diff --git a/contributors/segundev.txt b/contributors/segundev.txt new file mode 100644 index 0000000..aac641e --- /dev/null +++ b/contributors/segundev.txt @@ -0,0 +1 @@ +Jayeola Gbenga diff --git a/contributors/yabebalFantaye.txt b/contributors/yabebalFantaye.txt deleted file mode 100755 index 05cad46..0000000 --- a/contributors/yabebalFantaye.txt +++ /dev/null @@ -1 +0,0 @@ -Yabebal Tadesse Fantaye diff --git a/twitter_mining/Challenge_Distribution.ipynb b/twitter_mining/Challenge_Distribution.ipynb new file mode 100644 index 0000000..2cf5234 --- /dev/null +++ b/twitter_mining/Challenge_Distribution.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge_Distribution\n", + "\n", + "In this challenge you consider the real estate data from the city of Ames, Iowa. The details of every real estate transaction in Ames is recorded by the City Assessor's office (https://www.openintro.org/stat/data/ames.csv).\n", + "\n", + "**Based on this data, perform the following tasks**:\n", + "\n", + "- Take a random sample of size 50 from price. Using this sample, what is your best point estimate of the population mean?\n", + "\n", + "\n", + "- Since you have access to the population, simulate the sampling distribution for the average home price in Ames by taking 5000 samples from the population of size 50 and computing 5000 sample means. Store these means in a vector called sample_means50. Plot the data, then describe the shape of this sampling distribution. Based on this sampling distribution, what would you guess the mean home price of the population to be? Finally, calculate and report the population mean.\n", + "\n", + "\n", + "- Change your sample size from 50 to 150, then compute the sampling distribution using the same method as above, and store these means in a new vector called sample_means150. Describe the shape of this sampling distribution, and compare it to the sampling distribution for a sample size of 50. Based on this sampling distribution, what would you guess to be the mean sale price of homes in Ames?\n", + "\n", + "\n", + "- Of the sampling distributions from 2 and 3, which has a smaller spread? If we’re concerned with making estimates that are more often close to the true value, would we prefer a distribution with a large or small spread?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'\n", + "plt.style.use(\"seaborn\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('ames.csv')\n", + "price = df['SalePrice']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "187271.76" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "price.sample(50 , random_state = 42).mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "sample_mean50 = []\n", + "\n", + "for i in range(5000):\n", + " sample_price = np.array(list(price.sample(50).values))\n", + " sample_mean50.append(sample_price.mean())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 329, + "width": 490 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(sample_mean50, alpha = 0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Describe the shape\n", + "\n", + "The mean sample distribution has a large spread with a near like near like normal distribution with the and this is due to the central limit theorem as the number of samples increases the distribution takes a normal form.\n", + "\n", + "The mean is assumed to lie in between the interval of 17500 - 18500 due most of the data point are contained between the interval" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best guess of the population mean using the sample mean is : 180892.92521599997\n", + "The population mean is : 180796.0600682594\n" + ] + } + ], + "source": [ + "print('The best guess of the population mean using the sample mean is : ', np.array(sample_mean50).mean())\n", + "print('The population mean is :',price.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 3" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "sample_mean150 = []\n", + "\n", + "for i in range(5000):\n", + " sample_price = np.array(list(price.sample(150).values))\n", + " sample_mean150.append(sample_price.mean())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 333, + "width": 490 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(sample_mean150, color = 'g', alpha = 0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 3" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 329, + "width": 490 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(sample_mean50, alpha = 0.5)\n", + "plt.hist(sample_mean150, color = 'g', alpha = 0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best guess of the population mean using the sample mean is : 180707.19084\n" + ] + } + ], + "source": [ + "print('The best guess of the population mean using the sample mean is : ', np.array(sample_mean150).mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The sample_mean150 have a relatively smaller spread than the sample_mean50. The spread which is shows how far the data points \n", + "are far from the average and if large then the estimate(mean of the sample distribution) is not reliable due to less precision.\n", + "\n", + "A smaller spread shows the data points are closer to the mean reveals a better estimates " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/twitter_mining/Chanllenge_Hypothesis_Testing.ipynb b/twitter_mining/Chanllenge_Hypothesis_Testing.ipynb new file mode 100644 index 0000000..7e8a0e8 --- /dev/null +++ b/twitter_mining/Chanllenge_Hypothesis_Testing.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge_Hypothesis Testing\n", + "\n", + "In this challenge, you will consider the state of North Carolina data set containing information on births recorded in this state. This data set is useful to researchers studying the relation between habits and practices of expectant mothers and the birth of their children. \n", + "\n", + "**The following is the variable descriptions**\n", + "\n", + "| variable | description |\n", + "| ---------------- | ------------|\n", + "| `fage` | father's age in years. |\n", + "| `mage` | mother's age in years. |\n", + "| `mature` | maturity status of mother. |\n", + "| `weeks` | length of pregnancy in weeks. |\n", + "| `premie` | whether the birth was classified as premature (premie) or full-term. |\n", + "| `visits` | number of hospital visits during pregnancy. |\n", + "| `marital` | whether mother is `married` or `not married` at birth. |\n", + "| `gained` | weight gained by mother during pregnancy in pounds. |\n", + "| `weight` | weight of the baby at birth in pounds. |\n", + "| `lowbirthweight` | whether baby was classified as low birthweight (`low`) or not (`not low`). |\n", + "| `gender` | gender of the baby, `female` or `male`. |\n", + "| `habit` | status of the mother as a `nonsmoker` or a `smoker`. |\n", + "| `whitemom` | whether mom is `white` or `not white`. |\n", + "\n", + "**Based on this data, perform the following tasks**:\n", + "\n", + "\n", + "- Make a side-by-side boxplot of habit and weight. What does the plot highlight about the relationship between these two variables?\n", + "\n", + "- Write the hypotheses for testing if the average weights of babies born to smoking and non-smoking mothers are different.\n", + "\n", + "- Conduct a hypothesis test evaluating whether the average weight gained by younger mothers is different than the average weight gained by mature mothers.\n", + "\n", + "- Construct a confidence interval for the difference between the weights of babies born to smoking and non-smoking mothers." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "data = pd.read_csv('nc (1).csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import statsmodels.api as sm\n", + "import numpy as np\n", + "import scipy.stats as stats\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'\n", + "plt.style.use(\"seaborn\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 343, + "width": 492 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "boxplot = sns.boxplot(x='habit', y='weight', data=data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The distribution of both Smokers and Non Smokers are quite close due to the interquartile range nearly overlaps\n", + "and the median line are quite close to each other but this cannot be guaranteed as the statisfical significance \n", + "of equality is needed to be tested using the t-test or z-test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Research Question\n", + "\n", + "Is there a significant difference between the population average of average weights of babies born to smoking and non-smoking mothers\n", + " \n", + "**Population**: States of North Carolina on recorded births. \n", + "**Parameter of Interest**: $\\mu_1 - \\mu_2$, Babies weights. \n", + "\n", + "**Null Hypothesis:** $\\mu_1 = \\mu_2$ \n", + "**Alternative Hypthosis:** $\\mu_1 \\neq \\mu_2$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 3" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "avg_of_young_mom = data['gained'][data['mature'] == 'younger mom'].mean()\n", + "std_of_young_mom = data['gained'][data['mature'] == 'younger mom'].std()\n", + "avg_of_mature_mom = data['gained'][data['mature'] == 'mature mom'].mean()\n", + "std_of_young_mom = data['gained'][data['mature'] == 'mature mom'].std()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "young_mom = data['gained'][data['mature'] == 'younger mom']\n", + "mature_mom = data['gained'][data['mature'] == 'mature mom']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.3150120052394427, 0.18850586777828726)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sm.stats.ztest(young_mom.dropna(), mature_mom.dropna() ,alternative='two-sided')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "we reject the null hypothesis that the average weigths are the same due to the p-value (0.1885) is greater than\n", + "0.05, therefore we fail to fail to reject the Alternative Hypothesis that the average are different" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['nonsmoker', 'smoker', nan], dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['habit'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "avg_of_nonsmoker = data['weight'][data['habit'] == 'nonsmoker'].mean()\n", + "avg_of_smoker = data['weight'][data['habit'] == 'smoker'].mean()\n", + "\n", + "lower_nonsmoker = np.percentile(avg_of_nonsmoker , 2.5)\n", + "upper_nonsmoker = np.percentile(avg_of_nonsmoker, 97.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "lower_smoker = np.percentile(avg_of_smoker , 2.5)\n", + "upper_smoker = np.percentile(avg_of_smoker, 97.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/twitter_mining/Matplotlib Task.ipynb b/twitter_mining/Matplotlib Task.ipynb new file mode 100644 index 0000000..c04951c --- /dev/null +++ b/twitter_mining/Matplotlib Task.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matplolib Task" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(\"pandas_tasks.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Instruction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Using the loaded dataset, make a boxplot, barplot, histogram, and a scatter plot. \n", + "* Ensure to provide appropriate labels, and titles where necessary. \n", + "* In each plot, ensure to use all parameters used in the Matplolib tutorial.\n", + "* Optionally, add additional parameters for each plot for some extra points.\n", + "* For each of the plot, choose appropriate columns in the dataset. The explanation for each of the variables has been provided in a word document called `Variable_Codebook.docx`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'\n", + "plt.style.use(\"seaborn\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(7094, 37)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Q1'].min()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 60] 5877\n", + "(60, 100] 802\n", + "(0, 18] 415\n", + "Name: age_group, dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['age_group'] = pd.cut(x = data['Q1'], bins = [0, 18 , 60 , 100 ])\n", + "data['age_group'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BoxPlot" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 362, + "width": 499 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "male_ages = data['Q1'][data['Q2'] == 1]\n", + "female_ages = data['Q1'][data['Q2'] == 2]\n", + "ages = [male_ages , female_ages]\n", + "labels = ['Male', 'Female']\n", + "\n", + "plt.boxplot(ages , widths = 0.3, labels = labels , patch_artist = True,\n", + " boxprops = dict(facecolor = 'g')\n", + " )\n", + "plt.ylabel('Ages', fontsize = 12)\n", + "plt.xlabel('Gender',fontsize = 12)\n", + "plt.title(\"A Boxplot Comparing both Ages of Male and Female Respondents\", fontsize = 15)\n", + "plt.show();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barplot" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 358, + "width": 593 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "Male = list(data['Q3'][data['Q2'] == 1].value_counts().sort_index())\n", + "Female = list(data['Q3'][data['Q2'] == 2].value_counts().sort_index())\n", + "ind = np.arange(4) # creates a numpy array from 0 to 3. it will be used on the x-axis\n", + "width = 0.4 # will be used to specify the width of the bar\n", + "plt.xticks(ind + width / 2, ('Married', 'Divorced', 'Widowed', 'Single'))\n", + "plt.bar(ind, Male, width, label=\"Males\")\n", + "plt.bar(ind + width, Female, width, label=\"Females\")\n", + "plt.title(\"A Bar Plot comparing the Total numbers of the Relationship Status of Respondents according to their Gender\")\n", + "plt.xlabel(\"Marital Status\")\n", + "plt.ylabel(\"Total Numbers\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Histogram" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 362, + "width": 510 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data['Q1'], bins=10 , color = 'c')\n", + "plt.title(\"A Histogram showing the distribution of Ages between 15 and 100\")\n", + "plt.grid(axis = 'y', alpha = 0.75)\n", + "plt.xlabel(\"Value\", fontsize = 12)\n", + "plt.ylabel(\"Frequency\", fontsize = 12)\n", + "plt.xticks(fontsize = 12)\n", + "plt.yticks( fontsize = 12)\n", + "plt.show();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scatter Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 344, + "width": 599 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "latitude = data['Latitude']\n", + "longitude = data['Longitude']\n", + "scale = np.random.rand(200)\n", + "scale = 100 * scale ** 2\n", + "plt.scatter(latitude, longitude, s=scale)\n", + "plt.title(\"A scatter plot showing the relationship between two lists of randomly generated integers between 150 and 275\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/twitter_mining/Numpy Task.ipynb b/twitter_mining/Numpy Task.ipynb new file mode 100644 index 0000000..b50a025 --- /dev/null +++ b/twitter_mining/Numpy Task.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Numpy Tasks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task_1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 1. Create a numpy array with 6X5 2D array from 10 upwards" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[10, 11, 12, 13, 14],\n", + " [15, 16, 17, 18, 19],\n", + " [20, 21, 22, 23, 24],\n", + " [25, 26, 27, 28, 29],\n", + " [30, 31, 32, 33, 34],\n", + " [35, 36, 37, 38, 39]])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "array = np.arange(10,40).reshape(6,5)\n", + "array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 2. From question 1, reshape the numpy array to any shape of your choice and return the max value from each column." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([30, 31, 32, 33, 34, 35, 36, 37, 38, 39])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "array.reshape(3,10).max(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 3. Create a numpy array A with np.ones with a shape of (3,4). Create another array using the np.arange with a shape of (3,4). " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3],\n", + " [ 4, 5, 6, 7],\n", + " [ 8, 9, 10, 11]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "array1 = np.ones((3,4))\n", + "array2 = np.arange(12).reshape(3,4)\n", + "array2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 4. Perform arithmetic operations (+,-,*,/) on the two arrays from question 3." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The addition of both arrays \n", + " [[ 1. 2. 3. 4.]\n", + " [ 5. 6. 7. 8.]\n", + " [ 9. 10. 11. 12.]]\n", + "\n", + "+++++++++++++++++++++++++++++++++\n", + "\n", + "The difference of both arrays \n", + " [[ 1. 0. -1. -2.]\n", + " [ -3. -4. -5. -6.]\n", + " [ -7. -8. -9. -10.]]\n", + "\n", + "+++++++++++++++++++++++++++++++++\n", + "\n", + "The product of both arrays \n", + " [[ 0. 1. 2. 3.]\n", + " [ 4. 5. 6. 7.]\n", + " [ 8. 9. 10. 11.]]\n", + "\n", + "+++++++++++++++++++++++++++++++++\n", + "\n", + "The division of both arrays \n", + " [[ inf 1. 0.5 0.33333333]\n", + " [0.25 0.2 0.16666667 0.14285714]\n", + " [0.125 0.11111111 0.1 0.09090909]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in true_divide\n", + " after removing the cwd from sys.path.\n" + ] + } + ], + "source": [ + "add = array1 + array2\n", + "diff = array1 - array2\n", + "prod = array1 * array2\n", + "div = array1 / array2\n", + "\n", + "print('The addition of both arrays \\n', add)\n", + "print('\\n+++++++++++++++++++++++++++++++++\\n')\n", + "print('The difference of both arrays \\n', diff)\n", + "print('\\n+++++++++++++++++++++++++++++++++\\n')\n", + "print('The product of both arrays \\n', prod)\n", + "print('\\n+++++++++++++++++++++++++++++++++\\n')\n", + "print('The division of both arrays \\n', div)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 5. From question 4, transpose the results of each arithmetic operation." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The transpose of the arrays addition \n", + " [[ 1. 5. 9.]\n", + " [ 2. 6. 10.]\n", + " [ 3. 7. 11.]\n", + " [ 4. 8. 12.]]\n", + "\n", + "+++++++++++++++++++++++++++++++++\n", + "\n", + "The transpose of the arrays difference \n", + " [[ 1. -3. -7.]\n", + " [ 0. -4. -8.]\n", + " [ -1. -5. -9.]\n", + " [ -2. -6. -10.]]\n", + "\n", + "+++++++++++++++++++++++++++++++++\n", + "\n", + "The transpose of the arrays multiplication \n", + " [[ 0. 4. 8.]\n", + " [ 1. 5. 9.]\n", + " [ 2. 6. 10.]\n", + " [ 3. 7. 11.]]\n", + "\n", + "+++++++++++++++++++++++++++++++++\n", + "\n", + "The transpose of the arrays division \n", + " [[ inf 0.25 0.125 ]\n", + " [1. 0.2 0.11111111]\n", + " [0.5 0.16666667 0.1 ]\n", + " [0.33333333 0.14285714 0.09090909]]\n", + "\n", + "+++++++++++++++++++++++++++++++++\n", + "\n" + ] + } + ], + "source": [ + "print('The transpose of the arrays addition \\n', add.T)\n", + "print('\\n+++++++++++++++++++++++++++++++++\\n')\n", + "print('The transpose of the arrays difference \\n', diff.T)\n", + "print('\\n+++++++++++++++++++++++++++++++++\\n')\n", + "print('The transpose of the arrays multiplication \\n', prod.T)\n", + "print('\\n+++++++++++++++++++++++++++++++++\\n')\n", + "print('The transpose of the arrays division \\n', div.T)\n", + "print('\\n+++++++++++++++++++++++++++++++++\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/twitter_mining/Pandas Task_1.ipynb b/twitter_mining/Pandas Task_1.ipynb new file mode 100644 index 0000000..c720219 --- /dev/null +++ b/twitter_mining/Pandas Task_1.ipynb @@ -0,0 +1,1261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas Tasks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the task below, you will practice what you have learnt in the pandas tutorial. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 1. Identify two ways of creating a pandas DataFrame and create a dataframe with ten columns and twenty rows for each way " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# the first dataframe is created using the widely used pandas dataframe creation by \n", + "# specifying the values in a variable and the columns (headers) in a another variable\n", + "# the data was generated using the numpy random generator\n", + "\n", + "data = np.random.rand(20,10)\n", + "columns = list('ABCDEFGHIJ')\n", + "\n", + "\n", + "df1 = pd.DataFrame(data , columns = columns) # --------------> first dataframe\n", + "\n", + "\n", + "# the second dataframe was created using dictionary to hold the data.\n", + "# pandas automatically sees the keys as the column header and the values \n", + "# from each key as the row. The rows from this example was stored using the list\n", + "# data types and were generated by list operation\n", + "# There do say most programmers are very lazy\n", + "\n", + "\n", + "data2 = {\n", + " \n", + " \n", + " 'Name' : ['Gbenga','Femi','Ade','Bose','Bolu','Ayo','David','Esther','Ifeoma','Akeem','Wale','Abeeb','Muraina',\n", + " 'Segun','Tosin','Siju','Deji','Blessing','Funmi','Yinka'],\n", + " 'Sex' : ['male','female'] * 10 ,\n", + " 'Job' : ['Teacher','Banker','Footballer','Trader'] * 5,\n", + " 'Job Status' : ['yes'] * 20,\n", + " 'Age' : list(np.arange(20,100,4)),\n", + " 'weight' : list(np.random.rand(20)),\n", + " 'Account' : ['Yes', 'No'] * 10,\n", + " 'Transfer' : [0 , 1] * 10,\n", + " 'Count' : list(np.arange(20)),\n", + " 'Religion' : ['Christian' ,'Muslim'] * 10\n", + "}\n", + "\n", + "df2 = pd.DataFrame(data2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B C D E F G \\\n", + "0 0.305239 0.231272 0.088222 0.251882 0.385132 0.234758 0.645410 \n", + "1 0.894179 0.129102 0.520792 0.780366 0.785802 0.368216 0.207315 \n", + "2 0.306956 0.049282 0.675463 0.113234 0.869604 0.930263 0.696827 \n", + "3 0.272259 0.779780 0.380540 0.685579 0.785072 0.976008 0.412942 \n", + "4 0.622153 0.963447 0.755817 0.541017 0.254235 0.708141 0.150924 \n", + "5 0.811146 0.476762 0.855972 0.305416 0.808352 0.544968 0.843353 \n", + "6 0.523588 0.727435 0.659603 0.762771 0.462791 0.180671 0.026311 \n", + "7 0.290624 0.776585 0.648164 0.913340 0.240606 0.354553 0.271786 \n", + "8 0.526691 0.020449 0.372602 0.057064 0.331893 0.809467 0.815766 \n", + "9 0.469389 0.373859 0.917139 0.301419 0.361257 0.166470 0.001263 \n", + "10 0.586595 0.103862 0.542455 0.172138 0.231164 0.621282 0.303060 \n", + "11 0.874637 0.765251 0.446922 0.863143 0.934134 0.781621 0.578874 \n", + "12 0.372582 0.014192 0.141197 0.718072 0.985635 0.727378 0.832513 \n", + "13 0.747853 0.361451 0.307679 0.430487 0.333176 0.512498 0.421828 \n", + "14 0.806012 0.289871 0.438636 0.135046 0.456787 0.491738 0.091800 \n", + "15 0.633796 0.121392 0.551617 0.938901 0.037063 0.551676 0.989032 \n", + "16 0.369000 0.534016 0.841040 0.079688 0.757663 0.511047 0.485882 \n", + "17 0.922850 0.980879 0.299093 0.918864 0.870944 0.175901 0.749300 \n", + "18 0.585918 0.606926 0.210228 0.821542 0.206155 0.557342 0.548108 \n", + "19 0.042375 0.285725 0.220813 0.964579 0.865262 0.464721 0.220354 \n", + "\n", + " H I J \n", + "0 0.986175 0.760495 0.491813 \n", + "1 0.248793 0.587673 0.763330 \n", + "2 0.397656 0.459233 0.064965 \n", + "3 0.512427 0.956594 0.887955 \n", + "4 0.664176 0.261850 0.161177 \n", + "5 0.562328 0.826649 0.036099 \n", + "6 0.011348 0.820640 0.175180 \n", + "7 0.248880 0.084472 0.741984 \n", + "8 0.421695 0.876327 0.676441 \n", + "9 0.745675 0.487949 0.341857 \n", + "10 0.199532 0.592424 0.846125 \n", + "11 0.161245 0.364697 0.604686 \n", + "12 0.394161 0.892131 0.595044 \n", + "13 0.602016 0.756884 0.319003 \n", + "14 0.136814 0.859572 0.175451 \n", + "15 0.429235 0.139587 0.049697 \n", + "16 0.118199 0.556479 0.377192 \n", + "17 0.169334 0.537206 0.577845 \n", + "18 0.521981 0.054173 0.617419 \n", + "19 0.071457 0.548230 0.684320 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameSexJobJob StatusAgeweightAccountTransferCountReligion
0GbengamaleTeacheryes200.637354Yes00Christian
1FemifemaleBankeryes240.607694No11Muslim
2AdemaleFootballeryes280.040372Yes02Christian
3BosefemaleTraderyes320.999901No13Muslim
4BolumaleTeacheryes360.239623Yes04Christian
5AyofemaleBankeryes400.571719No15Muslim
6DavidmaleFootballeryes440.251429Yes06Christian
7EstherfemaleTraderyes480.307864No17Muslim
8IfeomamaleTeacheryes520.714834Yes08Christian
9AkeemfemaleBankeryes560.409580No19Muslim
10WalemaleFootballeryes600.338789Yes010Christian
11AbeebfemaleTraderyes640.460655No111Muslim
12MurainamaleTeacheryes680.893463Yes012Christian
13SegunfemaleBankeryes720.750401No113Muslim
14TosinmaleFootballeryes760.150023Yes014Christian
15SijufemaleTraderyes800.503903No115Muslim
16DejimaleTeacheryes840.853731Yes016Christian
17BlessingfemaleBankeryes880.880703No117Muslim
18FunmimaleFootballeryes920.840192Yes018Christian
19YinkafemaleTraderyes960.922895No119Muslim
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" + ], + "text/plain": [ + " Name Sex Job Job Status Age weight Account Transfer \\\n", + "0 Gbenga male Teacher yes 20 0.637354 Yes 0 \n", + "1 Femi female Banker yes 24 0.607694 No 1 \n", + "2 Ade male Footballer yes 28 0.040372 Yes 0 \n", + "3 Bose female Trader yes 32 0.999901 No 1 \n", + "4 Bolu male Teacher yes 36 0.239623 Yes 0 \n", + "5 Ayo female Banker yes 40 0.571719 No 1 \n", + "6 David male Footballer yes 44 0.251429 Yes 0 \n", + "7 Esther female Trader yes 48 0.307864 No 1 \n", + "8 Ifeoma male Teacher yes 52 0.714834 Yes 0 \n", + "9 Akeem female Banker yes 56 0.409580 No 1 \n", + "10 Wale male Footballer yes 60 0.338789 Yes 0 \n", + "11 Abeeb female Trader yes 64 0.460655 No 1 \n", + "12 Muraina male Teacher yes 68 0.893463 Yes 0 \n", + "13 Segun female Banker yes 72 0.750401 No 1 \n", + "14 Tosin male Footballer yes 76 0.150023 Yes 0 \n", + "15 Siju female Trader yes 80 0.503903 No 1 \n", + "16 Deji male Teacher yes 84 0.853731 Yes 0 \n", + "17 Blessing female Banker yes 88 0.880703 No 1 \n", + "18 Funmi male Footballer yes 92 0.840192 Yes 0 \n", + "19 Yinka female Trader yes 96 0.922895 No 1 \n", + "\n", + " Count Religion \n", + "0 0 Christian \n", + "1 1 Muslim \n", + "2 2 Christian \n", + "3 3 Muslim \n", + "4 4 Christian \n", + "5 5 Muslim \n", + "6 6 Christian \n", + "7 7 Muslim \n", + "8 8 Christian \n", + "9 9 Muslim \n", + "10 10 Christian \n", + "11 11 Muslim \n", + "12 12 Christian \n", + "13 13 Muslim \n", + "14 14 Christian \n", + "15 15 Muslim \n", + "16 16 Christian \n", + "17 17 Muslim \n", + "18 18 Christian \n", + "19 19 Muslim " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 2. Show a summary of your dataframe with column names, non null count and dtypes for the columns" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20 entries, 0 to 19\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 A 20 non-null float64\n", + " 1 B 20 non-null float64\n", + " 2 C 20 non-null float64\n", + " 3 D 20 non-null float64\n", + " 4 E 20 non-null float64\n", + " 5 F 20 non-null float64\n", + " 6 G 20 non-null float64\n", + " 7 H 20 non-null float64\n", + " 8 I 20 non-null float64\n", + " 9 J 20 non-null float64\n", + "dtypes: float64(10)\n", + "memory usage: 1.7 KB\n" + ] + } + ], + "source": [ + "df1.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20 entries, 0 to 19\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Name 20 non-null object \n", + " 1 Sex 20 non-null object \n", + " 2 Job 20 non-null object \n", + " 3 Job Status 20 non-null object \n", + " 4 Age 20 non-null int64 \n", + " 5 weight 20 non-null float64\n", + " 6 Account 20 non-null object \n", + " 7 Transfer 20 non-null int64 \n", + " 8 Count 20 non-null int64 \n", + " 9 Religion 20 non-null object \n", + "dtypes: float64(1), int64(3), object(6)\n", + "memory usage: 1.7+ KB\n" + ] + } + ], + "source": [ + "df2.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 3. Again show a summary of your dataframe, this time, showing the mean, count, standard deviation, minimum number etc for all the numerical columns" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ABCDEFGHIJ
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std0.2453350.3196360.2448890.3324320.2993340.2417990.2993940.2538980.2787670.278714
min0.0423750.0141920.0882220.0570640.0370630.1664700.0012630.0113480.0541730.036099
25%0.3534890.1271740.3055320.2319460.3124780.3648000.2170950.1673120.4355990.175383
50%0.5563040.3676550.4838570.6132980.4597890.5287330.4538550.3959080.5720760.534829
75%0.7623930.7368890.6635680.8319420.8225790.7129500.7099450.5320680.8221420.678411
max0.9228500.9808790.9171390.9645790.9856350.9760080.9890320.9861750.9565940.887955
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AgeweightTransferCount
count20.00000020.00000020.00000020.00000
mean58.0000000.5687560.5000009.50000
std23.6643190.2861020.5129895.91608
min20.0000000.0403720.0000000.00000
25%39.0000000.3310580.0000004.75000
50%58.0000000.5897060.5000009.50000
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" + ], + "text/plain": [ + " Age weight Transfer Count\n", + "count 20.000000 20.000000 20.000000 20.00000\n", + "mean 58.000000 0.568756 0.500000 9.50000\n", + "std 23.664319 0.286102 0.512989 5.91608\n", + "min 20.000000 0.040372 0.000000 0.00000\n", + "25% 39.000000 0.331058 0.000000 4.75000\n", + "50% 58.000000 0.589706 0.500000 9.50000\n", + "75% 77.000000 0.843577 1.000000 14.25000\n", + "max 96.000000 0.999901 1.000000 19.00000" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 4. Show the number of unique values in all the columns in your dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "A 20\n", + "B 20\n", + "C 20\n", + "D 20\n", + "E 20\n", + "F 20\n", + "G 20\n", + "H 20\n", + "I 20\n", + "J 20\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Name 20\n", + "Sex 2\n", + "Job 4\n", + "Job Status 1\n", + "Age 20\n", + "weight 20\n", + "Account 2\n", + "Transfer 2\n", + "Count 20\n", + "Religion 2\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/twitter_mining/Power BI visualization task.ipynb b/twitter_mining/Power BI visualization task.ipynb new file mode 100644 index 0000000..0fbfc6e --- /dev/null +++ b/twitter_mining/Power BI visualization task.ipynb @@ -0,0 +1,85 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Power BI Dashboard Task" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Section 1: Explore the dashboard KPIs visualization challenge and answer the following questions\n", + "* What is the total number of subscribers in the dataset? 751.06k\n", + "* What is the total revenue? 5.22bn\n", + "* What is the total number of calls for MTN Ipulse tariff type? 20.11\n", + "* Which cluster_tag is the top voice user? DORMANT\n", + "* What is the average voice usage for all the customers? 45.71bn\n", + "* What is the share of weekday onnet voice usage? 71.08\n", + "* What is the share of weekend onnet voice usage? 70.62\n", + "* What is the share of weekend offnet voice usage? 28.95\n", + "* What is the share of weekend voice usage (secs)? 0.00\n", + "* Which cluster has the highest number of calls? DORMANT\n", + "* Which cluster has the lowest voice usage? `PLATINUM`\n", + "* Which cluster has the highest night usage? `PLATINUM`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Section 2: Explore the dashboard KPIs visualization challenge and answer the following questions\n", + "* What is the average data usage? 1.58T\n", + "* What is the average data usage for women? 1.58T\n", + "* What is the average data usage in ABIA state? 1.58T\n", + "* What is the average data usage in BAUCHI state? 1.58T\n", + "* Which cluster has the highest in bundle data usage? \n", + "* What is the average data revenue? 50.58K\n", + "* What is the average data usage for microsegment 1.10? 1.58T\n", + "* What is the average data usage for the microsegment 0.80? 1.58T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Section 3: Explore the dashboard KPIs visualization challenge and answer the following questions\n", + "* What is the total lifetime value of the customers? 10.36BN\n", + "* What is the average sms usage? 23.68M\n", + "* What can you diagnose when looking on voice usage trend? \n", + "* What can you diagnose when looking on sms usage trend? clicking on the sms usage changes the revenue and Usage from \n", + "* bn from thousand " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/twitter_mining/Seaborn Task.ipynb b/twitter_mining/Seaborn Task.ipynb new file mode 100644 index 0000000..8469dfa --- /dev/null +++ b/twitter_mining/Seaborn Task.ipynb @@ -0,0 +1,855 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Seaborn Tasks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tasks 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the pandas_tasks.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "sns.set()\n", + "\n", + "data = pd.read_csv(\"pandas_tasks.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 1. Make a scatter plot of the Longitude and Latitude columns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.relplot(x=\"Longitude\", y=\"Latitude\", height= 8, aspect=1, data=data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 2. Make a scatter plot of the Longitude and Latitude columns. This time, plot with 'hue' = Q2 and compare it over Q3" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.relplot(x=\"Longitude\", y=\"Latitude\", height= 8, aspect=1, hue=\"Q2\",data=data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 3. Make a boxplot of Longitude and Q2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(x='Q2', y='Longitude', data=data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question 4. Make a count plot of all the categorical variables in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "cat = data[['Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8_1', 'Q8_2', 'Q8_3', 'Q8_4', 'Q8_5', 'Q8_6', 'Q8_7', 'Q8_8',\n", + " 'Q8_9', 'Q8_10', 'Q8_11', 'Q9','Q10','Q11','Q12','Q13','Q14','Q15','Q16','Q17','Q18','Q19','mobile_money','savings',\n", + " 'borrowing','insurance','mobile_money_classification']]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Gender = sns.countplot(x='Q2', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q3 = sns.countplot(x='Q3', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q4 = sns.countplot(x='Q4', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q5 = sns.countplot(x='Q5', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q6 = sns.countplot(x='Q6', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q7 = sns.countplot(x='Q7', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q8_4 = sns.countplot(x='Q8_4', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q8_5 = sns.countplot(x='Q8_5', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q8_6 = sns.countplot(x='Q8_6', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q8_7 = sns.countplot(x='Q8_7', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q8_8 = sns.countplot(x='Q8_8', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q8_9 = sns.countplot(x='Q8_9', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q8_10 = sns.countplot(x='Q8_10', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q8_11 = sns.countplot(x='Q8_11', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q9 = sns.countplot(x='Q9', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q10 = sns.countplot(x='Q10', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q11 = sns.countplot(x='Q11', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q12 = sns.countplot(x='Q12', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "EOL while scanning string literal (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m Q13 = sns.countplot(x='Q13, data=data)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOL while scanning string literal\n" + ] + } + ], + "source": [ + "Q13 = sns.countplot(x='Q13, data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q14 = sns.countplot(x='Q14', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q15 = sns.countplot(x='Q15', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yZdzH8oE1EOiwOwKAUWrcOMegf2xbNj118OBBPfbYY6qoqJDX65UkzZ49W3v37tW+ffsUCoVUV1cnj8ejqVOnKjExUU1NTZKk2tpaeTwexcfHKzU1VfX19ZKkmpoaeTweqyIDAIZg2ZnG+vXr1dvbq7Vr10bHlixZorVr12rZsmXq7e1Venq6srKyJEkVFRUqKSlRZ2enZs6cGf2mwFWrVqmwsFDr1q1TSkqKnn76aasiAwCG4IiM8a/gG2x6Knfl721IZK2N5XlMTwE4Y7ZNTwEAxh5KAwBgjNIAABijNAAAxigNAIAxSgMAYIzSAAAYozQAAMYoDQCAMUoDAGCM0gAAGKM0AADGKA0AgDFKAwBgjNIAABijNAAAxigNAIAxSgMAYIzSAAAYozQAAMYoDQCAMUoDAGCM0gAAGKM0AADGKA0AgDFKAwBgjNIAABijNAAAxigNAIAxSgMAYMxpdwAAiHUXXTheCYlj63DZ19uvo8e6T/t+Y+tVAAALJCQ69aviP9odY1gVrV58RvezfHqqs7NTt956q/bv3y9JamhoUHZ2tjIyMlRZWRndb8+ePcrJyVFmZqaKi4vV398vSTpw4IDy8vKUlZWlpUuXqqury+rIAICvYWlp7Ny5U3feeaeam5slST09PSoqKlJVVZXq6+u1e/dubdu2TZK0YsUKlZaWasuWLYpEIqqurpYklZWVKTc3Vz6fT7NmzVJVVZWVkQEAg7C0NKqrq7Vq1Sq53W5J0q5duzR9+nRNmzZNTqdT2dnZ8vl8amlpUU9Pj+bMmSNJysnJkc/nUzAYVGNjozIzMweMAwDsYemaxurVqwdsHzp0SC6XK7rtdrvl9/tPGne5XPL7/Wpvb1dSUpKcTueAcQCAPUZ0ITwcDsvhcES3I5GIHA7H147/7+eJvro9lEmTks4u9Cjkck2wOwKAUeBMjhUjWhpTpkxRIBCIbgcCAbnd7pPGW1tb5Xa7lZycrI6ODoVCIcXFxUX3Px1tbZ0KhyMnjY/lA2sg0GF3BGBMGavHi1MdK8aNcwz6x/aIfrhv9uzZ2rt3r/bt26dQKKS6ujp5PB5NnTpViYmJampqkiTV1tbK4/EoPj5eqampqq+vlyTV1NTI4/GMZGQAwAlG9EwjMTFRa9eu1bJly9Tb26v09HRlZWVJkioqKlRSUqLOzk7NnDlT+fn5kqRVq1apsLBQ69atU0pKip5++umRjAwAOMGIlMa7774b/T0tLU1vvvnmSftcccUV+uMfT/7wzNSpU/XKK69Ymg8AYIZrTwEAjFEaAABjlAYAwBilAQAwRmkAAIxRGgAAY5QGAMAYpQEAMEZpAACMURoAAGOUBgDAGKUBADBGaQAAjI3opdEBjB4TJyQo/rxEu2MMq2BPr4509NkdY1SjNACcUvx5iarPv8/uGMNqwcsbJErjrDA9BQAwRmkAAIxRGgAAY5QGAMAYpQEAMEZpAACMURoAAGOUBgDAGKUBADBGaQAAjHEZEeAEF16UqMSEBLtjDLvevj4dO9prdwyMAZQGcILEhATdu+FHdscYdi/d94wkSgNnj+kpAIAxSgMAYIzSAAAYozQAAMYoDQCAsVHx7qk//elPWrdunfr7+3XPPfcoLy/P7khjysUXJciZMLa+1lOS+vt61X6Ub2kDhlPMl4bf71dlZaU2bdqkhIQELVmyRPPmzdNll11md7Qxw5mQqKbyB+2OMeyuWvl/kigNYDjFfGk0NDRo/vz5mjhxoiQpMzNTPp9Pjz/+uNH9x41zfO1t37j4gmHJGGsGe85fJ+HCSRYksd+ZvBbfSEq2IIn9zuS1GP+Nsff/4kxeB0m6aOL5w5zEfqd6LYZ6fRyRSCRiVaDh8MILL+j48eMqKCiQJL3++uvatWuXfvGLX9icDADOPTG/EB4Oh+Vw/P/mi0QiA7YBACMn5ktjypQpCgQC0e1AICC3221jIgA4d8V8aVxzzTXavn27Dh8+rO7ubr399tvyeDx2xwKAc1LML4RPnjxZBQUFys/PVzAY1OLFi3XllVfaHQsAzkkxvxAOAIgdMT89BQCIHZQGAMAYpQEAMEZpAACMURoW6+zs1K233qr9+/fbHcVWzz33nLxer7xer8rLy+2OY6tnnnlGCxYskNfr1YYNG+yOExOefPJJFRYW2h3DVnfffbe8Xq8WLlyohQsXaufOnXZHOqWYf8vtaLZz506VlJSoubnZ7ii2amho0Pvvv6/NmzfL4XDowQcf1DvvvKObb77Z7mgj7oMPPtCOHTv05ptvqr+/XwsWLFB6erq+9a1v2R3NNtu3b9fmzZt13XXX2R3FNpFIRM3NzXrvvffkdMb2YZkzDQtVV1dr1apV5/wn2F0ulwoLC5WQkKD4+HjNmDFDBw4csDuWLa6++mq9/PLLcjqdamtrUygU0vnnj70L4Zk6cuSIKisr9eijj9odxVaff/65JOn+++/XbbfdpldffdXmRF8vtittlFu9erXdEWLC5ZdfHv29ublZb731ll577TUbE9krPj5ezz77rF588UVlZWVp8uTJdkeyTWlpqQoKCnTw4EG7o9jq2LFjSktL089//nMFg0Hl5+fr0ksv1bXXXmt3tJNwpoER8+mnn+r+++/XypUr9c1vftPuOLZavny5tm/froMHD6q6utruOLZ4/fXXlZKSorS0NLuj2G7u3LkqLy/XhAkTlJycrMWLF2vbtm12xzolzjQwIpqamrR8+XIVFRXJ6/XaHcc2n332mfr6+vSd73xH48ePV0ZGhj7++GO7Y9mivr5egUBACxcu1NGjR3X8+HH96le/UlFRkd3RRtyHH36oYDAYLdBIJBKzaxucacByBw8e1GOPPaaKiopzujAkaf/+/SopKVFfX5/6+vq0detWXXXVVXbHssWGDRtUV1en2tpaLV++XDfccMM5WRiS1NHRofLycvX29qqzs1ObN2+O2TeKxGaVYUxZv369ent7tXbt2ujYkiVLdOedd9qYyh7p6enatWuXFi1apLi4OGVkZJzzRQrp+uuv186dO7Vo0SKFw2Hl5uZq7ty5dsc6JS5YCAAwxvQUAMAYpQEAMEZpAACMURoAAGOUBgDAGKUBADBGaQDDrL+/X7/97W+VnZ2t7Oxseb1elZWVqb29fcB+77//vhYuXDhg7OOPP9bdd9+tRYsWKScnR7t37x7J6MCQ+HAfMMxWrFihcDisV199VRdddJGCwaBeeuklLVmyRG+88YacTqfWrVunjRs3DrhYYXd3tx544AGtXr1a6enp+vOf/6yf/vSn8vl8Nj4bYCBKAxhGu3btUmNjo9555x2NHz9e0pdXtX3ooYf00Ucf6bXXXtOll16q7u5urV27VpWVldH7/u1vf9O0adOUnp4uSbrxxht1ySWX2PI8gK/D9BQwjD766CPNmjUrWhgnuvbaa/XRRx/ppptuUlFRkZKSkgbcvnfvXrlcLhUVFSknJ0f33XefQqHQSEUHjFAawAga7Ko9/f392rZtm+644w5t2rRJd911lx5++GH19fWNYEJgcJQGMIy++93v6p///Ke6u7slSX19fdEF8B07dmj27Nlfe1+3260ZM2ZE97npppsUCoX0xRdfWB8cMERpAMPoyiuv1Lx581RYWKijR4/qiy++UF5enpYtW6aPP/5YeXl5X3tfj8ej/fv3R98x1djYKIfDwboGYgoL4cAwe+qpp/Tiiy/qrrvukiQFg0HFxcXpggsu0NatW3X77bef8n4ul0vPP/+8ysrK1N3drYSEBP3mN79RYmLiSMYHBsWl0YERcuzYMe3evVvXXHON3VGAM0ZpAACMsaYBADBGaQAAjFEaAABjlAYAwBilAQAwRmkAAIz9Px5L8FacRm4kAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q16 = sns.countplot(x='Q16', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ZV57DmgaAiOtrTcPoSqOgoEA5OTnKzc2V3+/Xyy+/rMLCQj377LMDFhQAMPQZrWm0t7eHp4tiY2N1//3391q4BgBcHIwXwk9/f0RbW5sMZrUAABcYo+mp+++/X3PnztV3v/td2Ww2NTY2atmyZZHOBgAYYoxKY968eUpKStL27dsVExOjH/3oR/rmN78Z6WwAgCHGqDQk6frrr9f1118fySwAgCHunD5PAwBwcaI0AADGKA0AgDFKAwBgjNIAABijNAAAxigNAIAxSgMAYIzSAAAYozQAAMYoDQCAMUoDAGCM0gAAGKM0AADGKA0AgDFKAwBgjNIAABijNAAAxigNAICxiJbG008/rYyMDGVkZKi8vFyS1NjYqMzMTKWmpqqysjK87969e5WVlaW0tDQVFRWpp6dHknTgwAHl5OQoPT1dixcvVldXVyQjAwDOImKl0djYqK1bt2rjxo2qqanRP//5T9XV1amwsFBVVVWqr6/Xnj171NDQIEnKz89XcXGxNm3aJMuyVF1dLUkqLS1Vdna2PB6PkpKSVFVVFanIAIA+RKw0nE6nCgoKNHLkSDkcDl1zzTVqbm7WpEmTNHHiRNntdmVmZsrj8ai1tVXd3d2aOnWqJCkrK0sej0eBQEA7duxQWlpar3EAQHRErDSuu+66cAk0Nzfr9ddfl81mk9PpDO/jcrnk9Xp16NChXuNOp1Ner1ft7e2Ki4uT3W7vNQ4AiA57pP+Ajz76SD/5yU+0bNkyxcTEqLm5OfyYZVmy2WwKhUKy2WxfGf/v19N9ebsv48bFnVf+ocTpHBPtCAAuchEtjaamJi1dulSFhYXKyMjQe++9J5/PF37c5/PJ5XIpISGh13hbW5tcLpfi4+PV0dGhYDComJiY8P79cfhwp0Ihq9fYcH3x9fk6oh0BwAVuxAjbWb/Zjtj01MGDB/Wzn/1MFRUVysjIkCRNmTJF+/btU0tLi4LBoOrq6uR2uzVhwgTFxsaqqalJklRbWyu32y2Hw6Hk5GTV19dLkmpqauR2uyMVGQDQh4hdaaxZs0Z+v18rVqwIj82fP18rVqzQkiVL5Pf7lZKSovT0dElSRUWFli9frs7OTk2ePFm5ubmSpJKSEhUUFGj16tVKTEzUypUrIxUZANAHm2VZVt+7DV9nmp7KXvZSlBKdm3XlOUxPAYi4qE1PAQAuPJQGAMAYpQEAMEZpAACMURoAAGOUBgDAGKUBADBGaQAAjFEaAABjlAYAwBilAQAwRmkAAIxRGgAAY5QGAMAYpQEAMEZpAACMURoAAGOUBgDAGKUBADBGaQAAjFEaAABjlAYAwBilAQAwRmkAAIxRGgAAYxEvjc7OTt1xxx36/PPPJUmNjY3KzMxUamqqKisrw/vt3btXWVlZSktLU1FRkXp6eiRJBw4cUE5OjtLT07V48WJ1dXVFOjIA4AwiWhq7du3SPffco+bmZklSd3e3CgsLVVVVpfr6eu3Zs0cNDQ2SpPz8fBUXF2vTpk2yLEvV1dWSpNLSUmVnZ8vj8SgpKUlVVVWRjAwAOIuIlkZ1dbVKSkrkcrkkSbt379akSZM0ceJE2e12ZWZmyuPxqLW1Vd3d3Zo6daokKSsrSx6PR4FAQDt27FBaWlqvcQBAdNgj+eSPP/54r+1Dhw7J6XSGt10ul7xe71fGnU6nvF6v2tvbFRcXJ7vd3mscABAdES2NLwuFQrLZbOFty7Jks9nOOP7fr6f78nZfxo2LO7/QQ4jTOSbaEQBc5Aa1NBISEuTz+cLbPp9PLpfrK+NtbW1yuVyKj49XR0eHgsGgYmJiwvv3x+HDnQqFrF5jw/XF1+friHYEABe4ESNsZ/1me1B/5HbKlCnat2+fWlpaFAwGVVdXJ7fbrQkTJig2NlZNTU2SpNraWrndbjkcDiUnJ6u+vl6SVFNTI7fbPZiRAQCnGdQrjdjYWK1YsUJLliyR3+9XSkqK0tPTJUkVFRVavny5Ojs7NXnyZOXm5kqSSkpKVFBQoNWrVysxMVErV64czMgAgNPYLMuy+t5t+DrT9FT2speilOjcrCvPYXoKQMQNqekpAMDwRmkAAIxRGgAAY5QGAMAYpQEAMEZpAACMURoAAGOUBgDAGKUBADBGaQAAjFEaAABjlAYAwBilAQAwNqi3RsfguOLykbKPjI12DGM9p/xqP3Yq2jEAGKA0LkD2kbFqKn8o2jGMfWfZ/0qiNIDhgOkpAIAxSgMAYIzpKQCD5vLLRmtk7PB62Tnl79Gx4yejHWPIGF5/ewCGtZGxdv226C/RjtEvhY/Pi3aEIYXpKQCAMUoDAGCM0gAAGKM0AADGWAgHgAFy+WUjNTJ2+NyNQZJO+f06dtz8zbWUBgAMkJGxsVr5q59EO0a//E/ZM+rPHRmYngIAGBsWpfHaa69p9uzZSk1N1UsvvRTtOABw0Rry01Ner1eVlZXasGGDRo4cqfnz52v69Om69tprox0NGHBjx4yUY9TwmhMPdPt1tIMbTl4shnxpNDY26qabbtLYsWMlSWlpafJ4PHr44YeNjh8xwva141decemAZRwsZzqXrzPysnERTDLw+nNuFzLHqFj99X/+X7Rj9MvMlRUa0RUw3v/ysZdEME1k9Off52Vjh9f/Pan3+fV1rjbLsqxIBzofzzzzjE6cOKG8vDxJ0iuvvKLdu3fr17/+dZSTAcDFZ8ivaYRCIdls/9d8lmX12gYADJ4hXxoJCQny+XzhbZ/PJ5fLFcVEAHDxGvKlcfPNN2vbtm06cuSITp48qTfeeENutzvasQDgojTkF8LHjx+vvLw85ebmKhAIaN68ebrhhhuiHQsALkpDfiEcADB0DPnpKQDA0EFpAACMURoAAGOUBgDAGKUxwD744ANlZGREO8aA6+zs1B133KHPP/882lEG3NNPP62MjAxlZGSovLw82nEG3FNPPaXZs2crIyNDa9eujXaciHjiiSdUUFAQ7RgRce+99yojI0Nz5szRnDlztGvXrqjmGfI/cjuc1NTU6He/+50cDke0owyoXbt2afny5Wpubo52lAHX2NiorVu3auPGjbLZbHrooYf05ptv6vvf/360ow2I9957T9u3b9err76qnp4ezZ49WykpKbr66qujHW3AbNu2TRs3btStt94a7SgDzrIsNTc3669//avs9qHxcs2VxgDp6OjQ5s2btXLlymhHGXDV1dUqKSm5IN+J73Q6VVBQoJEjR8rhcOiaa67RgQMHoh1rwNx44416/vnnZbfbdfjwYQWDQV1yyfC7YeCZHD16VJWVlVq0aFG0o0TEp59+Kkl68MEHdeedd+rFF1+MciKuNAbMmDFj9Ic//OGCnL55/PHHox0hYq677rrw75ubm/X666/r5ZdfjmKigedwOLRq1So999xzSk9P1/jx46MdacAUFxcrLy9PBw8ejHaUiDh+/LhmzJihRx99VIFAQLm5ubrqqqt0yy23RC0TVxqApI8++kgPPvigli1bpm984xvRjjPgli5dqm3btungwYOqrq6OdpwB8corrygxMVEzZsyIdpSImTZtmsrLyzVmzBjFx8dr3rx5amhoiGomrjTOw1NPPaUtW7ZI+uI/5W233RblRDgXTU1NWrp0qQoLCy+4H2L45JNPdOrUKX3rW9/S6NGjlZqaqg8++CDasQZEfX29fD6f5syZo2PHjunEiRP67W9/q8LCwmhHGzA7d+5UIBAIF6NlWdFf27AwoPbv32/NnDkz2jEiYubMmdb+/fujHWNAHThwwJo+fbrV2NgY7SgR8dZbb1lZWVmW3++3/H6/9cADD1h1dXXRjjXg1q9fb/3yl7+MdowBt2XLFmvu3LlWd3e31dHRYWVmZlrvv/9+VDNxpYGL2po1a+T3+7VixYrw2Pz583XPPfdEMdXASUlJ0e7duzV37lzFxMQoNTX1gruaupDNnDlTu3bt0ty5cxUKhZSdna1p06ZFNRM3LAQAGGMhHABgjNIAABijNAAAxigNAIAxSgMAYIzSAAAY430awADr6enRc889p9dee02SFAqFdOONN2rp0qW64oorwvtt3bpVTz75pGprayV9cZfk029d3tHRIa/Xq4aGBl155ZWDexLAGfA+DWCA5eXlKRQK6bHHHtPll1+uQCCgP/3pT/rLX/6i9evXy263a/Xq1Vq3bp3Gjx+vurq6rzxHIBDQggULdNddd2n+/PlROAvg6zE9BQyg3bt3a8eOHVqxYoUuv/xySV/cZXbhwoW6+uqr9fLLL2vr1q06efJkr3ehf9mzzz6r+Ph4CgNDDtNTwAB6//33lZSUpNGjR3/lsVtuuUXvvPOOFi5cqNtvv13vvvvu1z7HkSNHtHbtWm3YsCHScYF+40oDGEQms8HV1dW67bbbNHHixEFIBPQPpQEMoG9/+9v6xz/+oZMnT0qSTp06pfb2dknS9u3bNWXKlD6fo76+XllZWRHNCZwrSgMYQDfccIOmT5+ugoICHTt2TPv371dOTo6WLFmiDz74QDk5OWc9/tixY/rss8+ifidT4EwoDWCAPfnkk0pKStKCBQv0yCOPKBAIKCYmRpdeeqk2b9581mNbWlrkdDrlcDgGKS3QP/zILTBIjh8/rj179ujmm2+OdhTgnFEaAABjTE8BAIxRGgAAY5QGAMAYpQEAMEZpAACMURoAAGP/H5KO605xO3pZAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q17 = sns.countplot(x='Q17', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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hQ3rwwQdVWVkpr9crSbr66qu1b98+7d+/X/39/aqvr5fH49GUKVOUnJyspqYmSVJdXZ08Ho8SExOVnp4uv98vSaqtrZXH47EqMgBgCJYdaaxfv149PT1as2ZNbGzhwoVas2aNli5dqp6eHmVmZionJ0eSVFlZqdLSUnV2dmr69Omxz+9YuXKlioqKtG7dOk2ePFmPP/64VZEBAENwREf5B2MMNj2Vv+J3NiSy1qYKH9NTAL4026anAACjj+WvngJwdpowLkmJ5yXbHWNYhbt7dLSj1+4YZzVKA8DnSjwvWf6CH9kdY1jNeW6DRGl8JUxPAQCMURoAAGOUBgDAGKUBADBGaQAAjFEaAABjlAYAwBilAQAwRmkAAIxRGgAAY5QGAMAYpQEAMEZpAACMURoAAGOUBgDAGKUBADBGaQAAjFEaAABjlAYAwBilAQAwRmkAAIxRGgAAY5QGAMAYpQEAMEZpAACMURoAAGOUBgDAGKUBADBGaQAAjFEaAABjlpdGZ2en5s6dqwMHDkiSGhoalJubq6ysLFVVVcX227t3r/Ly8pSdna2SkhL19fVJkg4ePCifz6ecnBwtWbJEXV1dVkcGAHwBS0tj165duvPOO9Xc3CxJ6u7uVnFxsaqrq+X3+7Vnzx5t27ZNkrR8+XKVlZVpy5YtikajqqmpkSSVl5crPz9fgUBAM2bMUHV1tZWRAQCDsLQ0ampqtHLlSrndbknS7t27NW3aNE2dOlVOp1O5ubkKBAJqaWlRd3e3Zs6cKUnKy8tTIBBQOBxWY2OjsrOzB4wDAOzhtPLGV61aNWD78OHDcrlcsW23261gMHjauMvlUjAYVHt7u1JSUuR0OgeMn4mJE1O+wj04O7lc4+yOAMQt/j6+GktL47MikYgcDkdsOxqNyuFwfOH4f7+e6rPbQ2lr61QkEj1tfDT/xwmFOuyOgFFgtP6N8PcxuDFjHIM+2R7RV0+lpaUpFArFtkOhkNxu92njra2tcrvdSk1NVUdHh/r7+wfsDwCwx4iWxtVXX619+/Zp//796u/vV319vTwej6ZMmaLk5GQ1NTVJkurq6uTxeJSYmKj09HT5/X5JUm1trTwez0hGBgCcYkSnp5KTk7VmzRotXbpUPT09yszMVE5OjiSpsrJSpaWl6uzs1PTp01VQUCBJWrlypYqKirRu3TpNnjxZjz/++EhGBgCcYkRK4+233459n5GRoddff/20fa644gq98sorp41PmTJFzz//vKX5AABmOCMcAGCM0gAAGKM0AADGKA0AgDFKAwBgjNIAABijNAAAxigNAIAxSgMAYIzSAAAYozQAAMYoDQCAMUoDAGCM0gAAGKM0AADGKA0AgDFKAwBgjNIAABijNAAAxigNAIAxSgMAYIzSAAAYozQAAMYoDQCAMUoDAGCM0gAAGKM0AADGKA0AgDGn3QFgv4vGJ8mZlGx3jGHX19uj9mO9dscARhVKA3ImJaup4gG7Ywy72Sv+nyRKAxhOTE8BAIxRGgAAY5QGAMDYWbGm8Yc//EHr1q1TX1+f7rnnHvl8PrsjATiHjL9wrJKSz4qHS2O9PX06dvzkGV8v7n8LwWBQVVVVeu2115SUlKSFCxfquuuu02WXXWZ3NADniKRkp35d8ordMYZV8aoFX+p6cV8aDQ0Nuv766zVhwgRJUnZ2tgKBgB566CGj648Z4/jCy/7voguGJWO8Gew+f5GkCydakMR+Z/q7SBmXpOSkJIvS2Kent1edHWf+SrKx/zf6/l98mb8PSRo/4fxhTmK/z/tdDPX7cUSj0ahVgYbD008/rRMnTqiwsFCS9PLLL2v37t165JFHbE4GAOeeuF8Ij0Qicjj+13zRaHTANgBg5MR9aaSlpSkUCsW2Q6GQ3G63jYkA4NwV96Vxww03aMeOHTpy5IhOnjypN998Ux6Px+5YAHBOivuF8EmTJqmwsFAFBQUKh8NasGCBrrrqKrtjAcA5Ke4XwgEA8SPup6cAAPGD0gAAGKM0AADGKA0AgDFKw2KdnZ2aO3euDhw4YHcUWz355JPyer3yer2qqKiwO46tnnjiCc2ZM0der1cbNmywO05cePTRR1VUVGR3DFvdfffd8nq9mjdvnubNm6ddu3bZHelzxf1Lbs9mu3btUmlpqZqbm+2OYquGhgZt375dmzdvlsPh0AMPPKC33npLt9xyi93RRty7776rnTt36vXXX1dfX5/mzJmjzMxMXXLJJXZHs82OHTu0efNmffe737U7im2i0aiam5v1pz/9SU5nfD8sc6RhoZqaGq1cufKcP4Pd5XKpqKhISUlJSkxM1KWXXqqDBw/aHcsW1157rZ577jk5nU61tbWpv79f558/+t4Iz9TRo0dVVVWlxYsX2x3FVh9//LEk6b777tNtt92mF154weZEXyy+K+0st2rVKrsjxIXLL7889n1zc7PeeOMNvfjiizYmsldiYqLWrl2rZ599Vjk5OZo0aZLdkWxTVlamwsJCHTp0yO4otjp+/LgyMjL0q1/9SuFwWAUFBfrGN76hG2+80e5op+FIAyPmgw8+0H333acVK1bo61//ut1xbLVs2TLt2LFDhw4dUk1Njd1xbPHyyy9r8uTJysjIsDuK7WbNmqWKigqNGzdOqampWrBggbZt22Z3rM/FkQZGRFNTk5YtW6bi4mJ5vV6749jmo48+Um9vr6688kqNHTtWWVlZev/99+2OZQu/369QKKR58+bp2LFjOnHihH7961+ruLjY7mgj7r333lM4HI4VaDQajdu1DY40YLlDhw7pwQcfVGVl5TldGJJ04MABlZaWqre3V729vdq6datmz55tdyxbbNiwQfX19aqrq9OyZct08803n5OFIUkdHR2qqKhQT0+POjs7tXnz5rh9oUh8VhlGlfXr16unp0dr1qyJjS1cuFB33nmnjanskZmZqd27d2v+/PlKSEhQVlbWOV+kkG666Sbt2rVL8+fPVyQSUX5+vmbNmmV3rM/FGxYCAIwxPQUAMEZpAACMURoAAGOUBgDAGKUBADBGaQAAjFEawDDr6+vTM888o9zcXOXm5srr9aq8vFzt7e0D9tu+fbvmzZs3YOytt95Sbm6u5s2bp4KCAn3yyScjGR0YEudpAMOssLBQkUhEDz/8sMaPH69wOKyNGzfqlVde0auvviqn06l169Zp06ZNmjRpkurr6yVJ3d3duv7661VXV6dp06Zp48aNamho0DPPPGPzPQL+hyMNYBjt3r1bjY2NWrNmjcaPHy/p03e1XbRokS655BK9+OKL2r59u06ePDngDHlJ6u/vVzQaVUdHhySpq6tLycnJI34fgMHwNiLAMPrb3/6mGTNmaOzYsaddduONN+qdd97RokWL9P3vf19//etfB1x+wQUXqLy8XAsXLtSECRMUiUTO6beQR3ziSAMYQYPNBr///vt66qmn5Pf7tX37di1evFhLly4d9DrASKM0gGF0zTXX6B//+IdOnjwpSert7Y0tgO/cuVNXX331F153+/btuuaaa/S1r31NkuTz+fTBBx+ctoAO2InSAIbRVVddpeuuu05FRUU6duyY/vOf/8jn82np0qV6//335fP5vvC63/72t9XY2KjW1lZJ0h//+EddfPHFSk1NHan4wJBY0wCG2WOPPaZnn31Wd911lyQpHA4rISFBF1xwgbZu3arbb7/9c6+XkZGh+++/X3fffbcSExM1fvx4VVdXj2R0YEi85BYYIcePH9eePXt0ww032B0F+NIoDQCAMdY0AADGKA0AgDFKAwBgjNIAABijNAAAxigNAICx/w8ZKPdD1IAUNAAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q19 = sns.countplot(x='Q19', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mobile_money = sns.countplot(x='mobile_money', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "savings = sns.countplot(x='savings', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "borrowing = sns.countplot(x='borrowing', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "insurance = sns.countplot(x='insurance', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mobile_money_classification = sns.countplot(x='mobile_money_classification', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/twitter_mining/data/ames.csv b/twitter_mining/data/ames.csv new file mode 100644 index 0000000..81873bf --- /dev/null +++ b/twitter_mining/data/ames.csv @@ -0,0 +1,2931 @@ 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Sdng","None",0,"Gd","TA","Wood","Gd","TA","Av","GLQ",427,"Unf",0,132,559,"GasA","Gd","Y","SBrkr",1080,672,0,1752,0,0,2,0,4,1,"TA",8,"Typ",0,NA,"Basment",1988,"RFn",2,492,"TA","TA","Y",325,12,0,0,0,0,NA,NA,NA,0,6,2010,"WD ","Normal",164000 +18,527258010,20,"RL",88,11394,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","StoneBr","Norm","Norm","1Fam","1Story",9,2,2010,2010,"Hip","CompShg","VinylSd","VinylSd","Stone",350,"Gd","TA","PConc","Ex","TA","Av","GLQ",1445,"Unf",0,411,1856,"GasA","Ex","Y","SBrkr",1856,0,0,1856,1,0,1,1,1,1,"Ex",8,"Typ",1,"Ex","Attchd",2010,"Fin",3,834,"TA","TA","Y",113,0,0,0,0,0,NA,NA,NA,0,6,2010,"New","Partial",394432 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+37,528108120,60,"RL",102,12858,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NridgHt","Norm","Norm","1Fam","2Story",9,5,2009,2010,"Gable","CompShg","VinylSd","VinylSd","Stone",162,"Ex","TA","PConc","Ex","TA","No","Unf",0,"Unf",0,1590,1590,"GasA","Ex","Y","SBrkr",1627,707,0,2334,0,0,2,1,3,1,"Ex",10,"Typ",1,"Gd","Attchd",2009,"Fin",3,751,"TA","TA","Y",144,133,0,0,0,0,NA,NA,NA,0,1,2010,"New","Partial",376162 +38,528112020,20,"RL",98,11478,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NridgHt","Norm","Norm","1Fam","1Story",8,5,2007,2008,"Gable","CompShg","VinylSd","VinylSd","Stone",200,"Gd","TA","PConc","Ex","TA","No","GLQ",1218,"Unf",0,486,1704,"GasA","Ex","Y","SBrkr",1704,0,0,1704,1,0,2,0,3,1,"Gd",7,"Typ",1,"Gd","Attchd",2008,"RFn",3,772,"TA","TA","Y",0,50,0,0,0,0,NA,NA,NA,0,5,2010,"WD ","Normal",306000 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+73,531367050,60,"RL",76,10142,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","SawyerW","Norm","Norm","1Fam","2Story",7,5,2004,2004,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",656,"Unf",0,300,956,"GasA","Ex","Y","SBrkr",956,1128,0,2084,1,0,2,1,4,1,"Gd",8,"Typ",0,NA,"BuiltIn",2004,"RFn",2,618,"TA","TA","Y",0,45,0,0,0,0,NA,NA,NA,0,1,2010,"WD ","Normal",233000 +74,531369060,60,"RL",70,11920,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","SawyerW","Norm","Norm","1Fam","2Story",7,5,2004,2004,"Gable","CompShg","VinylSd","VinylSd","BrkFace",122,"Gd","TA","PConc","Gd","TA","Av","Unf",0,"Unf",0,831,831,"GasA","Ex","Y","SBrkr",831,828,0,1659,0,0,2,1,3,1,"Gd",8,"Typ",0,NA,"Attchd",2004,"RFn",2,484,"TA","TA","Y",144,68,0,0,0,0,NA,NA,NA,0,4,2010,"WD ","Normal",181000 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Sdng","None",0,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,1055,1055,"GasA","Ex","Y","SBrkr",1055,790,0,1845,0,0,2,1,3,1,"Gd",8,"Typ",1,"TA","Attchd",1992,"RFn",2,462,"TA","TA","Y",635,104,0,0,0,0,NA,"GdPrv","Shed",400,5,2010,"WD ","Normal",189000 +78,531452020,120,"RL",55,7892,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","SawyerW","Norm","Norm","TwnhsE","1Story",6,5,1979,1979,"Gable","CompShg","Plywood","Plywood","None",0,"TA","TA","CBlock","Gd","TA","No","Unf",0,"Unf",0,918,918,"GasA","TA","Y","SBrkr",918,0,0,918,0,0,2,0,2,1,"TA",5,"Typ",1,"TA","Attchd",1979,"Unf",1,264,"TA","TA","Y",28,0,0,0,0,0,NA,NA,NA,0,4,2010,"WD ","Normal",99500 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+81,531453010,20,"RL",81,9672,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","SawyerW","Norm","Norm","1Fam","1Story",6,5,1984,1985,"Hip","CompShg","HdBoard","Plywood","None",0,"TA","TA","PConc","Gd","TA","No","GLQ",338,"Unf",0,702,1040,"GasA","TA","Y","SBrkr",1097,0,0,1097,0,0,2,0,3,1,"TA",6,"Typ",0,NA,"Attchd",1986,"Unf",2,480,"TA","TA","Y",0,0,0,0,0,0,NA,"GdPrv",NA,0,5,2010,"WD ","Normal",152000 +82,531453040,60,"RL",70,8400,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","SawyerW","Norm","Norm","1Fam","2Story",6,6,1980,1981,"Gable","CompShg","HdBoard","HdBoard","BrkFace",130,"TA","TA","CBlock","Gd","TA","No","Unf",0,"Unf",0,650,650,"GasA","TA","Y","SBrkr",888,676,0,1564,0,0,2,1,3,1,"TA",7,"Typ",1,"TA","Attchd",1980,"Unf",2,476,"TA","TA","Y",0,50,0,0,204,0,NA,"MnPrv",NA,0,4,2010,"WD ","Normal",171000 +83,531477050,30,"RH",70,9800,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","SawyerW","Feedr","Norm","1Fam","1Story",5,5,1920,1950,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","Fa","BrkTil","TA","TA","No","Unf",0,"Unf",0,816,816,"GasA","TA","N","FuseA",1012,0,0,1012,0,0,1,0,2,1,"TA",5,"Typ",0,NA,"Detchd",1920,"Unf",1,429,"TA","TA","Y",121,0,80,0,0,0,NA,NA,NA,0,4,2010,"WD ","Normal",67500 +84,532351140,90,"RM",68,8930,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Sawyer","RRAe","Norm","Duplex","1.5Fin",6,5,1978,1978,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","Slab",NA,NA,NA,NA,0,NA,0,0,0,"GasA","TA","Y","SBrkr",1318,584,0,1902,0,0,2,0,4,2,"TA",8,"Typ",0,NA,"Attchd",1978,"Unf",2,539,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,4,2010,"WD ","Normal",112000 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+103,533223020,160,"FV",24,2572,"Pave",NA,"Reg","Lvl","AllPub","FR2","Gtl","Somerst","Norm","Norm","Twnhs","2Story",7,5,1999,1999,"Hip","CompShg","MetalSd","MetalSd","None",0,"Gd","TA","PConc","Gd","TA","No","ALQ",604,"Unf",0,92,696,"GasA","Ex","Y","SBrkr",696,720,0,1416,1,0,2,1,3,1,"Gd",6,"Typ",0,NA,"Detchd",1999,"Unf",2,484,"TA","TA","Y",0,44,0,0,0,0,NA,NA,NA,0,5,2010,"WD ","Normal",155000 +104,533223100,160,"FV",NA,2403,"Pave",NA,"IR1","Lvl","AllPub","FR2","Gtl","Somerst","Norm","Norm","TwnhsE","2Story",7,5,2003,2003,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",244,"Unf",0,286,530,"GasA","Ex","Y","SBrkr",530,550,0,1080,0,0,2,1,2,1,"Gd",4,"Typ",0,NA,"Attchd",2003,"RFn",2,496,"TA","TA","Y",0,50,0,0,0,0,NA,NA,NA,0,6,2010,"WD ","Normal",147110 +105,533238045,20,"FV",57,12853,"Pave","Pave","IR1","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","1Story",8,5,2010,2010,"Gable","CompShg","CemntBd","CmentBd","None",0,"Gd","TA","PConc","Ex","Po","No","GLQ",1032,"Unf",0,610,1642,"GasA","Ex","Y","SBrkr",1418,0,0,1418,1,0,1,1,1,1,"Gd",6,"Typ",1,"Gd","Attchd",2010,"RFn",3,852,"TA","TA","Y",160,192,0,224,0,0,NA,NA,NA,0,4,2010,"New","Partial",267916 +106,533244030,60,"FV",68,7379,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","2Story",8,5,2000,2000,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",484,"Unf",0,491,975,"GasA","Ex","Y","SBrkr",975,873,0,1848,1,0,2,1,3,1,"Gd",7,"Typ",1,"TA","Attchd",2000,"RFn",2,592,"TA","TA","Y",280,184,0,0,0,0,NA,NA,NA,0,4,2010,"WD ","Normal",254000 +107,533253060,120,"RL",30,4420,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Greens","Norm","Norm","TwnhsE","1Story",8,5,1980,1980,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"Gd","TA","CBlock","Gd","TA","Gd","GLQ",728,"Unf",0,0,728,"GasA","TA","Y","SBrkr",788,0,0,788,1,0,1,0,1,1,"Gd",3,"Typ",1,"TA","Detchd",1980,"Fin",2,484,"TA","TA","Y",133,42,0,0,0,0,NA,NA,NA,0,6,2010,"WD ","Normal",155000 +108,533253210,120,"RL",40,3880,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Greens","Norm","Norm","Twnhs","1Story",8,6,1978,1978,"Gable","CompShg","Wd Sdng","Plywood","None",0,"Gd","TA","CBlock","Gd","TA","Gd","GLQ",1052,"Unf",0,174,1226,"GasA","TA","Y","SBrkr",1226,0,0,1226,1,0,1,0,1,1,"Gd",4,"Typ",1,"TA","Attchd",1978,"Fin",2,484,"TA","TA","Y",223,78,0,0,0,0,NA,NA,NA,0,1,2010,"WD ","Normal",206000 +109,533352170,60,"RL",NA,13517,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Sawyer","RRAe","Norm","1Fam","2Story",6,8,1976,2005,"Gable","CompShg","HdBoard","Plywood","BrkFace",289,"Gd","TA","CBlock","TA","TA","No","GLQ",533,"Unf",0,192,725,"GasA","Ex","Y","SBrkr",725,754,0,1479,0,0,2,1,3,1,"Gd",6,"Typ",0,NA,"Attchd",1976,"RFn",2,475,"TA","TA","Y",0,44,0,0,0,0,NA,NA,NA,0,3,2010,"WD ","Normal",130500 +110,534102025,20,"FV",80,8000,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","1Story",7,5,2002,2002,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",833,"Unf",0,659,1492,"GasA","Ex","Y","SBrkr",1492,0,0,1492,1,0,2,0,3,1,"Gd",6,"Typ",1,"Gd","Attchd",2002,"RFn",2,596,"TA","TA","Y",277,137,0,0,0,0,NA,NA,NA,0,4,2010,"WD ","Normal",230000 +111,534129040,20,"RL",NA,10456,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",6,6,1967,1967,"Hip","CompShg","HdBoard","HdBoard","BrkFace",120,"TA","TA","CBlock","TA","TA","No","GLQ",506,"Unf",0,1323,1829,"GasA","Gd","Y","SBrkr",1829,0,0,1829,1,0,2,0,4,1,"TA",8,"Typ",0,NA,"Attchd",1967,"RFn",2,535,"TA","TA","Y",0,76,0,0,0,0,NA,NA,NA,0,5,2010,"WD ","Normal",218500 +112,534151175,60,"RL",80,10791,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","2Story",6,5,1993,1993,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","TA","PConc","Gd","TA","Mn","GLQ",1137,"Unf",0,143,1280,"GasA","Ex","Y","SBrkr",1280,1215,0,2495,1,0,2,1,4,1,"Gd",9,"Typ",1,"TA","Attchd",1993,"Unf",2,660,"TA","TA","Y",224,32,0,0,0,0,NA,NA,NA,0,3,2010,"WD ","Normal",243500 +113,534152050,20,"RL",NA,10603,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",6,7,1977,2001,"Gable","CompShg","Plywood","Plywood","BrkFace",28,"TA","TA","PConc","TA","TA","Mn","ALQ",1200,"Unf",0,410,1610,"GasA","Gd","Y","SBrkr",1610,0,0,1610,1,0,2,0,3,1,"Gd",6,"Typ",2,"TA","Attchd",1977,"RFn",2,480,"TA","TA","Y",168,68,0,0,0,0,NA,NA,NA,0,2,2010,"WD ","Normal",205000 +114,534152070,50,"RL",NA,18837,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1.5Fin",6,5,1978,1978,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","TA","PConc","Gd","TA","Mn","ALQ",687,"LwQ",46,491,1224,"GasA","TA","Y","SBrkr",1287,604,0,1891,0,1,3,0,3,1,"TA",7,"Typ",1,"TA","Attchd",1978,"RFn",2,678,"TA","TA","Y",0,69,0,0,0,0,NA,NA,NA,0,4,2010,"WD ","Normal",212500 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","Normal",136000 +218,905101310,90,"RL",72,10778,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Sawyer","Norm","Norm","Duplex","1Story",4,5,1968,1968,"Hip","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","TA","TA","No","Unf",0,"Unf",0,1768,1768,"GasA","TA","N","SBrkr",1768,0,0,1768,0,0,2,0,4,2,"TA",8,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"Y",0,0,0,0,0,0,NA,NA,NA,0,4,2010,"WD ","Normal",136500 +219,905102060,190,"RL",75,11625,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Sawyer","Norm","Norm","2fmCon","1Story",5,4,1965,1965,"Hip","CompShg","Plywood","HdBoard","None",0,"TA","TA","PConc","TA","TA","Mn","BLQ",841,"Unf",0,198,1039,"GasA","Ex","Y","SBrkr",1039,0,0,1039,1,0,1,1,3,1,"TA",6,"Typ",0,NA,"Attchd",1965,"Unf",2,504,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,4,2010,"WD ","Normal",131500 +220,905103060,20,"RL",NA,11341,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Sawyer","Norm","Norm","1Fam","1Story",5,6,1957,1996,"Hip","CompShg","Wd Sdng","Wd 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+353,527146010,120,"RL",57,8013,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","StoneBr","Norm","Norm","TwnhsE","1Story",8,5,1995,1996,"Gable","CompShg","CemntBd","CmentBd","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",741,"Unf",0,846,1587,"GasA","Ex","Y","SBrkr",1734,0,0,1734,1,0,2,0,2,1,"Gd",6,"Typ",0,NA,"Attchd",1995,"RFn",2,528,"TA","TA","Y",52,50,0,0,0,0,NA,NA,NA,0,9,2009,"WD ","Normal",239500 +354,527161040,80,"RL",57,8923,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Gilbert","Norm","Norm","1Fam","SLvl",7,5,1998,1998,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",189,"Unf",0,195,384,"GasA","Gd","Y","SBrkr",751,631,0,1382,0,0,2,1,3,1,"TA",7,"Typ",1,"TA","BuiltIn",1998,"Fin",2,396,"TA","TA","Y",256,0,0,0,0,0,NA,NA,NA,0,2,2009,"WD ","Normal",177500 +355,527162110,60,"RL",74,10141,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","Gilbert","Norm","Norm","1Fam","2Story",7,5,1998,1998,"Gable","CompShg","VinylSd","VinylSd","BrkFace",40,"TA","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,832,832,"GasA","Gd","Y","SBrkr",885,833,0,1718,0,0,2,1,3,1,"TA",7,"Typ",1,"TA","Attchd",1998,"Fin",2,427,"TA","TA","Y",0,94,0,0,291,0,NA,NA,NA,0,12,2009,"WD ","Normal",185000 +356,527162120,60,"RL",60,7500,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Gilbert","Norm","Norm","1Fam","2Story",6,5,1998,1999,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",476,"Unf",0,476,952,"GasA","Gd","Y","SBrkr",952,684,0,1636,1,0,2,1,3,1,"TA",7,"Typ",1,"TA","Attchd",1998,"Fin",2,440,"TA","TA","Y",0,84,0,0,0,0,NA,NA,NA,0,10,2009,"WD ","Normal",191000 +357,527163040,60,"RL",59,7837,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Gilbert","Norm","Norm","1Fam","2Story",6,7,1993,1994,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,799,799,"GasA","Gd","Y","SBrkr",799,772,0,1571,0,0,2,1,3,1,"TA",7,"Typ",1,"TA","Attchd",1993,"RFn",2,380,"TA","TA","Y",0,40,0,0,0,0,NA,NA,NA,0,5,2009,"WD ","Normal",178000 +358,527163070,60,"RL",NA,9765,"Pave",NA,"IR2","Lvl","AllPub","Corner","Gtl","Gilbert","Norm","Norm","1Fam","2Story",6,8,1993,1993,"Gable","CompShg","VinylSd","VinylSd","BrkFace",68,"Ex","Gd","PConc","Gd","Gd","No","ALQ",310,"Unf",0,370,680,"GasA","Gd","Y","SBrkr",680,790,0,1470,0,0,2,1,3,1,"TA",6,"Typ",1,"TA","BuiltIn",1993,"Fin",2,420,"TA","TA","Y",232,63,0,0,0,0,NA,NA,"Shed",480,4,2009,"WD ","Normal",185000 +359,527163130,60,"RL",NA,8803,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Gilbert","Norm","Norm","1Fam","2Story",6,5,1994,1995,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","PConc","Gd","TA","No","GLQ",600,"Unf",0,107,707,"GasA","Gd","Y","SBrkr",707,809,0,1516,1,0,2,1,3,1,"Gd",7,"Typ",1,"TA","Attchd",1994,"Fin",2,409,"TA","TA","Y",0,46,0,0,0,0,NA,NA,NA,0,7,2009,"WD ","Normal",181316 +360,527164020,20,"RL",58,7250,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Gilbert","Norm","Norm","1Fam","1Story",6,5,1993,1993,"Gable","CompShg","HdBoard","HdBoard","BrkFace",45,"TA","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,1181,1181,"GasA","Ex","Y","SBrkr",1190,0,0,1190,0,0,2,0,3,1,"Gd",6,"Typ",1,"TA","Attchd",1993,"Unf",2,430,"TA","TA","Y",0,21,0,0,0,0,NA,NA,NA,0,11,2009,"WD ","Normal",166000 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Sdng","AsbShng","None",0,"Fa","Fa","CBlock","TA","Fa","No","Unf",0,"Unf",0,721,721,"GasA","Gd","Y","SBrkr",841,0,0,841,0,0,1,0,2,1,"TA",4,"Typ",0,NA,"CarPort",1950,"Unf",1,294,"TA","TA","N",250,0,24,0,0,0,NA,NA,NA,0,9,2009,"WD ","Normal",82000 +554,531479020,45,"RH",60,9000,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","SawyerW","Norm","Norm","1Fam","1.5Unf",6,3,1928,1950,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","BrkTil","Fa","Fa","No","Unf",0,"Unf",0,784,784,"GasA","TA","N","FuseA",784,0,0,784,0,0,1,0,2,1,"TA",5,"Typ",0,NA,"Detchd",1950,"Unf",2,360,"Fa","Fa","N",0,0,91,0,0,0,NA,NA,NA,0,10,2009,"WD ","Normal",76000 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+567,533210010,160,"FV",40,3951,"Pave","Pave","Reg","Lvl","AllPub","Corner","Gtl","Somerst","Norm","Norm","TwnhsE","2Story",6,5,2009,2009,"Gable","CompShg","VinylSd","VinylSd","Stone",76,"Gd","TA","PConc","Gd","TA","Av","Unf",0,"Unf",0,612,612,"GasA","Ex","Y","SBrkr",612,612,0,1224,0,0,2,1,2,1,"Gd",4,"Typ",0,NA,"Detchd",2009,"RFn",2,528,"TA","TA","Y",0,234,0,0,0,0,NA,NA,NA,0,6,2009,"New","Partial",164500 +568,533210020,160,"FV",30,3000,"Pave","Pave","Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","TwnhsE","2Story",6,5,2009,2009,"Gable","CompShg","VinylSd","VinylSd","Stone",76,"Gd","TA","PConc","Gd","TA","Av","GLQ",294,"Unf",0,318,612,"GasA","Ex","Y","SBrkr",612,612,0,1224,0,0,2,1,2,1,"Gd",4,"Typ",0,NA,"Detchd",2009,"RFn",2,528,"TA","TA","Y",0,234,0,0,0,0,NA,NA,NA,0,6,2009,"New","Partial",173733 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Sdng","BrkFace",102,"Gd","TA","CBlock","Gd","Gd","Mn","GLQ",983,"Unf",0,692,1675,"GasA","Ex","Y","SBrkr",1688,0,0,1688,1,0,2,0,2,1,"Ex",6,"Typ",1,"Ex","Attchd",1988,"RFn",2,528,"TA","TA","Y",0,48,0,0,141,0,NA,NA,NA,0,6,2008,"WD ","Normal",226500 +1019,527302050,20,"RL",78,12090,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",6,6,1981,1981,"Gable","CompShg","MetalSd","MetalSd","BrkFace",210,"TA","Gd","CBlock","Gd","TA","No","GLQ",588,"LwQ",228,606,1422,"GasA","TA","Y","SBrkr",1422,0,0,1422,0,0,2,0,3,1,"Gd",7,"Typ",1,"TA","Attchd",1981,"Fin",2,576,"TA","TA","Y",276,0,0,0,0,0,NA,"GdPrv",NA,0,6,2008,"WD ","Normal",175500 +1020,527302070,20,"RL",NA,10825,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",7,7,1983,1983,"Gable","CompShg","WdShing","Plywood","BrkFace",174,"Gd","TA","CBlock","Gd","TA","Mn","GLQ",747,"Unf",0,513,1260,"GasA","TA","Y","SBrkr",1260,0,0,1260,1,0,2,0,3,1,"TA",6,"Typ",0,NA,"Attchd",1983,"Unf",2,598,"TA","TA","Y",152,0,0,0,0,0,NA,NA,NA,0,7,2008,"WD ","Normal",181900 +1021,527302185,20,"RL",56,18559,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","NWAmes","Norm","Norm","1Fam","1Story",7,5,1978,1978,"Hip","CompShg","Plywood","Plywood","BrkFace",383,"Gd","Gd","CBlock","Gd","TA","No","GLQ",1206,"Rec",186,656,2048,"GasA","TA","Y","SBrkr",2064,0,0,2064,1,0,2,0,3,1,"Gd",7,"Typ",2,"Fa","Attchd",1978,"Fin",2,550,"TA","TA","Y",200,0,0,0,0,0,NA,NA,NA,0,8,2008,"WD ","Normal",235000 +1022,527302210,20,"RL",85,14450,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",7,5,1979,1979,"Hip","CompShg","Plywood","Plywood","BrkFace",194,"Gd","TA","CBlock","Gd","Fa","No","ALQ",864,"LwQ",449,469,1782,"GasA","TA","Y","SBrkr",1782,0,0,1782,0,1,2,0,3,1,"Gd",6,"Typ",1,"TA","Attchd",1979,"Fin",2,551,"TA","TA","Y",467,0,0,0,0,0,NA,NA,NA,0,3,2008,"WD ","Normal",220000 +1023,527325070,60,"RL",NA,12227,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","NWAmes","PosN","Norm","1Fam","2Story",6,7,1977,1995,"Gable","CompShg","HdBoard","HdBoard","BrkFace",424,"TA","Gd","CBlock","Gd","Gd","No","ALQ",896,"Unf",0,434,1330,"GasA","TA","Y","SBrkr",1542,1330,0,2872,1,0,2,1,4,1,"TA",11,"Typ",1,"TA","Attchd",1977,"Fin",2,619,"TA","TA","Y",550,282,0,0,0,0,NA,NA,NA,0,7,2008,"WD ","Normal",272000 +1024,527325240,20,"RL",90,13068,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","NWAmes","Norm","Norm","1Fam","1Story",6,5,1976,1976,"Hip","CompShg","HdBoard","HdBoard","BrkFace",621,"TA","TA","PConc","Gd","TA","No","ALQ",890,"Rec",48,273,1211,"GasA","TA","Y","SBrkr",1211,0,0,1211,1,0,2,0,3,1,"Gd",6,"Typ",1,"Po","Attchd",1976,"Fin",2,461,"TA","TA","Y",0,0,0,174,0,0,NA,"MnPrv",NA,0,11,2008,"WD ","Normal",161000 +1025,527326130,20,"RL",NA,15611,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","NWAmes","Norm","Norm","1Fam","1Story",5,6,1977,1977,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","PConc","Gd","TA","Av","ALQ",767,"LwQ",93,266,1126,"GasA","TA","Y","SBrkr",1126,0,0,1126,0,1,2,0,3,1,"Ex",6,"Typ",0,NA,"Attchd",1977,"RFn",2,540,"TA","TA","Y",180,0,0,0,0,0,NA,NA,NA,0,3,2008,"WD ","Abnorml",175000 +1026,527354160,60,"RL",80,10400,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","2Story",7,6,1980,1988,"Gable","CompShg","HdBoard","HdBoard","BrkFace",280,"TA","TA","CBlock","Gd","TA","Mn","Unf",0,"Unf",0,738,738,"GasA","TA","Y","SBrkr",1277,767,0,2044,0,0,2,1,3,1,"TA",7,"Min1",1,"TA","Attchd",1980,"Unf",2,489,"TA","TA","Y",28,73,0,0,0,0,NA,NA,NA,0,3,2008,"WD ","Normal",185000 +1027,527357110,60,"RL",75,9743,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","2Story",5,6,1969,1969,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","CBlock","TA","Fa","No","BLQ",280,"Unf",0,440,720,"GasA","Gd","Y","SBrkr",720,588,0,1308,0,0,1,1,3,1,"TA",7,"Typ",0,NA,"Detchd",1969,"Unf",2,484,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","Normal",146900 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+1104,528354100,60,"RL",97,10990,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","NoRidge","Norm","Norm","1Fam","2Story",7,5,1996,1997,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Ex","TA","No","GLQ",851,"Unf",0,213,1064,"GasA","Ex","Y","SBrkr",1064,1061,0,2125,1,0,2,1,4,1,"Gd",12,"Typ",2,"TA","Attchd",1996,"RFn",2,576,"TA","TA","Y",168,0,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","Normal",279500 +1105,528363020,60,"RL",NA,11929,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","NoRidge","Norm","Norm","1Fam","2Story",8,7,1995,1995,"Gable","CompShg","VinylSd","VinylSd","BrkFace",466,"Gd","TA","PConc","Gd","TA","Av","Unf",0,"Unf",0,1242,1242,"GasA","Ex","Y","SBrkr",1251,1250,0,2501,0,0,2,1,4,1,"Gd",9,"Typ",1,"TA","Attchd",1995,"RFn",3,751,"TA","TA","Y",192,87,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","Normal",290000 +1106,528363110,20,"RL",91,10437,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NoRidge","Norm","Norm","1Fam","1Story",8,6,1995,1995,"Hip","CompShg","MetalSd","MetalSd","BrkFace",660,"Gd","Gd","PConc","Gd","TA","Gd","GLQ",1696,"Unf",0,413,2109,"GasA","Ex","Y","SBrkr",2113,0,0,2113,1,0,2,1,2,1,"Gd",7,"Typ",1,"TA","Attchd",1995,"Fin",3,839,"TA","TA","Y",236,46,0,0,0,0,NA,NA,NA,0,8,2008,"WD ","Normal",350000 +1107,528365060,60,"RL",96,10542,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NoRidge","Norm","Norm","1Fam","2Story",7,5,1993,1994,"Hip","CompShg","Wd Sdng","ImStucc","BrkFace",651,"Gd","TA","PConc","Gd","TA","Gd","GLQ",1173,"Unf",0,138,1311,"GasA","Ex","Y","SBrkr",1325,1093,0,2418,1,0,2,1,3,1,"Gd",9,"Typ",1,"TA","Attchd",1993,"RFn",3,983,"TA","TA","Y",250,154,216,0,0,0,NA,NA,NA,0,8,2008,"WD ","Normal",341000 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+1110,528390020,20,"RL",91,14303,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","NoRidge","Norm","Norm","1Fam","1Story",8,5,1994,1994,"Hip","CompShg","HdBoard","HdBoard","BrkFace",554,"Gd","TA","PConc","Gd","TA","Gd","GLQ",1314,"Unf",0,672,1986,"GasA","Ex","Y","SBrkr",1987,0,0,1987,1,0,2,0,2,1,"Gd",7,"Typ",1,"TA","Attchd",1994,"Fin",2,691,"TA","TA","Y",262,36,0,0,0,0,NA,NA,NA,0,8,2008,"WD ","Normal",301500 +1111,528427010,20,"RL",92,11932,"Pave",NA,"Reg","Lvl","AllPub","FR2","Gtl","Somerst","Feedr","Norm","1Fam","1Story",8,5,2007,2008,"Gable","CompShg","VinylSd","VinylSd","Stone",186,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,1580,1580,"GasA","Ex","Y","SBrkr",1580,0,0,1580,0,0,2,0,3,1,"Gd",7,"Typ",0,NA,"Attchd",2008,"RFn",3,830,"TA","TA","Y",0,24,0,0,0,0,NA,NA,NA,0,6,2008,"ConLD","Partial",235128 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+1116,528431060,20,"RL",78,10206,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","1Story",8,5,2007,2007,"Gable","CompShg","VinylSd","VinylSd","Stone",468,"TA","TA","PConc","Gd","TA","No","GLQ",33,"Unf",0,1530,1563,"GasA","Ex","Y","SBrkr",1563,0,0,1563,0,0,2,0,3,1,"Gd",6,"Typ",1,"Gd","Attchd",2007,"RFn",3,758,"TA","TA","Y",144,99,0,0,0,0,NA,NA,NA,0,10,2008,"WD ","Normal",245000 +1117,528431080,20,"RL",91,12274,"Pave",NA,"IR1","Lvl","AllPub","FR2","Gtl","Somerst","Norm","Norm","1Fam","1Story",7,5,2008,2008,"Gable","CompShg","VinylSd","VinylSd","Stone",256,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,1417,1417,"GasA","Ex","Y","SBrkr",1428,0,0,1428,0,0,2,0,3,1,"Ex",6,"Typ",0,NA,"Attchd",2008,"RFn",2,554,"TA","TA","Y",0,60,0,0,0,0,NA,NA,NA,0,7,2008,"New","Partial",227680 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+1128,528480160,60,"FV",65,8125,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","2Story",7,6,2007,2007,"Gable","CompShg","CemntBd","CmentBd","",NA,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,813,813,"GasA","Ex","Y","SBrkr",822,843,0,1665,0,0,2,1,3,1,"Gd",7,"Typ",0,NA,"Attchd",2007,"RFn",2,562,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,5,2008,"WD ","Normal",205950 +1129,528482020,20,"FV",85,10625,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","1Story",8,5,2007,2007,"Gable","CompShg","VinylSd","VinylSd","BrkFace",292,"Gd","TA","PConc","Gd","TA","Av","Unf",0,"Unf",0,1660,1660,"GasA","Ex","Y","SBrkr",1660,0,0,1660,0,0,2,0,3,1,"Gd",7,"Typ",1,"Gd","Attchd",2007,"Fin",3,660,"TA","TA","Y",133,120,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","Normal",256000 +1130,528488140,20,"FV",62,7500,"Pave","Pave","Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","1Story",7,5,2007,2008,"Gable","CompShg","CemntBd","CmentBd","Stone",210,"Gd","TA","PConc","Gd","TA","No","GLQ",902,"Unf",0,316,1218,"GasA","Ex","Y","SBrkr",1218,0,0,1218,1,0,2,0,2,1,"Gd",6,"Typ",0,NA,"Attchd",2008,"Fin",2,462,"TA","TA","Y",168,168,0,0,0,0,NA,NA,NA,0,4,2008,"New","Partial",230348 +1131,528490080,60,"FV",64,8791,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","2Story",6,5,2003,2003,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","Rec",503,"Unf",0,361,864,"GasA","Ex","Y","SBrkr",864,864,0,1728,0,0,2,1,3,1,"Gd",7,"Typ",0,NA,"Attchd",2003,"RFn",2,673,"TA","TA","Y",216,56,0,0,0,0,NA,NA,NA,0,5,2008,"WD ","Normal",207500 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Sdng","None",0,"TA","TA","PConc","TA","Gd","No","BLQ",53,"ALQ",799,132,984,"GasA","TA","Y","SBrkr",984,0,0,984,1,0,1,0,3,1,"TA",5,"Typ",1,"TA","Attchd",1963,"RFn",1,384,"TA","TA","Y",145,56,0,0,0,0,NA,"MnPrv","Shed",400,8,2008,"WD ","Normal",128000 +1151,532353050,20,"RL",NA,12968,"Pave",NA,"IR2","Lvl","AllPub","Inside","Gtl","Sawyer","Norm","Norm","1Fam","1Story",5,6,1962,1962,"Hip","CompShg","HdBoard","Plywood","None",0,"TA","TA","CBlock","TA","TA","No","ALQ",737,"Unf",0,175,912,"GasA","TA","Y","SBrkr",912,0,0,912,1,0,1,0,2,1,"TA",4,"Typ",0,NA,"Detchd",1962,"Unf",1,352,"TA","TA","Y",140,0,0,0,176,0,NA,NA,NA,0,9,2008,"WD ","Normal",144000 +1152,532354150,20,"RL",75,8100,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Sawyer","Feedr","Norm","1Fam","1Story",5,8,1961,2007,"Gable","CompShg","Wd Sdng","Wd 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+1192,534177180,60,"RL",90,10800,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","2Story",6,7,1974,1997,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","TA","TA","No","ALQ",956,"Rec",182,384,1522,"GasA","TA","Y","SBrkr",1548,1066,0,2614,0,0,2,1,4,1,"TA",9,"Typ",1,"TA","Attchd",1974,"RFn",2,624,"TA","TA","Y",38,243,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","Normal",240000 +1193,534201240,60,"RL",73,8814,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NAmes","Feedr","Norm","1Fam","2Story",5,6,1968,1968,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","TA","CBlock","TA","TA","No","LwQ",732,"Unf",0,0,732,"GasA","Ex","Y","SBrkr",732,732,0,1464,0,0,1,1,4,1,"TA",7,"Typ",0,NA,"Attchd",1968,"Unf",2,470,"TA","TA","Y",0,40,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","Normal",141000 +1194,534202130,20,"RL",72,8872,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","NAmes","Norm","Norm","1Fam","1Story",5,8,1965,2008,"Gable","CompShg","VinylSd","VinylSd","BrkFace",300,"TA","TA","CBlock","TA","TA","No","ALQ",595,"Unf",0,317,912,"GasA","Ex","Y","SBrkr",912,0,0,912,1,0,1,0,2,1,"Gd",5,"Typ",0,NA,"Detchd",1992,"Unf",2,576,"TA","TA","Y",0,240,0,0,0,0,NA,NA,NA,0,12,2008,"WD ","Normal",147000 +1195,534204030,20,"RL",65,8125,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NAmes","Norm","Norm","1Fam","1Story",5,7,1965,2005,"Hip","CompShg","HdBoard","HdBoard","None",0,"Gd","TA","CBlock","TA","TA","No","ALQ",260,"Rec",456,196,912,"GasA","Ex","Y","SBrkr",925,0,0,925,1,0,1,0,3,1,"TA",5,"Typ",0,NA,"Detchd",1978,"Unf",2,576,"TA","TA","Y",233,0,0,0,0,0,NA,NA,NA,0,7,2008,"WD ","Normal",135000 +1196,534225110,90,"RL",72,11072,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NAmes","Norm","Norm","Duplex","1Story",5,5,1965,1965,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","TA","TA","No","Unf",0,"Unf",0,1728,1728,"GasA","TA","Y","SBrkr",1728,0,0,1728,0,0,2,0,6,2,"TA",10,"Typ",0,NA,"Detchd",1987,"Unf",2,576,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,11,2008,"WD ","Normal",145000 +1197,534225120,90,"RL",74,13101,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NAmes","Norm","Norm","Duplex","1Story",5,5,1965,1965,"Gable","CompShg","HdBoard","HdBoard","BrkFace",108,"TA","TA","CBlock","TA","TA","No","LwQ",231,"Unf",0,1497,1728,"GasA","TA","Y","SBrkr",1728,0,0,1728,0,0,2,0,6,2,"TA",10,"Typ",0,NA,"Detchd",1987,"Unf",2,576,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,11,2008,"WD ","Normal",142600 +1198,534250010,90,"RL",87,9246,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Feedr","Norm","Duplex","1Story",5,5,1973,1973,"Gable","CompShg","Plywood","Plywood","BrkFace",564,"TA","TA","CBlock","TA","TA","No","Unf",0,"Unf",0,1656,1656,"GasA","TA","Y","SBrkr",1656,0,0,1656,0,0,2,0,4,2,"TA",8,"Typ",0,NA,"Detchd",1973,"Unf",2,506,"TA","TA","Y",0,211,0,0,0,0,NA,NA,NA,0,11,2008,"WD ","Normal",135000 +1199,534250335,60,"RL",NA,13355,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","2Story",7,6,1971,1971,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","Gd","Gd","No","ALQ",583,"Unf",0,242,825,"GasA","TA","Y","SBrkr",845,825,0,1670,0,0,1,1,4,1,"TA",7,"Typ",0,NA,"Attchd",1971,"Fin",2,464,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,5,2008,"WD ","Normal",170000 +1200,534250370,60,"RL",NA,8963,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","2Story",8,9,1976,1996,"Hip","CompShg","VinylSd","VinylSd","BrkFace",289,"Ex","Gd","CBlock","TA","Gd","No","GLQ",575,"ALQ",80,487,1142,"GasA","Ex","Y","SBrkr",1175,1540,0,2715,0,1,3,1,4,1,"Gd",11,"Typ",2,"TA","BuiltIn",1994,"Fin",2,831,"TA","TA","Y",0,204,0,0,0,0,NA,NA,NA,0,7,2008,"WD ","Normal",299800 +1201,534250410,60,"RL",76,9120,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","2Story",6,6,1974,1974,"Hip","CompShg","HdBoard","HdBoard","BrkFace",270,"Gd","TA","CBlock","TA","TA","No","ALQ",442,"Unf",0,459,901,"GasA","TA","Y","SBrkr",943,933,0,1876,0,0,2,1,4,1,"Gd",8,"Typ",1,"TA","Attchd",1974,"RFn",2,540,"Gd","TA","Y",0,69,0,0,0,0,NA,NA,NA,0,7,2008,"WD ","Normal",185000 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+1373,904100040,20,"RL",55,8250,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Sawyer","Feedr","Norm","1Fam","1Story",5,5,1968,1968,"Hip","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","TA","TA","No","BLQ",250,"LwQ",492,210,952,"GasA","Ex","Y","SBrkr",1211,0,0,1211,0,0,1,0,3,1,"TA",5,"Typ",1,"TA","Attchd",1968,"Unf",1,322,"TA","TA","Y",0,63,0,0,0,0,NA,NA,NA,0,8,2008,"WD ","Normal",134000 +1374,904100160,50,"RL",115,21286,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Sawyer","Norm","Norm","1Fam","1.5Fin",5,5,1948,1950,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","CBlock","TA","TA","No","Unf",0,"Unf",0,720,720,"GasA","TA","Y","SBrkr",720,551,0,1271,0,0,2,0,4,1,"TA",7,"Typ",1,"Gd","Attchd",1948,"Unf",1,312,"TA","TA","Y",0,0,108,0,0,0,NA,NA,NA,0,8,2008,"WD ","Normal",135000 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+1489,908102260,20,"RL",75,9937,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",5,6,1965,1965,"Gable","CompShg","HdBoard","HdBoard","None",0,"Gd","TA","CBlock","TA","TA","No","BLQ",830,"LwQ",290,136,1256,"GasA","Gd","Y","SBrkr",1256,0,0,1256,1,0,1,1,3,1,"TA",6,"Typ",0,NA,"Attchd",1965,"Fin",1,276,"TA","TA","Y",736,68,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","Normal",147500 +1490,908103260,85,"RL",64,12102,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","SFoyer",5,5,1976,1976,"Gable","CompShg","HdBoard","Plywood","BrkFace",222,"TA","TA","CBlock","Gd","Gd","Gd","ALQ",456,"Unf",0,0,456,"GasA","Ex","Y","SBrkr",1033,0,0,1033,0,1,1,0,3,1,"TA",6,"Typ",0,NA,"BuiltIn",1976,"RFn",2,504,"Fa","TA","Y",224,0,0,0,0,0,NA,NA,NA,0,4,2008,"WD ","Family",165000 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+1493,908128050,85,"RL",90,10012,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","SFoyer",4,5,1972,1972,"Gable","CompShg","Plywood","Plywood","None",0,"TA","TA","CBlock","Gd","TA","Av","BLQ",920,"Rec",180,38,1138,"GasA","TA","Y","SBrkr",1181,0,0,1181,1,0,2,0,3,1,"TA",6,"Typ",0,NA,"Detchd",1974,"RFn",2,588,"TA","TA","Y",0,0,180,0,0,0,NA,"MnPrv",NA,0,4,2008,"WD ","Normal",137500 +1494,908128110,60,"RL",60,6931,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","2Story",7,5,2003,2004,"Gable","CompShg","VinylSd","VinylSd","Stone",92,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,746,746,"GasA","Ex","Y","SBrkr",760,896,0,1656,0,0,2,1,3,1,"Gd",7,"Typ",1,"Gd","BuiltIn",2003,"Fin",2,397,"TA","TA","Y",178,128,0,0,0,0,NA,NA,NA,0,7,2008,"WD ","Normal",165400 +1495,908151040,80,"RL",NA,9638,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Edwards","Norm","Norm","1Fam","SLvl",6,6,1977,1977,"Hip","CompShg","Plywood","Plywood","None",0,"TA","TA","CBlock","TA","TA","Av","ALQ",368,"Rec",120,541,1029,"GasA","TA","Y","SBrkr",1117,0,0,1117,1,0,1,0,3,1,"TA",6,"Typ",1,"Fa","Attchd",1977,"RFn",2,542,"TA","TA","Y",292,0,0,0,0,0,NA,NA,NA,0,11,2008,"WD ","Normal",147000 +1496,908152110,60,"RL",72,7200,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","2Story",6,7,1976,2001,"Hip","CompShg","Plywood","Plywood","None",0,"TA","TA","CBlock","TA","TA","Av","ALQ",288,"Unf",0,396,684,"GasA","TA","Y","SBrkr",684,714,0,1398,0,0,1,1,3,1,"TA",6,"Typ",1,"TA","Attchd",1976,"Fin",2,440,"TA","TA","Y",0,0,0,0,0,0,NA,"MnPrv",NA,0,2,2008,"WD ","Normal",160500 +1497,908152270,20,"RL",60,7024,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",4,5,2005,2005,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","PConc","Gd","TA","No","GLQ",1024,"Unf",0,108,1132,"GasA","Ex","Y","SBrkr",1132,0,0,1132,1,0,1,1,2,1,"Gd",5,"Typ",0,NA,"Attchd",2005,"Fin",2,451,"TA","TA","Y",252,64,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","Normal",176000 +1498,908154080,20,"RL",123,47007,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",5,7,1959,1996,"Gable","CompShg","Plywood","Plywood","None",0,"TA","TA","Slab",NA,NA,NA,NA,0,NA,0,0,0,"GasA","TA","Y","SBrkr",3820,0,0,3820,NA,NA,3,1,5,1,"Ex",11,"Typ",2,"Gd","Attchd",1959,"Unf",2,624,"TA","TA","Y",0,372,0,0,0,0,NA,NA,NA,0,7,2008,"WD ","Normal",284700 +1499,908154235,60,"RL",313,63887,"Pave",NA,"IR3","Bnk","AllPub","Corner","Gtl","Edwards","Feedr","Norm","1Fam","2Story",10,5,2008,2008,"Hip","ClyTile","Stucco","Stucco","Stone",796,"Ex","TA","PConc","Ex","TA","Gd","GLQ",5644,"Unf",0,466,6110,"GasA","Ex","Y","SBrkr",4692,950,0,5642,2,0,2,1,3,1,"Ex",12,"Typ",3,"Gd","Attchd",2008,"Fin",2,1418,"TA","TA","Y",214,292,0,0,0,480,"Gd",NA,NA,0,1,2008,"New","Partial",160000 +1500,908186040,180,"RM",35,3675,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","TwnhsE","SLvl",6,5,2005,2005,"Gable","CompShg","VinylSd","VinylSd","BrkFace",80,"TA","TA","PConc","Gd","TA","Gd","GLQ",459,"Unf",0,88,547,"GasA","Ex","Y","SBrkr",1072,0,0,1072,1,0,1,0,2,1,"TA",5,"Typ",0,NA,"Basment",2005,"RFn",2,525,"TA","TA","Y",0,28,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","Normal",148000 +1501,908201100,90,"RL",65,6040,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","Duplex","1Story",4,5,1955,1955,"Gable","CompShg","AsbShng","Plywood","None",0,"TA","TA","PConc",NA,NA,NA,NA,0,NA,0,0,0,"GasA","TA","N","FuseP",1152,0,0,1152,0,0,2,0,2,2,"Fa",6,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"N",0,0,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","AdjLand",82000 +1502,908201110,90,"RL",65,6012,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","Edwards","Norm","Norm","Duplex","1Story",4,5,1955,1955,"Gable","CompShg","AsbShng","Plywood","None",0,"TA","Fa","PConc",NA,NA,NA,NA,0,NA,0,0,0,"GasA","TA","N","SBrkr",1152,0,0,1152,0,0,2,0,2,2,"TA",6,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"N",0,0,0,0,0,0,NA,NA,NA,0,6,2008,"WD ","AdjLand",82000 +1503,908201120,90,"RL",92,12108,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","Duplex","1Story",4,4,1955,1955,"Gable","CompShg","VinylSd","VinylSd","BrkFace",270,"TA","TA","CBlock","TA","TA","No","ALQ",133,"Unf",0,1307,1440,"GasA","TA","N","FuseF",1440,0,0,1440,0,0,2,0,4,2,"Fa",8,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"Y",0,0,0,0,0,0,NA,NA,NA,0,9,2008,"WD ","Normal",118000 +1504,908204160,90,"RL",74,6845,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","Duplex","1Story",4,5,1955,1955,"Gable","CompShg","WdShing","Wd Shng","BrkCmn",58,"TA","TA","Slab",NA,NA,NA,NA,0,NA,0,0,0,"GasA","TA","N","FuseF",1152,0,0,1152,0,0,2,0,2,2,"TA",6,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"N",0,0,0,0,0,0,NA,NA,NA,0,7,2008,"WD ","Normal",82500 +1505,908225170,190,"RL",56,6931,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","2fmCon","1Story",4,5,1955,1955,"Hip","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","CBlock","TA","TA","Av","ALQ",784,"Unf",0,0,784,"GasA","TA","N","FuseP",784,0,0,784,1,0,1,0,2,1,"TA",4,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"Y",112,0,0,0,0,0,NA,NA,NA,0,5,2008,"WD ","Normal",91900 +1506,908226120,50,"RL",60,12180,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",5,7,1938,2007,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","PConc","TA","TA","No","Unf",0,"Unf",0,585,585,"GasA","Gd","Y","FuseF",585,468,0,1053,0,0,1,1,2,1,"Ex",5,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"N",42,0,0,0,0,0,NA,NA,NA,0,1,2008,"WD ","Family",120000 +1507,908250040,50,"RL",57,8050,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",5,8,1947,1993,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","Gd","Slab",NA,NA,NA,NA,0,NA,0,0,0,"GasA","Gd","Y","SBrkr",929,208,0,1137,0,0,1,1,4,1,"TA",8,"Min1",0,NA,NA,NA,NA,0,0,NA,NA,"Y",0,0,0,0,0,0,NA,NA,NA,0,4,2008,"WD ","Normal",96000 +1508,908250090,20,"RL",68,9520,"Pave",NA,"Reg","Bnk","AllPub","Inside","Mod","Edwards","Norm","Norm","1Fam","1Story",4,5,1953,1953,"Gable","CompShg","MetalSd","MetalSd","Stone",115,"TA","TA","CBlock","Gd","TA","No","Rec",767,"Unf",0,144,911,"GasA","TA","Y","SBrkr",930,0,0,930,0,0,1,0,2,1,"TA",5,"Typ",0,NA,"Attchd",1953,"RFn",1,286,"TA","TA","Y",134,0,0,0,0,0,NA,"MnPrv","Gar2",3000,5,2008,"WD ","Normal",99000 +1509,908275130,50,"RL",70,11767,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",5,6,1946,1995,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","TA","CBlock","TA","TA","No","BLQ",352,"Unf",0,416,768,"GasA","Ex","Y","SBrkr",768,432,0,1200,0,0,1,0,3,1,"TA",6,"Typ",0,NA,"Detchd",1946,"Unf",1,240,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,5,2008,"WD ","Normal",130500 +1510,908275180,50,"RL",60,8172,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",4,6,1954,1972,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","TA","PConc","TA","TA","No","Unf",0,"Unf",0,941,941,"GasA","Ex","Y","SBrkr",997,473,0,1470,0,0,2,0,4,1,"TA",7,"Typ",0,NA,"Detchd",1958,"Unf",1,548,"TA","TA","Y",0,0,0,0,156,0,NA,NA,NA,0,5,2008,"WD ","Normal",135000 +1511,908275280,50,"RL",59,16466,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",5,7,1955,1955,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","Gd","PConc","TA","TA","No","Unf",0,"Unf",0,816,816,"GasA","TA","Y","SBrkr",872,521,0,1393,0,0,1,1,3,1,"TA",8,"Typ",0,NA,"Attchd",1955,"Unf",1,300,"TA","TA","Y",121,0,0,0,265,0,NA,NA,NA,0,4,2008,"WD ","Normal",135500 +1512,908276140,80,"RL",62,7692,"Pave",NA,"Reg","Bnk","AllPub","Inside","Mod","Edwards","Norm","Norm","1Fam","SLvl",4,6,1954,1954,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","CBlock","Ex","TA","Av","Unf",0,"Unf",0,416,416,"GasA","Gd","Y","FuseA",1204,0,0,1204,0,0,1,0,3,1,"TA",6,"Typ",0,NA,"Basment",1954,"Unf",1,312,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,11,2008,"WD ","Abnorml",100000 +1513,909100150,30,"RL",67,5142,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",4,7,1923,2008,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","PConc","TA","TA","No","ALQ",224,"Unf",0,725,949,"GasA","TA","Y","SBrkr",949,343,0,1292,0,0,1,1,3,1,"Gd",6,"Typ",0,NA,"Detchd",1923,"Unf",1,205,"TA","TA","N",0,0,183,0,0,0,NA,NA,NA,0,6,2008,"WD ","Normal",108000 +1514,909100170,30,"RL",67,5604,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",5,6,1925,1950,"Gable","CompShg","Stucco","Stucco","None",0,"TA","TA","CBlock","TA","TA","No","Rec",468,"Unf",0,396,864,"GasA","TA","N","FuseA",864,0,0,864,1,0,1,0,2,1,"TA",5,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"Y",0,0,96,0,0,0,NA,NA,NA,0,4,2008,"WD ","Normal",98000 +1515,909101060,30,"RL",45,8248,"Pave","Grvl","Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",3,3,1914,1950,"Gable","CompShg","Stucco","Stucco","None",0,"TA","TA","BrkTil","TA","TA","No","BLQ",41,"Unf",0,823,864,"GasA","TA","N","FuseF",864,0,0,864,1,0,1,0,2,1,"TA",5,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"N",0,0,100,0,0,0,NA,NA,NA,0,9,2008,"WD ","Normal",67000 +1516,909101180,190,"RL",55,5687,"Pave","Grvl","Reg","Bnk","AllPub","Inside","Gtl","SWISU","Norm","Norm","2fmCon","2Story",5,6,1912,2000,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","Fa","PConc","TA","Fa","No","Rec",210,"Unf",0,570,780,"GasA","Ex","N","SBrkr",936,780,0,1716,1,0,2,0,6,1,"Fa",9,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"N",0,184,0,0,0,0,NA,NA,NA,0,3,2008,"WD ","Normal",135900 +1517,909131170,70,"RH",NA,12155,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","SWISU","Norm","Norm","1Fam","2Story",6,8,1925,1950,"Gable","CompShg","Wd Sdng","Wd 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Sdng","None",0,"Gd","Gd","PConc","TA","Gd","Gd","ALQ",351,"Rec",823,1043,2217,"GasA","Ex","Y","SBrkr",2217,0,0,2217,1,0,2,0,4,1,"Gd",8,"Typ",1,"TA","Attchd",1970,"Unf",2,621,"TA","TA","Y",81,207,0,0,224,0,NA,NA,NA,0,10,2007,"WD ","Normal",268000 +1616,527105140,60,"RL",NA,12394,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","Gilbert","Norm","Norm","1Fam","2Story",7,5,2003,2003,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","Gd","Unf",0,"Unf",0,847,847,"GasA","Ex","Y","SBrkr",847,886,0,1733,0,0,2,1,3,1,"Gd",7,"Typ",1,"Gd","BuiltIn",2003,"Fin",2,433,"TA","TA","Y",100,48,0,0,0,0,NA,NA,NA,0,10,2007,"WD ","Family",225000 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+1637,527212060,60,"RL",98,12328,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","StoneBr","Norm","Norm","1Fam","2Story",8,5,2005,2005,"Hip","CompShg","VinylSd","VinylSd","Stone",146,"Gd","TA","PConc","Ex","TA","Mn","GLQ",986,"Unf",0,163,1149,"GasA","Ex","Y","SBrkr",1164,1377,0,2541,1,0,3,1,4,1,"Ex",10,"Typ",1,"Gd","BuiltIn",2005,"Fin",3,729,"TA","TA","Y",120,32,0,0,0,0,NA,NA,NA,0,7,2007,"WD ","Normal",349265 +1638,527216080,20,"RL",52,51974,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","StoneBr","PosN","Norm","1Fam","1Story",9,5,2006,2007,"Hip","CompShg","VinylSd","VinylSd","BrkFace",710,"Ex","TA","PConc","Ex","TA","Av","GLQ",1101,"Unf",0,1559,2660,"GasA","Ex","Y","SBrkr",2338,0,0,2338,1,0,2,1,4,1,"Gd",8,"Typ",2,"Gd","Attchd",2005,"Fin",3,1110,"Gd","TA","Y",0,135,0,0,322,0,NA,NA,NA,0,6,2007,"New","Partial",591587 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+1643,527256040,20,"RL",81,13870,"Pave",NA,"IR1","HLS","AllPub","Inside","Gtl","StoneBr","PosA","PosA","1Fam","1Story",10,5,2006,2007,"Hip","CompShg","CemntBd","CmentBd","BrkFace",250,"Ex","TA","PConc","Ex","TA","Gd","GLQ",1152,"Unf",0,590,1742,"GasA","Ex","Y","SBrkr",2042,0,0,2042,1,0,2,0,3,1,"Ex",8,"Typ",1,"Gd","Attchd",2007,"Fin",3,724,"TA","TA","Y",240,52,0,0,174,0,NA,NA,NA,0,10,2007,"New","Partial",455000 +1644,527301080,20,"RL",NA,12546,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","NWAmes","Norm","Norm","1Fam","1Story",6,7,1981,1981,"Gable","CompShg","MetalSd","MetalSd","BrkFace",310,"Gd","Gd","CBlock","Gd","TA","No","BLQ",678,"Unf",0,762,1440,"GasA","Ex","Y","SBrkr",1440,0,0,1440,0,0,2,0,3,1,"Gd",7,"Typ",1,"TA","Attchd",1981,"Fin",2,467,"TA","TA","Y",0,0,99,0,0,0,NA,NA,NA,0,4,2007,"WD ","Normal",182900 +1645,527301280,20,"RL",NA,10960,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",6,5,1984,1984,"Hip","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","Gd","TA","No","GLQ",256,"Unf",0,1028,1284,"GasA","TA","Y","SBrkr",1284,0,0,1284,1,0,2,0,3,1,"TA",5,"Typ",0,NA,"Attchd",1984,"Unf",2,480,"TA","TA","Y",0,0,192,0,0,0,NA,NA,NA,0,4,2007,"COD","Abnorml",174000 +1646,527302020,80,"RL",78,12090,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","SLvl",6,7,1984,2003,"Hip","CompShg","VinylSd","VinylSd","BrkFace",74,"TA","TA","CBlock","Gd","TA","No","Unf",0,"Unf",0,585,585,"GasA","Ex","Y","SBrkr",1140,728,0,1868,0,0,3,1,3,1,"TA",7,"Typ",1,"TA","BuiltIn",1984,"Fin",2,477,"TA","TA","Y",268,112,0,0,147,0,NA,NA,NA,0,1,2007,"WD ","Abnorml",178000 +1647,527302060,60,"RL",78,12090,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","2Story",7,7,1981,2003,"Gable","CompShg","MetalSd","MetalSd","BrkFace",306,"Gd","TA","CBlock","Gd","TA","No","ALQ",321,"Unf",0,404,725,"GasA","Ex","Y","SBrkr",725,754,0,1479,0,0,2,1,3,1,"Gd",6,"Typ",1,"TA","Attchd",1981,"RFn",2,484,"TA","TA","Y",167,72,0,0,0,0,NA,NA,NA,0,3,2007,"WD ","Normal",176000 +1648,527302090,20,"RL",93,12299,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",7,6,1978,1985,"Gable","CompShg","Plywood","Plywood","Stone",67,"TA","TA","CBlock","Gd","TA","No","GLQ",1328,"Unf",0,201,1529,"GasA","TA","Y","SBrkr",1664,0,0,1664,1,0,2,0,3,1,"Gd",7,"Typ",1,"TA","Attchd",1978,"Fin",2,663,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,9,2007,"WD ","Normal",213000 +1649,527325090,60,"RL",61,11339,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","PosA","Norm","1Fam","2Story",7,5,1979,1979,"Hip","WdShake","HdBoard","Plywood","BrkFace",549,"TA","TA","CBlock","Gd","TA","No","ALQ",758,"Unf",0,22,780,"GasA","TA","Y","SBrkr",1085,845,0,1930,1,0,2,1,4,1,"Gd",9,"Typ",1,"TA","Attchd",1979,"Fin",2,481,"TA","TA","Y",192,72,0,0,0,0,NA,NA,NA,0,3,2007,"WD ","Normal",192000 +1650,527325110,60,"RL",92,11952,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","PosA","Norm","1Fam","2Story",7,6,1977,1977,"Mansard","WdShake","WdShing","Plywood","None",0,"TA","TA","CBlock","Gd","TA","No","Unf",0,"Unf",0,808,808,"GasA","TA","Y","SBrkr",1161,808,0,1969,0,0,2,1,3,1,"TA",8,"Typ",1,"Gd","Attchd",1977,"RFn",2,534,"TA","TA","Y",0,0,0,0,276,0,NA,NA,NA,0,11,2007,"WD ","Normal",190000 +1651,527327050,60,"RL",NA,12046,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","2Story",6,6,1976,1976,"Gable","CompShg","Plywood","Plywood","BrkFace",298,"TA","TA","CBlock","TA","TA","No","LwQ",156,"Unf",0,692,848,"GasA","TA","Y","SBrkr",1118,912,0,2030,0,0,2,1,4,1,"Gd",8,"Typ",1,"TA","Attchd",1976,"Fin",2,551,"TA","TA","Y",0,224,0,0,0,0,NA,NA,NA,0,6,2007,"WD ","Normal",195000 +1652,527328020,80,"RL",NA,10395,"Pave",NA,"IR1","Lvl","AllPub","FR2","Gtl","NWAmes","Norm","Norm","1Fam","SLvl",6,6,1978,1978,"Gable","CompShg","HdBoard","HdBoard","BrkFace",233,"TA","TA","CBlock","Gd","TA","Av","ALQ",605,"Unf",0,427,1032,"GasA","TA","Y","SBrkr",1032,0,0,1032,0,1,2,0,3,1,"TA",6,"Typ",1,"TA","Attchd",1978,"Unf",2,564,"TA","TA","Y",0,0,0,0,0,0,NA,"MnPrv","Shed",500,7,2007,"WD ","Normal",148000 +1653,527328050,20,"RL",79,11850,"Pave",NA,"Reg","Lvl","AllPub","FR2","Gtl","NWAmes","Norm","Norm","1Fam","1Story",6,6,1984,1984,"Gable","CompShg","Plywood","Plywood","BrkFace",98,"TA","TA","CBlock","Gd","TA","No","ALQ",781,"Unf",0,372,1153,"GasA","TA","Y","SBrkr",1177,0,0,1177,0,0,2,0,3,1,"Gd",6,"Typ",0,NA,"Attchd",1984,"Fin",2,495,"TA","TA","Y",204,103,0,0,0,0,NA,"MnPrv",NA,0,8,2007,"WD ","Normal",151500 +1654,527353080,60,"RL",80,11584,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","SLvl",7,6,1979,1979,"Hip","CompShg","HdBoard","HdBoard","BrkFace",96,"TA","TA","CBlock","TA","TA","No","GLQ",315,"Rec",110,114,539,"GasA","TA","Y","SBrkr",1040,685,0,1725,0,0,2,1,3,1,"TA",6,"Typ",1,"TA","Attchd",1979,"RFn",2,550,"TA","TA","Y",0,88,216,0,0,0,NA,NA,NA,0,11,2007,"WD ","Normal",197000 +1655,527354050,20,"RL",80,10400,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",6,5,1979,1999,"Gable","CompShg","MetalSd","MetalSd","BrkFace",253,"TA","TA","CBlock","Gd","TA","No","GLQ",903,"Unf",0,356,1259,"GasA","Ex","Y","SBrkr",1353,0,0,1353,1,0,2,0,2,1,"TA",5,"Typ",1,"TA","Attchd",1979,"RFn",2,478,"TA","TA","Y",240,141,0,0,0,0,NA,NA,NA,0,8,2007,"WD ","Normal",192350 +1656,527356050,60,"RL",80,10793,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","RRAn","Norm","1Fam","2Story",5,5,1969,1969,"Mansard","CompShg","WdShing","HdBoard","BrkFace",263,"TA","TA","CBlock","TA","TA","No","Rec",493,"BLQ",287,0,780,"GasA","Ex","Y","SBrkr",780,840,0,1620,0,0,2,1,4,1,"TA",7,"Min1",0,NA,"Attchd",1969,"Fin",2,462,"TA","TA","Y",208,0,0,0,0,0,NA,"GdWo",NA,0,4,2007,"WD ","Normal",152000 +1657,527357020,20,"RL",128,13001,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","PosN","Norm","1Fam","1Story",6,5,1971,1971,"Hip","CompShg","HdBoard","HdBoard","BrkFace",176,"TA","TA","CBlock","TA","TA","Mn","ALQ",492,"BLQ",121,1012,1625,"GasA","TA","Y","SBrkr",1220,0,0,1220,0,1,2,0,2,1,"TA",6,"Typ",1,"TA","Attchd",1971,"Unf",2,944,"TA","TA","Y",0,0,249,0,0,0,NA,NA,NA,0,9,2007,"WD ","Normal",170000 +1658,527358060,20,"RL",70,12243,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",5,6,1971,1971,"Gable","CompShg","Plywood","Plywood","None",0,"TA","TA","CBlock","Gd","TA","Av","ALQ",998,"Unf",0,486,1484,"GasA","Gd","Y","SBrkr",1484,0,0,1484,0,0,2,0,3,1,"TA",7,"Typ",1,"TA","Attchd",1971,"Unf",2,487,"TA","TA","Y",224,0,0,0,180,0,NA,NA,NA,0,2,2007,"WD ","Normal",175000 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+1823,532376040,20,"RL",85,6970,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","Sawyer","Feedr","Norm","1Fam","1Story",4,5,1961,1961,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","CBlock","TA","TA","No","ALQ",932,"Unf",0,108,1040,"GasA","TA","Y","SBrkr",1120,0,0,1120,1,0,1,1,3,1,"Fa",5,"Typ",0,NA,"Attchd",1961,"RFn",2,544,"TA","TA","Y",168,0,0,0,0,0,NA,NA,"Shed",400,5,2007,"WD ","Normal",129000 +1824,532376090,20,"RL",40,13673,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Sawyer","RRAe","Norm","1Fam","1Story",5,5,1962,1962,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","Gd","CBlock","TA","TA","No","Unf",0,"Unf",0,1140,1140,"GasA","TA","Y","SBrkr",1696,0,0,1696,0,0,1,1,3,1,"TA",8,"Min2",1,"TA","Attchd",1962,"RFn",1,349,"TA","TA","Y",0,30,0,0,0,0,NA,NA,NA,0,3,2007,"WD ","Normal",143900 +1825,532377050,20,"RL",84,7476,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","Sawyer","Norm","Norm","1Fam","1Story",5,7,1968,1968,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","TA","TA","No","Rec",812,"Unf",0,228,1040,"GasA","TA","Y","SBrkr",1040,0,0,1040,1,0,1,1,3,1,"TA",5,"Typ",0,NA,"Detchd",1991,"Fin",2,686,"TA","TA","Y",188,0,0,0,0,0,NA,NA,NA,0,6,2007,"WD ","Normal",145000 +1826,532377140,20,"RL",NA,9945,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Sawyer","Norm","Norm","1Fam","1Story",5,5,1961,1961,"Hip","CompShg","Wd Sdng","Wd Sdng","BrkFace",57,"TA","TA","CBlock","TA","TA","No","Rec",827,"Unf",0,161,988,"GasA","TA","Y","SBrkr",988,0,0,988,1,0,1,0,3,1,"TA",5,"Typ",0,NA,"Detchd",1963,"Unf",2,572,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,10,2007,"WD ","Normal",128500 +1827,532378240,20,"RL",NA,6173,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Sawyer","Norm","Norm","1Fam","1Story",5,6,1967,1967,"Gable","CompShg","HdBoard","Wd Sdng","BrkFace",75,"TA","TA","CBlock","TA","TA","No","GLQ",599,"Unf",0,277,876,"GasA","TA","Y","SBrkr",902,0,0,902,0,0,1,0,3,1,"TA",6,"Typ",0,NA,"Attchd",1967,"Unf",1,288,"TA","TA","Y",0,0,0,0,0,0,NA,"MnPrv",NA,0,8,2007,"WD ","Normal",125500 +1828,532476080,60,"RL",NA,19522,"Pave",NA,"IR1","Bnk","AllPub","Inside","Gtl","Veenker","Norm","Norm","1Fam","2Story",7,5,1990,1990,"Gable","CompShg","HdBoard","HdBoard","BrkFace",272,"Gd","TA","PConc","Gd","TA","No","GLQ",727,"Unf",0,496,1223,"GasA","Gd","Y","SBrkr",1271,1232,0,2503,1,0,2,1,3,1,"Gd",7,"Typ",1,"TA","Attchd",1990,"RFn",2,564,"TA","TA","Y",0,99,0,0,182,0,NA,NA,NA,0,2,2007,"WD ","Normal",300000 +1829,532477040,60,"RL",NA,17542,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Veenker","Norm","Norm","1Fam","2Story",7,7,1974,2003,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"Gd","TA","CBlock","TA","TA","Gd","LwQ",125,"ALQ",1031,36,1192,"GasA","TA","Y","SBrkr",1516,651,0,2167,1,0,2,1,3,1,"Gd",9,"Typ",2,"Gd","Attchd",1974,"RFn",2,518,"TA","TA","Y",220,47,0,0,0,0,NA,"MnPrv",NA,0,7,2007,"WD ","Normal",294000 +1830,532479050,20,"RL",44,10751,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Sawyer","RRAe","Norm","1Fam","1Story",5,5,1974,1974,"Gable","CompShg","Plywood","Plywood","BrkFace",44,"TA","TA","CBlock","Fa","TA","Gd","ALQ",787,"Unf",0,250,1037,"GasA","TA","Y","SBrkr",1037,0,0,1037,1,0,1,0,2,1,"TA",5,"Typ",0,NA,"Attchd",1974,"RFn",2,431,"TA","TA","Y",136,47,0,0,0,0,NA,NA,NA,0,8,2007,"WD ","Normal",129000 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Sdng","BrkFace",336,"Gd","TA","PConc","Ex","TA","No","GLQ",482,"Unf",0,971,1453,"GasA","Ex","Y","SBrkr",1453,1357,0,2810,0,0,2,1,4,1,"Gd",9,"Typ",1,"Ex","Attchd",1990,"RFn",2,750,"Gd","Gd","Y",500,0,0,0,0,0,NA,NA,NA,0,6,2007,"WD ","Normal",302000 +1834,533130170,50,"RL",85,10678,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NoRidge","Norm","Norm","1Fam","1.5Fin",8,5,1992,2000,"Hip","CompShg","HdBoard","HdBoard","BrkFace",337,"Gd","TA","PConc","Gd","TA","No","GLQ",700,"Unf",0,983,1683,"GasA","Ex","Y","SBrkr",2129,743,0,2872,0,0,2,1,4,1,"Gd",9,"Typ",1,"TA","Attchd",1992,"Fin",2,541,"TA","TA","Y",0,33,0,0,0,0,NA,NA,NA,0,4,2007,"WD ","Normal",285000 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+1845,533221030,160,"FV",NA,2117,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","TwnhsE","2Story",6,5,2000,2000,"Gable","CompShg","MetalSd","MetalSd","BrkFace",513,"Gd","TA","PConc","Gd","TA","No","GLQ",420,"Unf",0,336,756,"GasA","Ex","Y","SBrkr",756,756,0,1512,0,0,2,1,2,1,"Gd",4,"Typ",1,"TA","Detchd",2000,"Unf",2,440,"TA","TA","Y",0,32,0,0,0,0,NA,NA,NA,0,6,2007,"WD ","Normal",168500 +1846,533221060,160,"FV",24,2117,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","Twnhs","2Story",6,5,2000,2000,"Gable","CompShg","MetalSd","MetalSd","BrkFace",216,"Gd","TA","PConc","Gd","TA","No","GLQ",417,"Unf",0,339,756,"GasA","Ex","Y","SBrkr",769,804,0,1573,0,0,2,1,3,1,"Gd",4,"Typ",0,NA,"Detchd",2000,"Unf",2,440,"TA","TA","Y",0,32,0,0,0,0,NA,NA,NA,0,9,2007,"WD ","Normal",177000 +1847,533221100,160,"FV",NA,2117,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","Twnhs","2Story",6,5,2000,2000,"Gable","CompShg","MetalSd","MetalSd","BrkFace",456,"Gd","TA","PConc","Gd","TA","No","GLQ",436,"Unf",0,320,756,"GasA","Ex","Y","SBrkr",769,756,0,1525,0,0,2,1,3,1,"Gd",5,"Typ",1,"TA","Detchd",2000,"Unf",2,440,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,6,2007,"WD ","Normal",177000 +1848,533223050,160,"FV",NA,5105,"Pave",NA,"IR2","Lvl","AllPub","FR2","Gtl","Somerst","Norm","Norm","TwnhsE","2Story",7,5,2004,2004,"Gable","CompShg","MetalSd","MetalSd","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",239,"Unf",0,312,551,"GasA","Ex","Y","SBrkr",551,551,0,1102,0,0,2,1,2,1,"Gd",4,"Typ",0,NA,"Detchd",2004,"Unf",2,480,"TA","TA","Y",0,60,0,0,0,0,NA,NA,NA,0,3,2007,"WD ","Normal",148800 +1849,533236040,160,"FV",24,2645,"Pave","Pave","Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","Twnhs","2Story",8,5,1999,2000,"Gable","CompShg","MetalSd","MetalSd","BrkFace",456,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,776,776,"GasA","Ex","Y","SBrkr",764,677,0,1441,0,0,2,1,2,1,"Gd",5,"Typ",0,NA,"Detchd",1999,"Unf",2,492,"TA","TA","Y",206,0,0,0,0,0,NA,NA,NA,0,11,2007,"WD ","Normal",174000 +1850,533241030,60,"FV",66,7399,"Pave","Pave","IR1","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","2Story",7,5,1997,1998,"Hip","CompShg","VinylSd","VinylSd","BrkFace",1600,"Gd","TA","PConc","Gd","TA","No","BLQ",649,"Unf",0,326,975,"GasA","Ex","Y","SBrkr",975,975,0,1950,0,0,2,1,3,1,"Gd",7,"Typ",1,"TA","Detchd",1997,"RFn",2,576,"TA","TA","Y",0,10,0,0,198,0,NA,NA,NA,0,6,2007,"WD ","Normal",239000 +1851,533242080,20,"FV",73,7321,"Pave","Pave","IR1","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","1Story",7,5,1999,2000,"Gable","CompShg","CemntBd","CmentBd","None",0,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,1339,1339,"GasA","Ex","Y","SBrkr",1358,0,0,1358,0,0,2,0,3,1,"Gd",5,"Typ",0,NA,"Detchd",1999,"Unf",2,625,"TA","TA","Y",176,174,0,0,0,0,NA,NA,NA,0,12,2007,"COD","Normal",204000 +1852,533242090,60,"FV",NA,8010,"Pave","Pave","IR1","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","2Story",8,5,2003,2004,"Hip","CompShg","CemntBd","CmentBd","None",0,"Gd","TA","PConc","Ex","TA","No","GLQ",964,"Unf",0,90,1054,"GasA","Ex","Y","SBrkr",1072,976,0,2048,1,0,2,1,3,1,"Gd",8,"Typ",2,"Gd","Detchd",2003,"Unf",2,552,"TA","TA","Y",0,48,0,0,180,0,NA,NA,NA,0,8,2007,"WD ","Normal",301000 +1853,533244040,60,"FV",106,8413,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","2Story",8,5,1998,1998,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","Gd","PConc","Ex","TA","Mn","GLQ",901,"Unf",0,319,1220,"GasA","Ex","Y","SBrkr",1220,1142,0,2362,1,0,2,1,3,1,"Gd",8,"Typ",2,"TA","Attchd",1998,"RFn",2,1105,"Gd","TA","Y",147,0,36,0,144,0,NA,NA,NA,0,6,2007,"WD ","Normal",312500 +1854,533250190,120,"RL",50,9466,"Pave",NA,"IR2","Lvl","AllPub","FR2","Gtl","Veenker","Norm","Norm","TwnhsE","1Story",8,5,1994,1995,"Gable","CompShg","BrkFace","BrkFace","None",0,"Gd","TA","PConc","Gd","TA","Gd","LwQ",457,"ALQ",1037,0,1494,"GasA","Ex","Y","SBrkr",1494,0,0,1494,1,0,1,1,1,1,"Gd",5,"Typ",1,"Gd","Attchd",1994,"Fin",2,478,"TA","TA","Y",0,30,0,0,217,0,NA,NA,NA,0,5,2007,"WD ","Normal",279700 +1855,533251110,20,"RL",80,12000,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Veenker","Norm","Norm","1Fam","1Story",7,6,1980,1980,"Hip","CompShg","VinylSd","MetalSd","BrkFace",600,"Gd","TA","CBlock","Gd","TA","No","GLQ",1732,"Unf",0,270,2002,"GasA","Ex","Y","SBrkr",2362,0,0,2362,1,0,2,1,3,1,"Gd",8,"Typ",1,"TA","Attchd",1980,"RFn",2,546,"Gd","TA","Y",180,16,0,0,0,0,NA,NA,NA,0,3,2007,"WD ","Normal",255000 +1856,533251130,80,"RL",NA,16157,"Pave",NA,"IR1","Lvl","AllPub","FR2","Gtl","Veenker","Feedr","Norm","1Fam","SLvl",5,7,1978,1978,"Gable","CompShg","Plywood","Plywood","None",0,"TA","TA","PConc","Gd","TA","Gd","ALQ",680,"Rec",391,289,1360,"GasA","Ex","Y","SBrkr",1432,0,0,1432,1,0,1,1,2,1,"Gd",5,"Typ",1,"TA","Attchd",1978,"Unf",2,588,"TA","TA","Y",168,180,0,0,0,0,NA,NA,NA,0,6,2007,"WD ","Normal",194000 +1857,533252130,20,"RL",90,10768,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","Veenker","Norm","Norm","1Fam","1Story",5,8,1976,2004,"Gable","CompShg","Plywood","Plywood","None",0,"Gd","Gd","CBlock","Gd","TA","Gd","ALQ",1157,"Unf",0,280,1437,"GasA","TA","Y","SBrkr",1437,0,0,1437,1,0,2,0,3,1,"Gd",6,"Typ",1,"Fa","Attchd",1976,"RFn",2,528,"TA","TA","Y",0,21,0,0,180,0,NA,NA,NA,0,7,2007,"WD ","Normal",218000 +1858,533253180,120,"RL",40,3840,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Greens","Norm","Norm","TwnhsE","1Story",8,6,1978,1998,"Gable","CompShg","Wd Sdng","Plywood","None",0,"Gd","TA","CBlock","Gd","TA","Gd","GLQ",881,"LwQ",176,0,1057,"GasA","TA","Y","SBrkr",1295,0,0,1295,1,0,1,0,1,1,"Gd",4,"Typ",2,"TA","Attchd",1978,"Fin",2,571,"TA","TA","Y",133,89,0,0,0,0,NA,NA,NA,0,7,2007,"WD ","Normal",213750 +1859,533254100,80,"RL",80,9600,"Pave",NA,"Reg","Lvl","AllPub","FR2","Gtl","Veenker","Feedr","Norm","1Fam","SLvl",6,7,1976,1994,"Hip","CompShg","Plywood","Plywood","BrkFace",360,"Gd","Gd","CBlock","TA","TA","No","Unf",0,"Unf",0,528,528,"GasA","Ex","Y","SBrkr",1094,761,0,1855,0,0,2,1,3,1,"TA",7,"Typ",1,"TA","Attchd",1976,"RFn",2,512,"TA","TA","Y",113,100,0,0,0,0,NA,NA,NA,0,8,2007,"WD ","Normal",187000 +1860,533254130,20,"RL",80,9600,"Pave",NA,"Reg","Lvl","AllPub","FR2","Gtl","Veenker","Feedr","Norm","1Fam","1Story",6,8,1976,1976,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","TA","CBlock","Gd","TA","Gd","ALQ",978,"Unf",0,284,1262,"GasA","Ex","Y","SBrkr",1262,0,0,1262,0,1,2,0,3,1,"TA",6,"Typ",1,"TA","Attchd",1976,"RFn",2,460,"TA","TA","Y",298,0,0,0,0,0,NA,NA,NA,0,5,2007,"WD ","Normal",181500 +1861,533350020,20,"RL",94,17778,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Veenker","Norm","Norm","1Fam","1Story",8,5,1981,1981,"Hip","CompShg","BrkFace","BrkFace","None",0,"Gd","TA","CBlock","Gd","TA","Gd","ALQ",1632,"Rec",829,0,2461,"GasA","Gd","Y","SBrkr",2497,0,0,2497,1,0,2,0,2,1,"Gd",7,"Typ",2,"Gd","Attchd",1981,"RFn",2,676,"TA","TA","Y",266,0,0,0,0,0,NA,NA,NA,0,3,2007,"WD ","Normal",373000 +1862,533352075,90,"RL",NA,18890,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Sawyer","Feedr","RRAe","Duplex","1.5Fin",5,5,1977,1977,"Shed","CompShg","Plywood","Plywood","None",1,"TA","TA","CBlock","Gd","TA","No","GLQ",498,"Rec",211,652,1361,"GasA","Ex","Y","SBrkr",1361,1259,0,2620,0,0,2,2,4,2,"TA",12,"Typ",1,"TA","BuiltIn",1977,"RFn",2,600,"TA","TA","N",155,24,145,0,0,0,NA,NA,"Gar2",8300,8,2007,"WD ","Normal",190000 +1863,534104090,60,"FV",NA,7050,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Somerst","Norm","Norm","1Fam","2Story",7,5,2001,2001,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",738,"Unf",0,319,1057,"GasA","Ex","Y","SBrkr",1057,872,0,1929,1,0,2,1,3,1,"Gd",7,"Typ",1,"TA","Attchd",2001,"Fin",2,650,"TA","TA","Y",0,235,0,0,0,0,NA,NA,NA,0,5,2007,"WD ","Normal",226000 +1864,534125120,20,"RL",78,11700,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","RRAn","Norm","1Fam","1Story",6,6,1968,1968,"Gable","CompShg","MetalSd","MetalSd","BrkFace",41,"TA","TA","CBlock","TA","TA","No","Unf",0,"Unf",0,912,912,"GasA","Ex","Y","SBrkr",1152,0,0,1152,1,0,1,1,3,1,"TA",7,"Typ",0,NA,"Attchd",1968,"RFn",2,412,"TA","TA","Y",0,126,0,0,0,0,NA,NA,NA,0,9,2007,"CWD","Normal",143450 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Sdng","None",0,"TA","TA","BrkTil","TA","Fa","No","LwQ",425,"Unf",0,459,884,"GasA","TA","Y","FuseA",959,408,0,1367,0,0,1,0,3,1,"TA",6,"Typ",1,"Gd","Detchd",1978,"Unf",1,560,"TA","TA","Y",0,0,0,0,120,0,NA,NA,NA,0,11,2007,"WD ","Normal",127500 +2021,903234160,30,"RM",60,6180,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","BrkSide","Norm","Norm","1Fam","1Story",6,5,1926,1950,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","BrkTil","TA","TA","No","Unf",0,"Unf",0,960,960,"GasA","TA","N","SBrkr",986,0,0,986,0,0,1,0,2,1,"TA",5,"Typ",1,"Gd","Detchd",1926,"Unf",1,180,"TA","TA","Y",0,128,0,0,0,0,NA,NA,NA,0,5,2007,"WD ","Normal",102000 +2022,903236130,70,"RM",47,7755,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","OldTown","Norm","Norm","1Fam","2Story",6,8,1918,1995,"Gable","CompShg","Wd Sdng","Wd 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Shng","None",0,"TA","Gd","PConc","TA","TA","No","Unf",0,"Unf",0,768,768,"GasA","Ex","Y","SBrkr",752,624,0,1376,0,0,1,0,3,1,"Gd",7,"Typ",0,NA,"Detchd",1977,"Unf",2,576,"TA","TA","P",0,54,144,0,0,0,NA,NA,NA,0,2,2007,"WD ","Normal",165000 +2025,903425270,50,"RL",60,8550,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","BrkSide","RRAn","Norm","1Fam","1.5Fin",5,5,1926,1950,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","PConc","Fa","TA","No","Unf",0,"Unf",0,750,750,"GasA","TA","Y","SBrkr",960,356,0,1316,0,0,1,0,4,1,"TA",7,"Typ",0,NA,"Detchd",1965,"Unf",2,576,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,10,2007,"ConLw","Family",90000 +2026,903425280,70,"RL",60,8730,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","BrkSide","RRAn","Norm","1Fam","2Story",6,7,1915,2003,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","BrkTil","TA","TA","No","Unf",0,"Unf",0,698,698,"GasA","Ex","Y","FuseA",698,698,0,1396,0,0,1,0,3,1,"TA",7,"Typ",0,NA,"Detchd",2003,"Unf",1,384,"TA","TA","Y",0,0,0,0,259,0,NA,NA,NA,0,7,2007,"WD ","Normal",153575 +2027,903426010,70,"RM",NA,5700,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","OldTown","Feedr","Norm","1Fam","2Story",7,6,1929,1990,"Gable","CompShg","Wd Sdng","Wd Shng","None",0,"TA","TA","BrkTil","TA","TA","No","Rec",336,"Unf",0,336,672,"GasA","Gd","N","FuseA",672,672,0,1344,1,0,1,1,3,1,"Gd",6,"Typ",1,"Gd","Attchd",1979,"Unf",2,456,"TA","TA","Y",0,0,70,0,0,0,NA,"GdPrv",NA,0,9,2007,"WD ","Normal",140000 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Sdng","None",0,"TA","TA","PConc","TA","TA","No","Unf",0,"Unf",0,637,637,"GasA","Ex","Y","FuseF",1108,0,0,1108,0,0,1,0,3,1,"Gd",6,"Typ",1,"Gd","Attchd",1930,"Unf",1,280,"TA","TA","N",0,0,205,0,0,0,NA,"GdPrv",NA,0,6,2007,"WD ","Normal",139400 +2033,903453080,80,"RM",120,13200,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","IDOTRR","Norm","Norm","1Fam","SLvl",6,6,1963,1963,"Gable","CompShg","HdBoard","HdBoard","BrkFace",234,"TA","TA","CBlock","TA","TA","No","BLQ",375,"Unf",0,366,741,"GasA","Fa","Y","SBrkr",1497,797,0,2294,0,0,3,0,5,1,"TA",9,"Typ",1,"Gd","Attchd",1963,"Unf",2,658,"TA","TA","Y",0,110,0,0,0,0,NA,NA,NA,0,4,2007,"WD ","Normal",202500 +2034,903454010,70,"RM",60,6882,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","IDOTRR","Norm","Norm","1Fam","2Story",6,7,1914,2006,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","PConc","TA","TA","No","Unf",0,"Unf",0,684,684,"GasA","TA","Y","SBrkr",773,582,0,1355,0,0,1,1,3,1,"Gd",7,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"Y",136,0,115,0,0,0,NA,NA,NA,0,3,2007,"WD ","Normal",127000 +2035,903454090,50,"RM",50,9000,"Pave",NA,"Reg","Bnk","AllPub","Inside","Gtl","IDOTRR","Norm","Norm","1Fam","1.5Fin",6,6,1937,1950,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","Gd","PConc","TA","TA","No","Unf",0,"Unf",0,780,780,"GasA","TA","Y","SBrkr",780,595,0,1375,0,0,1,1,3,1,"Gd",6,"Typ",1,"Gd","Detchd",1979,"Unf",1,544,"TA","TA","P",0,162,0,0,126,0,NA,NA,NA,0,12,2007,"WD ","Normal",141000 +2036,903456110,60,"RM",60,9780,"Pave","Grvl","Reg","Lvl","AllPub","Inside","Gtl","IDOTRR","Norm","Norm","1Fam","2Story",7,9,1950,2005,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","Ex","CBlock","TA","TA","No","LwQ",354,"Rec",398,224,976,"GasA","Ex","Y","SBrkr",976,976,0,1952,0,0,1,1,4,1,"Gd",8,"Typ",2,"TA","Detchd",1950,"Fin",1,299,"TA","TA","Y",285,0,0,0,216,0,NA,NA,NA,0,4,2007,"WD ","Normal",212300 +2037,903457040,50,"RM",60,10320,"Pave","Grvl","Reg","Lvl","AllPub","Inside","Gtl","IDOTRR","Norm","Norm","1Fam","1.5Fin",6,5,1915,1950,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","TA","CBlock","TA","Fa","No","LwQ",375,"Unf",0,763,1138,"GasA","Gd","Y","SBrkr",1138,1042,0,2180,0,0,1,1,3,1,"TA",7,"Typ",1,"Gd","Detchd",2006,"Unf",2,720,"TA","TA","N",0,0,170,0,0,0,NA,NA,NA,0,5,2007,"WD ","Normal",161000 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+2070,905226170,20,"RL",85,13770,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","Sawyer","Feedr","Norm","1Fam","1Story",5,6,1958,1998,"Gable","CompShg","Plywood","Plywood","BrkFace",340,"TA","TA","CBlock","TA","TA","Mn","Rec",190,"BLQ",873,95,1158,"GasA","TA","Y","SBrkr",1176,0,0,1176,1,0,1,0,3,1,"TA",6,"Typ",2,"Gd","Attchd",1958,"Unf",1,303,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,10,2007,"VWD","Normal",137000 +2071,905228020,20,"RL",NA,9000,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Sawyer","Norm","Norm","1Fam","1Story",4,7,1945,1950,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"Fa","TA","Slab",NA,NA,NA,NA,0,NA,0,0,0,"GasA","TA","N","FuseA",998,0,0,998,0,0,1,0,3,1,"TA",5,"Min2",0,NA,"2Types",1952,"Unf",2,460,"Fa","TA","Y",0,0,140,0,0,0,NA,NA,NA,0,5,2007,"WD ","Normal",89500 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Sdng","None",0,"Gd","TA","CBlock","Gd","Gd","Gd","LwQ",51,"GLQ",915,0,966,"GasA","Ex","Y","SBrkr",1743,0,0,1743,2,0,0,1,0,1,"Gd",5,"Typ",2,"Fa","Attchd",1976,"Fin",2,529,"TA","TA","Y",646,0,0,0,0,0,NA,NA,NA,0,5,2007,"WD ","Normal",279000 +2120,907125040,20,"RL",NA,14217,"Pave",NA,"IR2","Lvl","AllPub","CulDSac","Gtl","CollgCr","Norm","Norm","1Fam","1Story",5,5,1994,1994,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","PConc","Gd","TA","No","ALQ",550,"Unf",0,472,1022,"GasA","Gd","Y","SBrkr",1022,0,0,1022,0,1,1,0,3,1,"TA",6,"Typ",0,NA,"Detchd",1995,"Unf",2,747,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,8,2007,"WD ","Normal",139500 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+2133,907192120,20,"RL",72,10665,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","CollgCr","Norm","Norm","1Fam","1Story",7,5,2003,2003,"Gable","CompShg","VinylSd","VinylSd","BrkFace",167,"Gd","TA","PConc","Gd","TA","Av","GLQ",1013,"Unf",0,440,1453,"GasA","Ex","Y","SBrkr",1479,0,0,1479,1,0,2,0,3,1,"Gd",7,"Typ",0,NA,"Attchd",2003,"RFn",2,558,"TA","TA","Y",144,29,0,0,0,0,NA,NA,NA,0,6,2007,"WD ","Normal",226000 +2134,907192150,60,"RL",72,11317,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","CollgCr","Norm","Norm","1Fam","2Story",7,5,2003,2003,"Gable","CompShg","VinylSd","VinylSd","BrkFace",101,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,840,840,"GasA","Ex","Y","SBrkr",840,828,0,1668,0,0,2,1,3,1,"Gd",8,"Typ",0,NA,"Attchd",2003,"RFn",2,500,"TA","TA","Y",144,68,0,0,0,0,NA,NA,NA,0,9,2007,"WD ","Normal",180000 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+2141,907203060,20,"RL",70,7700,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","CollgCr","Norm","Norm","1Fam","1Story",5,5,1972,1972,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","CBlock","TA","TA","No","LwQ",138,"Rec",468,276,882,"GasA","TA","Y","SBrkr",882,0,0,882,1,0,1,0,3,1,"TA",5,"Typ",0,NA,"Detchd",1980,"Unf",2,461,"TA","TA","Y",96,0,0,0,0,0,NA,"MnPrv",NA,0,3,2007,"WD ","Normal",112500 +2142,907203110,20,"RL",120,10356,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","CollgCr","Norm","Norm","1Fam","1Story",5,6,1975,1975,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","TA","TA","Av","BLQ",716,"Unf",0,253,969,"GasA","TA","Y","SBrkr",969,0,0,969,0,0,1,1,3,1,"TA",5,"Typ",0,NA,"Attchd",1975,"Unf",2,440,"TA","TA","Y",0,0,0,0,0,0,NA,"MnPrv",NA,0,1,2007,"WD ","Normal",122000 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+2165,907420050,20,"RL",64,8334,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","CollgCr","Norm","Norm","1Fam","1Story",6,5,2006,2007,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,1330,1330,"GasA","Ex","Y","SBrkr",1330,0,0,1330,0,0,2,0,3,1,"Gd",6,"Typ",1,"Gd","Attchd",2006,"Fin",2,437,"TA","TA","Y",0,23,0,0,0,0,NA,NA,NA,0,7,2007,"New","Partial",167840 +2166,907420060,60,"RL",64,8333,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","CollgCr","Norm","Norm","1Fam","2Story",7,5,2006,2006,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,738,738,"GasA","Gd","Y","SBrkr",738,753,0,1491,0,0,2,1,3,1,"Gd",7,"Typ",1,"Gd","Attchd",2006,"RFn",2,484,"TA","TA","Y",100,30,0,0,0,0,NA,NA,NA,0,5,2007,"WD ","Normal",168675 +2167,907420110,60,"RL",64,9045,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","CollgCr","Norm","Norm","1Fam","2Story",5,5,2005,2005,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","Mn","Unf",0,"Unf",0,768,768,"GasA","Ex","Y","SBrkr",768,768,0,1536,0,0,2,1,3,1,"Gd",6,"Typ",0,NA,"Attchd",2005,"Fin",2,400,"TA","TA","Y",0,40,0,0,0,0,NA,NA,NA,0,6,2007,"WD ","Normal",167000 +2168,908102100,20,"RL",70,9170,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","Edwards","Feedr","Norm","1Fam","1Story",5,7,1965,1965,"Hip","CompShg","MetalSd","MetalSd","None",0,"TA","TA","CBlock","TA","TA","No","ALQ",698,"GLQ",96,420,1214,"GasA","Ex","Y","SBrkr",1214,0,0,1214,1,0,1,0,2,1,"TA",6,"Typ",0,NA,"Detchd",1965,"Unf",2,461,"Fa","Fa","Y",0,0,184,0,0,0,NA,"GdPrv","Shed",400,4,2007,"WD ","Normal",140000 +2169,908102160,85,"RL",75,9825,"Pave",NA,"Reg","Low","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","SFoyer",5,5,1967,1967,"Gable","CompShg","HdBoard","HdBoard","BrkFace",162,"TA","TA","CBlock","Gd","TA","Gd","ALQ",936,"Unf",0,0,936,"GasA","Gd","Y","SBrkr",936,0,0,936,1,0,1,0,2,1,"TA",4,"Typ",1,"Fa","Attchd",1967,"Unf",1,384,"TA","TA","Y",405,0,0,0,0,0,NA,NA,"Shed",450,8,2007,"WD ","Abnorml",118500 +2170,908103090,20,"RL",67,8308,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",4,6,1963,1963,"Gable","CompShg","VinylSd","VinylSd","Stone",20,"TA","Gd","CBlock","TA","TA","No","BLQ",132,"LwQ",841,115,1088,"GasA","TA","Y","SBrkr",1088,0,0,1088,0,0,1,0,2,1,"TA",4,"Typ",0,NA,"Detchd",2002,"Unf",2,520,"TA","TA","P",0,0,0,0,0,0,NA,NA,NA,0,6,2007,"COD","Normal",110000 +2171,908127040,20,"RL",73,10921,"Pave",NA,"Reg","HLS","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",4,5,1965,1965,"Hip","CompShg","HdBoard","HdBoard","BrkFace",48,"TA","TA","CBlock","TA","TA","No","Rec",520,"Unf",0,440,960,"GasA","TA","Y","FuseF",960,0,0,960,1,0,1,0,3,1,"TA",6,"Typ",0,NA,"Attchd",1965,"Fin",1,432,"TA","TA","P",120,0,0,0,0,0,NA,NA,NA,0,5,2007,"WD ","Normal",94750 +2172,908127060,50,"RL",74,16287,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",5,6,1925,1950,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","Gd","CBlock","TA","TA","No","ALQ",130,"BLQ",105,666,901,"GasA","TA","Y","SBrkr",901,450,0,1351,1,0,1,0,3,1,"TA",7,"Typ",1,"Gd","Detchd",1975,"Unf",2,576,"TA","TA","N",0,0,43,0,100,0,NA,NA,NA,0,7,2007,"WD ","Normal",122000 +2173,908127070,20,"RL",80,8240,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",6,6,1960,1960,"Hip","CompShg","HdBoard","HdBoard","Stone",198,"TA","TA","CBlock","TA","TA","No","Unf",0,"Unf",0,1179,1179,"GasA","Gd","Y","SBrkr",1179,0,0,1179,0,0,1,0,2,1,"TA",5,"Min2",0,NA,"Attchd",1960,"Fin",2,622,"TA","TA","P",0,0,0,0,0,0,NA,"GdPrv",NA,0,6,2007,"WD ","Normal",110000 +2174,908127100,90,"RL",81,11841,"Grvl",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","Duplex","SFoyer",6,5,1990,1990,"Gable","CompShg","HdBoard","HdBoard","BrkFace",104,"TA","Gd","CBlock","Gd","TA","Av","GLQ",816,"Unf",0,0,816,"GasA","TA","Y","SBrkr",816,0,0,816,1,0,1,0,3,1,"TA",5,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"Y",0,32,0,0,0,0,NA,NA,NA,0,5,2007,"WD ","Normal",118500 +2175,908152060,80,"RL",65,6285,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","SLvl",6,6,1976,1976,"Gable","CompShg","HdBoard","Plywood","None",0,"TA","Fa","CBlock","TA","TA","Av","GLQ",504,"Unf",0,456,960,"GasA","TA","Y","SBrkr",1044,0,0,1044,1,0,1,0,3,1,"TA",7,"Typ",1,"Fa","Detchd",1976,"Unf",2,528,"TA","Fa","Y",228,0,0,0,0,0,NA,NA,NA,0,4,2007,"WD ","Normal",133500 +2176,908152070,20,"RL",NA,7917,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","Edwards","Norm","Norm","1Fam","1Story",6,7,1976,1976,"Hip","CompShg","HdBoard","HdBoard","BrkFace",174,"TA","Gd","CBlock","TA","Gd","No","BLQ",751,"Unf",0,392,1143,"GasA","TA","Y","SBrkr",1113,0,0,1113,1,0,1,1,3,1,"TA",6,"Typ",1,"Fa","Attchd",1987,"RFn",1,504,"TA","Gd","Y",370,30,0,0,0,0,NA,"GdPrv",NA,0,5,2007,"WD ","Normal",148000 +2177,908152180,90,"RL",NA,9555,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Edwards","Norm","Norm","Duplex","2Story",5,6,1979,1979,"Gable","CompShg","Plywood","Plywood","None",0,"TA","TA","Slab",NA,NA,NA,NA,0,NA,0,0,0,"GasA","TA","Y","SBrkr",1100,1133,0,2233,0,0,2,1,5,2,"TA",11,"Typ",0,NA,"Attchd",1979,"Fin",2,579,"TA","Gd","Y",0,0,0,0,0,0,NA,NA,NA,0,2,2007,"WD ","Normal",141000 +2178,908152260,20,"RL",74,8536,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","Edwards","Norm","Norm","1Fam","1Story",5,5,2006,2007,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,1125,1125,"GasA","Gd","Y","SBrkr",1125,0,0,1125,0,0,1,1,2,1,"TA",5,"Typ",0,NA,"Attchd",2007,"Unf",2,430,"TA","TA","Y",80,64,0,0,0,0,NA,NA,NA,0,4,2007,"New","Partial",155000 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Sdng","None",0,"TA","TA","BrkTil","TA","TA","No","LwQ",144,"ALQ",278,238,660,"GasA","TA","Y","SBrkr",740,125,0,865,1,0,1,0,2,1,"TA",4,"Typ",1,"Gd","Detchd",1920,"Unf",1,216,"TA","TA","Y",0,0,84,0,0,0,NA,NA,NA,0,8,2007,"WD ","Normal",108500 +2190,908250100,20,"RL",68,9450,"Pave",NA,"Reg","Bnk","AllPub","Inside","Mod","Edwards","Norm","Norm","1Fam","1Story",4,5,1954,1954,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","TA","CBlock","TA","TA","No","LwQ",552,"Unf",0,342,894,"GasA","Ex","Y","SBrkr",894,0,0,894,0,0,1,0,3,1,"TA",5,"Typ",0,NA,"Detchd",1999,"Unf",2,400,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,5,2007,"WD ","Abnorml",110000 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Sdng","BrkFace",252,"TA","TA","BrkTil","Gd","TA","No","ALQ",569,"Unf",0,162,731,"GasA","Ex","Y","SBrkr",981,787,0,1768,1,0,1,1,3,1,"Gd",7,"Typ",2,"TA","Detchd",1939,"Unf",1,240,"TA","TA","Y",0,0,264,0,0,0,NA,"MnPrv",NA,0,6,2007,"WD ","Normal",175000 +2202,909252150,70,"RL",63,4000,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Crawfor","Norm","Norm","1Fam","2Story",7,8,1930,1995,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","BrkTil","Gd","TA","No","GLQ",246,"Unf",0,285,531,"GasA","TA","Y","SBrkr",567,531,0,1098,1,0,1,0,2,1,"TA",5,"Typ",1,"Gd","Detchd",1930,"Unf",1,216,"TA","TA","Y",0,0,0,0,0,0,NA,"MnPrv",NA,0,6,2007,"WD ","Normal",148500 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Sdng","None",0,"TA","TA","CBlock",NA,NA,NA,NA,0,NA,0,0,0,"GasA","Fa","Y","SBrkr",1226,0,0,1226,0,0,1,1,2,1,"TA",7,"Min1",1,"Gd","Attchd",1951,"Fin",2,400,"TA","TA","Y",174,24,120,0,228,0,NA,NA,NA,0,6,2007,"WD ","Normal",153500 +2214,909275310,70,"RL",86,22420,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Crawfor","Feedr","Norm","1Fam","2Story",6,6,1918,1950,"Hip","CompShg","Wd Sdng","Stucco","None",0,"TA","TA","BrkTil","Gd","TA","No","BLQ",1128,"Unf",0,242,1370,"GasW","TA","N","FuseA",1370,1254,0,2624,1,0,2,1,4,1,"TA",10,"Typ",1,"Gd","Detchd",1918,"Unf",3,864,"TA","TA","N",0,0,0,0,0,0,NA,NA,NA,0,11,2007,"WD ","Normal",239000 +2215,909276160,70,"RL",78,12168,"Pave",NA,"Reg","HLS","AllPub","Inside","Mod","Crawfor","Norm","Norm","1Fam","2Story",8,6,1934,1998,"Gable","CompShg","BrkFace","Wd 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+2276,921128030,20,"RL",85,12633,"Pave",NA,"IR1","HLS","AllPub","Inside","Gtl","Timber","PosA","PosA","1Fam","1Story",10,5,2006,2007,"Hip","CompShg","MetalSd","MetalSd","BrkFace",242,"Ex","TA","PConc","Ex","TA","Gd","Unf",0,"Unf",0,1824,1824,"GasA","Ex","Y","SBrkr",1824,0,0,1824,0,0,2,0,3,1,"Ex",8,"Typ",1,"Gd","Attchd",2006,"Fin",3,932,"TA","TA","Y",160,36,0,0,108,0,NA,NA,NA,0,9,2007,"New","Partial",392000 +2277,921205070,60,"RL",88,12665,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Timber","Norm","Norm","1Fam","2Story",8,5,2005,2006,"Hip","CompShg","VinylSd","VinylSd","BrkFace",245,"Gd","TA","PConc","Gd","Gd","Gd","Unf",0,"Unf",0,1094,1094,"GasA","Ex","Y","SBrkr",1133,1349,0,2482,0,0,2,1,4,1,"Gd",9,"Typ",1,"Gd","BuiltIn",2005,"Fin",3,642,"TA","TA","Y",144,39,0,0,0,0,NA,NA,NA,0,2,2007,"WD ","Normal",281213 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+2280,923203010,120,"RM",62,6710,"Pave",NA,"IR1","Lvl","AllPub","FR3","Gtl","Mitchel","Norm","Norm","TwnhsE","SFoyer",6,5,1996,1997,"Gable","CompShg","VinylSd","VinylSd","BrkFace",134,"TA","TA","PConc","Ex","TA","Av","Rec",16,"GLQ",904,0,920,"GasA","Ex","Y","SBrkr",936,0,0,936,2,0,0,1,0,1,"TA",3,"Typ",0,NA,"Attchd",1996,"Fin",2,460,"TA","TA","Y",0,40,0,0,0,0,NA,NA,NA,0,6,2007,"WD ","Normal",140000 +2281,923204050,20,"RL",94,25286,"Pave",NA,"Reg","HLS","AllPub","Inside","Mod","Mitchel","Norm","Norm","1Fam","1Story",4,5,1963,1963,"Gable","CompShg","HdBoard","Plywood","None",0,"TA","TA","PConc","TA","TA","Gd","ALQ",633,"Unf",0,431,1064,"GasA","Gd","Y","SBrkr",1040,0,0,1040,1,0,1,0,3,1,"TA",5,"Typ",0,NA,"Attchd",1963,"Unf",2,648,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,1,2007,"WD ","Normal",132250 +2282,923204140,90,"RL",100,25000,"Pave",NA,"Reg","Low","AllPub","Inside","Gtl","Mitchel","Norm","Norm","Duplex","1Story",5,4,1967,1967,"Gable","CompShg","HdBoard","Plywood","None",0,"TA","TA","CBlock","TA","TA","Av","Unf",0,"Unf",0,1632,1632,"GasA","TA","Y","SBrkr",1632,0,0,1632,0,0,2,0,4,2,"TA",8,"Typ",0,NA,"Attchd",1967,"Unf",2,576,"TA","TA","P",0,0,0,0,0,0,NA,NA,NA,0,6,2007,"WD ","Normal",143000 +2283,923205025,190,"RL",NA,32463,"Pave",NA,"Reg","Low","AllPub","Inside","Mod","Mitchel","Norm","Norm","2fmCon","1Story",4,4,1961,1975,"Gable","CompShg","MetalSd","MetalSd","Stone",149,"TA","Gd","CBlock","TA","TA","Av","BLQ",1159,"Unf",0,90,1249,"GasA","Ex","Y","SBrkr",1622,0,0,1622,1,0,1,0,3,1,"TA",7,"Typ",1,"TA","2Types",1975,"Fin",4,1356,"TA","TA","Y",439,0,0,0,0,0,NA,NA,NA,0,3,2007,"WD ","Normal",168000 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Sdng","Stone",28,"TA","TA","CBlock","TA","TA","No","BLQ",468,"Rec",539,171,1178,"GasA","Gd","Y","SBrkr",1293,0,0,1293,1,0,2,0,2,1,"TA",5,"Typ",0,NA,"Attchd",1954,"RFn",2,452,"TA","TA","Y",280,0,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",149900 +2307,526302040,20,"RL",85,10533,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NAmes","Norm","Norm","1Fam","1Story",6,6,1956,1956,"Hip","CompShg","VinylSd","VinylSd","BrkFace",244,"TA","Gd","CBlock","TA","TA","No","Rec",773,"Unf",0,235,1008,"GasA","TA","Y","SBrkr",1024,0,0,1024,1,0,1,0,2,1,"Gd",5,"Typ",2,"TA","Attchd",1956,"Unf",1,313,"TA","TA","Y",0,0,0,0,280,0,NA,NA,NA,0,8,2006,"WD ","Normal",157500 +2308,526302120,20,"RL",NA,11765,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","NAmes","Norm","Norm","1Fam","1Story",5,5,1957,1957,"Gable","CompShg","Wd Sdng","Wd Sdng","Stone",302,"TA","TA","CBlock","TA","TA","Mn","Rec",1127,"Unf",0,490,1617,"GasA","Fa","Y","SBrkr",1797,0,0,1797,0,0,1,1,3,1,"TA",7,"Typ",1,"TA","Attchd",1957,"Unf",3,963,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,6,2006,"WD ","Normal",169000 +2309,526303060,20,"RL",NA,39384,"Pave",NA,"IR1","Low","AllPub","CulDSac","Sev","NAmes","Norm","Norm","1Fam","1Story",6,6,1957,1957,"Gable","CompShg","Wd Sdng","Wd Sdng","Stone",902,"TA","TA","CBlock","Gd","TA","Gd","ALQ",1110,"Unf",0,595,1705,"GasA","Ex","Y","SBrkr",1390,0,0,1390,1,0,1,1,1,1,"Ex",4,"Min1",2,"Gd","Attchd",1957,"Unf",2,550,"TA","TA","Y",0,189,0,0,0,0,NA,NA,NA,0,10,2006,"WD ","Abnorml",252000 +2310,526354020,20,"RL",90,11727,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","NAmes","Norm","Norm","1Fam","1Story",7,6,1969,1969,"Gable","CompShg","HdBoard","HdBoard","BrkFace",434,"TA","Gd","CBlock","TA","TA","Mn","Unf",0,"Unf",0,1851,1851,"GasA","Gd","Y","SBrkr",1851,0,0,1851,0,0,2,0,3,1,"TA",8,"Typ",1,"TA","Attchd",1969,"Fin",2,506,"TA","TA","Y",0,146,0,0,0,0,NA,NA,NA,0,3,2006,"WD ","Normal",192100 +2311,527105060,60,"RL",60,8238,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Gilbert","Norm","Norm","1Fam","2Story",6,5,1997,1998,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","PConc","TA","TA","No","GLQ",700,"Unf",0,113,813,"GasA","Ex","Y","SBrkr",813,712,0,1525,1,0,2,1,3,1,"TA",6,"Typ",1,"TA","Attchd",1997,"Fin",2,400,"TA","TA","Y",421,72,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",183500 +2312,527106010,60,"RL",NA,13006,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","Gilbert","Norm","Norm","1Fam","2Story",7,5,1997,1997,"Gable","CompShg","HdBoard","HdBoard","BrkFace",285,"TA","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,964,964,"GasA","Gd","Y","SBrkr",993,1243,0,2236,0,0,2,1,4,1,"Gd",8,"Typ",1,"TA","BuiltIn",1997,"Fin",2,642,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,11,2006,"WD ","Normal",222000 +2313,527107020,60,"RL",NA,13041,"Pave",NA,"IR2","Lvl","AllPub","Inside","Gtl","Gilbert","Norm","Norm","1Fam","2Story",6,5,1995,1995,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,781,781,"GasA","Gd","Y","SBrkr",781,890,0,1671,0,0,2,1,3,1,"TA",7,"Typ",1,"TA","Attchd",1995,"Fin",2,423,"TA","TA","Y",0,84,0,0,0,0,NA,NA,NA,0,2,2006,"WD ","Normal",170000 +2314,527107030,60,"RL",NA,13031,"Pave",NA,"IR2","Lvl","AllPub","Corner","Gtl","Gilbert","Norm","Norm","1Fam","2Story",6,5,1995,1996,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","PConc","Gd","TA","No","ALQ",592,"Unf",0,99,691,"GasA","Gd","Y","SBrkr",691,807,0,1498,0,0,2,1,3,1,"TA",6,"Typ",1,"TA","Attchd",1995,"Fin",2,409,"TA","TA","Y",315,44,0,0,0,0,NA,NA,NA,0,7,2006,"WD ","Normal",187500 +2315,527107120,60,"RL",54,9783,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Gilbert","Norm","Norm","1Fam","2Story",6,5,1996,1996,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,821,821,"GasA","Gd","Y","SBrkr",821,955,0,1776,0,0,2,1,3,1,"TA",7,"Typ",1,"TA","BuiltIn",1996,"Fin",2,443,"TA","TA","Y",286,116,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",202000 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+2334,527212040,60,"RL",82,12438,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","StoneBr","Norm","Norm","1Fam","2Story",8,5,2006,2006,"Hip","CompShg","VinylSd","VinylSd","BrkFace",466,"Ex","TA","PConc","Ex","Gd","No","Unf",0,"Unf",0,1234,1234,"GasA","Ex","Y","SBrkr",1264,1312,0,2576,0,0,2,1,4,1,"Ex",10,"Typ",1,"Gd","BuiltIn",2006,"Fin",3,666,"TA","TA","Y",324,100,0,0,0,0,NA,NA,NA,0,7,2006,"New","Partial",361919 +2335,527214060,60,"RL",82,16052,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","StoneBr","Norm","Norm","1Fam","2Story",10,5,2006,2006,"Hip","CompShg","VinylSd","VinylSd","Stone",734,"Ex","TA","PConc","Ex","TA","No","GLQ",1206,"Unf",0,644,1850,"GasA","Ex","Y","SBrkr",1850,848,0,2698,1,0,2,1,4,1,"Ex",11,"Typ",1,"Gd","Attchd",2006,"RFn",3,736,"TA","TA","Y",250,0,0,0,0,0,NA,NA,NA,0,7,2006,"New","Partial",535000 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+2346,527327080,60,"RL",NA,18800,"Pave",NA,"IR1","Lvl","AllPub","FR2","Gtl","NWAmes","Norm","Norm","1Fam","2Story",6,5,1976,1976,"Gable","CompShg","HdBoard","HdBoard","BrkFace",120,"TA","TA","PConc","Gd","TA","Mn","GLQ",712,"Unf",0,84,796,"GasA","TA","Y","SBrkr",790,784,0,1574,1,0,2,1,3,1,"TA",6,"Typ",1,"TA","Attchd",1976,"Fin",2,566,"TA","TA","Y",306,111,0,0,0,0,NA,NA,NA,0,7,2006,"WD ","Normal",190000 +2347,527328010,85,"RL",NA,10464,"Pave",NA,"IR1","Lvl","AllPub","FR3","Gtl","NWAmes","Norm","Norm","1Fam","SFoyer",6,6,1980,1980,"Gable","CompShg","HdBoard","HdBoard","BrkFace",130,"TA","TA","CBlock","Gd","TA","Av","GLQ",850,"Unf",0,138,988,"GasA","TA","Y","SBrkr",1102,0,0,1102,1,0,1,0,2,1,"TA",5,"Typ",1,"TA","Attchd",1980,"RFn",2,582,"TA","TA","Y",140,22,0,0,0,0,NA,NA,NA,0,6,2006,"WD ","Normal",169000 +2348,527354040,20,"RL",80,10400,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",7,5,1988,1988,"Gable","CompShg","Wd Sdng","Wd Sdng","BrkFace",102,"TA","TA","CBlock","Gd","TA","Av","GLQ",929,"Unf",0,916,1845,"GasA","Gd","Y","SBrkr",1872,0,0,1872,0,1,2,0,3,1,"TA",6,"Typ",1,"TA","Attchd",1988,"Fin",2,604,"TA","TA","Y",197,39,0,0,0,0,NA,NA,NA,0,6,2006,"WD ","Normal",241500 +2349,527355060,60,"RL",81,10530,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","PosA","Norm","1Fam","2Story",7,5,1978,1978,"Gable","CompShg","Plywood","Plywood","BrkFace",68,"TA","TA","CBlock","TA","TA","No","Unf",0,"Unf",0,945,945,"GasA","TA","Y","SBrkr",945,912,0,1857,0,0,2,1,4,1,"TA",8,"Typ",1,"TA","Attchd",1978,"RFn",2,482,"TA","TA","Y",400,105,0,0,0,0,NA,"GdPrv",NA,0,7,2006,"WD ","Normal",188900 +2350,527355280,20,"RL",79,10240,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","1Story",6,6,1980,1980,"Gable","CompShg","Plywood","Plywood","BrkFace",157,"TA","Gd","CBlock","Gd","TA","No","BLQ",625,"LwQ",1061,0,1686,"GasA","TA","Y","SBrkr",1686,0,0,1686,1,0,2,0,3,1,"TA",7,"Typ",1,"TA","Attchd",1980,"Unf",2,612,"TA","TA","Y",384,131,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",207500 +2351,527356020,60,"RL",80,16692,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","RRAn","Norm","1Fam","2Story",7,5,1978,1978,"Gable","CompShg","Plywood","Plywood","BrkFace",184,"TA","TA","CBlock","Gd","TA","No","BLQ",790,"LwQ",469,133,1392,"GasA","TA","Y","SBrkr",1392,1392,0,2784,1,0,3,1,5,1,"Gd",12,"Typ",2,"TA","Attchd",1978,"RFn",2,564,"TA","TA","Y",0,112,0,0,440,519,"Fa","MnPrv","TenC",2000,7,2006,"WD ","Normal",250000 +2352,527356060,60,"RL",85,11475,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","NWAmes","RRAn","Norm","1Fam","2Story",6,6,1975,1975,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","CBlock","Gd","TA","No","ALQ",550,"Unf",0,163,713,"GasA","TA","Y","SBrkr",811,741,0,1552,1,0,2,1,3,1,"TA",6,"Typ",1,"TA","Attchd",1975,"RFn",2,434,"TA","TA","Y",209,208,0,0,0,0,NA,"MnPrv",NA,0,2,2006,"WD ","Normal",179900 +2353,527358090,85,"RL",NA,9927,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","SFoyer",7,5,1976,1976,"Gable","CompShg","VinylSd","Wd Shng","Stone",252,"Gd","TA","CBlock","Ex","TA","Gd","GLQ",1005,"Unf",0,42,1047,"GasA","TA","Y","SBrkr",1083,0,0,1083,1,0,1,0,2,1,"TA",5,"Typ",1,"Fa","Attchd",1976,"RFn",2,596,"TA","TA","Y",444,0,40,0,0,0,NA,NA,NA,0,7,2006,"WD ","Normal",172000 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+2480,531451290,60,"RL",70,9370,"Pave",NA,"IR2","Lvl","AllPub","Inside","Gtl","SawyerW","Norm","Norm","1Fam","2Story",6,5,1992,1993,"Gable","CompShg","HdBoard","HdBoard","None",0,"Gd","TA","PConc","Gd","TA","No","ALQ",758,"Unf",0,78,836,"GasA","Ex","Y","SBrkr",844,887,0,1731,1,0,2,1,3,1,"Gd",7,"Typ",0,NA,"Attchd",1992,"RFn",2,462,"TA","TA","Y",307,85,0,0,224,0,NA,NA,"Othr",3000,10,2006,"WD ","Family",248500 +2481,531452080,120,"RL",50,7175,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","SawyerW","Norm","Norm","TwnhsE","1Story",6,5,1990,1991,"Gable","CompShg","Plywood","Plywood","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",904,"Unf",0,494,1398,"GasA","Gd","Y","SBrkr",1398,0,0,1398,1,0,2,0,2,1,"Gd",5,"Typ",0,NA,"Attchd",1990,"RFn",2,542,"TA","TA","Y",0,46,0,0,0,0,NA,"MnPrv",NA,0,7,2006,"WD ","Normal",172000 +2482,531452100,120,"RL",50,7175,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","SawyerW","Norm","Norm","TwnhsE","1Story",6,5,1991,1991,"Gable","CompShg","Plywood","Plywood","None",0,"Gd","TA","PConc","Gd","TA","No","GLQ",278,"Unf",0,939,1217,"GasA","Gd","Y","SBrkr",1217,0,0,1217,0,0,2,0,2,1,"Gd",5,"Typ",0,NA,"Attchd",1991,"RFn",2,484,"TA","TA","Y",0,64,0,0,0,0,NA,NA,NA,0,6,2006,"WD ","Normal",151000 +2483,531452210,60,"RL",NA,9019,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","SawyerW","RRAe","Norm","1Fam","2Story",6,5,1994,1994,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","PConc","Gd","TA","No","ALQ",274,"Unf",0,362,636,"GasA","Ex","Y","SBrkr",636,684,0,1320,0,0,2,1,3,1,"Gd",6,"Typ",0,NA,"Attchd",1994,"Fin",2,472,"TA","TA","Y",0,40,0,0,0,0,NA,NA,NA,0,7,2006,"WD ","Normal",150000 +2484,531478010,20,"RH",NA,8900,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","SawyerW","Norm","Norm","1Fam","1Story",4,4,1966,1966,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","TA","TA","No","Rec",1056,"Unf",0,0,1056,"GasA","TA","Y","SBrkr",1056,0,0,1056,1,0,1,0,2,1,"TA",5,"Typ",0,NA,"Detchd",1966,"Unf",1,384,"TA","TA","Y",0,42,0,0,0,0,NA,"MnPrv",NA,0,11,2006,"WD ","Normal",107000 +2485,531479030,20,"RH",60,7800,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","SawyerW","Norm","Norm","1Fam","1Story",5,5,1965,1965,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","CBlock","TA","TA","No","BLQ",641,"Unf",0,187,828,"GasA","Gd","Y","SBrkr",965,0,0,965,1,0,1,0,3,1,"TA",6,"Typ",0,NA,"Detchd",1979,"Unf",1,300,"TA","TA","Y",421,0,0,0,0,0,NA,"MnPrv",NA,0,7,2006,"WD ","Abnorml",119900 +2486,532351060,20,"RL",70,9100,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Sawyer","RRAe","Norm","1Fam","1Story",5,4,1962,1962,"Hip","CompShg","Wd Sdng","Wd Sdng","BrkFace",51,"TA","TA","CBlock","TA","TA","No","LwQ",36,"Unf",0,952,988,"GasA","Ex","Y","SBrkr",988,0,0,988,1,0,1,0,3,1,"TA",6,"Typ",0,NA,"Detchd",1977,"Unf",2,624,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,7,2006,"WD ","Normal",129500 +2487,532351090,90,"RM",70,9100,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Sawyer","RRAe","Norm","Duplex","1Story",5,5,1963,1963,"Gable","CompShg","HdBoard","HdBoard","BrkFace",336,"TA","TA","CBlock","TA","TA","No","Rec",1332,"Unf",0,396,1728,"GasA","TA","Y","SBrkr",1728,0,0,1728,1,0,2,0,6,2,"TA",10,"Typ",0,NA,"Detchd",1963,"Unf",2,504,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,11,2006,"ConLI","Abnorml",125000 +2488,532351150,90,"RM",68,8927,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Sawyer","RRAe","Norm","Duplex","1.5Fin",6,6,1977,1977,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","Gd","CBlock",NA,NA,NA,NA,0,NA,0,0,0,"GasA","TA","Y","SBrkr",1286,368,0,1654,0,0,2,0,4,2,"TA",10,"Typ",0,NA,"Attchd",1977,"RFn",2,528,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,8,2006,"WD ","Normal",119500 +2489,532353030,20,"RL",NA,9240,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Sawyer","Norm","Norm","1Fam","1Story",5,6,1962,2002,"Hip","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","TA","TA","No","BLQ",612,"Unf",0,252,864,"GasA","Gd","Y","SBrkr",1211,0,0,1211,0,0,1,0,2,1,"TA",6,"Min1",1,"TA","Detchd",1972,"Unf",2,576,"TA","TA","Y",161,0,0,0,0,0,NA,"MnPrv",NA,0,3,2006,"WD ","Normal",144000 +2490,532353180,80,"RL",88,8471,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","Sawyer","Norm","Norm","1Fam","SLvl",6,7,1977,1995,"Gable","CompShg","HdBoard","Plywood","BrkFace",46,"TA","TA","CBlock","Gd","Gd","Av","ALQ",506,"Unf",0,0,506,"GasA","TA","Y","SBrkr",1212,0,0,1212,1,0,1,0,3,1,"TA",6,"Typ",1,"TA","Attchd",1978,"Unf",2,492,"TA","TA","Y",292,12,0,0,0,0,NA,"GdWo",NA,0,7,2006,"WD ","Normal",151000 +2491,532376080,20,"RL",NA,9308,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Sawyer","RRNe","Norm","1Fam","1Story",5,5,1965,1965,"Gable","CompShg","HdBoard","HdBoard","None",0,"TA","TA","CBlock","TA","TA","No","Rec",554,"Unf",0,430,984,"GasA","TA","Y","SBrkr",984,0,0,984,0,0,1,0,3,1,"TA",5,"Typ",0,NA,"Attchd",1965,"Unf",1,310,"TA","TA","Y",0,0,0,0,0,0,NA,"MnPrv",NA,0,7,2006,"WD ","Normal",126000 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+2518,533252020,20,"RL",NA,11250,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Veenker","Norm","Norm","1Fam","1Story",6,6,1977,1977,"Gable","CompShg","Plywood","Plywood","None",0,"Gd","TA","CBlock","Gd","TA","No","ALQ",767,"Unf",0,441,1208,"GasA","TA","Y","SBrkr",1208,0,0,1208,1,0,1,1,3,1,"TA",6,"Typ",1,"TA","Attchd",1977,"RFn",2,546,"TA","TA","Y",198,42,0,0,0,0,NA,NA,NA,0,6,2006,"WD ","Normal",165000 +2519,533253030,120,"RL",NA,3760,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Greens","Norm","Norm","Twnhs","1Story",8,5,1980,1980,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"Gd","TA","CBlock","Gd","TA","Gd","GLQ",1009,"LwQ",182,44,1235,"GasA","Gd","Y","SBrkr",1235,0,0,1235,1,0,1,0,1,1,"TA",4,"Typ",3,"TA","Attchd",1980,"Fin",2,484,"TA","TA","Y",133,78,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",214000 +2520,533253050,120,"RL",36,3640,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Greens","Norm","Norm","Twnhs","1Story",8,5,1980,1980,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"Gd","TA","CBlock","Gd","TA","Gd","GLQ",860,"Unf",0,176,1036,"GasA","TA","Y","SBrkr",1036,0,0,1036,1,0,1,0,1,1,"Gd",4,"Typ",1,"TA","Detchd",1980,"Fin",2,484,"TA","TA","Y",133,108,0,0,0,0,NA,NA,NA,0,9,2006,"WD ","Normal",188000 +2521,533253090,120,"RL",40,3874,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Greens","Norm","Norm","TwnhsE","1Story",8,5,1980,1980,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"Gd","TA","CBlock","Gd","TA","Gd","GLQ",807,"Unf",0,419,1226,"GasA","TA","Y","SBrkr",1226,0,0,1226,1,0,1,0,1,1,"Gd",4,"Typ",1,"Fa","Attchd",1980,"Fin",2,484,"TA","TA","Y",133,78,0,0,0,0,NA,NA,NA,0,7,2006,"WD ","Normal",190000 +2522,533253160,120,"RL",40,3876,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Greens","Norm","Norm","Twnhs","1Story",8,5,1978,1978,"Gable","CompShg","Wd 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+2524,534125210,190,"RL",79,13110,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","NWAmes","RRAn","Feedr","2fmCon","1Story",5,6,1972,1972,"Gable","CompShg","Plywood","Plywood","BrkFace",144,"TA","TA","CBlock","Gd","TA","No","GLQ",962,"Unf",0,191,1153,"GasA","Ex","Y","SBrkr",1193,0,0,1193,1,0,2,0,2,1,"TA",6,"Typ",0,NA,"Attchd",1972,"Fin",2,501,"TA","TA","Y",140,153,0,0,0,0,NA,"MnPrv",NA,0,7,2006,"WD ","Normal",146500 +2525,534126060,80,"RL",102,10192,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","NWAmes","Norm","Norm","1Fam","SLvl",7,6,1968,1992,"Gable","CompShg","MetalSd","MetalSd","BrkFace",143,"TA","TA","CBlock","TA","TA","No","Unf",0,"Unf",0,570,570,"GasA","Gd","Y","SBrkr",1222,698,0,1920,0,0,3,0,4,1,"Gd",8,"Typ",1,"TA","Attchd",1968,"RFn",2,487,"TA","TA","Y",0,98,0,0,0,0,NA,"GdPrv",NA,0,9,2006,"WD ","Normal",170000 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Sdng","None",0,"TA","TA","CBlock","TA","Fa","No","Rec",444,"Unf",0,550,994,"GasA","Gd","Y","SBrkr",1216,639,0,1855,0,0,2,0,4,1,"TA",7,"Typ",0,NA,"Attchd",1954,"Unf",1,325,"TA","TA","Y",182,0,0,0,0,0,NA,NA,NA,0,10,2006,"WD ","Normal",136900 +2743,905452150,50,"RL",75,9525,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",6,5,1953,1953,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","TA","CBlock","TA","TA","No","LwQ",468,"Unf",0,532,1000,"GasA","TA","Y","SBrkr",1068,541,0,1609,0,0,1,1,5,1,"TA",7,"Typ",0,NA,"Attchd",1953,"Unf",1,305,"Fa","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,7,2006,"WD ","Normal",149900 +2744,905475150,20,"RL",57,6420,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",5,7,1952,1952,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","PConc","Ex","Gd","Mn","LwQ",210,"ALQ",551,219,980,"GasA","Fa","Y","FuseA",1148,0,0,1148,0,1,1,0,2,1,"TA",6,"Typ",0,NA,"Detchd",1952,"Unf",1,308,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,9,2006,"WD ","Normal",123500 +2745,905477010,20,"RL",77,8335,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","Edwards","Norm","Norm","1Fam","1Story",5,5,1954,1954,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","Slab",NA,NA,NA,NA,0,NA,0,0,0,"GasA","Gd","Y","SBrkr",1124,0,0,1124,0,0,1,0,3,1,"TA",5,"Min2",1,"Gd",NA,NA,NA,0,0,NA,NA,"N",0,36,190,0,0,0,NA,NA,NA,0,4,2006,"WD ","Normal",93000 +2746,905480160,50,"RL",50,7585,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Artery","Norm","1Fam","1.5Fin",5,3,1948,1950,"Gable","CompShg","MetalSd","MetalSd","None",0,"TA","TA","CBlock","Fa","Fa","Mn","Unf",0,"Unf",0,810,810,"GasA","Fa","Y","FuseA",1002,454,0,1456,1,1,1,0,4,1,"TA",7,"Typ",1,"TA","Detchd",1954,"Unf",1,280,"TA","TA","P",0,0,0,0,0,0,NA,NA,NA,0,8,2006,"WD ","Normal",91500 +2747,906202040,20,"RL",NA,11200,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","SawyerW","Norm","Norm","1Fam","1Story",6,5,1985,1985,"Gable","CompShg","Wd Sdng","Wd Shng","BrkFace",85,"Gd","TA","CBlock","Gd","TA","No","GLQ",1258,"Unf",0,40,1298,"GasA","TA","Y","SBrkr",1298,0,0,1298,1,0,2,0,3,1,"Gd",5,"Typ",1,"TA","Attchd",1985,"Unf",2,403,"TA","TA","Y",165,26,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",180000 +2748,906204180,60,"RL",88,12128,"Pave",NA,"IR1","Bnk","AllPub","Inside","Gtl","SawyerW","Norm","Norm","1Fam","2Story",6,4,1989,1989,"Gable","CompShg","HdBoard","HdBoard","BrkFace",232,"Gd","TA","CBlock","Gd","TA","No","ALQ",549,"Unf",0,319,868,"GasA","Ex","Y","SBrkr",1313,1246,0,2559,0,0,2,1,4,1,"Ex",9,"Typ",1,"TA","Attchd",1989,"RFn",2,506,"TA","TA","Y",0,245,0,0,168,0,NA,"MnPrv",NA,0,11,2006,"WD ","Abnorml",209000 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+2759,906392100,20,"RL",78,11645,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","CollgCr","Norm","Norm","1Fam","1Story",7,5,2005,2005,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","Av","GLQ",1300,"Unf",0,434,1734,"GasA","Ex","Y","SBrkr",1734,0,0,1734,1,0,2,0,3,1,"Gd",7,"Typ",0,NA,"Attchd",2005,"Fin",2,660,"TA","TA","Y",160,24,0,0,0,0,NA,NA,NA,0,1,2006,"WD ","Normal",260000 +2760,906392120,20,"RL",78,11645,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","CollgCr","Norm","Norm","1Fam","1Story",8,5,2005,2006,"Hip","CompShg","VinylSd","VinylSd","BrkFace",198,"Gd","TA","PConc","Ex","TA","Gd","GLQ",1122,"Unf",0,448,1570,"GasA","Ex","Y","SBrkr",1590,0,0,1590,1,0,2,1,2,1,"Ex",6,"Typ",0,NA,"Attchd",2005,"Fin",3,754,"TA","TA","Y",176,80,0,0,0,0,NA,NA,NA,0,8,2006,"New","Partial",294900 +2761,906394010,60,"RL",91,10984,"Pave",NA,"IR1","Lvl","AllPub","Corner","Gtl","CollgCr","Norm","Norm","1Fam","2Story",7,5,2005,2006,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","Gd","Unf",0,"Unf",0,945,945,"GasA","Ex","Y","SBrkr",945,864,0,1809,0,0,2,1,3,1,"Gd",8,"Typ",0,NA,"Attchd",2005,"RFn",2,638,"TA","TA","Y",144,54,0,0,0,0,NA,NA,NA,0,5,2006,"New","Partial",209700 +2762,906394040,20,"RL",78,9316,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","CollgCr","Norm","Norm","1Fam","1Story",7,5,2005,2005,"Gable","CompShg","VinylSd","VinylSd","BrkFace",140,"Gd","TA","PConc","Gd","TA","Gd","GLQ",56,"Unf",0,1558,1614,"GasA","Ex","Y","SBrkr",1614,0,0,1614,0,0,2,0,3,1,"Gd",7,"Typ",1,"Gd","Attchd",2005,"Fin",2,576,"TA","TA","Y",100,45,0,0,0,0,NA,NA,NA,0,3,2006,"WD ","Normal",225000 +2763,906394060,60,"RL",78,9316,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","CollgCr","Norm","Norm","1Fam","2Story",7,5,2005,2005,"Gable","CompShg","VinylSd","VinylSd","BrkFace",532,"Gd","TA","PConc","Gd","TA","Av","Unf",0,"Unf",0,784,784,"GasA","Ex","Y","SBrkr",784,812,0,1596,0,0,2,1,3,1,"Gd",6,"Typ",0,NA,"Attchd",2005,"RFn",2,610,"TA","TA","Y",144,45,0,0,0,0,NA,NA,NA,0,6,2006,"WD ","Normal",181000 +2764,906420020,60,"RL",80,10041,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","SawyerW","Norm","Norm","1Fam","2Story",8,5,1992,1992,"Gable","CompShg","HdBoard","HdBoard","None",0,"Gd","TA","PConc","Gd","TA","Mn","GLQ",789,"Unf",0,119,908,"GasA","Ex","Y","SBrkr",927,988,0,1915,1,0,2,1,3,1,"Gd",8,"Typ",1,"TA","Attchd",1992,"Fin",2,506,"TA","TA","Y",120,150,0,0,0,0,NA,NA,NA,0,2,2006,"WD ","Abnorml",220000 +2765,906426090,20,"RL",NA,36500,"Pave",NA,"IR1","Low","AllPub","Inside","Mod","ClearCr","Norm","Norm","1Fam","1Story",5,5,1964,1964,"Gable","CompShg","Wd Sdng","Wd Sdng","BrkCmn",621,"TA","Gd","CBlock","TA","TA","Av","Rec",812,"Unf",0,812,1624,"GasA","Fa","Y","SBrkr",1582,0,0,1582,0,1,2,0,4,1,"TA",7,"Typ",0,NA,"Attchd",1964,"Unf",2,390,"TA","TA","N",168,198,0,0,0,0,NA,NA,NA,0,6,2006,"WD ","Normal",190000 +2766,906475050,80,"RL",NA,21453,"Pave",NA,"IR1","Low","AllPub","CulDSac","Sev","ClearCr","Norm","Norm","1Fam","SLvl",6,5,1969,1969,"Flat","Metal","Plywood","Plywood","None",0,"TA","TA","CBlock","TA","TA","Gd","ALQ",938,"Unf",0,0,938,"GasA","Ex","Y","SBrkr",988,0,0,988,1,0,1,0,1,1,"TA",4,"Typ",2,"TA","Attchd",1969,"Unf",2,540,"TA","TA","Y",0,130,0,130,0,0,NA,NA,NA,0,10,2006,"WD ","Normal",180000 +2767,906475200,20,"RL",62,70761,"Pave",NA,"IR1","Low","AllPub","Inside","Mod","ClearCr","Norm","Norm","1Fam","1Story",7,5,1975,1975,"Gable","WdShngl","Plywood","Plywood","None",0,"TA","TA","CBlock","Gd","TA","Gd","ALQ",655,"Unf",0,878,1533,"GasA","TA","Y","SBrkr",1533,0,0,1533,1,0,2,0,2,1,"Gd",5,"Typ",2,"TA","Attchd",1975,"Unf",2,576,"TA","TA","Y",200,54,0,0,0,0,NA,NA,NA,0,12,2006,"WD ","Normal",280000 +2768,906476140,20,"RL",80,12000,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","ClearCr","Norm","Norm","1Fam","1Story",6,5,1968,1968,"Gable","CompShg","Plywood","Plywood","None",0,"TA","Fa","CBlock","Gd","Fa","No","LwQ",853,"Unf",0,535,1388,"GasA","Gd","Y","SBrkr",1388,0,0,1388,1,0,2,0,3,1,"TA",6,"Typ",1,"Po","Attchd",1968,"RFn",2,522,"TA","TA","Y",0,58,0,0,0,0,NA,NA,NA,0,7,2006,"COD","Abnorml",158900 +2769,907125020,85,"RL",57,8846,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","CollgCr","Norm","Norm","1Fam","SFoyer",5,5,1996,1996,"Gable","CompShg","VinylSd","VinylSd","None",0,"Gd","TA","PConc","Gd","TA","Av","GLQ",298,"Unf",0,572,870,"GasA","Ex","Y","SBrkr",914,0,0,914,0,0,1,0,2,1,"TA",5,"Typ",0,NA,"Detchd",1998,"Unf",2,576,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,7,2006,"WD ","Normal",148000 +2770,907125080,20,"RL",95,13015,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","CollgCr","Norm","Norm","1Fam","1Story",5,6,1996,1996,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","Gd","PConc","Gd","TA","No","Unf",0,"Unf",0,1100,1100,"GasA","Ex","Y","SBrkr",1100,0,0,1100,0,0,1,1,3,1,"TA",5,"Typ",0,NA,"Attchd",1996,"RFn",2,462,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,6,2006,"WD ","Normal",145000 +2771,907130110,60,"RL",65,12438,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","CollgCr","PosN","Norm","1Fam","2Story",6,5,1995,1995,"Gable","CompShg","VinylSd","VinylSd","BrkFace",68,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,781,781,"GasA","Ex","Y","SBrkr",795,704,0,1499,0,0,2,1,3,1,"Gd",6,"Typ",1,"TA","Attchd",1995,"RFn",2,473,"TA","TA","Y",413,91,0,0,0,0,NA,NA,NA,0,8,2006,"WD ","Normal",187000 +2772,907131070,20,"RL",NA,8685,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","CollgCr","Norm","Norm","1Fam","1Story",7,5,1998,1998,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","PConc","Gd","TA","Mn","GLQ",846,"Unf",0,579,1425,"GasA","Ex","Y","SBrkr",1425,0,0,1425,1,0,2,0,3,1,"TA",5,"Typ",0,NA,"Attchd",1998,"RFn",2,591,"TA","TA","Y",0,130,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",193000 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Sdng","None",0,"TA","TA","CBlock","Fa","Fa","No","LwQ",836,"Unf",0,0,836,"GasA","TA","Y","FuseF",1220,0,0,1220,0,0,1,0,2,1,"TA",6,"Typ",0,NA,"Detchd",1951,"Unf",2,396,"TA","TA","Y",0,0,0,0,0,0,NA,NA,NA,0,4,2006,"COD","Normal",102000 +2820,908103310,90,"RL",38,7840,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Edwards","Norm","Norm","Duplex","SFoyer",6,5,1975,1975,"Flat","Tar&Grv","Plywood","Wd Shng","BrkFace",355,"TA","TA","CBlock","Gd","TA","Gd","GLQ",976,"Unf",0,0,976,"GasA","TA","Y","SBrkr",1012,0,0,1012,0,2,2,0,4,0,"TA",4,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"Y",0,0,0,0,0,0,NA,NA,NA,0,10,2006,"WD ","AdjLand",127500 +2821,908103320,90,"RL",35,9400,"Pave",NA,"IR1","Lvl","AllPub","CulDSac","Gtl","Edwards","Norm","Norm","Duplex","SFoyer",6,5,1975,1975,"Flat","Tar&Grv","WdShing","Plywood","BrkFace",250,"TA","TA","CBlock","Gd","Gd","Gd","GLQ",945,"Unf",0,0,945,"GasA","TA","Y","SBrkr",980,0,0,980,0,2,2,0,4,0,"TA",4,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"Y",0,0,0,0,0,0,NA,NA,NA,0,10,2006,"WD ","AdjLand",127500 +2822,908127050,20,"RL",73,16133,"Pave",NA,"Reg","HLS","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",5,4,1969,1969,"Gable","CompShg","HdBoard","Plywood","None",0,"TA","TA","CBlock","Gd","TA","Mn","ALQ",847,"Unf",0,329,1176,"GasA","TA","Y","SBrkr",1176,0,0,1176,1,0,1,0,2,1,"TA",5,"Typ",0,NA,"Attchd",1969,"Unf",1,360,"TA","TA","Y",0,92,0,0,112,0,NA,NA,NA,0,12,2006,"WD ","Abnorml",119900 +2823,908128100,60,"RL",62,7162,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","2Story",7,5,2003,2004,"Hip","CompShg","HdBoard","Stucco","BrkFace",190,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,796,796,"GasA","Ex","Y","SBrkr",806,918,0,1724,0,0,2,1,3,1,"Gd",8,"Typ",1,"Gd","BuiltIn",2003,"Fin",2,616,"TA","TA","Y",168,57,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",196500 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+2828,908186090,180,"RM",35,3675,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","TwnhsE","SFoyer",6,5,2005,2006,"Gable","CompShg","VinylSd","VinylSd","BrkFace",82,"TA","TA","PConc","Gd","TA","Gd","GLQ",547,"Unf",0,0,547,"GasA","Gd","Y","SBrkr",1072,0,0,1072,1,0,1,0,2,1,"TA",5,"Typ",0,NA,"Basment",2005,"Fin",2,525,"TA","TA","Y",0,44,0,0,0,0,NA,NA,NA,0,10,2006,"New","Partial",142500 +2829,908188110,160,"RM",24,2522,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","Twnhs","2Story",7,5,2004,2004,"Gable","CompShg","VinylSd","VinylSd","Stone",50,"Gd","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,970,970,"GasA","Ex","Y","SBrkr",970,739,0,1709,0,0,2,0,3,1,"Gd",7,"Maj1",0,NA,"Detchd",2004,"Unf",2,380,"TA","TA","Y",0,40,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",134000 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+2834,908225180,50,"RL",56,6956,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",4,7,1948,1950,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","Gd","CBlock","Fa","TA","Mn","Unf",0,"Unf",0,624,624,"GasA","Ex","Y","SBrkr",624,312,0,936,0,0,1,0,3,1,"TA",5,"Typ",0,NA,"Attchd",1948,"Unf",1,265,"TA","Po","Y",0,0,0,0,0,0,NA,NA,NA,0,4,2006,"WD ","Normal",97900 +2835,908227010,50,"RL",72,7822,"Pave",NA,"Reg","Bnk","AllPub","Corner","Gtl","Edwards","Artery","Norm","1Fam","1.5Fin",6,3,1915,1950,"Gable","CompShg","AsbShng","AsbShng","None",0,"TA","Fa","BrkTil","Fa","Fa","No","Unf",0,"Unf",0,832,832,"GasA","TA","Y","FuseF",846,492,0,1338,0,0,2,0,3,1,"TA",7,"Typ",0,NA,"Detchd",1974,"Unf",2,528,"TA","TA","N",0,0,208,0,0,0,NA,"GdPrv",NA,0,5,2006,"WD ","AdjLand",92000 +2836,908227030,50,"RL",62,8707,"Pave",NA,"Reg","Lvl","AllPub","FR2","Gtl","Edwards","Feedr","Norm","1Fam","1.5Fin",4,5,1924,1950,"Gable","CompShg","Wd Sdng","Wd Sdng","None",0,"TA","TA","BrkTil","TA","TA","No","Unf",0,"Unf",0,1093,1093,"GasA","TA","N","FuseF",1093,576,0,1669,0,0,1,1,4,1,"TA",9,"Min2",0,NA,"Attchd",1924,"Unf",1,288,"Fa","TA","Y",0,0,56,0,0,0,NA,NA,NA,0,5,2006,"WD ","AdjLand",107000 +2837,908227040,50,"RL",58,8410,"Pave",NA,"Reg","Lvl","AllPub","FR2","Gtl","Edwards","Feedr","Norm","1Fam","1.5Fin",5,3,1910,1996,"Gambrel","CompShg","Wd Sdng","VinylSd","None",0,"TA","Fa","PConc","TA","TA","No","Unf",0,"Unf",0,658,658,"GasA","TA","Y","SBrkr",658,526,0,1184,0,0,1,0,5,1,"TA",8,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"N",0,151,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","AdjLand",81000 +2838,908228040,50,"RL",60,9738,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",5,7,1924,1950,"Gable","CompShg","AsbShng","AsbShng","None",0,"TA","Gd","BrkTil","TA","TA","No","BLQ",392,"Unf",0,392,784,"GasA","Gd","Y","SBrkr",949,272,0,1221,1,0,1,0,4,1,"TA",7,"Typ",0,NA,"Attchd",1965,"Unf",1,392,"TA","TA","Y",0,0,236,0,0,0,NA,NA,NA,0,3,2006,"WD ","Normal",104900 +2839,908275220,20,"RL",60,8172,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",5,7,1955,1990,"Hip","CompShg","WdShing","Plywood","None",0,"TA","TA","CBlock","TA","TA","No","Rec",167,"Unf",0,697,864,"GasA","TA","Y","SBrkr",864,0,0,864,1,0,1,0,2,1,"TA",5,"Typ",0,NA,"Detchd",1957,"Unf",2,572,"TA","TA","N",0,0,0,0,0,0,NA,NA,NA,0,4,2006,"WD ","Normal",115000 +2840,908275290,20,"RL",60,16012,"Pave",NA,"IR1","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1Story",4,4,1954,1968,"Hip","CompShg","Wd Sdng","Wd Sdng","BrkFace",60,"TA","TA","CBlock","TA","TA","No","Rec",691,"Unf",0,263,954,"GasA","Ex","Y","SBrkr",1482,0,0,1482,0,1,2,0,3,1,"TA",6,"Min1",1,"Gd","2Types",1956,"Unf",2,609,"TA","TA","Y",0,30,0,0,0,0,NA,"MnPrv",NA,0,10,2006,"WD ","Abnorml",125000 +2841,909101050,50,"RL",45,8248,"Pave","Grvl","Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",5,7,1918,1950,"Gable","CompShg","Stucco","Stucco","None",0,"TA","TA","BrkTil","TA","TA","No","Unf",0,"Unf",0,686,686,"GasW","Gd","Y","SBrkr",686,564,0,1250,0,1,1,1,3,1,"Fa",7,"Typ",0,NA,"Detchd",1955,"Unf",1,280,"TA","TA","P",207,0,96,0,0,0,NA,NA,NA,0,7,2006,"WD ","Normal",116000 +2842,909101080,50,"RL",45,8248,"Pave","Grvl","Reg","Lvl","AllPub","Inside","Gtl","Edwards","Norm","Norm","1Fam","1.5Fin",4,4,1922,1950,"Gable","CompShg","Wd Sdng","Wd 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+2901,916477010,20,"RL",95,13618,"Pave",NA,"Reg","Lvl","AllPub","Corner","Gtl","Timber","Norm","Norm","1Fam","1Story",8,5,2005,2006,"Gable","CompShg","VinylSd","VinylSd","Stone",198,"Gd","TA","PConc","Ex","Gd","Av","GLQ",1350,"Unf",0,378,1728,"GasA","Ex","Y","SBrkr",1960,0,0,1960,1,0,2,0,3,1,"Gd",8,"Typ",2,"Gd","Attchd",2005,"Fin",3,714,"TA","TA","Y",172,38,0,0,0,0,NA,NA,NA,0,11,2006,"New","Partial",320000 +2902,921205030,20,"RL",88,11443,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Timber","Norm","Norm","1Fam","1Story",8,5,2005,2006,"Hip","CompShg","VinylSd","VinylSd","BrkFace",208,"Gd","TA","PConc","Ex","TA","Gd","GLQ",1460,"Unf",0,408,1868,"GasA","Ex","Y","SBrkr",2028,0,0,2028,1,0,2,0,2,1,"Gd",7,"Typ",2,"Gd","Attchd",2005,"RFn",3,880,"TA","TA","Y",326,66,0,0,0,0,NA,NA,NA,0,3,2006,"New","Partial",369900 +2903,921205050,20,"RL",88,11577,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Timber","Norm","Norm","1Fam","1Story",9,5,2005,2006,"Hip","CompShg","VinylSd","VinylSd","BrkFace",382,"Ex","TA","PConc","Gd","TA","Gd","GLQ",1455,"Unf",0,383,1838,"GasA","Ex","Y","SBrkr",1838,0,0,1838,1,0,2,0,3,1,"Ex",9,"Typ",1,"Gd","Attchd",2005,"Fin",3,682,"TA","TA","Y",161,225,0,0,0,0,NA,NA,NA,0,9,2006,"New","Partial",359900 +2904,923125030,20,"A (agr)",125,31250,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Mitchel","Artery","Norm","1Fam","1Story",1,3,1951,1951,"Gable","CompShg","CBlock","VinylSd","None",0,"TA","Fa","CBlock",NA,NA,NA,NA,0,NA,0,0,0,"GasA","TA","Y","FuseA",1600,0,0,1600,0,0,1,1,3,1,"TA",6,"Mod",0,NA,"Attchd",1951,"Unf",1,270,"Fa","TA","N",0,0,135,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",81500 +2905,923202025,90,"RM",78,7020,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Mitchel","Norm","Norm","Duplex","SFoyer",7,5,1997,1997,"Gable","CompShg","MetalSd","MetalSd","BrkFace",200,"TA","Gd","PConc","Gd","TA","Gd","GLQ",1243,"Unf",0,45,1288,"GasA","Gd","Y","SBrkr",1368,0,0,1368,2,0,2,0,2,2,"TA",8,"Typ",0,NA,"Attchd",1997,"Fin",4,784,"TA","TA","Y",0,48,0,0,0,0,NA,NA,NA,0,11,2006,"WD ","Normal",215000 +2906,923203090,120,"RM",32,4500,"Pave",NA,"Reg","Lvl","AllPub","FR2","Gtl","Mitchel","Norm","Norm","Twnhs","1Story",6,5,1998,1998,"Hip","CompShg","VinylSd","VinylSd","BrkFace",116,"TA","TA","PConc","Ex","TA","No","GLQ",897,"Unf",0,319,1216,"GasA","Ex","Y","SBrkr",1216,0,0,1216,1,0,2,0,2,1,"TA",5,"Typ",0,NA,"Attchd",1998,"Unf",2,402,"TA","TA","Y",0,125,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",164000 +2907,923203100,120,"RM",32,4500,"Pave",NA,"Reg","Lvl","AllPub","FR2","Gtl","Mitchel","Norm","Norm","TwnhsE","1Story",6,5,1998,1998,"Hip","CompShg","VinylSd","VinylSd","BrkFace",443,"TA","Gd","PConc","Ex","Gd","No","GLQ",1201,"Unf",0,36,1237,"GasA","Ex","Y","SBrkr",1337,0,0,1337,1,0,2,0,2,1,"TA",5,"Typ",0,NA,"Attchd",1998,"Fin",2,405,"TA","TA","Y",0,199,0,0,0,0,NA,NA,NA,0,3,2006,"WD ","Normal",153500 +2908,923205120,20,"RL",90,17217,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","Mitchel","Norm","Norm","1Fam","1Story",5,5,2006,2006,"Gable","CompShg","VinylSd","VinylSd","None",0,"TA","TA","PConc","Gd","TA","No","Unf",0,"Unf",0,1140,1140,"GasA","Ex","Y","SBrkr",1140,0,0,1140,0,0,1,0,3,1,"TA",6,"Typ",0,NA,NA,NA,NA,0,0,NA,NA,"Y",36,56,0,0,0,0,NA,NA,NA,0,7,2006,"WD ","Abnorml",84500 +2909,923225190,160,"RM",41,2665,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","MeadowV","Norm","Norm","TwnhsE","2Story",5,6,1977,1977,"Gable","CompShg","CemntBd","CmentBd","None",0,"TA","TA","PConc","Gd","TA","Mn","Unf",0,"Unf",0,264,264,"GasA","TA","Y","SBrkr",616,688,0,1304,0,0,1,1,3,1,"TA",5,"Typ",1,"TA","BuiltIn",1977,"RFn",1,336,"TA","TA","Y",141,0,0,0,0,0,NA,NA,NA,0,5,2006,"WD ","Normal",104500 +2910,923225240,160,"RM",41,2665,"Pave",NA,"Reg","Lvl","AllPub","Inside","Gtl","MeadowV","Norm","Norm","TwnhsE","2Story",5,6,1977,1977,"Gable","CompShg","CemntBd","CmentBd","None",0,"TA","TA","PConc","TA","TA","No","ALQ",548,"Rec",173,36,757,"GasA","Ex","Y","SBrkr",925,550,0,1475,0,0,2,0,4,1,"TA",6,"Typ",1,"TA","Attchd",1977,"RFn",1,336,"TA","TA","Y",104,26,0,0,0,0,NA,NA,NA,0,7,2006,"WD ","Normal",127000 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low,male,nonsmoker,white +30,24,younger mom,38,full term,15,not married,35,9.13,not low,male,nonsmoker,white +22,24,younger mom,39,full term,14,not married,18,7.31,not low,female,nonsmoker,not white +NA,24,younger mom,38,full term,16,married,15,6.31,not low,female,nonsmoker,white +29,24,younger mom,44,full term,18,not married,30,6.25,not low,male,nonsmoker,white +32,24,younger mom,37,full term,12,not married,28,8.5,not low,male,nonsmoker,white +NA,24,younger mom,41,full term,6,married,60,8.31,not low,male,nonsmoker,not white +24,24,younger mom,38,full term,17,married,13,6.56,not low,female,nonsmoker,not white +27,24,younger mom,39,full term,10,not married,35,7.13,not low,female,nonsmoker,white +22,24,younger mom,39,full term,10,not married,35,5.94,not low,male,nonsmoker,NA +28,24,younger mom,39,full term,11,married,NA,7.5,not low,female,nonsmoker,white +25,24,younger mom,41,full term,16,not married,37,7.19,not low,female,nonsmoker,not white +47,24,younger mom,40,full term,12,not married,46,6.88,not low,male,nonsmoker,white +46,24,younger mom,38,full term,14,not married,58,6.63,not low,female,nonsmoker,white +NA,24,younger mom,38,full term,12,married,0,7.06,not low,female,nonsmoker,not white +NA,24,younger mom,39,full term,20,married,26,7.19,not low,male,nonsmoker,white +28,24,younger mom,38,full term,15,not married,21,6.94,not low,female,nonsmoker,white +34,24,younger mom,37,full term,12,not married,20,7.81,not low,male,nonsmoker,not white +25,24,younger mom,40,full term,15,not married,29,8.5,not low,female,nonsmoker,white +28,24,younger mom,38,full term,12,not married,28,7.25,not low,male,nonsmoker,white +19,24,younger mom,40,full term,11,married,70,8.5,not low,female,nonsmoker,white +28,24,younger mom,40,full term,12,not married,29,7.06,not low,female,smoker,white +20,24,younger mom,38,full term,11,married,50,5.75,not low,male,smoker,not white +NA,24,younger mom,39,full term,6,married,30,6.13,not low,female,smoker,not white +26,24,younger mom,38,full term,13,not married,NA,7.19,not low,female,smoker,white +30,24,younger mom,41,full term,12,not married,40,8.13,not low,male,smoker,white +32,24,younger mom,39,full term,8,not married,23,6.88,not low,female,smoker,white +NA,24,younger mom,37,full term,5,not married,34,7.38,not low,male,smoker,white +NA,24,younger mom,38,full term,5,married,NA,7.13,not low,female,smoker,white +NA,24,younger mom,40,full term,12,married,47,7.5,not low,female,smoker,not white +NA,24,younger mom,39,full term,8,married,22,5,low,female,smoker,white +32,24,younger mom,36,premie,12,not married,12,5.81,not low,male,nonsmoker,white +29,24,younger mom,34,premie,12,not married,31,7.5,not low,female,nonsmoker,not white +NA,24,younger mom,35,premie,3,married,13,8.13,not low,female,nonsmoker,white +NA,24,younger mom,34,premie,7,married,18,5.06,low,male,nonsmoker,white +24,25,younger mom,41,full term,14,married,30,7.38,not low,female,nonsmoker,white +NA,25,younger mom,37,full term,11,married,31,6.25,not low,female,nonsmoker,not white +NA,25,younger mom,38,full term,11,married,27,8.75,not low,male,nonsmoker,not white +26,25,younger mom,39,full term,16,not married,0,6.94,not low,female,nonsmoker,not white +30,25,younger mom,40,full term,12,not married,35,8.13,not low,male,nonsmoker,white +NA,25,younger mom,40,full term,15,married,38,8.25,not low,male,nonsmoker,not white +23,25,younger mom,41,full term,12,not married,20,8,not low,female,nonsmoker,white +24,25,younger mom,37,full term,15,married,20,6.31,not low,male,nonsmoker,not white +32,25,younger mom,41,full term,13,not married,50,7.75,not low,female,nonsmoker,white +32,25,younger mom,37,full term,13,not married,50,7.75,not low,male,nonsmoker,white +NA,25,younger mom,38,full term,17,not married,38,7.06,not low,male,nonsmoker,white +29,25,younger mom,38,full term,18,not married,20,7.88,not low,male,nonsmoker,white +23,25,younger mom,37,full term,8,not married,53,7.69,not low,female,nonsmoker,white +24,25,younger mom,40,full term,15,married,36,7.94,not low,male,nonsmoker,white +NA,25,younger mom,39,full term,14,not married,20,6.88,not low,male,nonsmoker,white +26,25,younger mom,39,full term,9,not married,25,6.75,not low,male,nonsmoker,not white +25,25,younger mom,39,full term,18,not married,28,7.94,not low,male,nonsmoker,white +36,25,younger mom,40,full term,13,not married,34,8.75,not low,female,nonsmoker,not white +NA,25,younger mom,40,full term,14,married,48,8.56,not low,male,nonsmoker,white +31,25,younger mom,41,full term,14,not married,27,7.38,not low,female,nonsmoker,white +35,25,younger mom,37,full term,30,not married,60,3.94,low,female,nonsmoker,white +27,25,younger mom,40,full term,14,not married,47,8.75,not low,female,nonsmoker,white +NA,25,younger mom,40,full term,8,married,18,8.19,not low,male,nonsmoker,white +28,25,younger mom,40,full term,7,not married,31,9.19,not low,male,nonsmoker,white +NA,25,younger mom,38,full term,9,married,30,6.06,not low,female,nonsmoker,white +27,25,younger mom,40,full term,11,not married,26,7.44,not low,male,nonsmoker,white +NA,25,younger mom,39,full term,6,married,40,7.38,not low,female,nonsmoker,white +24,25,younger mom,42,full term,18,married,33,8.06,not low,female,nonsmoker,white +27,25,younger mom,39,full term,15,not married,31,7.56,not low,female,nonsmoker,white +34,25,younger mom,38,full term,6,not married,50,6.88,not low,male,nonsmoker,white +32,25,younger mom,41,full term,14,not married,32,7.75,not low,male,nonsmoker,white +29,25,younger mom,39,full term,10,not married,29,7.63,not low,female,nonsmoker,white +29,25,younger mom,41,full term,8,not married,33,9.38,not low,male,nonsmoker,white +24,25,younger mom,38,full term,22,not married,35,8.31,not low,female,nonsmoker,white +25,25,younger mom,38,full term,19,not married,13,7.63,not low,female,nonsmoker,white +NA,25,younger mom,39,full term,12,not married,41,6.88,not low,female,nonsmoker,white +NA,25,younger mom,37,full term,20,not married,20,5.63,not low,female,nonsmoker,not white +27,25,younger mom,40,full term,15,not married,32,8.38,not low,female,nonsmoker,white +30,25,younger mom,40,full term,15,not married,13,9,not low,male,nonsmoker,white +25,25,younger mom,38,full term,12,not married,24,8.75,not low,female,nonsmoker,white +26,25,younger mom,38,full term,10,not married,26,7.94,not low,male,nonsmoker,white +26,25,younger mom,40,full term,10,not married,25,7,not low,male,nonsmoker,white +34,25,younger mom,40,full term,12,married,44,5.81,not low,female,smoker,white +NA,25,younger mom,39,full term,5,not married,37,6,not low,female,smoker,not white +23,25,younger mom,37,full term,14,not married,33,7.31,not low,female,smoker,white +21,25,younger mom,39,full term,15,married,30,9.06,not low,male,smoker,not white +25,25,younger mom,38,full term,10,married,35,5.94,not low,female,smoker,white +NA,25,younger mom,40,full term,8,married,23,6.5,not low,female,smoker,not white +33,25,younger mom,35,premie,15,not married,18,6.75,not low,male,nonsmoker,white +NA,25,younger mom,36,premie,8,married,30,5.19,low,male,nonsmoker,white +24,25,younger mom,29,premie,10,married,0,1.69,low,male,nonsmoker,not white +NA,25,younger mom,35,premie,0,married,20,6.13,not low,male,nonsmoker,not white +23,25,younger mom,34,premie,11,not married,40,7.13,not low,male,smoker,white +30,25,younger mom,35,premie,15,not married,40,4.5,low,male,smoker,white +27,26,younger mom,38,full term,14,not married,32,6.88,not low,female,nonsmoker,white +28,26,younger mom,39,full term,15,not married,45,7.19,not low,female,nonsmoker,white +28,26,younger mom,40,full term,14,married,21,5.94,not low,female,nonsmoker,white +33,26,younger mom,38,full term,15,not married,27,9,not low,male,nonsmoker,white +30,26,younger mom,39,full term,12,not married,43,7.25,not low,male,nonsmoker,white +35,26,younger mom,41,full term,12,married,30,7.19,not low,female,nonsmoker,white +28,26,younger mom,39,full term,15,not married,25,6.94,not low,female,nonsmoker,white +29,26,younger mom,39,full term,15,not married,27,7.13,not low,female,nonsmoker,white +30,26,younger mom,39,full term,15,not married,35,7.56,not low,female,nonsmoker,not white +32,26,younger mom,39,full term,13,married,55,8.25,not low,female,nonsmoker,white +25,26,younger mom,39,full term,12,not married,40,8.38,not low,male,nonsmoker,white +NA,26,younger mom,42,full term,15,married,35,6.75,not low,female,nonsmoker,not white +33,26,younger mom,39,full term,11,married,27,5.38,low,female,nonsmoker,not white +28,26,younger mom,40,full term,13,married,38,7.13,not low,female,nonsmoker,white +29,26,younger mom,38,full term,14,not married,38,8.75,not low,female,nonsmoker,white +30,26,younger mom,41,full term,13,not married,29,7.75,not low,male,nonsmoker,white +29,26,younger mom,40,full term,12,not married,38,8.44,not low,male,nonsmoker,not white +27,26,younger mom,39,full term,16,not married,38,8,not low,female,nonsmoker,white +NA,26,younger mom,40,full term,15,married,30,8.19,not low,female,nonsmoker,white +29,26,younger mom,38,full term,12,not married,25,7.94,not low,female,nonsmoker,white +NA,26,younger mom,41,full term,6,married,0,7.38,not low,female,nonsmoker,white +26,26,younger mom,41,full term,10,not married,68,9.31,not low,male,nonsmoker,white +27,26,younger mom,40,full term,15,not married,24,8.88,not low,male,nonsmoker,white +28,26,younger mom,40,full term,13,married,35,8.06,not low,female,nonsmoker,white +25,26,younger mom,41,full term,12,married,20,7.06,not low,male,nonsmoker,white +32,26,younger mom,41,full term,14,married,19,6.56,not low,female,nonsmoker,white +29,26,younger mom,41,full term,8,married,39,8.19,not low,male,nonsmoker,white +30,26,younger mom,39,full term,14,not married,40,8.31,not low,male,nonsmoker,white +NA,26,younger mom,39,full term,6,married,20,5.88,not low,male,nonsmoker,not white +NA,26,younger mom,41,full term,20,married,20,7.44,not low,male,nonsmoker,not white +32,26,younger mom,40,full term,12,married,40,7.44,not low,male,nonsmoker,white +23,26,younger mom,40,full term,4,married,15,6.75,not low,male,nonsmoker,white +25,26,younger mom,43,full term,9,not married,23,7.44,not low,female,nonsmoker,white +30,26,younger mom,38,full term,18,not married,37,7.5,not low,female,nonsmoker,white +NA,26,younger mom,37,full term,16,married,22,6.56,not low,male,nonsmoker,white +32,26,younger mom,40,full term,15,not married,55,7,not low,female,nonsmoker,white +26,26,younger mom,42,full term,15,not married,36,7.69,not low,male,nonsmoker,white +NA,26,younger mom,38,full term,14,married,40,8,not low,male,smoker,white +25,26,younger mom,38,full term,9,not married,43,7.31,not low,female,smoker,not white +NA,26,younger mom,44,full term,14,married,25,9.13,not low,male,smoker,white +42,26,younger mom,40,full term,8,not married,38,7.13,not low,male,smoker,white +22,26,younger mom,39,full term,12,not married,31,7.31,not low,male,smoker,white +NA,26,younger mom,42,full term,15,married,30,7.38,not low,male,smoker,white +29,26,younger mom,39,full term,14,not married,65,7.75,not low,female,smoker,white +31,26,younger mom,36,premie,18,not married,40,8,not low,male,nonsmoker,white +27,26,younger mom,31,premie,20,married,10,2.25,low,female,nonsmoker,white +29,26,younger mom,36,premie,10,married,21,8.19,not low,female,nonsmoker,white +25,26,younger mom,33,premie,10,not married,13,3.44,low,male,nonsmoker,white +35,26,younger mom,34,premie,14,not married,48,6.5,not low,male,nonsmoker,not white +NA,26,younger mom,32,premie,18,married,19,4,low,male,nonsmoker,not white +29,26,younger mom,36,premie,4,not married,0,7.5,not low,male,nonsmoker,white +28,27,younger mom,40,full term,16,not married,28,8,not low,male,nonsmoker,white +33,27,younger mom,41,full term,14,not married,58,8,not low,male,nonsmoker,white +26,27,younger mom,39,full term,8,not married,20,6.63,not low,male,nonsmoker,not white +28,27,younger mom,40,full term,14,not married,36,6.75,not low,female,nonsmoker,not white +28,27,younger mom,39,full term,13,married,61,7.44,not low,male,nonsmoker,white +30,27,younger mom,39,full term,13,not married,50,6.25,not low,male,nonsmoker,white +31,27,younger mom,38,full term,12,not married,31,6.56,not low,female,nonsmoker,white +27,27,younger mom,37,full term,13,not married,38,5.63,not low,female,nonsmoker,white +32,27,younger mom,38,full term,11,not married,14,4.56,low,female,nonsmoker,white +37,27,younger mom,41,full term,8,not married,15,8.5,not low,female,nonsmoker,white +30,27,younger mom,39,full term,10,not married,37,8.88,not low,male,nonsmoker,white +40,27,younger mom,37,full term,5,married,15,6.25,not low,male,nonsmoker,white +31,27,younger mom,40,full term,13,married,3,7.06,not low,female,nonsmoker,not white +30,27,younger mom,43,full term,12,not married,17,7.94,not low,male,nonsmoker,white +31,27,younger mom,39,full term,5,married,35,6.31,not low,male,nonsmoker,not white +33,27,younger mom,39,full term,9,married,40,9.06,not low,male,nonsmoker,white +45,27,younger mom,39,full term,14,not married,43,9.13,not low,male,nonsmoker,white +28,27,younger mom,40,full term,12,married,21,7.06,not low,female,nonsmoker,not white +32,27,younger mom,38,full term,9,not married,29,8.25,not low,female,nonsmoker,white +36,27,younger mom,40,full term,11,married,18,7.75,not low,male,nonsmoker,not white +30,27,younger mom,40,full term,15,not married,40,8.25,not low,male,nonsmoker,white +31,27,younger mom,37,full term,14,not married,48,5.75,not low,female,nonsmoker,white +NA,27,younger mom,39,full term,16,not married,25,7.69,not low,female,nonsmoker,white +NA,27,younger mom,41,full term,10,married,20,6.69,not low,male,nonsmoker,white +39,27,younger mom,37,full term,13,not married,50,7.75,not low,male,nonsmoker,white +NA,27,younger mom,37,full term,10,married,37,6.81,not low,female,nonsmoker,white +33,27,younger mom,40,full term,NA,married,10,8,not low,female,nonsmoker,white +32,27,younger mom,43,full term,11,not married,30,6.56,not low,female,nonsmoker,not white +44,27,younger mom,40,full term,10,married,24,9.63,not low,female,nonsmoker,white +35,27,younger mom,38,full term,14,not married,22,8.25,not low,male,nonsmoker,white +27,27,younger mom,39,full term,14,not married,31,7.56,not low,female,nonsmoker,white +37,27,younger mom,39,full term,16,not married,34,7.19,not low,male,nonsmoker,white +NA,27,younger mom,39,full term,8,married,12,7.25,not low,female,smoker,not white +NA,27,younger mom,38,full term,12,married,40,6.25,not low,male,smoker,not white +34,27,younger mom,39,full term,16,not married,24,8.88,not low,male,smoker,white +39,27,younger mom,39,full term,12,not married,58,8.56,not low,male,smoker,white +24,27,younger mom,39,full term,15,married,42,6.75,not low,female,smoker,not white +31,27,younger mom,39,full term,11,not married,23,7.56,not low,female,smoker,white +27,27,younger mom,39,full term,11,not married,5,7.81,not low,male,smoker,white +30,27,younger mom,39,full term,15,not married,22,8.38,not low,male,smoker,white +33,27,younger mom,41,full term,15,not married,38,7,not low,male,smoker,white +30,27,younger mom,34,premie,15,not married,30,7.69,not low,female,nonsmoker,white +NA,27,younger mom,25,premie,10,married,30,1.38,low,male,nonsmoker,not white +34,27,younger mom,32,premie,17,married,20,4.06,low,female,nonsmoker,white +27,27,younger mom,35,premie,10,not married,19,7.38,not low,male,nonsmoker,white +33,27,younger mom,36,premie,11,married,14,5.56,not low,male,nonsmoker,not white +28,27,younger mom,33,premie,6,not married,18,4.75,low,male,smoker,white +NA,28,younger mom,40,full term,13,married,0,7.44,not low,female,nonsmoker,not white +28,28,younger mom,44,full term,17,not married,32,6.75,not low,female,nonsmoker,white +36,28,younger mom,39,full term,15,not married,40,8.38,not low,male,nonsmoker,white +NA,28,younger mom,40,full term,5,married,31,7.81,not low,female,nonsmoker,white +27,28,younger mom,39,full term,22,not married,21,8.13,not low,male,nonsmoker,white +NA,28,younger mom,39,full term,16,married,35,8.06,not low,male,nonsmoker,not white +25,28,younger mom,39,full term,12,not married,47,7.38,not low,male,nonsmoker,white +28,28,younger mom,40,full term,15,not married,45,9.25,not low,male,nonsmoker,white +30,28,younger mom,40,full term,15,not married,28,7.63,not low,female,nonsmoker,white +28,28,younger mom,40,full term,15,not married,0,8.38,not low,male,nonsmoker,white +30,28,younger mom,39,full term,13,married,0,7.5,not low,female,nonsmoker,white +33,28,younger mom,37,full term,NA,married,72,8,not low,female,nonsmoker,not white +28,28,younger mom,39,full term,13,not married,35,8.13,not low,female,nonsmoker,white +33,28,younger mom,39,full term,10,married,39,7.31,not low,male,nonsmoker,white +30,28,younger mom,38,full term,8,not married,0,7.56,not low,female,nonsmoker,white +45,28,younger mom,37,full term,13,not married,13,7.44,not low,male,nonsmoker,not white +31,28,younger mom,39,full term,20,not married,35,7.06,not low,female,nonsmoker,not white +27,28,younger mom,42,full term,15,not 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mom,40,full term,16,not married,20,6.38,not low,female,nonsmoker,white +29,28,younger mom,38,full term,10,not married,34,7.13,not low,male,nonsmoker,white +24,28,younger mom,39,full term,15,not married,33,5.56,not low,female,nonsmoker,white +35,28,younger mom,38,full term,10,not married,25,6.88,not low,male,nonsmoker,white +28,28,younger mom,40,full term,15,not married,32,7.75,not low,female,nonsmoker,white +34,28,younger mom,38,full term,12,not married,NA,7.25,not low,female,nonsmoker,white +28,28,younger mom,40,full term,12,not married,20,7.75,not low,female,nonsmoker,not white +33,28,younger mom,41,full term,10,not married,30,8.13,not low,male,nonsmoker,not white +NA,28,younger mom,38,full term,10,not married,70,8.19,not low,male,nonsmoker,white +30,28,younger mom,38,full term,12,not married,41,8.5,not low,male,nonsmoker,not white +23,28,younger mom,39,full term,13,married,34,5.69,not low,female,nonsmoker,white +31,28,younger mom,38,full term,15,not married,25,6.38,not 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mom,39,full term,11,not married,17,8.5,not low,male,nonsmoker,white +30,29,younger mom,38,full term,14,not married,36,7.88,not low,female,nonsmoker,white +31,29,younger mom,38,full term,14,not married,39,7.94,not low,male,nonsmoker,white +28,29,younger mom,39,full term,12,not married,30,7,not low,female,nonsmoker,white +42,29,younger mom,40,full term,8,not married,25,7.81,not low,female,nonsmoker,white +30,29,younger mom,39,full term,14,not married,30,5.38,low,male,nonsmoker,not white +35,29,younger mom,39,full term,6,not married,NA,6.25,not low,male,nonsmoker,white +34,29,younger mom,38,full term,16,not married,35,6.19,not low,female,nonsmoker,white +32,29,younger mom,39,full term,11,not married,28,7.88,not low,female,nonsmoker,not white +NA,29,younger mom,39,full term,18,not married,25,6.5,not low,male,nonsmoker,white +30,29,younger mom,38,full term,17,not married,55,7,not low,female,nonsmoker,white +29,29,younger mom,37,full term,10,married,40,7.13,not low,female,nonsmoker,not white 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married,25,9.25,not low,male,nonsmoker,not white +33,33,younger mom,39,full term,14,not married,0,7.38,not low,female,nonsmoker,white +36,33,younger mom,39,full term,13,not married,41,7.38,not low,male,nonsmoker,white +36,33,younger mom,39,full term,15,not married,44,7.56,not low,male,nonsmoker,white +36,33,younger mom,39,full term,12,not married,0,8.81,not low,male,nonsmoker,white +36,33,younger mom,38,full term,10,not married,20,6,not low,male,nonsmoker,white +27,33,younger mom,38,full term,12,not married,17,6.88,not low,female,nonsmoker,white +34,33,younger mom,39,full term,10,not married,40,8.06,not low,male,nonsmoker,white +31,33,younger mom,38,full term,10,not married,24,6.69,not low,female,nonsmoker,not white +34,33,younger mom,38,full term,18,not married,6,7.06,not low,female,nonsmoker,white +33,33,younger mom,38,full term,13,not married,18,6.88,not low,male,nonsmoker,white +35,33,younger mom,37,full term,13,not married,21,9.63,not low,male,nonsmoker,white +31,33,younger mom,37,full term,14,not married,24,7.88,not low,male,nonsmoker,white +42,33,younger mom,41,full term,15,not married,30,8.88,not low,female,nonsmoker,white +33,33,younger mom,37,full term,14,not married,0,7.81,not low,male,nonsmoker,not white +35,33,younger mom,41,full term,16,married,41,7.38,not low,female,nonsmoker,white +35,33,younger mom,37,full term,15,not married,30,7.5,not low,male,nonsmoker,white +33,33,younger mom,39,full term,30,not married,0,7.31,not low,male,nonsmoker,not white +33,33,younger mom,37,full term,10,not married,35,8.25,not low,female,nonsmoker,not white +40,33,younger mom,39,full term,10,not married,23,6.56,not low,male,nonsmoker,white +32,33,younger mom,40,full term,12,married,36,8.81,not low,female,nonsmoker,not white +31,33,younger mom,39,full term,NA,married,40,7.88,not low,female,nonsmoker,white +34,33,younger mom,39,full term,12,not married,0,8.31,not low,female,nonsmoker,white +38,33,younger mom,38,full term,14,not married,30,5.88,not low,male,nonsmoker,white +37,33,younger mom,38,full term,13,not married,18,8.63,not low,male,nonsmoker,white +27,33,younger mom,40,full term,15,not married,35,9.31,not low,male,nonsmoker,white +34,33,younger mom,40,full term,25,married,25,7.88,not low,male,nonsmoker,not white +32,33,younger mom,40,full term,13,not married,77,9.63,not low,female,nonsmoker,white +38,33,younger mom,40,full term,15,not married,45,10.06,not low,female,nonsmoker,not white +34,33,younger mom,38,full term,16,not married,60,7.13,not low,male,nonsmoker,white +35,33,younger mom,40,full term,16,not married,30,6.81,not low,male,nonsmoker,white +48,33,younger mom,40,full term,11,not married,0,8.19,not low,male,smoker,white +34,33,younger mom,43,full term,13,not married,25,9.13,not low,male,smoker,white +34,33,younger mom,35,premie,10,married,20,5.63,not low,female,nonsmoker,white +37,33,younger mom,36,premie,13,not married,23,6.56,not low,female,nonsmoker,white +37,33,younger mom,36,premie,11,not married,15,6.31,not 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married,40,6.88,not low,male,nonsmoker,white +35,34,younger mom,40,full term,13,not married,36,8.5,not low,male,nonsmoker,white +35,34,younger mom,45,full term,10,not married,34,8.5,not low,male,nonsmoker,white +37,34,younger mom,37,full term,18,not married,0,4.75,low,female,nonsmoker,not white +34,34,younger mom,37,full term,13,not married,30,8.75,not low,male,nonsmoker,white +33,34,younger mom,39,full term,10,not married,40,6.44,not low,female,nonsmoker,white +36,34,younger mom,38,full term,14,not married,15,7,not low,female,nonsmoker,white +32,34,younger mom,37,full term,10,not married,25,7.25,not low,female,nonsmoker,white +33,34,younger mom,40,full term,13,not married,25,8.25,not low,male,nonsmoker,white +35,34,younger mom,37,full term,14,not married,36,8.5,not low,male,nonsmoker,white +35,34,younger mom,40,full term,15,not married,20,7.44,not low,male,nonsmoker,white +40,34,younger mom,37,full term,5,not married,25,7.75,not low,female,nonsmoker,white +36,34,younger mom,41,full term,15,not married,28,8.5,not low,female,nonsmoker,white +40,34,younger mom,39,full term,12,not married,23,8.13,not low,male,nonsmoker,white +36,34,younger mom,41,full term,10,not married,NA,6.94,not low,male,nonsmoker,white +34,34,younger mom,37,full term,15,not married,20,7.25,not low,male,nonsmoker,white +22,34,younger mom,39,full term,12,not married,57,9.69,not low,male,nonsmoker,not white +38,34,younger mom,40,full term,18,not married,0,6.31,not low,female,nonsmoker,white +31,34,younger mom,42,full term,20,not married,40,6.75,not low,male,nonsmoker,white +34,34,younger mom,37,full term,17,not married,35,6.88,not low,female,nonsmoker,white +40,34,younger mom,44,full term,12,not married,28,8.75,not low,male,nonsmoker,white +30,34,younger mom,40,full term,10,not married,22,9.31,not low,male,nonsmoker,white +26,34,younger mom,39,full term,16,not married,15,7.88,not low,female,nonsmoker,not white +35,34,younger mom,40,full term,16,not married,38,7.63,not low,female,nonsmoker,white 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[Practical Introduction to Web Scraping in Python](https://realpython.com/python-web-scraping-practical-introduction/)\n", + "2. [Web Scraping using Python](https://www.datacamp.com/community/tutorials/web-scraping-using-python)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# $ python3 -m venv venv\n", + "# $ . ./venv/bin/activate" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: requests in c:\\users\\user\\anaconda3\\lib\\site-packages (2.22.0)\n", + "Requirement already satisfied: BeautifulSoup4 in c:\\users\\user\\anaconda3\\lib\\site-packages (4.8.2)\n", + "Requirement already satisfied: fire in c:\\users\\user\\anaconda3\\lib\\site-packages (0.3.1)\n", + "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests) (3.0.4)\n", + "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests) (2019.11.28)\n", + "Requirement already satisfied: idna<2.9,>=2.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests) (2.8)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests) (1.25.8)\n", + "Requirement already satisfied: soupsieve>=1.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from BeautifulSoup4) (1.9.5)\n", + "Requirement already satisfied: six in c:\\users\\user\\anaconda3\\lib\\site-packages (from fire) (1.14.0)\n", + "Requirement already satisfied: termcolor in c:\\users\\user\\anaconda3\\lib\\site-packages (from fire) (1.1.0)\n" + ] + } + ], + "source": [ + "#Better\n", + "!pip install requests BeautifulSoup4 fire" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "from requests import get\n", + "from requests.exceptions import RequestException\n", + "from contextlib import closing\n", + "from bs4 import BeautifulSoup\n", + "import pandas as pd\n", + "import os, sys\n", + "import pandas as pd\n", + "import fire" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#%%writefile ../pyscrap_url.py\n", + "\n", + "def simple_get(url):\n", + " \"\"\"\n", + " Attempts to get the content at `url` by making an HTTP GET request.\n", + " If the content-type of response is some kind of HTML/XML, return the\n", + " text content, otherwise return None.\n", + " \"\"\"\n", + " try:\n", + " with closing(get(url, stream=True)) as resp:\n", + " if is_good_response(resp):\n", + " return resp.content #.encode(BeautifulSoup.original_encoding)\n", + " else:\n", + " return None\n", + "\n", + " except RequestException as e:\n", + " log_error('Error during requests to {0} : {1}'.format(url, str(e)))\n", + " return None\n", + "\n", + "\n", + "def is_good_response(resp):\n", + " \"\"\"\n", + " Returns True if the response seems to be HTML, False otherwise.\n", + " \"\"\"\n", + " content_type = resp.headers['Content-Type'].lower()\n", + " return (resp.status_code == 200 \n", + " and content_type is not None \n", + " and content_type.find('html') > -1)\n", + "\n", + "\n", + "def log_error(e):\n", + " \"\"\"\n", + " It is always a good idea to log errors. \n", + " This function just prints them, but you can\n", + " make it do anything.\n", + " \"\"\"\n", + " print(e)\n", + " \n", + "def get_elements(url, tag='',search={}, fname=None):\n", + " \"\"\"\n", + " Downloads a page specified by the url parameter\n", + " and returns a list of strings, one per tag element\n", + " \"\"\"\n", + " \n", + " if isinstance(url,str):\n", + " response = simple_get(url)\n", + " else:\n", + " #if already it is a loaded html page\n", + " response = url\n", + "\n", + " if response is not None:\n", + " html = BeautifulSoup(response, 'html.parser')\n", + " \n", + " res = []\n", + " if tag: \n", + " for li in html.select(tag):\n", + " for name in li.text.split('\\n'):\n", + " if len(name) > 0:\n", + " res.append(name.strip())\n", + " \n", + " \n", + " if search:\n", + " soup = html \n", + " \n", + " \n", + " r = ''\n", + " if 'find' in search.keys():\n", + " print('findaing',search['find'])\n", + " soup = soup.find(**search['find'])\n", + " r = soup\n", + "\n", + " \n", + " if 'find_all' in search.keys():\n", + " print('findaing all of',search['find_all'])\n", + " r = soup.find_all(**search['find_all'])\n", + " \n", + " if r:\n", + " for x in list(r):\n", + " if len(x) > 0:\n", + " res.extend(x)\n", + " \n", + " return res\n", + "\n", + " # Raise an exception if we failed to get any data from the url\n", + " raise Exception('Error retrieving contents at {}'.format(url)) \n", + " \n", + " \n", + "if get_ipython().__class__.__name__ == '__main__':\n", + " fire(get_tag_elements)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the most influential twitter users in Africa" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "def influential(url , tag):\n", + " # scrape the website using beautifulsoup\n", + " data = get_elements(url, tag= tag)\n", + " \n", + " # remove unrelated data from \n", + " infl_data = data[:-5]\n", + " \n", + " # preprocess the scraped data to remove punctuation marks\n", + " infl_list = []\n", + " marks = '—()'\n", + " for i in infl_data:\n", + " for m in marks:\n", + " i = i.replace(m,\"\")\n", + " infl_list.append(i.strip(' ').split('@'))\n", + " \n", + " lst = infl_list[-10:][::-1]\n", + " \n", + " # create a dataframe containing the list of influential users \n", + " df = pd.DataFrame(lst, columns = ['Influencers','Twitter Handle'])\n", + " \n", + " # Save as csv file \n", + " return df.to_csv('InluencersInAfrica')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "influential('https://africafreak.com/100-most-influential-twitter-users-in-africa',tag='h2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get The Twitter Handle for African Government Response to covid19" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def govern(url, attribute):\n", + " \n", + " # Get the data from the url\n", + " \n", + " response = simple_get(url)\n", + " \n", + " res = get_elements(response, search={'find_all':{'class_':attribute}})\n", + " \n", + " # Get the government twitter handle from the scraped data\n", + " tweet_handle = []\n", + "\n", + " count = 1\n", + " for index, value in enumerate(res):\n", + " if index == count:\n", + " tweet_handle.append(value)\n", + " count += 3\n", + " \n", + " # preprocess the data to remove punctuation marks\n", + " a = []\n", + " marks = '—()'\n", + " for i in tweet_handle:\n", + " for m in marks:\n", + " i = i.replace(m,\"\")\n", + " a.append(i.strip(' ').split('@'))\n", + " \n", + " # create a dataframe and save as a csv file \n", + " df1 = pd.DataFrame(a, columns = ['Government','Twitter Handle'])\n", + " \n", + " # Get 10 government handles\n", + " df1 = df1[:10]\n", + " return df1.to_csv('GovernmentHandle')" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "findaing all of {'class_': 'twitter-tweet'}\n" + ] + } + ], + "source": [ + "govern('https://www.atlanticcouncil.org/blogs/africasource/african-leaders-respond-to-coronavirus-on-twitter/#east-africa', attribute = 'twitter-tweet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Web scrapping using bash script\n", + "If the web site has a quite simple HTML, you can easily use curl to perform the request and then extract the needed values using bash commands grep, cut , sed, ..\n", + "\n", + "This tutorial is adapted from [this](https://medium.com/@LiliSousa/web-scraping-with-bash-690e4ee7f98d) medium article" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Couldn't find program: 'bash'\n" + ] + } + ], + "source": [ + "%%bash \n", + "\n", + "# curl the page and save content to tmp_file\n", + "#url = \"https://www.atlanticcouncil.org/blogs/africasource/african-leaders-respond-to-coronavirus-on-twitter/#east-africa\"\n", + "#curl -X GET $url -o tmp_file\n", + "\n", + "\n", + "#!/bin/bash\n", + "\n", + "# write headers to CSV file\n", + "echo \"Name, twitter_id\" >> extractData.csv\n", + "n=\"1\"\n", + "while [ $n -lt 2 ]\n", + "do\n", + " \n", + " #get title\n", + " title=$(cat tmp_file | grep \"class=\\\"twitter-tweet\\\"\" | cut -d ';' -f1 )\n", + " echo $title\n", + " #get author\n", + " #twitter_id=$(cat tmp_file |grep -A1 \"class=\\\"css-901oao css-16my406 r-1qd0xha r-ad9z0x r-bcqeeo r-qvutc0\\\"\" | tail -1)\n", + "\n", + " #echo \"$title, $twitter_id\" >> extractData.csv\n", + " #echo \"$title, $twitter_id\"\n", + " \n", + " n=$[$n+1]\n", + "\n", + "done" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/twitter_mining/segundev_twittermininng.ipynb b/twitter_mining/segundev_twittermininng.ipynb new file mode 100644 index 0000000..5701300 --- /dev/null +++ b/twitter_mining/segundev_twittermininng.ipynb @@ -0,0 +1,3458 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "rziXcUSBskS3" + }, + "outputs": [], + "source": [ + "#from google.colab import drive\n", + "#drive.mount('/content/drive')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Twitter Developer Account\n", + "In order to use Twitter’s API, we have to create a developer account on the Twitter apps site.\n", + " * Log in or make a Twitter account at https://apps.twitter.com/.\n", + " * Create a new app (button on the top right)\n", + " \n", + "\n", + "\n", + "Fill in the app creation page with a unique name, a website name (use a placeholder website if you don’t have one), and a project description. Accept the terms and conditions and proceed to the next page.\n", + "\n", + "Once your project has been created, click on the “Keys and Access Tokens” tab. You should now be able to see your consumer secret and consumer key.\n", + "\n", + "\n", + "\n", + "You’ll also need a pair of access tokens. Scroll down and request those tokens. The page should refresh, and you should now have an access token and access token secret.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import necessary modules" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "UI2fIQFxrNLB", + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "import json\n", + "import re\n", + "import string\n", + "import pandas as pd\n", + "from pandas.io.json import json_normalize\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "3pf5Xapqrq0M" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Error loading punkt: \n" + ] + } + ], + "source": [ + "#Import the necessary methods from tweepy library \n", + "\n", + "#install tweepy if you don't have it\n", + "#!pip install tweepy\n", + "import tweepy\n", + "from tweepy.streaming import StreamListener\n", + "from tweepy import OAuthHandler\n", + "from tweepy import Stream\n", + "\n", + "#sentiment analysis package\n", + "#!pip install textblob\n", + "from textblob import TextBlob\n", + "\n", + "#general text pre-processor\n", + "#!pip install nltk\n", + "import nltk\n", + "from nltk.corpus import stopwords\n", + "nltk.download('punkt')\n", + "\n", + "#tweet pre-processor \n", + "#!pip install tweet-preprocessor\n", + "import preprocessor as ppr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Starting code\n", + "Below we define some starting codes (python classes and function) to illustrate and assist on how to fetch data from twitter and analyse them. \n", + "\n", + "### **Your task is**\n", + "1. Go through the code and understand it. Know what each function does\n", + "2. If you find error, fix it. Ask for help in the slack channel if you find serious mistake\n", + "3. Extend the code such that it will be useful for topics you choose to analyse\n", + "4. Make nice plots and share your finding (e.g. insight on the main covid19 twitter converstions about your country)\n", + "5. Submit what ever you managed to do by Wednesday morning. But you should keep using what you build to write blogs, share on facebook, etc. \n", + "\n", + "\n", + "[Reference used to build some of the functions here](https://towardsdatascience.com/extracting-twitter-data-pre-processing-and-sentiment-analysis-using-python-3-0-7192bd8b47cf)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class tweetsearch():\n", + " '''\n", + " This is a basic class to search and download twitter data.\n", + " You can build up on it to extend the functionalities for more \n", + " sophisticated analysis.\n", + " \n", + " '''\n", + " def __init__(self,cols=None,auth=None):\n", + " #\n", + " if cols is not None: # for simplicity and easy comprehension by readers\n", + " self.cols = cols\n", + " else:\n", + " self.cols = ['id', 'created_at', 'source', 'original_text','clean_text', \n", + " 'sentiment','polarity','subjectivity', 'lang',\n", + " 'favorite_count', 'retweet_count', 'original_author', \n", + " 'possibly_sensitive', 'hashtags',\n", + " 'user_mentions', 'place', 'place_coord_boundaries']\n", + " \n", + " if auth is None:\n", + " #Variables that contains the user credentials to access Twitter API \n", + " consumer_key = os.environ.get('TWITTER_API_KEY')\n", + " consumer_secret = os.environ.get('TWITTER_API_SECRET')\n", + " access_token = os.environ.get('TWITTER_ACCESS_TOKEN')\n", + " access_token_secret = os.environ.get('TWITTER_ACCESS_TOKEN_SECRET')\n", + "\n", + "\n", + "\n", + " #This handles Twitter authetification and the connection to Twitter \n", + " #Streaming API\n", + " auth = OAuthHandler(consumer_key, consumer_secret)\n", + " auth.set_access_token(access_token, access_token_secret)\n", + " \n", + "\n", + " # \n", + " self.auth = auth\n", + " self.api = tweepy.API(auth) \n", + " \n", + " \n", + "\n", + " def clean_tweets(self,twitter_text):\n", + "\n", + " #use pre processor\n", + " tweet = ppr.clean(twitter_text) # changed the object ###\n", + "\n", + " #HappyEmoticons\n", + " emoticons_happy = set([\n", + " ':-)', ':)', ';)', ':o)', ':]', ':3', ':c)', ':>', '=]', '8)', '=)', ':}',\n", + " ':^)', ':-D', ':D', '8-D', '8D', 'x-D', 'xD', 'X-D', 'XD', '=-D', '=D',\n", + " '=-3', '=3', ':-))', \":'-)\", \":')\", ':*', ':^*', '>:P', ':-P', ':P', 'X-P',\n", + " 'x-p', 'xp', 'XP', ':-p', ':p', '=p', ':-b', ':b', '>:)', '>;)', '>:-)',\n", + " '<3'\n", + " ])\n", + "\n", + " # Sad Emoticons\n", + " emoticons_sad = set([\n", + " ':L', ':-/', '>:/', ':S', '>:[', ':@', ':-(', ':[', ':-||', '=L', ':<',\n", + " ':-[', ':-<', '=\\\\', '=/', '>:(', ':(', '>.<', \":'-(\", \":'(\", ':\\\\', ':-c',\n", + " ':c', ':{', '>:\\\\', ';('\n", + " ])\n", + "\n", + " #Emoji patterns\n", + " emoji_pattern = re.compile(\"[\"\n", + " u\"\\U0001F600-\\U0001F64F\" # emoticons\n", + " u\"\\U0001F300-\\U0001F5FF\" # symbols & pictographs\n", + " u\"\\U0001F680-\\U0001F6FF\" # transport & map symbols\n", + " u\"\\U0001F1E0-\\U0001F1FF\" # flags (iOS)\n", + " u\"\\U00002702-\\U000027B0\"\n", + " u\"\\U000024C2-\\U0001F251\"\n", + " \"]+\", flags=re.UNICODE)\n", + "\n", + " #combine sad and happy emoticons\n", + " emoticons = emoticons_happy.union(emoticons_sad)\n", + "\n", + " stop_words = set(stopwords.words('english'))\n", + " word_tokens = nltk.word_tokenize(tweet)\n", + " \n", + " #after tweepy preprocessing the colon symbol left remain after \n", + " #removing mentions\n", + " tweet = re.sub(r':', '', tweet)\n", + " tweet = re.sub(r'‚Ķ', '', tweet)\n", + "\n", + " #replace consecutive non-ASCII characters with a space\n", + " tweet = re.sub(r'[^\\x00-\\x7F]+',' ', tweet)\n", + "\n", + " #remove emojis from tweet\n", + " tweet = emoji_pattern.sub(r'', tweet)\n", + "\n", + " #filter using NLTK library append it to a string\n", + " filtered_tweet = [w for w in word_tokens if not w in stop_words]\n", + "\n", + " #looping through conditions\n", + " filtered_tweet = [] \n", + " for w in word_tokens:\n", + " #check tokens against stop words , emoticons and punctuations\n", + " if w not in stop_words and w not in emoticons and w not in string.punctuation:\n", + " filtered_tweet.append(w)\n", + "\n", + " return ' '.join(filtered_tweet) \n", + " \n", + " \n", + " def get_user(self, csv, hname):\n", + " \n", + " \"\"\"\n", + " this would get all user information for proper scoring.\n", + " Information i would get here include favourite count and \n", + " statutes count, friends count and followers count\n", + " \n", + " \"\"\"\n", + " lnfl_list = []\n", + " df = pd.read_csv(csv)\n", + " handle = df[hname].to_list()\n", + " for h in handle:\n", + " try:\n", + " user = self.api.get_user(id=h)\n", + " json_str = json.dumps(user._json)\n", + " lnfl_list.append(json.loads(json_str))\n", + " except tweepy.error.TweepError:\n", + " print('user handle not found')\n", + " continue\n", + " df1 = json_normalize(lnfl_list) \n", + " df1.to_csv(csvfile)\n", + " \n", + " return df1\n", + " \n", + " \n", + "\n", + " def get_tweets(self, handle , csvfile=None):\n", + " \n", + " \n", + " df = pd.DataFrame(columns=self.cols) # commented out the line since data has been provided\n", + " \n", + " if not csvfile is None:\n", + " #If the file exists, then read the existing data from the CSV file.\n", + " if os.path.exists(csvfile):\n", + " df = pd.read_csv(csvfile, header = 0) # changed to pd.read_json from read_csv since the data provided\n", + " # was a json file\n", + " # the dataframe was returned containing the data (tweet data) \n", + " \n", + " \n", + " #page attribute in tweepy.cursor and iteration\n", + " \n", + " \"\"\"\n", + " this fetches users timeline to extract hashtags and mentions by the user\n", + " \n", + " \"\"\"\n", + " \n", + " for page in tweepy.Cursor(self.api.user_timeline, id = handle ,count=20, include_rts=False).pages(10):\n", + "\n", + "\n", + " for status in page:\n", + " \n", + " new_entry = []\n", + " status = status._json\n", + " \n", + " #filter by language\n", + " if status['lang'] != 'en':\n", + " continue\n", + "\n", + " \n", + " #if this tweet is a retweet update retweet count\n", + " if status['created_at'] in df['created_at'].values:\n", + " i = df.loc[df['created_at'] == status['created_at']].index[0]\n", + " #\n", + " cond1 = status['favorite_count'] != df.at[i, 'favorite_count']\n", + " cond2 = status['retweet_count'] != df.at[i, 'retweet_count']\n", + " if cond1 or cond2:\n", + " df.at[i, 'favorite_count'] = status['favorite_count']\n", + " df.at[i, 'retweet_count'] = status['retweet_count']\n", + " continue\n", + "\n", + " #calculate sentiment\n", + " filtered_tweet = self.clean_tweets(status['text'])\n", + " blob = TextBlob(filtered_tweet)\n", + " Sentiment = blob.sentiment \n", + " polarity = Sentiment.polarity\n", + " subjectivity = Sentiment.subjectivity\n", + "\n", + " new_entry += [status['id'], status['created_at'],\n", + " status['source'], status['text'],filtered_tweet, \n", + " Sentiment,polarity,subjectivity, status['lang'],\n", + " status['favorite_count'], status['retweet_count']]\n", + "\n", + " new_entry.append(status['user']['screen_name'])\n", + "\n", + " try:\n", + " is_sensitive = status['possibly_sensitive']\n", + " except KeyError:\n", + " is_sensitive = None\n", + "\n", + " new_entry.append(is_sensitive)\n", + "\n", + " hashtags = \", \".join([hashtag_item['text'] for \\\n", + " hashtag_item in status['entities']['hashtags']])\n", + " new_entry.append(hashtags) #append the hashtags\n", + "\n", + " #\n", + " mentions = \", \".join([mention['screen_name'] for \\\n", + " mention in status['entities']['user_mentions']])\n", + " new_entry.append(mentions) #append the user mentions\n", + "\n", + " try:\n", + " xyz = status['place']['bounding_box']['coordinates']\n", + " coordinates = [coord for loc in xyz for coord in loc]\n", + " except TypeError:\n", + " coordinates = None\n", + " #\n", + " new_entry.append(coordinates)\n", + "\n", + " try:\n", + " location = status['user']['location']\n", + " except TypeError:\n", + " location = ''\n", + " #\n", + " new_entry.append(location)\n", + "\n", + " #now append a row to the dataframe\n", + " single_tweet_df = pd.DataFrame([new_entry], columns=self.cols)\n", + " df = df.append(single_tweet_df, ignore_index=True)\n", + "\n", + " if not csvfile is None:\n", + " #save it to file\n", + " df.to_csv(csvfile, columns=self.cols, index=False, encoding=\"utf-8\")\n", + " \n", + " return df\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Search twitter and fetch data example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### fetch the User timeline of Influncers" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "user not found\n", + "user not found\n", + "user not found\n", + "user not found\n", + "user not found\n", + "user not found\n", + "user not found\n", + "user not found\n" + ] + } + ], + "source": [ + "tweets_file = 'data/influencers.csv' # changed the filename in the tweetfile variable as the name was not consistent \n", + "csv = 'InfluencersInAfrica.csv' # with the filename sent to us on the slack channel\n", + "df = pd.read_csv(csv)\n", + "handles = df['Handle'].to_list() \n", + " \n", + "ts = tweetsearch()\n", + "for handle in handles:\n", + " try:\n", + " df = ts.get_tweets(handle = handle, csvfile=tweets_file) #you saved the \n", + " except tweepy.error.TweepError:\n", + " print('user handle not found')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fetch the Timeline from the Government Handles" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "tweets_file = 'data/government.csv' # changed the filename in the tweetfile variable as the name was not consistent \n", + "csv = 'GovernmentHandle.csv' # with the filename sent to us on the slack channel\n", + "df = pd.read_csv(csv)\n", + "handles = df['Twitter Handle'].to_list() \n", + " \n", + "ts = tweetsearch()\n", + "for handle in handles:\n", + " try:\n", + " df = ts.get_tweets(handle = handle, csvfile=tweets_file) #you saved the \n", + " except tweepy.error.TweepError:\n", + " print('user handle not found')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fetch the User data of Influencers " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "users not found\n", + "users not found\n", + "users not found\n", + "users not found\n", + "users not found\n", + "users not found\n", + "users not found\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\user\\desktop\\10 academy\\pre accessment\\git-get-started\\twitter_mining\\lib\\site-packages\\ipykernel_launcher.py:119: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead\n" + ] + }, + { + "data": { + "text/html": [ + "
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idid_strnamescreen_namelocationdescriptionurlprotectedfollowers_countfriends_count...status.place.place_typestatus.place.namestatus.place.full_namestatus.place.country_codestatus.place.countrystatus.place.contained_withinstatus.place.bounding_box.typestatus.place.bounding_box.coordinatesstatus.retweeted_status.entities.mediastatus.retweeted_status.extended_entities.media
0305125998305125998Jeffrey GettlemangettlemanNew Delhi, IndiaSouth Asia bureau chief for the New York Times...http://t.co/AYD1lbjVvBFalse2570437...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12647594326475943A24 Mediaa24mediaGolden Ivy Plaza, Karen, NBOAfrica 24 produces compelling content that mak...https://t.co/5I7guDadfMFalse312853059...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
27201326772013267ScapegoatAndiMakinanaCape Town, South AfricaIn pursuit of scoops. I do not write headlines...https://t.co/pQLpRj9WO4False1012782838...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3625489039625489039Africa CheckAfricaCheckAfrica's first independent fact-checking websi...https://t.co/8bYLuvxpVNFalse680744592...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4401520924401520924James CopnallJamesCopnallBBC reporter + presenter. Author A Poisonous T...http://t.co/xrztQ2mzfHFalse219635050...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
88117102398117102398Julius Sello MalemaJulius_S_MalemaJohannesburg, South AfricaCommander in Chief of Economic Freedom Fighter...https://t.co/MrsRL5oNpKFalse3129686652...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
891469757514697575News24News24South AfricaSouth Africa's premier online news resource. F...https://t.co/TV9HgXREOiFalse3577999631...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9011025087817815572481102508781781557248jdwtweetSAPresidentMiami, FLNoneFalse1814...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
911796220417962204Gareth CliffGarethCliffSouth AfricaPresident of https://t.co/scMZ7lsVKF ⚜. Enquir...https://t.co/99Q8vPRprWFalse1974613356...cityPretoriaPretoria, South AfricaZASouth Africa[]Polygon[[[27.9483035, -25.9157727], [28.4198285, -25....NaNNaN
924633551146335511Trevor NoahTrevornoahNew York, NYComedian from South Africa. I was in the crowd...https://t.co/5zKr0YPMa5False10808461325...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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Willy Nyamitwe willynyamitwe \n", + "15 Chérif Mahamat Zene Cherif_MZ \n", + "16 Présidence RDC 🇨🇩 Presidence_RDC \n", + "17 Ali Bongo Ondimba PresidentABO \n", + "18 Présidence du Bénin PresidenceBenin \n", + "19 Roch KABORE rochkaborepf \n", + "20 Presidente Cabo Verde PresidenciaCV \n", + "21 Alassane Ouattara AOuattara_PRCI \n", + "22 State House of The Gambia Presidency_GMB \n", + "23 Nana Akufo-Addo NAkufoAddo \n", + "24 Alpha CONDÉ president_gn \n", + "25 Umaro Sissoco Embalo USEmbalo \n", + "26 Presidence Mali PresidenceMali \n", + "27 Mohamed Cheikh El Ghazouani محمد ولدالشيخ الغز... CheikhGhazouani \n", + "28 Issoufou Mahamadou IssoufouMhm \n", + "29 Muhammadu Buhari MBuhari \n", + "30 Macky Sall Macky_Sall \n", + "31 President Julius Maada Bio PresidentBio \n", + "32 Ministère de la Santé et de l'hygiène Publique MSPS_Togo \n", + "\n", + " location profile_location \\\n", + "0 Namibia NaN \n", + "1 Pretoria, South Africa NaN \n", + "2 Lusaka, Zambia NaN \n", + "3 Zimbabwe NaN \n", + "4 Djibouti NaN \n", + "5 Asmara; ERITREA NaN \n", + "6 Nairobi NaN \n", + "7 Rwanda, Africa NaN \n", + "8 Somalia NaN \n", + "9 South Sudan NaN \n", + "10 Sudan NaN \n", + "11 Dodoma, Tanzania NaN \n", + "12 Uganda NaN \n", + "13 Angola NaN \n", + "14 Burundi, Bujumbura NaN \n", + "15 Tchad NaN \n", + "16 Kinshasa, Rép. Dém du Congo NaN \n", + "17 Gabon NaN \n", + "18 République du Bénin NaN \n", + "19 Burkina Faso NaN \n", + "20 Cape Verde NaN \n", + "21 Ivory Coast NaN \n", + "22 Gambia NaN \n", + "23 Ghana NaN \n", + "24 Guinea NaN \n", + "25 Guinea Bissau NaN \n", + "26 Bamako NaN \n", + "27 Nouakchott NaN \n", + "28 Niger NaN \n", + "29 NaN \n", + "30 Sénégal NaN \n", + "31 Sierra Leone NaN \n", + "32 Lomé NaN \n", + "\n", + " description \\\n", + "0 President of the Republic of Namibia \n", + "1 This is the official Twitter page of The Presi... \n", + "2 The Ministry aims to address and share ideas w... \n", + "3 Official Twitter account of Emmerson Dambudzo ... \n", + "4 ORGANISME GOUVERNEMENTAL\\nSuivez toutes les ac... \n", + "5 Minister of Information \n", + "6 \n", + "7 President of the Republic of Rwanda, write to:... \n", + "8 9th and the current President of Federal Repub... \n", + "9 Official Twitter Account of the Revitalized Tr... \n", + "10 The official account of the Prime Minister of ... \n", + "11 Official English Account of the Chief Spokespe... \n", + "12 President of the Republic of Uganda \n", + "13 Conta oficial do Twitter do Ministério das Rel... \n", + "14 Ambassador & Senior Advisor to HE @GeneralNeva... \n", + "15 Ministre de la Communication, Porte-parole du ... \n", + "16 Bienvenue sur le compte officiel de la Préside... \n", + "17 Président de la République Gabonaise \n", + "18 Compte officiel de la Présidence de la Républi... \n", + "19 Président du Faso \n", + "20 Bio: President of the Republic of Cabo Verde.\\... \n", + "21 Profil officiel d’Alassane Ouattara, Président... \n", + "22 Official Twitter for the Office of The Preside... \n", + "23 Official Twitter account of Nana Addo Dankwa A... \n", + "24 Président de la République de Guinée -- Presid... \n", + "25 President of the Republic of Guinea-Bissau 🇬🇼\\... \n", + "26 Fil officiel de la Présidence de la République... \n", + "27 رئيس الجمهورية الإسلامية الموريتانية Président... \n", + "28 Compte officiel d'Issoufou Mahamadou, Présiden... \n", + "29 This is the official account of Muhammadu Buha... \n", + "30 Président de la République du Sénégal 🇸🇳 \n", + "31 H.E. Julius Maada Wonie Bio was inaugurated as... \n", + "32 \n", + "\n", + " url protected followers_count ... \\\n", + "0 https://t.co/f5BbkeEYSL False 192470 ... \n", + "1 https://t.co/lw3QfCqSCq False 1599341 ... \n", + "2 https://t.co/ShAx7bUDqc False 7170 ... \n", + "3 None False 546537 ... \n", + "4 https://t.co/ZElb6lvXnU False 2934 ... \n", + "5 https://t.co/fSQjSLmk6t False 66245 ... \n", + "6 https://t.co/vReQnpRV2z False 1104077 ... \n", + "7 https://t.co/bfKOFZyOav False 1984272 ... \n", + "8 None False 424425 ... \n", + "9 None False 2559 ... \n", + "10 https://t.co/2dtCXDjTvv False 371069 ... \n", + "11 https://t.co/o6LugkYaH3 False 4024 ... \n", + "12 https://t.co/98sFzWcbAF False 1813693 ... \n", + "13 https://t.co/EZEdzQzwGE False 3272 ... \n", + "14 https://t.co/IqiH1MwnKs False 107409 ... \n", + "15 https://t.co/D2MAAPTVot False 18635 ... \n", + "16 https://t.co/uISGEjLi7o False 340934 ... \n", + "17 https://t.co/HyRWdJhnrg False 173601 ... \n", + "18 https://t.co/VqAMM9C6kY False 30165 ... \n", + "19 https://t.co/47yRKu3CMx False 256351 ... \n", + "20 http://t.co/zrtxLrXOSg False 3283 ... \n", + "21 https://t.co/T70r91bUyq False 832898 ... \n", + "22 https://t.co/vnlfhzchiT False 10772 ... \n", + "23 https://t.co/Vz5z6hVzYV False 1508420 ... \n", + "24 https://t.co/lFypX5PDgC False 736 ... \n", + "25 https://t.co/fmjH8m2fpB False 8482 ... \n", + "26 https://t.co/4w4yX7r4qN False 228959 ... \n", + "27 https://t.co/yODYwZTv04 False 31805 ... \n", + "28 https://t.co/yqT76kKsIq False 116302 ... \n", + "29 None False 3272237 ... \n", + "30 https://t.co/FK2Eo1gPuQ False 1375730 ... \n", + "31 https://t.co/KHVHrCBNYA False 14907 ... \n", + "32 https://t.co/sYvXay8AhT False 1132 ... \n", + "\n", + " profile_location.country_code profile_location.country \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "17 NaN NaN \n", + "18 NaN NaN \n", + "19 NaN NaN \n", + "20 \n", + "21 \n", + "22 NaN NaN \n", + "23 NaN NaN \n", + "24 \n", + "25 \n", + "26 NaN NaN \n", + "27 NaN NaN \n", + "28 NaN NaN \n", + "29 NaN NaN \n", + "30 NaN NaN \n", + "31 NaN NaN \n", + "32 NaN NaN \n", + "\n", + " profile_location.contained_within profile_location.bounding_box \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 [] NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 [] NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "17 NaN NaN \n", + "18 NaN NaN \n", + "19 NaN NaN \n", + "20 [] NaN \n", + "21 [] NaN \n", + "22 NaN NaN \n", + "23 NaN NaN \n", + "24 [] NaN \n", + "25 [] NaN \n", + "26 NaN NaN \n", + "27 NaN NaN \n", + "28 NaN NaN \n", + "29 NaN NaN \n", + "30 NaN NaN \n", + "31 NaN NaN \n", + "32 NaN NaN \n", + "\n", + " status.entities.media \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 NaN \n", + "7 NaN \n", + "8 NaN \n", + "9 NaN \n", + "10 NaN \n", + "11 NaN \n", + "12 NaN \n", + "13 NaN \n", + "14 NaN \n", + "15 NaN \n", + "16 NaN \n", + "17 NaN \n", + "18 NaN \n", + "19 NaN \n", + "20 NaN \n", + "21 NaN \n", + "22 NaN \n", + "23 [{'id': 1284815212181434369, 'id_str': '128481... \n", + "24 NaN \n", + "25 NaN \n", + "26 [{'id': 1284214657348247559, 'id_str': '128421... \n", + "27 NaN \n", + "28 NaN \n", + "29 NaN \n", + "30 NaN \n", + "31 NaN \n", + "32 NaN \n", + "\n", + " status.extended_entities.media \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 NaN \n", + "7 NaN \n", + "8 NaN \n", + "9 NaN \n", + "10 NaN \n", + "11 NaN \n", + "12 NaN \n", + "13 NaN \n", + "14 NaN \n", + "15 NaN \n", + "16 NaN \n", + "17 NaN \n", + "18 NaN \n", + "19 NaN \n", + "20 NaN \n", + "21 NaN \n", + "22 NaN \n", + "23 [{'id': 1284815212181434369, 'id_str': '128481... \n", + "24 NaN \n", + "25 NaN \n", + "26 [{'id': 1284214657348247559, 'id_str': '128421... \n", + "27 NaN \n", + "28 NaN \n", + "29 NaN \n", + "30 NaN \n", + "31 NaN \n", + "32 NaN \n", + "\n", + " status.retweeted_status.quoted_status_id \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 NaN \n", + "7 NaN \n", + "8 NaN \n", + "9 NaN \n", + "10 NaN \n", + "11 NaN \n", + "12 NaN \n", + "13 NaN \n", + "14 NaN \n", + "15 NaN \n", + "16 NaN \n", + "17 NaN \n", + "18 NaN \n", + "19 NaN \n", + "20 NaN \n", + "21 NaN \n", + "22 NaN \n", + "23 NaN \n", + "24 1.258056e+18 \n", + "25 NaN \n", + "26 NaN \n", + "27 NaN \n", + "28 NaN \n", + "29 NaN \n", + "30 NaN \n", + "31 NaN \n", + "32 NaN \n", + "\n", + " status.retweeted_status.quoted_status_id_str status.quoted_status_id \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "17 NaN NaN \n", + "18 NaN NaN \n", + "19 NaN NaN \n", + "20 NaN NaN \n", + "21 NaN NaN \n", + "22 NaN NaN \n", + "23 NaN NaN \n", + "24 1258056308068139010 1.258056e+18 \n", + "25 NaN NaN \n", + "26 NaN NaN \n", + "27 NaN NaN \n", + "28 NaN NaN \n", + "29 NaN NaN \n", + "30 NaN NaN \n", + "31 NaN NaN \n", + "32 NaN NaN \n", + "\n", + " status.quoted_status_id_str \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 NaN \n", + "7 NaN \n", + "8 NaN \n", + "9 NaN \n", + "10 NaN \n", + "11 NaN \n", + "12 NaN \n", + "13 NaN \n", + "14 NaN \n", + "15 NaN \n", + "16 NaN \n", + "17 NaN \n", + "18 NaN \n", + "19 NaN \n", + "20 NaN \n", + "21 NaN \n", + "22 NaN \n", + "23 NaN \n", + "24 1258056308068139010 \n", + "25 NaN \n", + "26 NaN \n", + "27 NaN \n", + "28 NaN \n", + "29 NaN \n", + "30 NaN \n", + "31 NaN \n", + "32 NaN \n", + "\n", + "[33 rows x 121 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "csvfile = 'data/Governmentdata.csv'\n", + "csv = 'GovernmentHandle.csv'\n", + "\n", + "ts = tweetsearch()\n", + "ts.get_user(csv,hname='Twitter Handle')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis on Government Handles" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "gov = pd.read_csv('data/Governmentdata.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "gov_df = gov[['screen_name','followers_count','friends_count','statuses_count','favourites_count']]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\user\\desktop\\10 academy\\pre accessment\\git-get-started\\twitter_mining\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n", + "c:\\users\\user\\desktop\\10 academy\\pre accessment\\git-get-started\\twitter_mining\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \n" + ] + } + ], + "source": [ + "gov_df['Popularity_Score'] = gov_df['statuses_count'].add(gov_df['favourites_count'])\n", + "gov_df['Reach_Score'] = gov_df['followers_count'].sub(gov_df['friends_count'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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screen_namefollowers_countfriends_countstatuses_countfavourites_countPopularity_ScoreReach_Score
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1PresidencyZA1599341141888163189441599327
26PresidenceMali228959100111168173212900227958
6StateHouseKenya110407721490506191111103863
23NAkufoAddo1508420352709813472321508068
21AOuattara_PRCI83289823713947143832875
25USEmbalo8482181828606568938301
12KagutaMuseveni18136932866457667211813665
18PresidenceBenin3016566564994574330099
5hawelti662454354718735545365810
19rochkaborepf25635115144964754971256200
29MBuhari3272237264734847423272211
7PaulKagame1984272181286261634781984091
30Macky_Sall1375730171279653033261375559
22Presidency_GMB10772271440452189210745
17PresidentABO17360141738161754173597
16Presidence_RDC3409341251650991749340809
4MinSantedj2934127106558716522807
0hagegeingob1924705510872681355192415
15Cherif_MZ18635196753473122618439
13angola_Mirex327231273244711792960
2mohzambia71709583816310017075
11TZSpokesperson40243283618373992
20PresidenciaCV3283885715918062398
10SudanPMHamdok37106911565243695370954
3edmnangagwa54653711662865693546421
8M_Farmaajo424425259922621424423
9SouthSudanGov25594632093485572096
28IssoufouMhm116302173493352116285
31PresidentBio1490708308314907
32MSPS_Togo11322441451130
27CheikhGhazouani3180593003031796
24president_gn7362921627707
\n", + "
" + ], + "text/plain": [ + " screen_name followers_count friends_count statuses_count \\\n", + "14 willynyamitwe 107409 4656 40682 \n", + "1 PresidencyZA 1599341 14 18881 \n", + "26 PresidenceMali 228959 1001 11168 \n", + "6 StateHouseKenya 1104077 214 9050 \n", + "23 NAkufoAddo 1508420 352 7098 \n", + "21 AOuattara_PRCI 832898 23 7139 \n", + "25 USEmbalo 8482 181 828 \n", + "12 KagutaMuseveni 1813693 28 6645 \n", + "18 PresidenceBenin 30165 66 5649 \n", + "5 hawelti 66245 435 4718 \n", + "19 rochkaborepf 256351 151 4496 \n", + "29 MBuhari 3272237 26 4734 \n", + "7 PaulKagame 1984272 181 2862 \n", + "30 Macky_Sall 1375730 171 2796 \n", + "22 Presidency_GMB 10772 27 1440 \n", + "17 PresidentABO 173601 4 1738 \n", + "16 Presidence_RDC 340934 125 1650 \n", + "4 MinSantedj 2934 127 1065 \n", + "0 hagegeingob 192470 55 1087 \n", + "15 Cherif_MZ 18635 196 753 \n", + "13 angola_Mirex 3272 312 732 \n", + "2 mohzambia 7170 95 838 \n", + "11 TZSpokesperson 4024 32 836 \n", + "20 PresidenciaCV 3283 885 715 \n", + "10 SudanPMHamdok 371069 115 652 \n", + "3 edmnangagwa 546537 116 628 \n", + "8 M_Farmaajo 424425 2 599 \n", + "9 SouthSudanGov 2559 463 209 \n", + "28 IssoufouMhm 116302 17 349 \n", + "31 PresidentBio 14907 0 83 \n", + "32 MSPS_Togo 1132 2 44 \n", + "27 CheikhGhazouani 31805 9 30 \n", + "24 president_gn 736 29 21 \n", + "\n", + " favourites_count Popularity_Score Reach_Score \n", + "14 6259 46941 102753 \n", + "1 63 18944 1599327 \n", + "26 1732 12900 227958 \n", + "6 61 9111 1103863 \n", + "23 134 7232 1508068 \n", + "21 4 7143 832875 \n", + "25 6065 6893 8301 \n", + "12 76 6721 1813665 \n", + "18 94 5743 30099 \n", + "5 735 5453 65810 \n", + "19 475 4971 256200 \n", + "29 8 4742 3272211 \n", + "7 616 3478 1984091 \n", + "30 530 3326 1375559 \n", + "22 452 1892 10745 \n", + "17 16 1754 173597 \n", + "16 99 1749 340809 \n", + "4 587 1652 2807 \n", + "0 268 1355 192415 \n", + "15 473 1226 18439 \n", + "13 447 1179 2960 \n", + "2 163 1001 7075 \n", + "11 1 837 3992 \n", + "20 91 806 2398 \n", + "10 43 695 370954 \n", + "3 65 693 546421 \n", + "8 22 621 424423 \n", + "9 348 557 2096 \n", + "28 3 352 116285 \n", + "31 0 83 14907 \n", + "32 1 45 1130 \n", + "27 0 30 31796 \n", + "24 6 27 707 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gov_df.sort_values(by=['Popularity_Score','Reach_Score'], ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis for Influencers" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "inf = pd.read_csv('data/influencersdata.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\user\\desktop\\10 academy\\pre accessment\\git-get-started\\twitter_mining\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n", + "c:\\users\\user\\desktop\\10 academy\\pre accessment\\git-get-started\\twitter_mining\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " after removing the cwd from sys.path.\n" + ] + } + ], + "source": [ + "inf_df = inf[['screen_name','followers_count','friends_count','statuses_count','favourites_count']]\n", + "\n", + "inf_df['Popularity_Score'] = inf_df['statuses_count'].add(inf_df['favourites_count'])\n", + "inf_df['Reach_Score'] = inf_df['followers_count'].sub(inf_df['friends_count'])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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screen_namefollowers_countfriends_countstatuses_countfavourites_countPopularity_ScoreReach_Score
80UlrichJvV104247153030519998347436367434512166
89News24357799963132290411433240473577368
47CityofJoburgZA10027586197226530233984299286940786
30africatechie1061641644101334136604237938104520
2AndiMakinana1012782838142232902815126098440
........................
38africamedia_CPJ487035035487
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72SmithInAfrica69010169
7stateafrica801018
26ThisisAfrica600006
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" + ], + "text/plain": [ + " screen_name followers_count friends_count statuses_count \\\n", + "80 UlrichJvV 1042471 530305 19998 \n", + "89 News24 3577999 631 322904 \n", + "47 CityofJoburgZA 1002758 61972 265302 \n", + "30 africatechie 106164 1644 101334 \n", + "2 AndiMakinana 101278 2838 142232 \n", + ".. ... ... ... ... \n", + "38 africamedia_CPJ 487 0 35 \n", + "78 BBCAndrewH 7 0 6 \n", + "72 SmithInAfrica 69 0 1 \n", + "7 stateafrica 8 0 1 \n", + "26 ThisisAfrica 6 0 0 \n", + "\n", + " favourites_count Popularity_Score Reach_Score \n", + "80 347436 367434 512166 \n", + "89 1143 324047 3577368 \n", + "47 33984 299286 940786 \n", + "30 136604 237938 104520 \n", + "2 9028 151260 98440 \n", + ".. ... ... ... \n", + "38 0 35 487 \n", + "78 0 6 7 \n", + "72 0 1 69 \n", + "7 0 1 8 \n", + "26 0 0 6 \n", + "\n", + "[93 rows x 7 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inf_df.sort_values(by=['Popularity_Score','Reach_Score'], ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis on Government Handle Hashtags" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "# read the file for government timeline tweets\n", + "\n", + "\"\"\"\n", + "group the dataframe to author and extract their top ten retweets\n", + "\n", + "\"\"\"\n", + "\n", + "gov_users = pd.read_csv('data/government.csv')\n", + "top_retweet = gov_users.groupby('original_author').head(10).reset_index(drop=True)[['original_author','hashtags']]" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "COVID19 11\n", + "Ramaphosa 7\n", + "MalawiCabinet 6\n", + "Eritrea 4\n", + "Burundi 3\n", + "Name: hashtags, dtype: int64" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Unique hashtags by Government Users \n", + "\n", + "top_retweet['hashtags'].value_counts()[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis on Influencers Hashtags" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "group the dataframe to author and extract their top ten retweets\n", + "\n", + "\"\"\"\n", + "\n", + "infl_users = pd.read_csv('data/influencers.csv')\n", + "top_retweet = infl_users.groupby('original_author').head(10).reset_index(drop=True)[['original_author','hashtags']]" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MandelaDay 19\n", + "COVID19 13\n", + "COVID19, Masuku 6\n", + "Ethiopia 3\n", + "Malawi 3\n", + "Name: hashtags, dtype: int64" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Unique hashtags by Influencers \n", + "\n", + "top_retweet['hashtags'].value_counts()[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Barplot " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stream data and save it to file\n", + "In the above we saw how to search and fetch data, below we will see how we will stream data from twitter. Make sure you understand the difference between search and stream features of twitter api.\n", + "\n", + "### **SAME TASK AS ABOVE**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "r6lcy009rX_e" + }, + "outputs": [], + "source": [ + "#This is a basic listener that writes received tweets to file.\n", + "class StdOutListener(StreamListener):\n", + "\n", + " def __init__(self,fhandle, stop_at = 1000):\n", + " self.tweet_counter = 0\n", + " self.stop_at = stop_at\n", + " self.fhandle = fhandle\n", + " \n", + " \n", + " def on_data(self, data):\n", + " self.fhandle.write(data)\n", + " \n", + " #stop if enough tweets are obtained\n", + " self.tweet_counter += 1 \n", + " if self.tweet_counter < self.stop_at: \n", + " return True\n", + " else:\n", + " print('Max number of tweets reached: #tweets = ' + str(self.tweet_counter))\n", + " return False\n", + "\n", + " def on_error(self, status):\n", + " print (status)\n", + "\n", + "def stream_tweet_data(filename='data/tweets.json',\n", + " keywords=['COVID19Africa','COVID19Ethiopia'],\n", + " is_async=False):\n", + " # tweet topics to use as a filter. The tweets downloaded\n", + " # will have one of the topics in their text or hashtag \n", + "\n", + " print('saving data to file: ',filename)\n", + "\n", + " #print the tweet topics \n", + " print('TweetKeywords are: ',keywords)\n", + " print('For testing case, please interupt the downloading process using ctrl+x after about 5 mins ')\n", + " print('To keep streaming in the background, pass is_async=True')\n", + "\n", + " #Variables that contains the user credentials to access Twitter API \n", + " consumer_key = 'DieSYHRnaqfA7EWnoqdiwfcFT'\n", + " consumer_secret = 'f4HtCl4qH2dgnQ6gUCmxfo0POCMrgCadOVaCaanrmQSF46LTb9'\n", + " access_token = '1211889028020105217-PUjkEyesfD3Vdctyj7GBuWKTZyXEfv'\n", + " access_token_secret = 'yHQcq40XeuZojTkSbo1VSgkpAfoSpQCmb3fpgEaB5ZukG'\n", + "\n", + " #open file \n", + " fhandle=open(filename,'w')\n", + "\n", + " #This handles Twitter authetification and the connection to Twitter Streaming API\n", + " l = StdOutListener(fhandle)\n", + " auth = OAuthHandler(consumer_key, consumer_secret)\n", + " auth.set_access_token(access_token, access_token_secret)\n", + "\n", + " stream = Stream(auth, l)\n", + "\n", + " #This line filter Twitter Streams to capture data by the keywords: first argument to this code\n", + " stream.filter(track=keywords,is_async=is_async)\n", + "\n", + " return None\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use case of the above code" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "F8tcPcSMrNLL", + "outputId": "d7abd9c2-065c-40e8-f71b-e808d985c364" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving data to file: data/covid19_23june2020.json\n", + "TweetKeywords are: ['covid19', '#COVID19Africa']\n", + "For testing case, please interupt the downloading process using ctrl+x after about 5 mins \n", + "To keep streaming in the background, pass is_async=True\n", + "Max number of tweets reached: #tweets = 1000\n" + ] + } + ], + "source": [ + "tweets_file = 'data/covid19_23june2020.json'\n", + "stream_tweet_data(filename=tweets_file,keywords=['covid19','#COVID19Africa']) # commented this cell so as to throw error\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter twitter data and do basic analysis\n", + "**Extend it to gain more insight**" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "F8tcPcSMrNLL", + "outputId": "d7abd9c2-065c-40e8-f71b-e808d985c364" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saved numbers of tweets: 28\n" + ] + } + ], + "source": [ + "tweets_data = []\n", + "for line in open(tweets_file, \"r\"):\n", + " try:\n", + " tweet = json.loads(line)\n", + " x=tweet['text']\n", + " tweets_data.append(tweet)\n", + " except:\n", + " continue\n", + "\n", + "\n", + "print('saved numbers of tweets: ', len(tweet))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "hlFKyGnYrNLX" + }, + "outputs": [], + "source": [ + "tweets = pd.DataFrame(columns=['text','lang','country'])\n", + "\n", + "tweets['text'] = list(map(lambda tweet: tweet['text'], tweets_data))\n", + "tweets['lang'] = list(map(lambda tweet: tweet['lang'], tweets_data))\n", + "tweets['country'] = list(map(lambda tweet: tweet['place']['country'] if tweet['place'] != None else None, \n", + " tweets_data))\n", + "\n", + "\n", + "tweets_by_lang = tweets['lang'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "aEPPoCBtrNLd", + "outputId": "bfe0cee1-814d-4f30-f47d-205218b3adc9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.tick_params(axis='x', labelsize=15)\n", + "ax.tick_params(axis='y', labelsize=10)\n", + "ax.set_xlabel('Languages', fontsize=15)\n", + "ax.set_ylabel('Number of tweets' , fontsize=15)\n", + "ax.set_title('Top 5 languages', fontsize=15, fontweight='bold')\n", + "tweets_by_lang[:5].plot(ax=ax, kind='bar', color='red')\n", + "\n", + "tweets_by_country = tweets['country'].value_counts()\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.tick_params(axis='x', labelsize=15)\n", + "ax.tick_params(axis='y', labelsize=10)\n", + "ax.set_xlabel('Countries', fontsize=15)\n", + "ax.set_ylabel('Number of tweets' , fontsize=15)\n", + "ax.set_title('Top 5 countries', fontsize=15, fontweight='bold')\n", + "tweets_by_country[:5].plot(ax=ax, kind='bar', color='blue')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IQYFg6t5rNLj" + }, + "source": [ + "# Hashtag histogram. \n", + "\n", + "## Please write code that will help you answer the following questions\n", + " 1) What is the most used hashtag?\n", + " \n", + " 2) What is the most used referenced username?\n", + " \n", + " 3) What is the most retweeted tweet?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "user = pd.DataFrame(dict(df['user'])).T" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "p0VUwYR8rNLk" + }, + "outputs": [], + "source": [ + "# Question 1 : The most used hashtag\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Smart5GCitizen 8\n", + "CNRC_NRC 7\n", + "CofHEQ 7\n", + "vijay_rajan_159 6\n", + "Penkampar 5\n", + " ..\n", + "Ex_Catho 1\n", + "MARIS2911 1\n", + "samuraj_dr 1\n", + "Son_Of_Sher 1\n", + "DVEAProud 1\n", + "Name: screen_name, Length: 902, dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Question 2 : most referenced username\n", + "\n", + "user['screen_name'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "vistweet.ipynb", + "provenance": [] + }, + "hide_input": false, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "latex_envs": { + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 0 + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/twitter_mining/tenx_covid19_analysis.ipynb b/twitter_mining/tenx_covid19_analysis.ipynb new file mode 100644 index 0000000..694aa62 --- /dev/null +++ b/twitter_mining/tenx_covid19_analysis.ipynb @@ -0,0 +1,813 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zS_TY39-w9ai" + }, + "outputs": [], + "source": [ + "#attach your Google drive to read write file\n", + "#drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "BnW9-SpCweck" + }, + "outputs": [], + "source": [ + "## Importing the libraries:\n", + "import os, sys\n", + "\n", + "import cachetools.func\n", + "\n", + "import matplotlib\n", + "import matplotlib.colors as colors\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import seaborn as sns\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "oZKC5tkH_Ea-" + }, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IF3d39OEBoi5" + }, + "source": [ + "#WHO Health Observatory API wrapper\n", + "\n", + "Below is a python code that you can use to get health related data for any country in the world from World Health Organisation (WHO). To learn more about the description of the data available there check their [Global Health Observatory](https://www.who.int/data/gho) page. In particular, you can refer to the [indicators](https://www.who.int/data/gho/data/indicators) and [Data API](https://www.who.int/data/gho/info/gho-odata-api) pages." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "LSA251VHx_La" + }, + "outputs": [], + "source": [ + "def print_full(x):\n", + " \n", + " '''\n", + " This is to print nicely DataFrame wide tables\n", + " '''\n", + " pd.set_option('display.max_rows', len(x))\n", + " pd.set_option('display.max_columns', None)\n", + " pd.set_option('display.width', 2000)\n", + " pd.set_option('display.float_format', '{:20,.2f}'.format)\n", + " pd.set_option('display.max_colwidth', -1)\n", + " print(x)\n", + " pd.reset_option('display.max_rows')\n", + " pd.reset_option('display.max_columns')\n", + " pd.reset_option('display.width')\n", + " pd.reset_option('display.float_format')\n", + " pd.reset_option('display.max_colwidth')\n", + "\n", + "def agegrp_func(x):\n", + " '''\n", + " Function to format how age group is presented\n", + " WHO uses AGE20-30, we want just 20-30\n", + " '''\n", + " if 'AGELT1' in x:\n", + " y = '0-1'\n", + " elif 'AGE85' in x:\n", + " y = '85+'\n", + " else:\n", + " y=x.replace('AGE','')\n", + "\n", + " return y\n", + "\n", + "def agegrp_mid(x):\n", + " \n", + " '''\n", + " Function to calculate the mid age of an age group\n", + " '''\n", + " if 'AGELT1' in x:\n", + " y = -3\n", + " elif 'AGE85' in x:\n", + " y = 87\n", + " else:\n", + " try:\n", + " y=int(np.mean([int(i) for i in x.replace('AGE','').split('-')]))\n", + " except:\n", + " y = int(x.replace('AGE','').split('-')[0])\n", + " return y \n", + "\n", + "class whoapi():\n", + " '''\n", + " This class defines methods to get data from World Health Organisation (WHO) API\n", + " The different WHO health and related data is structured by country and indicator id (ids) \n", + " For example \n", + " Country = ETH\n", + " indicatorid='LIFE_0000000029' \n", + " describes the probability of dying in age groups for Ethiopia\n", + " Reference: for more information about available WHO data, refer\n", + " https://www.who.int/data/gho \n", + " '''\n", + " def __init__(self, dim='GHO',**kwargs):\n", + " #\n", + " self.url = f\"http://apps.who.int/gho/athena/api/{dim}\"\n", + " self.verbose = kwargs.get('verbose',0)\n", + "\n", + " @staticmethod\n", + " def age_sex_stem(df): \n", + " '''\n", + " To nicely format AGE and SEX columns\n", + " \n", + " '''\n", + " if 'AGEGROUP' in df.columns:\n", + " df['MidAge'] = df['AGEGROUP'].map(agegrp_mid)\n", + " df['AGEGROUP'] = df['AGEGROUP'].map(agegrp_func) \n", + " df = df.sort_values('MidAge').reset_index().drop(columns=['index'])\n", + " \n", + " if 'SEX' in df.columns:\n", + " df['SEX'] = df['SEX'].map(lambda x:{'MLE':'Male',\n", + " 'FMLE':'Female',\n", + " 'BTSX':'Both'}.get(x,x))\n", + " return df \n", + "\n", + " def get_ids(self,s):\n", + " df = self.dfid\n", + "\n", + " name = s\n", + " if isinstance(s,str):\n", + " name = [s] \n", + "\n", + " dflist = [] \n", + " for d in name:\n", + " dflist.append(df[df['id'].map(lambda x : d in x)])\n", + "\n", + " #try:\n", + " df = pd.concat(dflist)\n", + " #except:\n", + " # df = pd.DataFrame()\n", + "\n", + " return df\n", + "\n", + " def get_gho_data(self, id, fdict=None, \n", + " url=None,verbose=None):\n", + " '''\n", + " given \n", + " id: indicator id, \n", + " fdict: dictionary of filters e.g {'COUNTRY':ETH,'YEAR':2016} ethiopian data from 2016\n", + " url (optional)\n", + " verbose: feedback level (0 - less info to print; >=2 debug mode)\n", + " get data from WHO server and return it as pandas DataFrame \n", + " '''\n", + " #\n", + " verbose = self.verbose if verbose is None else verbose\n", + " #\n", + " ids = id\n", + " if isinstance(id,list):\n", + " ids = ','.join(id)\n", + "\n", + " if url is None:\n", + " url = f\"{self.url}/{ids}.csv\"\n", + "\n", + " if verbose>1:\n", + " print('API url:',url)\n", + "\n", + " if not fdict is None:\n", + " url += '?filter=' + \";\".join([f\"{k}:{v}\" for k, v in fdict.items()])\n", + " \n", + " df = pd.read_csv(url)\n", + " df = df.dropna(axis=1, how='all')\n", + " try:\n", + " df = df.drop(columns=['Comments'])\n", + " df = df.drop(columns=['Comments'])\n", + " except:\n", + " #print('not dropping Comments')\n", + " pass\n", + "\n", + " return df\n", + "\n", + " def country_data(self,ids,countries=['ETH'],year=2016): \n", + " '''\n", + " This is a wrapper to \n", + " method: get_gho_data \n", + " This method allows one easily to pass multiple countries in a list\n", + " '''\n", + " if isinstance(countries,str):\n", + " countries = [countries]\n", + "\n", + " dflist = []\n", + " verbose=self.verbose\n", + " for c in countries:\n", + " print(f'processing country={c}')\n", + " df = self.get_gho_data(ids,fdict={'COUNTRY':c,'YEAR':year},verbose=verbose)\n", + " print(df.shape)\n", + " verbose += 2\n", + " dflist.append(df)\n", + "\n", + " df = pd.concat(dflist)\n", + " print('all_df.shape',df.shape)\n", + " return df\n", + "\n", + " def death_rates(self,**kwargs):\n", + " '''\n", + " This method fetchs data for the death rate from WHO server. \n", + " if a list of countries in their iso-3 code (e.g ITH, ETH, USA..) passed\n", + " using keyword \n", + " countries=[ITH, ETH, USA], \n", + " The data will be returned only to these countries\n", + " '''\n", + " name = 'LIFE_0000000029'\n", + " try:\n", + " return self.age_sex_stem(self.country_data(name,**kwargs))\n", + " except:\n", + " return self.country_data(name,**kwargs)\n", + " \n", + " def prop_dying_ncd_3070(self, **kwargs):\n", + " name = 'NCDMORT3070'\n", + " df = self.country_data(name, **kwargs)\n", + " try:\n", + " return self.age_sex_stem(df)\n", + " except:\n", + " return df\n", + " \n", + " def dying_prob(self,**kwargs):\n", + " \n", + " '''\n", + " This method fetchs the probability of dying data at a given age from WHO serer \n", + " if a list of countries in their iso-3 code (e.g ITH, ETH, USA..) passed\n", + " using keyword \n", + " countries=[ITH, ETH, USA], \n", + " The data will be returned only to these countries\n", + " '''\n", + "\n", + " name = 'LIFE_0000000030'\n", + " df = self.country_data(name,**kwargs)\n", + " try:\n", + " return self.age_sex_stem(df)\n", + " except:\n", + " raise\n", + " return df\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "s_Dgyace63OV" + }, + "source": [ + "##Challenge 1\n", + "Below we use the `whoapi` class defined above to fetch data as pandas dataframe. The following plot is produced using the data stored in `df_pd` variable defined in the next cell. \n", + "\n", + "![image.png](data:image/png;base64,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)\n", + "\n", + "\n", + "#### **Your task is to make a plot similar to this plot. You may use different colors, but the x axis labels should be rotated to make it look nice and readable. The over all quality of the plot should be as good or better than this plot. We used something like the following to the above plot**\n", + "```python\n", + "sns.set_style(\"ticks\", {\"xtick.major\": len(xticks)})\n", + "with sns.axes_style(\"whitegrid\"):\n", + " chart = sns.relplot()\n", + "```\n", + " \n", + "**The plot should reflect your country (use your country iso-3 three letter code) and compare it to other countries that you like. Make other useful plots if you can.**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "colab_type": "code", + "id": "in2zrzDI27dY", + "outputId": "49d5658d-ad4e-4239-af58-cbdbcd2c4a85" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "processing country=NGA\n", + "(38, 9)\n", + "processing country=IND\n", + "API url: http://apps.who.int/gho/athena/api/GHO/LIFE_0000000030.csv\n", + "(38, 9)\n", + "all_df.shape (76, 9)\n", + "dying prob df.shape (76, 10)\n" + ] + } + ], + "source": [ + "'''\n", + "Here is a starter code to give you a hint\n", + "'''\n", + "#initiate the whoapi class\n", + "hapi = whoapi()\n", + "\n", + "#get data for dying probabilty of countries\n", + "df_pd = hapi.dying_prob(countries=[\"NGA\",\"IND\"])\n", + "\n", + "#plot data\n", + "print('dying prob df.shape',df_pd.shape)\n", + "xtickslabel = df_pd['AGEGROUP'].unique()\n", + "xticks = df_pd['MidAge'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GHOPUBLISHSTATEYEARREGIONCOUNTRYAGEGROUPSEXDisplay ValueNumericMidAge
0LIFE_0000000030PUBLISHED2016AFRNGA0-1Male0.0710.07128-3
1LIFE_0000000030PUBLISHED2016SEARIND0-1Male0.0340.03386-3
2LIFE_0000000030PUBLISHED2016AFRNGA0-1Female0.0620.06243-3
3LIFE_0000000030PUBLISHED2016SEARIND0-1Female0.0380.03755-3
4LIFE_0000000030PUBLISHED2016AFRNGA1-4Male0.0390.039142
\n", + "
" + ], + "text/plain": [ + " GHO PUBLISHSTATE YEAR REGION COUNTRY AGEGROUP SEX \\\n", + "0 LIFE_0000000030 PUBLISHED 2016 AFR NGA 0-1 Male \n", + "1 LIFE_0000000030 PUBLISHED 2016 SEAR IND 0-1 Male \n", + "2 LIFE_0000000030 PUBLISHED 2016 AFR NGA 0-1 Female \n", + "3 LIFE_0000000030 PUBLISHED 2016 SEAR IND 0-1 Female \n", + "4 LIFE_0000000030 PUBLISHED 2016 AFR NGA 1-4 Male \n", + "\n", + " Display Value Numeric MidAge \n", + "0 0.071 0.07128 -3 \n", + "1 0.034 0.03386 -3 \n", + "2 0.062 0.06243 -3 \n", + "3 0.038 0.03755 -3 \n", + "4 0.039 0.03914 2 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_pd.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Challenge One" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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krE8RgiC0f7KssLPQRQcbVNdCotqDWqNFVusxxDtbLA5JURQlFg2/8cYb+Hw+Hn74YQA++OADdu7c2WCR+R07dvCLX/wCk8mE0WhkwYIFxMWdvaJfIBAgPz+/yX0ajUa8YmkFhYWFhMPh1g5DENqlYneQ1z8vZWiKh2SNh+t9K/Bo41ETxn/lPUiGi1vYLjMzE71e32h7zPoQZFlusF6woigNPtfW1jJ9+nTeeecd+vfvz9tvv82jjz7KnDlzmn2Npm6qoKDgrP0DF6sPQbTR0ODBgy/o/NzcXNHGRWyjLcQg2mheG4u/OgiUktXJjud49DWyWaolbE5m0NDhjUYZnW8cZ/oxDTF8ZZSSkoLL5ar/7HK5SE7+tnNk//796PV6+vfvD8Add9zB5s2bYxWOIAhCm7WzsAybUUVHpwW9z0UteqSgD5XZ0WJDTiGGCWH48OFs2LCBiooK/H4/K1asYMSIEfX7O3fuTHFxMYcOHQJg1apVZGVlxSocQRCENikiK+w6WEYHG6j1JuKVcrza6Ktzra3lhpxCDF8ZOZ1OHn74YSZPnkwoFOK2226jf//+TJkyhalTp5KVlcWzzz7Lb3/7WxRFISEhgWeeeSZW4QiCILRJh05U4qsN07GjRGl1hA7qSmr0XSFUjLYFh5xCjOch5OTkkJOT02Dbm2++Wf/nkSNHMnLkyFiGIAiC0Kbt+mb+QbdENeWlLrpLYUJ6DXjAlNpyQ05BzFQWBEFoVXkHXMSZ1aQl2whWFANgUoWRNQYMcS035BREQhAEQWg14YjM7kMVdLApGE1G1J5SZCQI+pD1VrSWlqlyWueSLm7XFixbtow5c+YQDodRFIUJEybwy1/+kkmTJnHy5Ekslm/HFycmJvLWW28xbdo0FEXhueeeA6IlLO68804eeughrrvuuta6FUEQLrIDxyoJhCJ0tEugUmMNufAYHFj9brA4UemMLRqPSAgxVFJSwvPPP8/ChQuJi4vD6/UyadIkunaNvhecOXNmk30o06dP5+abb2bFihWMHj2aZ599lkGDBolkIAiXmJ2F0aH53ZO0lFaHSFW5CZnSkGoOtPiQUxAJIabcbjehUIja2loAzGYzzz33XJMzBL/LYrHwwgsv8Jvf/Ibq6mp27NjB/PnzWyJkQRBaUN6BMhIsapwJFg4U1zBI7aXKbIMaWrTKaR2REGKod+/eXHfddVx//fX06dOHoUOHkpOTQ+fOnQH405/+1OCV0Y033sgDDzwAwKBBg5g4cSIzZ85k6dKl6HS6VrkHQRBiIxiKUHCkgn5OMBhNeMuOAWA2RP9f17VgDaM6IiHE2FNPPcWDDz7I2rVrWbt2LbfffjsvvvgicPpXRhBdxGf79u3ExcWxbt26+tdMgiBcGvYddROOyHS0Sag1auSq6AgjlRQtL2dK797iMYmEEENr1qzB5/MxduxYbr31Vm699VYWLFjAhx9+eNZz//GPf2Cz2XjnnXf46U9/yrBhw+jeveX/AxEEITbyCl1IEvR06lAUBWNtGQGdHl3AQ0RjxOBIbPGYxLDTGDIYDLz00ksUFRUB0QJ/BQUF9OnT54znbdmyhQ8//JBZs2bRs2dPHnzwQf7whz8QDAZbImxBEFpA3gEXSRY1CfFmqnwRkqUK/IYkFG8Fit6KxmRt8ZhEQoih7Oxsfv3rX3P//fczZswYbrzxRtRqNQ899BAQ7UP47voHEyZMoLKykkceeYRZs2YRHx8PwN13343dbueVV15pzdsRBOEiqQ2EOXCskg42GYPBxImKWtLUbrA5kbwVYLS2+JBTEK+MYm7ixIlMnDix0fZ33333tGWn16xZ0+CzJEm88847MYpQEISWtudwBRFZoaNdhVqjxl3qQidFUBwJSCW1aEz2Fh9yCuIJQRAEocXtLHShkqBHcvRLP1RZAoBWH101UmNr+f4DEAlBEAShxeUdcJFsVZMQHx12rvN+U7JCkaOfE1JaJS6REARBEFpQbVDm4IkqOthkdEYj/qCMI1KOTxuH5K9CAQyp3VolNpEQBEEQWtDR0gCKAp0cEmqVmpPuIOlqN2FzMoq3HFlrxmBv+VnKIBKCIAhCizpcEkCtkujhjM5ILnFVE6/2ootPAW8Fit6CxmQ5SyuxIRKCIAhCCzpUXEuKTUW8Pfql7y+PzlDWx0eHnEqtNOQUREKIuaKiIjIyMpg5c2aD7QUFBQwaNIiFCxee9txRo0bVT2oTBKH9q/IEKK0K08EmozFGRxRJntLov412pEgQdSsNOQWREFqEw+Hg66+/JhKJ1G/77LPPiIuLa8WoBEFoafmHygHo/E3/QTiiYAm4CKgMEIpWRW6tIacgJqa1CLPZTO/evdmyZQvZ2dkArFu3jqFDhwLw3nvvsWjRIvx+P1qtlpdeeolu3b4dZRCJRHjhhRfYvHkzkUiEW265hXvuuac1bkUQhAuw84ALtQq6J0dL4JdWh0hTuwkYktD63ADoE9NaLb5LOiGs3nqMLzYfa7Q9EomgVqsvqO0RA5zcdGXPZh9/0003sXz5crKzs9m5cycZGRmEQiE8Hg+rV6/m3XffxWAw8MorrzB37lwef/zx+nMXLFgAwMcff0wwGOTee+8lMzOTIUOGXNA9CILQcmRZYeveUhKMEeyOaP9BUXktg9SVBB3dkLwVKEgYW2nIKVziCaEtGTVqFH/729+QZZnPP/+cm266icWLF2OxWHjppZdYunQpR44c4euvv25U/G7Dhg0UFBSwceNGAHw+H/v27RMJQRDakU27T1Fa4SM7pbZ+RnK1K1qyQp2QguI6gKIzo7c7Wi3GSzohjBrSiVFDOjXafroaQufC6/We0/F1r41yc3PZuHEjv//971m8eDGnTp3ijjvu4Gc/+xkjRowgMTGRgoKCBudGIhEeeeQRRo8eDUBFRcUFxy8IQstRFIX3V+zDatTQJymEShXtvq1bA0GyO+HwJhS9FbXO1Gpxik7lFnTTTTfx0ksvkZmZiUYTzcUmk4nOnTtzzz33kJWVxcqVKxt0PkO0auqCBQsIhUJ4vV7uuusuduzY0Rq3IAjCedi+z8Xhk9UMTAWjOfp0oCgKOn8ZMiowJyL53EhGCypD6/3Yu6SfENqaa6+9lunTp/P//t//q9+m1WqRZZmxY8eiKAqXX345Bw4caHDenXfeydGjR5k4cSLhcJhbbrmlvkNaEIS27/0VezHp1QzrLFEbjk5Ic3sjOCnHr4vDGK5FkkOozY5WG3IKIiHEXIcOHVi9ejUQfW2Ul5dXv++pp57CbDbzs5/9rMlz684DmDFjRmwDFQQhJnYfKmfvUTfDumqJT3BwsiRa2fRERZBOajeytTN4o8NRta045BTEKyNBEISYen/FXgw6FdkdQW/8tn+gtLyaOLUPQ0IqeCuA1h1yCiIhCIIgxMyB427yDpTRL0VNckLDJTED35SsUDtSokNOJRUGZ5dWiPJbIiEIgiDEyPsr9qHTqLiyI+i/NzJQ7Y2+OsKajOKpq3LautULREIQBEGIgaPF1WzZU0KfVA3OJDOS9O3XrTcQIS5SQVBlBL0lWuXU0HpF7erENCEsWbKEsWPHMnr0aObOndto/6FDh5g0aRLjx4/n3nvvpaqqKpbhCIIgtJj5X+xDo5YY3lHGaGn4uuikO1qyImhOBkDyu1EZra065BRimBBKSkp4+eWXmTdvHp988gnz58+nsLCwfr+iKDzwwANMmTKFxYsX06dPH+bMmROrcARBEFrMqTIva/NO0suppUNiw6cDgJPltaSq3WgdTqitRpIjqM2tV+W0TswSwvr168nOzsbhcGAymRgzZgzLli2r3797925MJhMjRowA4P777+enP/1prMIRBEFoMQtW7kOSJK7sGMZgtTXaX13uQivJaONT6oec6uxJLR1mI81KCOFwmMWLFwPgcrl45513kGX5jOeUlpaSlPTtDSYnJ1PyzfhbgGPHjpGYmMj//M//MHHiRJ544glMptabsh0rRUVFZGZmMmHChAb/nDp1KibXGjVq1EVvVxCE5iur9PN/uUX0SNLROclSX6biu6Tq6AgjbM76Iae6hPSWDLNJzZqY9uSTT+Lz+Rg/fjwqlYrc3FyKiorOOFlKlmUkSar/rChKg8/hcJjNmzfz3nvvkZWVxd/+9jeee+45nnvuuWYHn5+f3/iGNJpm1Rk611pE59uG3+8nMTGRefPmxTwOv9+PLMvn1ebFiCM3N1e00cbaaAsx/NDa+GyLG1lW6G12UVxhQlVV2WB/WAZTrQvZKHG4zENC6WFsqDhW5efYOcR3Me7l+5qVEHbs2MGnn34KQEJCAq+88goTJkw44zkpKSls3bq1/rPL5SI5Obn+c1JSEp07dyYrKwuAcePGMXXq1HMKPjMzE71e32BbQUFBfeG3mp1rqMlb3ei8i1H+Wtf7ShIvH3PW44xGIyqVqlExurKyMqZPn47L5UKSJH7/+98zfPhwXnvtNU6ePMmRI0eoqKjggQceYMOGDeTl5dG7d29efvllIpEITz75JAcOHMDlctG7d2/++te/NrhWWVkZM2fOpLi4uEH7TbkYxf4ABg8efEHn5+bmijYuYhttIYYfWhuVNQG2L1hBl2Q9g3qnYU9IaHTMpvzDpKorCegT6NqtB5RtQdFbyRoyFF186kWJ43QCgUCTP6TrNCshhEIhgsEgOl20Bkc4HD7rOXVfbhUVFRiNRlasWMHTTz9dv/+yyy6joqKCvXv30rt3b1avXk2/fv2aE067U1pa2iCB5uTksHv3biZMmMDYsWMpLS3lrrvu4pNPPgFg//79zJ8/n23btnH33XezZMkSunTpwtixY9m3bx81NTVotVrmz59PTU0NDz74IF9++WWDv79Zs2Zx6623ct111zVo32JpncW7BeGH4OMvCwmFZa7qGMFib7oMhcsDfTRusHeNbvBWgMHS6kNOoZkJ4ZprruHee+9lwoQJSJLEp59+ysiRI894jtPp5OGHH2by5MmEQiFuu+02+vfvz5QpU5g6dSpZWVn84x//YMaMGfj9flJSUnjhhRcuyk3Vsfa/Bmv/axptb+ny18nJySxatKjBtqFDh1JYWMgbb7wBRJPs8ePHAbjyyivRaDSkpaWRlJREjx49gOjfaVVVFUOHDsXhcDB37lz27dvHkSNH8Pl8Ddpfv349hw4d4tVXX23Q/vfXWhAE4eLw+EN8tu4wnRJ19EjWotY0/fVa4wniUPmIxKeALCP5K5FsyagMrd+H2qyE8Mc//pG5c+eyatUqNBoNN9xwA3feeedZz8vJySEnJ6fBtjfffLP+zwMGDODDDz88x5AvDbIs88Ybb5CWFq1dUlpaSkJCAitXrkSr/XbomaaJ/6hWrVrFq6++yuTJkxk/fjwejwdFURq1/+9//xuHw9GgfUEQYmPJVwepDUYY1kGF2d54ZFEdlc8NapBsTvBXIinyN0NOdS0Y7Wlia85BarWayZMnM3v2bF577TXuuuuuJnvOhebLzs7mgw8+AKCwsJCcnBz8fn+zzt2wYQM33XQTt956K1arlU2bNjW5hkJdR/a5ti8IwrnxB8Is+uogaXE6MlJ0qLVNf7nLioIpGF07+bsjjDRtYMgpnOUJ4Sc/+Qnvv/8+l112WYMRQnW2bdsWs8AudTNmzOB//ud/6p+gXnjhhWa/3//xj3/MH/7wB5YuXYpKpWLQoEEUFRU1an/mzJnn1b4gCOfms3WH8daGubGnDovdetrjKjxhUlRugmoTmm9KVgDokzq2VKhndMaE8MorrwDw9ttvN5hTIDTfd9dD+C6n08mrr77aqC/jN7/5zWnPfffdd+v/vGTJEqBxf0jd8U6ns75/QhCE2AmGIny8ppBku46+qWq0Ov1pjz1RESRN7SZiSY5++XorUFQaDCntICHUDRN97LHHGswyFgRBEKK+2HyUKm+QnEwdljP0HQCcrAiQqa5EiusV3eApR9ZZ0JnOfF5LaVZHQHp6Otu2bTvr7GRBEIQfknBE5oNVB0iwaslKkdAZDGc83lteilaSUdmd0Q2+tjPkFJo5yujgwYPcddddaDQadDpd/azjttqH8P1Z0UJsKYrSaJSTIPwQrMk9TnlVLWP66LA5zvwr31MbIVxZDEaiHQy1pUMAACAASURBVMpyBMlfhcqe2iaGnEIzE0JTpavbKrVaTSgUqp9EJ8ReKBQSCVj4wZFlhfkr92M3a7gsreHymE1ZlOsmTapARgWWRPC5kVBQW9rGkFM4h1dGu3btYsGCBcTHx7N9+3bS01u/EFNTHA4HJSUl4vVWC5FlmZKSkmbNXheES8mG/FMUl/sY1EGN3XbmEXz7TvnZeshLlr2GkN4GKnX9CCOtI/mM57akZj0hzJkzh3Xr1lFcXMw999zD3//+d44ePcpDDz0U6/jOWWJiIkVFRezbt++0x3y3DMf5Em1862LUQRKE9kRRFOYt24vVqOHydBmD5fRDTYNhmQ83VdDJGiYpUkKNJR0t1CcEQxsZcgrNTAhLly7lgw8+4PbbbycuLo4FCxZwxx13tMmEoFKp6NSp0xmPyc3NZcCAARd0HdFGQy6X64LOF4T2JHdvKcdKariyhwGH7cwdwl/sqqKsJsyTGXvAFaQqoS8WQPGWI6u06JM7tEzQzdCsV0Z1ncl1bDZbkyUVBEEQLnWKojB32V5MejVD02UMZ5jwedIdZPXuaq7vXIvDlQedBxM0RMvJ4ClH0VvQmk7/dNHSmpUQUlNTWbNmDZIkEQwGmT17dpvtQxAEQYil/EPlFBZVkpWuI8FubLQ8Zh1ZUfhgUwVGncRNms2g1aP0+rYoqOR1g77tDDmFZiaExx9/nLfffpt9+/YxcOBAvvrqKx5//PFYxyYIgtDmzF1WgEGnIjs91OTymHU27PdwxBXgnl5uNO4jKD1Hgu6bkUiRMFKgGrXJ2maGnEIz+xCcTif//ve/8fv9RCIRURNHEIQfpBPlQXYfqmBQZyNJdu1pi3xW+cJ8ut1NhlNLT/fXKJZE6PydBW18FUiAxuJoM0NOoZkJweVy8fHHH1NZ2XApuD/+8Y8xCUoQBKEtWp1XhU6jYliHEAb76cvJf7zFTTiiMCn9INIRN/IVd0WHmtapG3JqbztDTqGZr4weeOABdu7cWT8jVcxMFQThh+bIqWoOFgfISNXjjDOiVjW9DG/+cR95x3yM66fFcnw9SnJPSOre8KBvEoLR2TnWYZ+TZi+h+fe//z3WsQiCILRZ731egFoFV3UIYbLGN3lMICTz0eYKUuxaRrAV5DBK3xsaH+gpR1br0SemxTjqc9OsJ4R+/fqxf//+WMciCILQJh0sqmTT7mI6OSDFoT/t8pif7aik0hdhUlYQ1Yk86DoUzI1fLSnechS9GbWhbU3qbNYTwqBBg7j55ptJSkpqMP9g1apVMQtMEAShLVAUhdc/3olOq2JIUiVme2qTxx0vD/D1vhqG9zSTfmoJ6MwoPa5q8ljJ6wZLAip92xlyCs1MCG+99RYvvvjiWWcAC4IgXGpy95aw94ibK3pYiDdWNrk8ZkRWmL+xAotBzYTUU0j5RchZ40DbuBy2JIdRBT1Ixi5tasgpNDMh2Gw2xo4dG+tYBEEQ2pSIrPDmonzMBg3D0oJEwk1/gX+9t4YTFUHuucqOvvAjFFsKdGy6HIw2WAOApg1VOa3TrISQnZ3N888/z+jRoxuUsOjXr1/MAhMEQWhtq7Yc46TLy7V9rCTYJMqqahsdU+EJ8/mOSvqmGxkQ3IFUW4182UQ4zQxmbaA6+m97YkxjPx/NSgh16/cuX768fpskSaIPQRCES1YgFOHdzwqIt+oYnFyLye6EqoZzsRRF4cPNFSDB7QPUSJs3oKT2hfjTv16ve0IwpnY/7TGtpVkJoalF4gVBEC5lC/+vkEpPgHH9zcQ5tE3OO9hx1EfBCT8TBsfhOLYSAKX3dWdsVxOoRtYY0MU5YxL3hWhWQnj77beb3P7zn//8ogYjCILQFlR7gyz8vwOkxhvITAphbGLegT8o8/FWNx3idYxIdiNt2o3S82owOc7YtjZQjaK3tLkhp9DMhPDdOQjBYJAtW7YwbNiwmAUlCILQmt5bVkAgGGFkNwN2u6XJiqZLtrnx1EaYck0i6oJ3UQxWlG7Dz9q2LlSDypzS5oacQjMTwrPPPtvgc0lJCdOnT49JQIIgCK2puNzLio1H6ZZioltcGEMTKwIeKq1lwwEPI/tY6ejfi1RdjDzwZjjbqKFQAE0kgMpka3NDTqGZM5W/z+l0cuLEiYsdiyAIQqt7a3E+ACO6yNgdjV//hCMKCzZWEGdWc1M/I9Le1ShxHSAt8+yN+6I1jNTmtlXltM459yEoikJ+fj4JCaev9CcIgtAeFRZVsjG/mMyOZjraZXSGxhPLVu+uoqQqxC+vTcJwZB1S0It8+Z0gSWe/gLccAJ0j6WKHflGccx+CJEmkpaXx6KOPxiwoQRCElqYoCm8sjJaouLpTGKujcUey2xddI3lAZxP94vywcxNKhwHgaF6ROsXzTZXTtG4XNfaL5YyvjKZNm8a0adMabFMUhfLycl555ZWYBiYIgtCScgtK2HvUzcAuJlJsGjS6hq90FEXh/wolNGqJiUPikApWgkqDknFts68hV7sIq/RobW3zCeGMCaFnz56N/klMTGTp0qVs3779rI0vWbKEsWPHMnr0aObOnXva49asWcOoUaPOPXpBEISL4LslKoanBjE30Xew5ZCXoiqJcYPisHuPIZXsjxavM1ibdY1AIIBUUwo6Q5sccgpneWX0i1/8osHn9evX8+ijj5KTk8OMGTPO2HBJSQkvv/wyCxcuRKfTceeddzJ06FB69OjR4LiysjKef/758wxfEAThwq3ccoyTZV6u7Wslwa5qVN660hfmk61uUm0Kw7qbkNYuRzHFRctbN0M4FCJ0MBejr4xgXEdU+sZ9E21Bs0YZhcNhnn/+eX73u98xY8YMZs2ahdF45jG069evJzs7G4fDgclkYsyYMSxbtqzRcTNmzODXv/71+UUvCIJwgaIlKvZES1Qk1WKy2RrsVxSF/64vJxxRuL6ngur4NiRPGUqf60F99m7YSCSC59g+bMe+QmNPIhzXCVUbfUI4a0I4cuQIt99+O/n5+XzyySeMGTOmWQ2XlpaSlPTte7Lk5GRKSkoaHPOf//yHvn37MmBA01UBBUEQYu3jNYVUeYJc2U1LXJwZlarh1+K6/R72napl/OA4EnQBpP1rUBK6gDPjrG1HFJmq4iLij65CUmuwDBoDGn2bHHIKIClnWBz5o48+4vnnn+fnP/85DzzwwDk1PHv2bAKBAL/97W8BWLBgAfn5+fzpT38CoiOX/vSnP/HOO+9QXFzM5MmTm10zKRAIkJ+ff07xCIIgfJ8vEOHlRcXY9DC+qxuL3Yqk+nb4qNsH72+XSLPDhH4KScVbsFXsp6j7WIKGuDO2rcgKtdXV9KjYgN5fjr/bMJDUhOI6Iid2ifGdnVlmZiZ6vb7R9jM+70yfPh2VSsWcOXN4880367crioIkSWzbtu2056akpLB169b6zy6Xi+Tk5PrPy5Ytw+VyceuttxIKhSgtLeWuu+5i3rx5F3xTZ5Obm8vgwYPP+TzRhmijPbXRFmJo623886M8wmGFGwaa6NUpocGs5Iis8MmyYnSaML8YlYo9UoG0Zz90Hkx670FnvV5leRn2qjzU/jIsWdcQn9QJtcnGgaCl1f4+zvZj+owJ4ULKWw8fPpzXXnuNiooKjEYjK1as4Omnn67fP3XqVKZOnQpAUVERkydPPqdkIAiCcCGKy70s33iUrikmusVFGpWoWJVfxbHyIJOvTsRuVCNtXIas0iL1GnnWtqsrKzGX5qM+mY+hUz90aT1RgrWYMi6H3ftidUsX7IwJIT09/bwbdjqdPPzww0yePJlQKMRtt91G//79mTJlClOnTiUrK+u82xYEQbhQby3ORwJGNlGi4nh5gOU7qxjUxcRlXcxwcANSxVHK04aSqDtzDSJvdQ3aikPoj25AE5eCqd/VRGoqsGZehcZgieEdXbhmzVQ+Xzk5OeTk5DTY9t1XT3U6dOgg1lwQBKHFFB6PlqjI6mSmo11pUKIiGJZ5b20ZVoOaW66Ih6pTSPtWozgzqHH04EzrnPm9PmT3CexHvgKNHtvlY4l43BjSM9AlnP8P7JZyXsXtBEEQ2itFUXj9453otSqu7hjGFmdvsH/p9kpKq8PcOTwBs0ZG2v4x6Mwo/cedsV5RIFBLwF1M/KkNyAEvtsvHQiSCxhKHsUv7WG5YJARBEH5QthYUs++om4GdTTjtWtTab4eAHjjl56u9NVyVYaV3mhFpzwrwlqMMnABneFUUDoXwlrlIdu8kUl6Epf+1qK0JKOEQ5t5DUWm0LXFrF0wkBEEQfjAissK/Fu3GbNAwLC2A2f5t34E/KDNvfTlJNg05gxxQvBfp2DboNgwSu56+zUiEKlcJKbWHCR/bhaFrf/Qd+hCpqcDYcwgak+2057Y1IiEIgvCD8cXmaImKoV0NJNqMqDXfrpO8cEsF1f4IP70yEV3Yi7TzUxRbyhmL10UUmUpXKU7FRXj/WrQJaVj6XU2kphx9ajcMzs4tcVsXjUgIgiD8IITCCu99U6JiUHIAo/3bvoO8o162HvJyfZadzgk6pLxFEAmhXDYRVOom25MVhWpXGYlSFexbg0pvxDZkLHKtF5XegrFb+6vAIBKCIAg/COsKqqnyNi5RUeUL88GmCjrE6xidZYfDm5DKDqP0GwOW048pqq4ox6Z40B5ahxzwY7t8HKi1yKFaLH2GttnyFGciEoIgCJe8Kk+AdXs8pCUY6JcYxmiJlqxWlOhymMGwws+uSkRdU/LNENNe0PGy07fndmMMV2Nx7SRccRLrwOtR2xKJVJdh7H4ZGsuZy1q0VSIhCIJwyXv7092EIgojuko47FYkKfrVt7HQw54Tfn50mQOnBaQdH4PWiNI/57RDTL3VNWj8lSQGThI4ugtj90EYOmQgeyrQJXVC72ybq6E1h0gIgiBc0jbuOsmqLcfpFK+ie5yC/psSFWU1IT7Z6qZnioGre1uR9nwRLWt9hiGmoUCISE0ZqXov3t1fok3qiLnPcORaL5JGj6nHoEbVUtuT9hu5IAjCWbira3n5v9txWHQMTarCFh8dZirLCvPWlaOS4CfDE1CV7kc6lovSNRsSm/6FH6itBV8l6TYJ3/YVqAwWbINvQpFlwv4aLL2HotK1zYVvmkskBEEQLkmyrPDntzcRCEa4MdNEggl0umh15NV7qjnsCnDrFfHEqf3fDDF1nnaIaSAQwFdeQrJZIbDrC+RwEPsV41DpDESqXRi79Edjb5vrJJ8LkRAEQbgkzVu+l/3HKhneJ46uFj9aU/Q10ImKIMvyKunfycTgLiakvMUQDqIMnNjkCmjBYAhfWQkd47VYy/YSdhdju+wGNLZEwtVuNPHpGDqcfbGc9kAkBEEQLjm7D5fzwar9dHFaGJLgIT4hHkklEY4ozF1Xhkmn4sdD45GObkEqO4TSdzRYG//CD4dCeFzFpDs0qFyFaN3HMPW6HH1aD+SgD1QSlp6D23W/wXddGnchCILwDV9tiOf/vQWDXsMNvVQkxpvQ6KJzAj7bUcmpyhB3DEvAEnQh7V0VHWLaqfGCN5FwmCpXCel2FdqqY3h2fUnY6sSUkY0cCRP2VGPunY3KcOZy2O2JSAiCIFwyFEXhhXe3UukJMLq/g1RTCJM1OiP5RBWs2VPNsJ4W+qVqo1VMtUaUrMZVTCORCJWlJaRaFXQ1RdRs/wJtYkdqOw1GkiTC1eUYOvdFF+dsjduMGZEQBEG4ZCxdf5jcvaUM7hlPL3MNtvgEAGqDMl/sk0iwapgwOA6pYGV0iOmA8aBvuFJaNBmUkmKSMXqLqdn2BdqEdOxXjAOVmrDHjdaehLFTn9a4xZgSCUEQhEvC0VPV/O+i3aTEGxmW6ic+wYZao0ZRFD7YVE5NIDrEVF9RiHR0K0rXoZDUvUEbEUWmylVKkiGEOVBKzbblaONTsQ8dj6TRQiQIsoKp5xBUqpiuL9YqREIQBKHdC4UjzHp7E5IKbuxrJMmqQW+Mvtv/dHsl2474yO6s0M0WRtq55JshpqMatCErCtWlLuK1QWyhMqpzl6FxpGDLjiYDORJGCngx9hqCxmRtjduMOZEQBEFo9/7xYR6nyn2MykogXe/D8s0ayWv2VLN6dzVXZVgZ0kFB2tn0EFNZUaguK8Oh8eOIlFO99XM0jiTs2eNRaXTIkTCRqjLCCV0wJHVsrduMOZEQBEFo177eUcSqLcfp19VBP1s1cUlxSJKK3MNeFuW6GdDJxMQhcTjc+5BcB1H63NBoiGl1eTkWvMTJlVTnfo7Gnog9+2ZUWj1yOEy4yoWha3/k+E6tdJctQyQEQRDardIKH68tyCPOqmdExwgJDhNqrY59J/28v76M7k49P70qEZWnlPiSbSjJPaHz4AZtVJWXY47UkCBVUb31MzTWhIbJoNqFqdtlmC7BTuTvEwlBEIR2SZYVnvn3ZoLhCDf2t5JsimC02jhWHuDtL1047VruvSYZbdiLlPsBslrXqIpplduNIVxForqGmi1LUVvisA+7GZXOgBwORZNBj0EYO14aM5HPRiQEQRDapf98toeDRVVcnZlER1011oR4XNUh3lxVilmv4lejkjGqQkib34faGoo7jmwwxLSmsgpdbSXJGi81W5eiNjtwDJuISmdEDgcJV5dh6jUEY3qvVrzLlnXpjZsSBOGSl3fAxcI1hXRLszLAXk1CYhzeALy+qhQFuO96J3aDhLTlA6gpQRl8BwH/t193nqpqVP4KnFpP9MnAZMMxfCIqfV0yqMCckY0hpUur3WNrEE8IgiC0KzXeAH95bysmg4ZRPTUk2vXIKh1zVpXg8UeYMiqZZKsGKW9RdCnMrBxw9qw/3+vxoHjKSdV68W5ditpoiT4Z6E3IoSDhmgrMvX94yQBEQhAEoR2pK01R7Q0yZmAiyTofeouV//3SxanKEPeMTKJzgg6p4Aukk7uRM0ZBx28Xu/d5fchVLtIMXry5n6IymLEPn4jKYEYO1hL2VGDOGIbB2bkV77L1iIQgCEK7seirg+w4UMYVvRPpqq/AEZ/AvPUVHCiu5c7hCfRJN8KhDUiHN6F0uQK6D68/NxQIEaosIc3gw7d1KZLOiH34RNQGC3Kwloi3EnOf4Ricl/bQ0jMRCUEQhHbh4IlK/r10D6mJJoY4a4l3mFmyo4YdR33kDHJweTcLFOWh2rsKJa1ftKT1NyOKfF5fdLUzvQ9f7lIknQHH8FtRG63RZOCrwpI54pKedNYcolNZEIQ2LxiK8Mzbm1GrVIzpZybJ6GX9EYWv99Uwso+Va/vaoLQwWpYioStK//H1yaCmqho85aTpPPi3fYmk0eEYfgtqkxU56EP2e7FkjrzkKpeeD/GEIAhCm7d4s5tSt5/rLksiRV3FHreepTsqGdTFxPjBcUiVJ5G2fQhWJ8rgH4Nag6woVJWXo/GV08ESwnp8C5Jag2P4RNQmG3KtD9nnwZI5QiSDb4gnBEEQ2rQPVu0j/2gtWd3i6WWsojhg4sNNbjJSDfxkeCIqbznSlvdBb0G54ieg1ROJRKguL8cq+UjQ1uDZ+DmKJGEffgtqswO51osc8GPtf80lsRbyxRLTJ4QlS5YwduxYRo8ezdy5cxvtX7lyJRMmTGD8+PE8+OCDVFVVxTIcQRDakXBY5p8f5fHu53txmFVc2VnBF5aYu7GKtDgd94xMQhPyIG2eB5IK5Yq7QG8hHA5RWVpCvMZPnP84NZsXozJa8Xe/Co3FQaTWI5LBacQsIZSUlPDyyy8zb948PvnkE+bPn09hYWH9fo/Hw5NPPsmcOXNYvHgxGRkZvPbaa7EKRxCEdqTGG+Spf23k8/VH6JJq48ouQK2X97bUYjep+dWoZAwEo8kg6I8+GZjjCQQCVJcUR1dKK87Du2sNuqTOOK66DUVnIuLzoIRCWAdci8aW0Nq32ebELCGsX7+e7OxsHA4HJpOJMWPGsGzZsvr9oVCIJ554Aqcz+u4uIyODU6dOxSocQRDaiZMuD4/+42t2HHAxuHcyN2ZZMYcq+G9eBLUK7rsuGatOQdo6Hzxl0T4Deyp+rw+/6xQdbTLS/i/xH87D2G0gtqHjUGn10cQhh7D2H4nGGt/at9kmSYqiKLFo+I033sDn8/Hwww8D8MEHH7Bz506efvrpRsfW1tZy1113MWnSJCZOnHjWtgOBAPn5+Rc9ZkEQWtfh4lo+XFeBLyAzoKuRbmYvpkg1nx2xUxOEW/srJJtlnMe/xlJznJIOV+KxdyXo9aMJVJNsDGI5kYsq4CWQnkU4PjrBTKr1ggqCaZmgt7TyXba+zMxM9Hp9o+0x61SWZRnpO1UFFUVp8LlOTU0NDz30EL17925WMviu093U2eTm5jJ48OCzHyjaEG204zbaQgzNbUOWZZZvPMp/v94NSNwyshspmkrkWpmF+Toqa0Pcf52TnikGpPzPkWqOI/cdTUKXK9BVVKDTg1Mbwbv9a1AUbMNuRpfUEVmWCVeXobF0oLDWyOBhV8b8XtpyG2f7MR2zV0YpKSm4XK76zy6Xi+Tk5AbHlJaWctddd5GRkcGsWbNiFYogCG1YMCzzv0t28/rHuzDo1dwxqhtpKhcny7y8tTGINyAzoZ9Cz1QjFH6NdCwXpdswIl0up8rlwhSpJjlSjGfLYlRaA46rb48mg3CYSFUphqROWLNGgs7Q2rfa5sUsIQwfPpwNGzZQUVGB3+9nxYoVjBgxon5/JBLh/vvv56abbmL69OlNPj0IgnBp8/oDPPfvzSz66hCpCSZ+PKIzNu9x1h8M8HFegI6JOv7wo1Q6xgHHtqHa/yVKen/CPUZSWVxCvNqHw12AN28l2vg0HFffjsYSF61LVO3C0KU/xl6Xo9JoW/tW24WYvTJyOp08/PDDTJ48mVAoxG233Ub//v2ZMmUKU6dOpbi4mD179hCJRFi+fDkQfQUknhQE4YfhVJmH5/69lUMnq+jXLYGr+zoIlB7hw30yxVURrs+0ceMAB2qVRG31caTjX6Ek9SCQcQOe0mJSzCHUhzbiP3UQQ+d+WLKuQVKpifiqUUJBzH2vxpCU3tq32a7EdGJaTk4OOTk5Dba9+eabAGRlZbF3795YXl4QhDYqv7CUl+Ztp6yqlqsHppOVAscPHWX5/ghatcSvRiVHC9UBlB3GWbQWHKn4eo8lUFFKB3OYcP5KglUuzP2uxthtIJIkEa4uR9IasA4chcYS17o32Q6JmcqCILQYWZZZtbWIfy3aRTAUIefKLqQa/Gzc5WLbCZkuSXomX51InFkDigyFa5H2f0VIZyWQMY5ITQUddD5qty5HCQexDc1B7+yKLEcIVZWhdTixZFyBSmds7Vttl0RCEAShRQTDMv9dUcDHaw6i12m4fVQPVF4XizdXUVyjcG1fGz+6LPqKCH8V0o5PkCqOIadlclDVmU5hH05K8eWuRqUz4rj6x2hsicjhEJHqcgzpPTB2G4BKJb7Wzpf4mxMEIeZqvAFmf7STtXknSYozkjO8EyePHGP1Pj8qlYp7r0kks6MpevCpAqRdn4IsE+z3I6r0KVhcR0msKcS3fyOauBTsl49DZTAhB32EvdWYeg3GmNqjdW/yEiASgiAIMVXhCfHUW5vYd9RNjw4Orh2YzJbtB9lxIkynBB13j0gi3qKBSAhpzwqkY9tQ7KnUdL+eiKzQwRzGc6gA//ET6NMzsA68DkmtIeKpRFEUbP2vRetIPnsgwlmJhCAIQkzUBsNs3VPCu6vLcHsiXNHXSUaqniVfHqC4RmFEbys5g+LQqCWoLkHavhDJU0a4y1AqHX2xacLEq/34d6xBW1OOqXc2pp6XI0kSoSoXKqMVa5/haEzW1r7VS4ZICIIgXFSyLHPoRCUfrznI5j0lBEIRxgztjDrsZcGXx5AkiZ+PTKJ/JxMoChzZglTwBWgNePtOIKizkmYOoz65E8/hPFQGM/7OV5DU6wrkSJhwdRm6hA6YMi5HpdG19u1eUkRCEAThoimr9LFyy3FWbT5GcYUPZ7yJjFSJkhIXO474SI/Tcs/IJBKtWgj6kHYuQSrZj5zYDXfaMMw6iQ5yBf5tXxH0ezB06Y+5zzCqi04ih4KEa8oxdO6HsVM/VCqxvtfFJhKCIAgXzFcbZMd+F5+uPcyewxWo1RKjBnckPUHP0q/3U+FXMbynhZsvj0erlqD8CNKOTyDgpbbbCHzWTjiNYVTHcvGePIDaGo/jqh+jjU+NXiBUS8RXibn3MAzOzq17s5cwkRAEQThv4bDMwROVrNh0lI35xVR7g/Tq5GBAj0R27jvF/+V60agkJl2VwKCuFpBlpH1roHAtmOKp6j0BvclIeqCI2twNhCMhTBnZmHoORlKpkcNhwjXlSCjY+o8SaxjEmEgIgiCcl+JyL2t3nOSrHUUcPlmN3azjuiEdKSqp5sPVhahVcHWGmZ6OGjK7WsBXibT9Y6TKIkIp/ahO6k+yPoDq8Jf4y0+gjU/DMmAUGms8siwTqXFDJISxSyZBY41IBi1AJARBEM6Jxxdk+74S1mw/Qd7+MkLhCJndEggEw6zaehyNCq7qoeeGgUlYjWoOH6mBk7uRdi0FoKbbdajsTtI8hQR25yKr1FgGjMLQqR+SJCHXegn7qtEndcLQJSs6isiV28p3/cMgEoIgCM0SDMsUHnPzdd4JNu8uptTtJznOgEFvIv9QOVo1DO+qZvSAJOzWb9YpCQdJOrEBVeVBIrZUqtOHkaANIBUuJ1BTji61B5askagNZuRwiND/b++8w6SosgX+q9Q9eXryEGYIQwbJCkMaUXEAQYI8RBQQ1GXXgLKsz7Qm3FV3n4IChn2GNeDCIuIqKgZEXAVkyTkMCMwMDMOEntC5wn1/9NA6Msiw6tOF+n1ff9VdVffUuVWn77n33Fv31laiRMcR33UwjqSMnzfD5yG2Q7CxsTkjR8s8fLWjhA17jrPnsBtFlkhLiuaE249DDdK3hcxlneNJSXGFE1gmFG5GOvAF8UEv/qY9MVJySK/ag160Ezkq82yIIQAAIABJREFUloSLRuDMbB1exKa6HICYnG44m+TY00/8TNh33cbGpkEsy6LGZ7B6UxGb9pSyeX8ZNd4QcdEaHr9OdW2Qi1o6yGutkJGRjKKq4fcKju1E2v85ks+NHt+U4rgLaJUQg1ywAj3wzVBSWXNi+jxYQS+OjBbEtLgAOSrm5872eY3tEGxsbCJ4fCEqqgMcLfNQUu5h34FaSmoOcLikBlUJj/vXTYuL2sbTNyNAk/QEomJjw47gRAHS3lVItScwopMJth5MXFISzQrWwd4SpPhkXL3DQ0ktPYTuLkWJc5HQYTBqYtrPnHMbsB2Cjc15TSBk4K4JUFrhpfCEB69fx+vXqawJUFEdYN8RL6YVPldVJC5sn0rXZD9N4k3iXBkoqgLuYtj9CXJVMaYjDtG6PzGx0USXFqB/fQRNkiNDSQVgVJeBrBLdtifOjJZ2eOgXhP0kbGzOIwzDwl0boMztp7islooqPzU+nYpqPxXVAY6VefH49XppohwKvTuk0yHVJFlUk5ySgDM6BrOmFHP3SrSKr7HUKKQWPYnRZPSj2wj5PcjOWGLaXUSZFE96+86Y3hrMkI+opm2Izu5kr1nwC8R2CDY25zCWZVHjDVFe5edomZeScg9VnhAn3D4qqgOUlHvwB00AZAksEU6nKhJZGfEkOA1yOzbF6TtGslMiNjkNs6YCfft7OMr3gaKhNmmLZOrohVsICoGWlk1c50E4MlshyQonCvahu4+jJqYT17k/anzyz3hHbL4P2yHY2JyDmKbF4VI/h1Yf4ESljxNuP+VVfkorfQRCYQegyBLiW2nSkmJokRlPdkYcGfEKQvdTVngQV8AgMTkB0+/B2PQOUWV7kAA1qQlmoBajpADJEUV0Tk+iszujxLmwLAvL78EK+ZEsg5j2fXGkZdnzD/3COS8dgmlZWJZlG6fNOYnXr/OPzw/w8b+q8PjdhIxwJ4BDq2/v0U6VFpnxZKVF09SlEUUA1fCimZU4/QpOTcHUgjjkaNizkriyPUiWjhyTgOWrxXCXoKU0I6rTAJyZrZEUFcvQMarLEcJCdaUT0643oYPF9vxD/yGcdw7hcEkN//NWCSmffkbvTulc1jublk0Tf261bGx+FI4cr+HZpdvYfagSVQanQ0U3LYQAy4Lm6bFkpUbRLFEhxRlEM7w4FR9RioLDqSHJDixDRQ8GMGsryKjZQ8qxQ2AEQdEAEHqQ6NbdiGrRJRL+sQLe8AL3qoajeTuc6S1QYxLCSh069nPdDpuz5LxzCM3SYmmTqXG4LMA7n3/NO59/TWZKDL07ZnDphVm0aZ70c6to8x+EZVmEDItA0CCoW4R0E39Qp6xa/39thZqWYPkXB1n88X68AR1VkTBMQVKUSvtmsWQnyTSLDRJFkCinSZRDQXM4URwpGIZB0FODcbwQh+c4ccEKJE85BD31rqEmpBDd4gKczdqGWwNmeOI5Yego8UnEtO+DI6Upsqr9v+TZ5sfnvHMIqiLTJ0twWZZBRUBlv1vlQFmI9748xHtfHiI1MYoLO2VwSe9s2rdIQpKkn1tlm58By7Ko9elU1Qao8oSo9gSp8gSpqg1S4w1R6w3h8ev4gwYh3SRkhJ2BblgYpkVSrIRf/pqB3ZuR6vppR9OUun08tWgzOw9WoNW9K5AUq9EzpZrurZ1EOww0p4bmTEDVNPSQgV55DPNYIVJtCVH+cmL87vC7BACSFPkuJIWo7I7EtLwg8q6AFQqg17qRJQlHejbOJjl2R/E5wnnnEA4eLKZq65ccUVKIat6W3Jw4hnXUKXHL7Ck12F8e4sN1R1ix7giJsQ56d8rg4h7NuaBNKopi9zn8J6IbFrW+cCFe4wtRVRuksiZAVW2Qak+4gK/xBqn16XgDOj5fCGPxUUxLfK9cWZKIcig4NBlVlVFkCc0ZDtEcrQjw6vu7+WJrMfl9W9C/azPiYn781b1WrD3Ey8t3EQiZKIqEKSxyWzsY1BJqfRKZTTMxvVUYZYex3EWYNcdx+sqJsuqGlkpyPQeAouFIaYaW0hQtpRnFbh/pOTnh2Ue91Vh6ANkZR0zrrjjTsuyho+cY551DyE510jv2KDHmPihdy7GjLvYpWSgZbcjt0IrLnRaVbj87jwbZVxZi9cYiPt1QRLRToVeHDAZ1b0aPDvaC3j83Qd2MjJ2vqPJTXrf9uqicpeu/pMYbwuMLv2QV1M3TylFkCYcmE6UpOB0yidEq8apBSlJ8uKAHBALTEuhGODwU1E0CuoUvYOIJmlR5jAYkC2KjNQ4UV3Ng6XY+XHeE0Xk55F7QBIf2w/925VU+5i7awvYD5WhquKKSFitzRXuJnGaxOGuLiCtejzj0PpruJRzEkZBUB+LbY4tUDUdqc7Q6J6AmpCJJ36r4VO7HqC5DCAstuSkxTduiJqbZAzLOUc47h+BwpVPWbQJp+JFqSog+WsCF/l0oJ3YQKFUplpphpOTQr117LumZSG2tnx1Hathz3ORfO4/x5bZjaIpEq3SNkHaMCztnRl7pt/lx8AV0yr9VyFfUBCiv8lNR5aesKuwEvvvyFIBDldBki1injlODFKdE0xiIcqjEqIIYB3VbCYlwKDBkSfh08OkWft3EG9Kp8AUpLrWoCQiCDZT1sQ6JhGiJtDiZNmkaidEyidEKiTEKrmgFRZH4aPNxdpdLCAHx0QpfH63myTc2k9M8kfFD2nNRx4x/q8UphODDrw7z0js7CRlWeOioZTE4R+biHIh370NatwVZ9xEnKSgOJ0JWwpPNIZBUFUdKy0gLQIn7JixqGSEsnwehBxASSAIky8DZvAOO9Bb2YvbnAeedQ9CrTpC+/32U+DSUlj1xtusHpk7N0YPUHNlHWu0hEiuOwLpVVOAi5GpN31btGXRBC3whkx2HqthbEqLgRIBHX91AXLTC4J7Nye/XmhaZCT939n5RBHUTr1/H4wvH271+/dRtXZim1huipKwK79L3IuPkv02MQyLWIRHnELRKFMSlScQ6IT5KwhUtkxwrIytODpeUEZ/kwhMUeEICT8DCG7Q47rPwuC08ARNPwKShaJAsQVyUQpSikJkURfsYFVesgitGxRUt43KaJDgsNKGDGQKj7mMGwdDDI3FqQkimwbBMhav69+bzvX7WFHixBMQ5Zb4+Vs2jf/0XbbISuTa/Az3bZyDLjeunOlHpY86izez6ugJNDTubZokWE3MqyfDuR9p4ABCgRoXzI0xkVUPLaImWHG4ByDEJ4TUHDAMR8mHWVIBlImQJSXWiutJQ49PQ4hKRo+P5escuYlp2+SFmYPMfxHnnEGRHFEKS0Up2IJVsR2jRWGmtiW3ahbgBI0GSKS89TtnBvTjdX5Pt3opatRkdFTMumwubt6Nf/7bsLqykXI9hW6GP99ceYfmaI7TKjOXyvq24uHc2cdH/mSMtdMPCHzQIBA38IeOb70EDf9AkEDLwBwx8AR1fQMdfty0td7Pw81X4Agbeuo9hfn8MXlMgSpVwquBUIYYgWalO4hwScY5wTTw6SsGhaRjIhEwFrw6eoEVtwKLQY1JbZlLrNwkGg0RJ1TgkA006goaBQzKJUsKFeEtNEOuwiIu1iFFNohWLKDl83CGFPwoGkmkQ8NYQpcjgC0FNuNCXzFCj76FAIhOBKP6SK1zNye+Zw8baDN4/6MCyINYBh45W8/CL62mb5eLaoR3o2T79tAMYLEvw3pqvefW93RimhSxBPD6uaXGYNvp+5EPVWLIKioZkhpBkiGrTi3I5kZYdumCZBiIYQOiB8DxCgKQ6UBNSUF1pKDEu1NgEuz/A5vxzCEpMAr4+15Kalop7+xrE0R04ju9DPrYLoWiI5JYkNetMcp9+COVijld4KS7Yj1x2kFbVxTg9X8PeD2mpJNAmvTUDumZRJqez5pDF7lIvf/nHTl5avouLOqYxtF8O3dqmNboG+GMihMAbMKjxBKn2hKjyBKnxhkfK1HhCdaNnwsfc1V6st4/jD5pn7Ej9Ng4l/NEUCUnoxEUZxKqQFAfOxPB+pwZORUKRJRRFRpYlVFkGWQYEpmmFP4ZJdZUPhwhi1ASoDgWoNgI4hU6UHCJa0omWQqRJOi2kELGKTrQcPuaIDaHEWmdWWK/7nLxHkhweW6+oINdtT461j4pHyCpC1rAkBRMpHHaxTIRhIFk6ijCQLR3ZCiEbOhgBMIJICCxFQ3LEIjwVONyF9ANyk+M47sjmnxXpbNEzkTQHXxdX8dALX9E2K5HrhnakZ4f6i8IUn6jl6cVb2HvEjaYIWiul5Cfsp61UiFRrYarRCCRky0BNyiS6ZVe0jJagB5EOHyJUVYoka2gJySiJrVHjXKgxifY00zYNct45BABFVclsnUNm6xxCwSC1FeVU7FyHfmgLzspDKGUF4cIiqTlNmnSiSc+OCEcPisqDbDl4FP34AVqEjtLy6G6iSrbSBBihxHJZRjOqHRlsdieyfq/J2p0nSIrTuPTCbPJzW5GZEvuDdTctQWV1gNJKLyfcPrburGbj4a24awPUeENUe0LU+nRqfPppC3eHAvEOk3jVIF41SFT8xDkVtLhwTVmVTFQsNMlCxUSVLJS6fapkomChYCFbBhImsmVi6kE0GSRhIukmUshEFhayMFEwv5FTt1UxadBPnozZO+o+J/MtO0B1ImlRSA4nqHGgOUENfywtClQn5e5qUjOaRAp6S1IRsoKQFCxLYFkmlmmAaSIMHfQAkhkEI4hshBBGkGBNBXIwiGJUIRsBFN2Pw2qgM0GWkZ2xyM4Y5LiE8PeoGCTVQVXxQRy+CiQ9CIDliMVCIdNbwNWO3Yx3SBRbaWwLNOWg1YzDRwUPvvAVbZrGM3FYR3q0z+CLXTWsXryKaClInvMgA6IKSFeq6xyVE8nwo1g6UdmdcGZ1RNacCMvA8nvRUjLRMx0k9uqPHBVrdwLbNIqf1CEsX76c5557DsMwmDJlCtdee22943v27OG+++7D6/XSu3dvHn74YVT1/9dHOZxOUpo2I6XpOAxjNLXuKtx7NhAo2Iij4gBK5YeIXR9CQiZZGe3J7noBVp/L2bDrMJvVFNwnjqNWFZEeLKFV6CgtlP20AEYkaJTKaXwdSmPbF4W8+9leWmWlkJ/bioHdmxPlbDifQgiqPSFKK72UVvoorfRRUu6htNxLqduLp8ZDjPATL/uJlwLEyEEk2SBZMWgm60RJBk7NQEs0cGCgYaBJBprQUflmnyJ9q0btAARw+sE49bCEhI6CLhQMFEwhY6BgCRUhyQgpXGBZioIlq5iygq6oyIqCrKrIioqiqqiqiqKpaJqGoiqUuWtIa9I8XMjLSt2QSBlTWEhGCEvXEUYAoYciMXxhhCAQRPLWIpk6Tm8NhqcQydTDH0tHrttKohGtCCBGVlGj48IFaVRSuMB3xiI5opAc0ciaE0nVkBQNhAWWiUW4E5a6cjcUlEjJHIDw12BUnUCvPIbhLgUEQlIwHbE0FX6y5K3AVmqsKPYbTdlb0ZQnXi5DcsaQop9gQsw+umuHcUgmhhaHZShhR+yMJTqnK47U5kiKhuyIQkvNxpHSBCU+CVnRsHyb7I5gm7PiJyt9S0tLmTt3LsuWLcPhcDBhwgT69OlDmzZtIufceeed/OEPf6B79+7ce++9LFmyhIkTJ/5UKp0RVVVJSkslKW0YZv/L8dbUUnlgJ949a1HLCtAKPoeCz5FikujoTCMhMxtax4CWhY+2FHoc7Kj0YbhLiPUdI0sqZYCyjUEJYAqJo9XJHHwvncfeSSe9Q1diYxWKavfhLi2npqIMf1Ulhq+aaBEgXvITLweIlwJ0kwPEy37ipABq4ukLNUPIBIVKyFIJohESKn6hUitFYUrxCEkNx5plBUlRkWQZRVEI6SGio2OQJAlJDod1ZElClmVkOTw0U5ElFEmgyOGZMB2YREsWMgIhLKqr3CTExYEwEWY4tIIww/MlWAaIUHifbkHIjBSkUt1WCIsUI4RUsh7pO7XxM9ZtJRkUFUlRUYWEpsUgOTVkJRpJ05BkDUlVw1tFRVIUUDWkuvuArICsImkOkFVKjpfQvGlzhCRAgAQISQoXvJoDSXWEnYPmRNaiwg5D1UBWkdVw2OmQtJX4Nq0wPJUYNRWY3hosw49RWYpRdRy9sgQR8gFgKtGYahydpaP0dnyNFSNRLWJIivESFCo1ShKpVjmK7kVKakps87ZorgzUhBS0tGw0VzpqrD39is0P5ydzCGvXrqVv3764XOE1VvPz8/nwww+59dZbATh69CiBQIDu3bsDMHbsWObNm/ezOoRvoygKCUkuEi4cABcOwOfxUHnkANU7vkAp3UuiuwDJvT9yfhzQqe4DYDk1KqQktpmd8ZgqmhUgFTf9nfsZLO2Bos/xWxrR8neGT9b16xlCptaKolZEU2tFccxMJIiDkNAwUDElBZCQhIFTNnEQjrHHCD+xcpBoKUSK7CFKCqFI3wkdWXWfb3PqKM4zEpYqIUkSiYBUpYQLzrqaPZIUqeUT2V+3lcPx+8i5soQ/EMIZH4+kqMiKI1yIqyqyqiHJdQW5ooa/q3VbWQVFpq7Y5tixElKzWtQV9gpI4a2kqHV9BWFHGHYMWp1zVJAVDSQZSZYpkvYQ37VH+HxVQ1bDjuKswi5R8TgzWuCsm9TNsiysgBfL78HwujFqygkdP0Kw/Aih8mO4asoBk0IjheMihWjhY79oQhftCMmyFzI6ktCiA9HN2uBIbY6akGJ3Atv86PxkDuHEiROkpX2zLF56ejrbt28/7fG0tDRKS0t/KnV+MDFxccR07k7zzt0J+P189eUaWmc1D9cAa6uw/NVYAS8i6IeQD3Q/8bqfzkY1khFENoOYhsGxYBJuMxYLCVUyMUQ45CKkcJEmY6FJJk7ZIloxiVEMUmQfDsWLJMvhMeWSUhdHVggEdaJj48K1XCUGSU74pgVwsjZcF0+XFQVZ05AUB5KqIWtOZM1J0dGjtGzVCklWkSUJZBlJVsLjMGUFWZaRZAlZVsIFMCdbEuH9ALt376ZTp05A/Y4B6XSFaL394e+7du2gzQXdwi2ViDOp28p1hb4sISNHdDu5D8L6HNq0iaRevX7Qs7YKT/zoSzrKsowcEw8x8ThSmoR3dgEr5Mf0e9BrK/Ed2IJyZDfNy4sx/LXoThexrfqTfEE/YjOzw6Ege3Uxm5+Qn8y6LMuqN4xOCFHv95mON4adO3f+2/pt2rTp304LEJ+cRJnXC5ITEjLCnwawLAthWViWQJgm0XqQ6JAfKVSLFAqGC3dNCxfwiiNca1WUcKEshR2AV5LxyQqSItcVlOHXqk4Wtt4flBOQs5MoNDlNH8JpD9QnIYOdxRU/TJHYFHZ8XfzDZPDDn+3PJiO+LXRpC5aJ4a1GkjVCsfG4PcCBQqDwp9fBlnFeyfguP5lDyMzMZOPGjZHfZWVlpKen1zteVlYW+V1eXl7veGPo0qULTqfzrHXbtGkTvX5gLdKWYcv4pcv4Jehgy/hlyQgGg99bkf7JxqL169ePdevWUVlZid/v5+OPP2bQoEGR482aNcPpdEa83DvvvFPvuI2NjY3N/y8/mUPIyMhg5syZTJ48mdGjRzNixAi6du3KTTfdxI4dOwB44okneOyxxxg6dCg+n4/Jkyf/VOrY2NjY2JyBn7SHauTIkYwcObLevhdeeCHyvUOHDixduvSnVMHGxsbGppHYry/a2NjY2AC2Q7CxsbGxqcN2CDY2NjY2wH/o5Haibrm/UKjxUxJ/l2Aw+IP1sGXYMn7pMn4JOtgyfjkyTpaZJ8vQ7yKJ0x35BVNbW8v+/fvPfKKNjY2NzSm0a9eO+PhTJz78j3QIlmXh9XrRNO2s3262sbGxOV8RQqDrOrGxDU+J/h/pEGxsbGxsfnzsTmUbGxsbG8B2CDY2NjY2ddgOwcbGxsYGsB2CjY2NjU0dtkOwsbGxsQFsh2BjY2NjU4ftEGxsbGxsgP/QqSsay/Lly3nuuecwDIMpU6Zw7bXXNnheaWkpV111FV9++eUpxzweDxMmTOD555+nefPmp73WjBkzaNGiBZ999lnk3LVr1/LYY48RDAYZNmwYM2fOPG36k9d58sknmTVrVkTGPffcw+rVqwkEAmRnZ3PrrbcyZMiQ0+Zj6NChNG3aFIC8vDxcLhcLFy7E7XYTExPDhAkTTqvHggULWLFiBZWVlViWRWpqKnl5efz3f/93o/QwTZPZs2ezadMmysvLkWWZpKQkxo0bh67rjdbj6aef5qOPPqKysrKejKlTpzZaj4cffpjNmzcjSRK/+tWv2L17N263m9atW/O3v/2NyspKoqOjueyyy3j44YdR1dP/FRYuXMhHH31Ely5dcLvdPP744yxYsIDFixdTVlZGhw4dGD9+/GntC+BPf/pTJC3A559/zhNPPEFRURGKotC0aVNkWWb27Nl069Ytks7n8/Hggw+ya9cuSkpKSExMJDExEYDZs2dTWFjYKBu/9NJLiYuLo7CwEMMwyMrKQtM0Zs+ezRNPPMGuXbuIjo4mNTX1FB0AdF2nT58+uFwuKioqsCyL8ePHM2vWLG6++ebIQleXXHIJTz/9dIM6rFq1igULFlBeXo6u67hcLvr378/vf//7Rtv5kiVLePbZZykvL8flcpGSkkJxcTFt2rShpKTkjPb15ptvsnDhQqqqqnC73ei6TmJiIrquM2rUKPx+/xn1WLBgAZ988gkA1dXVHD9+nOTkZEaNGkVSUlKj7evLL7/kz3/+M5Zl0alTJ/7whz/gcDiA8IJh8+fPp6ioiGnTpnHXXXexYMEC3nrrLRISEgDOaHP/FuIc5fjx42Lw4MHC7XYLr9crRo4cKQoKCk45b/Xq1eLyyy8X7dq1O+XY1q1bxYgRI0Tnzp1FUVHRaa/15ptvip49e4rc3NzIuX6/X+Tl5YnCwkKh67qYNm2aWL16dYPpT16nY8eOIj8/v971hgwZIvr16yeuu+66783v6tWrxcCBA0W7du1EMBgUoVBIjBo1SgwaNEgMHDhQHD58WNx0003iyiuvbFCPNWvWiKuvvlqsXr1ajB8/Xlx33XVixYoVYvLkyeLjjz9ulB5LliwRM2bMEOvWrRPjx48XQ4cOFZs2bRL9+vUTQ4YMaZQe69evFxMmTBBr164V48ePFxdffLHYvXu3GDx4sDh48GCj9Fi2bJmYOXOmEEKIiooK0aNHD3HRRReJqVOnihEjRohhw4aJTZs2ienTp4vJkyeLN95447SyCgoKxMCBA8XIkSNFnz59xF133SWEEOLGG28UI0aMaNBuvsvatWvrpa2urhZ9+/YV+/fvFwMGDBDPP/+8eOSRRxpMO2fOHHH33XcLwzBE//79xZAhQ8Tx48eFEI238crKSpGfny8syxIDBgwQuq5HjlmWJXr16iUuvPBCMW/evNPmYceOHWLixIliwIABoqSkRIRCIXHNNdeIO+64Q3Tr1k0cOXJEnDhxQnTt2lX84x//OCV9YWGhGDBggNi0aZMYMGCAuOqqq8TKlSvFNddcI1avXt2o57phwwaRn58vampqhMfjEUOGDBErV64UAwYMEPn5+Y2yr2+zf/9+MWTIELFhwwYxZMgQUVFR0ej/mxBC+Hw+0aNHD3HFFVcIj8cjhg4dKgYPHtxo+xo0aJA4cOCAEEKI2267TSxZsiQit3fv3mLcuHGiXbt2Yty4cWLNmjVi+vTpYvPmzWfU64dwzoaM1q5dS9++fXG5XMTExJCfn8+HH354ynlLly5l/vz5DcpYsmQJDz744Peu9VxYWMjbb79N8+bNycvLi5y7fft2WrRoQVZWFqqqMnLkyAav/+3rOJ1OZsyYEZFRXV1NUVERSUlJ7Nixg3nz5mFZVoMyli5dyv333w+Aw+FA0zSioqJo0qQJrVq1okWLFuTl5RETE9OgHmlpadx99900bdqUe+65h7Zt21JaWkpOTg5FRUWN0qNt27bccsst9O3bl4ULF5KdnU1BQQHBYJBOnTo1So+LLrqI1157jdzc3EjtKRgMYpomiqI0So8xY8bw5z//GYCDBw8SDAa56aabqKqqonv37oRCIXr27MnAgQNRVfW0zyUUCvHAAw9w4403UlxczK9//evIsX/9619omgaEa+qnm2isqqqKuXPn1kt7+PDhSIsAYOXKlSxatIiFCxeekn7Pnj3k5+dz+PBhJEmitraW8ePHs3Dhwkbb+I4dOxBCMGbMGCorKxk9ejRXXnklCxcuZM2aNQQCAWJjY0+rw0kZRUVFmKbJjBkz2LJlC3PnzuXo0aNkZ2eTnZ1NWloabdu2ZfHixaek/+STTxg+fDhbt25l+PDhPPPMM/Tq1Yu5c+dGbOxMz3XFihVMnDiR+Ph4YmNjefnll3nhhRfo27cvHTp0aJR9fZuHHnqImTNnMnfuXGbOnNlo+zpJIBDA5/Nxxx13oCgKHo+Hrl27Ntq+TNPE4/FgmibBYDCyPrxpmvj9fsaMGQOAYRg4nU527tzJX/7yF0aOHNmgzV1yySXfm9/GcM46hBMnTpCWlhb5nZ6eTmlp6SnnzZ8/n3bt2jUo449//CO9e/c+7TUMw+C+++7j4Ycf5rLLLouEas7m+t++TlJSEl27do3s/9Of/kSHDh247bbb6NSpExs3bjztCnPz58+v17Q9fPgwBw4coLCwEJfLRTAYZNWqVQSDwQb1aNu2Ld27d6dt27a4XC5WrFhBmzZtWLFiBfv372+UHt27d4/cyx07dvDVV1/x6KOP0rVrV7Zs2dIoPQA0TWPevHmMGjUKl8vF9ddfT25uLs8//3yj74eqqtx3331MmjSJ4cOHk5ycjMvlYt26dSQlJUX08Pl8p9XjySef5KqrruLjjz+mefPmkab6+++/T2JiIv/zP/8DQE1NDc8++2yDMh544AFmzpwZSQvQsmVLjh8/zo4dO8jNzaVPnz4ALF68mDVr1tRL36lTJz788EPcbjc9evRAlmVGjx7N4sUP5uIrAAAPkElEQVSLOXbsWKNsLBQKMXDgQO655x4GDx5MdXU1Dz30EIsWLeKBBx5g4MCBXHHFFRG539UBQJIk0tLSGDp0KA6Hg+uvv56XX36ZtLQ0fD4fhmFQWlpKUVER5eXlp6Q/cuQIpmny+uuv88477zBp0iSmTJnC3/72N5555plGPdcjR45QWVnJDTfcwKhRo3jppZfQdZ2xY8eelX1BuMIYCARITEwkEAgwbNiws/q/QdiRt2/fnt/+9rfk5eWRlZXFzp07G21fDz30EJMmTWLgwIG43W6GDh0KwPr16+nQoUMkvNisWTPat29Px44dufPOO3n77be/1+Z+COesQ7Asq97Ed0KIH30ivJOFcJs2bX7062/atAmPx8Pbb7+Ny+VCURQmTZrE559/fsa0BQUFTJs2jfvuu49+/fqxZs0abrzxRnr16oWiKN+rx8m0U6ZM4f7772fMmDH4fL6z0mPDhg3MmDGDZ599lq+++grTNGnTps1Z6TFjxgzWrVuHy+Vi1qxZ7N27l717956VHt27d+fqq69mz5497N+/n/T0dPLy8igoKIjooapqg3qsWbOGkpISLMsiOTk5ErcPBAL89a9/5YMPPiAnJweAadOmNajHm2++SZMmTcjNza23PyEhgT/96U+88cYbHDx4kKysLBwOB+PGjTtFzvTp03E4HDz88MMADBo0iISEBMaNG8e8efMaZWOXXXYZv//97+nTpw8LFiwgPz+fHTt2kJaWRrNmzXjuuedwOp1ER0c3qAPAhAkT6NixI+vXr2fevHkMGDCAr776ivbt2yPLMmPGjOGPf/wjnTt3RlGUU9Kbpsm6desiFZ/09HQmTpzIP//5T/bv39+o52qaJps3b2bevHm89tprvPvuuwwYMIDc3FwuvPDCs7KvxYsXM3Xq1Mh2zZo1Z/1/e+WVVwgEAnz22Wd88cUXpKam0qJFi0bZV1lZGU888QTvvfceX375Jd26deOxxx6jrKyMOXPmEAqF+OyzzwCQZZnFixfzwgsvkJOTg6qqEZsrKSlh1KhRjBo1ihMnTkS+m6Z5Wr2/j3O2UzkzM5ONGzdGfpeVlZGens6oUaOAsIFfc801ZyVz0aJFkebwhAkT+Oijj3A4HLz11luRWtHJ+cYzMzMpKys75fqffvop8+bNA8JNvNtvv73Ba3366afs2rWLwYMHo6oq5eXlvPTSS2RkZJyix3fzcf3113PvvfeSl5eH0+mkvLycV155hRdffJH4+PjT6rFp0yZmzJjBNddcw+uvv869997Ll19+eVZ6fPzxx9x///3MmjWL/v37A+HO7ZOdsmfSY8SIEYRCIRwOB6FQiMsvv5yDBw/icDjYt29fo/S44IILiIuL44MPPqCsrIyKigqWLl2KJEnk5uaSlpbG66+/zosvvojL5UKSJHbs2MHvf/97ALp06YJlWRQUFPDFF1+g6zqGYbB9+3YkSUKSJK644gpcLhcAM2fOJDY29pS8bN26lbKyMkaNGkV1dTU+n49HH32Uu+66i8zMTO66667IzJNZWVkIIVBVNWKjM2bM4IILLmDmzJkcPHgQXdf561//yiWXXEJxcTHNmjVr0Ma+ez8yMzNJTU0lGAyi6zoQbkHt27ePQCDAZZddht/vj+Q9JyeHp59+mlWrVkX0qK2txeFwkJubS3JyMrIs07NnT9auXUtaWhpvvPEGAKNGjSIjI+OUe5Gamkpubi5xcXEkJibSqlUr9uzZg6ZpjX6uqampdO7cmdjYWEKhEIZh4HA48Hg8XHLJJY2281AoxIYNG3jkkUf44x//yOOPP84jjzxyVnZeWlrKiRMnGDduHCkpKQAMGzaMV155pVH2NWDAANq1a0d2djYQ7iC+44476NSpE2VlZSiKwrRp0wDYtWtXpBN93LhxABFbadKkCe+8804kfye//9v8pD0UPyMnO9wqKiqEz+cTV155pdi2bdtpz/++zsHBgwd/b6eyEELMmzdPzJs3L3JuIBAQgwYNEocPHxaGYYgbbrhBfPDBB98r42Tak9s9e/aIQYMGiU8//VRMnDhRTJs2TSxfvvy06Y8dOybatWsn1q5dK4QQYs+ePWLEiBFi0KBBYteuXeLyyy8X//Vf/9WgHseOHRN9+vQRy5cvF3369InIOCmnMXps27ZN5Obmitdff12MHTtWBINBEQwGI53DjdFj9erVYuzYseKTTz4RY8aMEVOmTBH/+Mc/xNSpU8X8+fMbpcfChQvFzTffLEzTFLW1tWLEiBFizpw54te//rW48sorxfDhw8U///lPkZ+fL6ZPny5eeOGF730uX331lbjuuuvEW2+9Je666y5RUVEhLrroIlFYWCjatWsn7rnnHvH8889/r4yTaYUQwjRNMXDgQLFs2TIxevRoMWPGDPH000+LkSNHntJp+Nprr4mHHnpIrFq1SgwdOlT069dPHD9+XIwcOVJs3LixUTb++uuvi+nTp4uVK1eKESNGiIsvvljs379fjBw5UixYsECMHj1azJkzRzzxxBMN6iCEEI8//ri45ZZbRH5+vti2bZsYOHCguOGGG8SMGTNE9+7dxeHDh8XOnTtF586dxbJly05Jv3XrVpGfny/WrFkj8vPzxbRp08TixYvF9OnTxSuvvNKo5/r++++LiRMnimAwKDZu3Ci6d+8u1q5de1Z2LoQQ27dvFxMmTIhsT3I2/7cVK1aISZMmiSuvvFJ4vV5hWZa47bbbRG5ubqPs68CBAyIvL0+UlZUJIYR47rnnIvbxxRdfROS2a9dO3H///eKhhx6K2JxlWQ3a3ODBgxvU9Ww4Z1sIGRkZzJw5k8mTJ6PrOuPGjasXn/+pcTqdPP7449x2220Eg0Hy8vIiMcLG0qFDB371q1/xyCOPUFFRweTJkxkxYsRpz3/ppZcAIrFHCMcfT3ZEJiQknFaPl156iWAwyB/+8Aeqq6uZPn06LpeLpKQkJkyY0Cg9nnvuOUzT5M0336SsrIxevXqRmprK2LFjkWWZN99884x65OXlsX37dubMmYPb7ebYsWO8+OKLXH755dx6660kJSWdUY8JEyawb98+Ro4ciSzLXHvttTgcDkpLS+nSpQvLli3j5ptvjnROTp48uVHP4yTJycnMnj2b3/zmN0C4tjZ16tRGpz85vPTJJ5+ktLSU4uJi9uzZw7XXXkuPHj3qnTt+/HjuvPNO5syZQ01NDQ6Hg8mTJzNx4kR69erVKBs/eT+efPJJKioqcDgc3HrrrUycOJEpU6ag6zqLFy/GsixuueWWU3QAuOWWW7j33nvxer1ce+21JCcnk52dzZ133smNN94YeQ6XXnopo0ePPiV9t27duPHGG3n00UepqamhsrKS4uJi+vfvz6RJk1BV9YzPdfjw4RQWFjJmzBiqq6tp3rx5JBw3dOjQRtkXQFFREZmZmZHtSc7m/1ZUVETXrl1JSEhg7NixaJrGBRdcwNVXX8277757RvvKycnh9ttvZ/LkySiKQosWLZg9ezYAAwYMYPfu3YwdOxb4pq+yb9++/OY3v0HXdXr27HmKzZ1s0f0Q7PUQbGxsbGyAc7hT2cbGxsbm7LAdgo2NjY0NYDsEGxsbG5s6bIdgY2NjYwPYDsHGxsbGpo5zdtipzfmNrusMHjyYDh068OKLL9Y7tnTpUv7+97/j9XoJhUJkZWVxxx13RGb4vPvuu1mzZg3Jycn10l111VWRIYT79u3jmWeeYe/evSiKgizLjBs3juuvvx5Jkli/fj033XQTrVq1iqT3er20adOGxx57jKSkJCA8fHHOnDns2LGDmJgYHA4HEyZMiLyAVFxczMiRI9myZUs9XV566SUKCgp4/PHHmT9/Pm+88QYZGRlIkoRpmqSkpPDggw/Wu76NzZmwHYLNOcknn3xChw4d2LlzJwcPHoxMMzFnzhw2bNjAU089RbNmzQBYt24d06dPZ9myZZH5qK6//npuuOGGBmXv3buXqVOn8sgjj0Tegq2srOTmm28GiIwPz87OrvfmqGma3Hbbbbz88svMmjWL4uJirrvuOu644w7mzJmDJEmUlpbyu9/9jiNHjjBr1qxG53f48OE88MADkd+vv/46s2bNYtmyZY2WYWNjh4xszkkWLVrEpZdeyvDhw3n11VcBKC8v59VXX+Xpp5+OOAOA3Nxc7r777sj0DWfiqaee4sYbb+Syyy6L7Dv5slpqaupp03k8HiorKyPzIv3v//4vI0aMYMyYMZH5bjIyMpg7dy6vvfYaJ06cOOt8fztPhw4d+rfT25yf2C0Em3OOAwcOsGXLFubNm0fnzp2ZNGkSM2fOZOvWreTk5DQ4nfl336595ZVXePfdd+vt+/Of/0z79u3ZuHFjg4uvtGvXrt7MuYWFhYwaNQrDMKisrCQzM5Nhw4YxZcoUALZs2dJgKyA1NZWcnBy2bdtGx44dzzr/hmGwdOnSyCyqNjaNxXYINuccixYtYvDgwSQlJZGUlETz5s1ZsmQJrVu3rneex+OJrDjl8/kYNmwYv/3tb4HvDxmJ78wq+uijj7J+/Xosy8Lv97Ny5UqgfsjorbfeYu7cuQwbNiyyjgKEC++GCIVCSJIUWTPhu1iWVe/YBx98EFm1TNd1OnfuzCOPPHL6m2Rj0wC2Q7A5p/D5fLzzzjs4HI7IgiEej4eFCxeyePFiDh06hNvtJikpibi4uEiBPX/+fNxud6Ou0aNHD/71r39FWgP33nsv8E0HcENcddVVbNu2jdtvv50lS5agqio9evRg/fr19UJPQGR+o5OztgYCgXoLqABUVFREZluFU/sQbGz+Hew+BJtziuXLl+Nyufjiiy9YtWoVq1atYuXKlfh8PjZv3szkyZO5/fbbOXbsWCTN0aNH2bx582lr499l1qxZ/OUvf2H16tWcnAosEAjwySeffK+M3/3ud5SUlESmiv7Nb37DihUrePvttyPnHD9+nN/+9rdcc801ZGRkEBsbS69evSL9IBB2GB9++CF5eXlndW9sbM6E3UKwOadYtGgRU6dOrbdIS0JCApMmTeKVV17hrbfe4t1332XWrFn4/X5qa2tJTExk+PDh9RYsb6gPoVu3bsyePZuOHTvy6quv8swzz/Dkk09Glvns06cPS5YsOa1uCQkJ/O53v+Oxxx7jiiuuoEmTJvz973/nqaee4vnnn0dVVZxOJ+PHj+fqq6+OpHviiSd49NFHueKKK5BlGUVRmDFjht1HYPOjY892amNjY2MD2CEjGxsbG5s6bIdgY2NjYwPYDsHGxsbGpg7bIdjY2NjYALZDsLGxsbGpw3YINjY2NjaA7RBsbGxsbOqwHYKNjY2NDQD/B84CXaU5NC8LAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_style(\"ticks\", {\"xtick.major\": len(xticks)})\n", + "with sns.axes_style(\"whitegrid\"):\n", + " chart = sns.lineplot(x = 'AGEGROUP', y='Numeric', hue = 'COUNTRY', data = df_pd)\n", + " chart = sns.lineplot(x = 'AGEGROUP', y='Numeric', hue = 'SEX', data = df_pd)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C__Ad1PW3WHY" + }, + "source": [ + "##Challenge 2\n", + "\n", + "Plot death rates as a function of age by calling the `death_rate` method. You can either use your choice of countries or use `countries=['ETH','ITA']`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "OdeXmt469bHs" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eG9_Gn6LOe8d" + }, + "source": [ + "##Challenge 3\n", + "\n", + "Plot the probability (%) of dying between age 30 and exact age 70 from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease. To obtain the data call the whoapi as follows \n", + "```python\n", + "df_pd_ncd = hapi.prop_dying_ncd_3070(countries=[\"ETH\",\"ITA\"])\n", + "print('dying prob df.shape',df_all_ncd.shape)\n", + "```\n", + "\n", + "Your plot should look as follows (but the contries being your country and multiple other contries you want to compare)\n", + "\n", + "![image.png](data:image/png;base64,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)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TXIcJHyZVB1p" + }, + "source": [ + "# COVID19 Data Analysis \n", + "\n", + "Below is a python class that you could use to download latest COVID19 data from John Hopkins Server for any contry in the world." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "WkPRlAiBY_4F" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4Gt-FuY5QQdj" + }, + "outputs": [], + "source": [ + "#----- \n", + "#-----\n", + "def fix_region_name(df, pairs = [[\"Mainland China\", \"China\"]]):\n", + " # fix region names\n", + " for p in pairs:\n", + " df['Country/Region'] = df['Country/Region'].str.replace(p[0],p[1])\n", + "\n", + " return df\n", + "\n", + "#-----\n", + "def merge_df_data(df1,df2):\n", + " return pd.merge(df1, df2,how='left' ,on=['Province/State','Country/Region'])\n", + "\n", + "#-----\n", + "def str_add_func(*args): \n", + " out = []\n", + " for x in args:\n", + " if isinstance(x,str):\n", + " out.append(x)\n", + " \n", + " return '_'.join(out)\n", + "\n", + "\n", + "class covid_data():\n", + " '''\n", + " Python class to obtain global COVID19 data from \n", + " John Hopkins GIT repository. This data is updated daily, \n", + " and the most upto date information available on the web. \n", + " '''\n", + " def __init__(self,**kwargs):\n", + "\n", + " #\n", + " nrow = kwargs.get('nrow',None)\n", + " self.confirmed, self.dead, self.recovered = self.get_csseg_data(nrow=nrow)\n", + "\n", + " @staticmethod\n", + " def create_ts(df):\n", + " ts=df\n", + " columns = ts['region']\n", + " ts=ts.drop(['Province/State', \n", + " 'Country/Region',\n", + " 'Lat', \n", + " 'Long',\n", + " 'Population'], \n", + " axis=1).set_index('region').T \n", + "\n", + " ts.columns = columns \n", + " ts=ts.fillna(0)\n", + " #\n", + " ts.index.name = 'Date'\n", + " return ts\n", + "\n", + " def search_agg(self, name,col='Country/Region',ts=True):\n", + " \n", + " if not isinstance(name,list):\n", + " name = [name]\n", + "\n", + " out = {}\n", + " for k,v in {'confirmed':self.confirmed,\n", + " 'dead':self.dead,\n", + " 'recovered':self.recovered}.items():\n", + "\n", + " #pd.columns(columns=)\n", + " df_list= [] \n", + " for n in name:\n", + " df = v[v[col]==n].set_index(col).filter(regex='/20')\n", + " df_list.append(df.sum(axis=0))\n", + "\n", + " df = pd.concat(df_list,axis=1, sort=False)\n", + " df.columns = name\n", + " out[k] = df\n", + "\n", + " # if ts: \n", + " # out[k] = self.create_ts(df)\n", + " # else:\n", + " # out[k] = df.T\n", + "\n", + " return out\n", + "\n", + " def search(self, name,col='Country/Region',ts=True):\n", + " \n", + " if not isinstance(name,list):\n", + " name = [name]\n", + "\n", + " out = {}\n", + " for k,v in {'confirmed':self.confirmed,\n", + " 'dead':self.dead,\n", + " 'recovered':self.recovered}.items():\n", + " if ts: \n", + " out[k] = self.create_ts(v[v[col].map(lambda x: x in name)])\n", + " else:\n", + " out[k] = v[v[col] in name].T\n", + " \n", + " return out\n", + "\n", + " @cachetools.func.ttl_cache(maxsize=128, ttl=24 * 60)\n", + " def get_csseg_data(self, nrow=None):\n", + " \n", + " url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master'\n", + " path = f'{url}/csse_covid_19_data/csse_covid_19_time_series' \n", + "\n", + " # \n", + " \n", + " url = f'{path}/time_series_covid19_confirmed_global.csv'\n", + " confirmed = fix_region_name(pd.read_csv(url, nrows=nrow, error_bad_lines=False))\n", + " #\n", + " url = f'{path}/time_series_covid19_deaths_global.csv'\n", + " dead = fix_region_name(pd.read_csv(url, nrows=nrow, error_bad_lines=False))\n", + " #\n", + " url = f'{path}/time_series_covid19_recovered_global.csv'\n", + " \n", + " recovered = fix_region_name(pd.read_csv(url, nrows=nrow, error_bad_lines=False))\n", + " print(confirmed.head())\n", + " #\n", + " return confirmed, dead, recovered\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XhnWba45PRCb" + }, + "source": [ + "## Challenge 4:\n", + "**Compare the number of COVID19 cases, deaths, and recovries with respect to your neighbouring contries**\n", + "\n", + "After successfully doing this and the previous challenges, share your final nice plot on twitter by adding #10AcademyCovid19Response hashtag. Include your twitter post links in this notebook, or as a comment when you submit. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CJIO5KIvW5kU" + }, + "source": [ + "**Your task is to produce a plot like this for your country and the neighbouring countries for #cases, #deaths, and #recoveries. Any improvement you can make is a plus.**\n", + "![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BDLlyrWsR79M" + }, + "source": [ + "## Example\n", + "\n", + "We can obtain the COVID19 number of confirmed cases, number of dead, and number of recovered counts for any subset of countries (the example below is for Ethiopia and its 5 neighbours) using the above class as follows: \n", + "\n", + "```python\n", + "cd = covid_data()\n", + "cd.confirmed.head()\n", + "countries = ['Sudan','Ethiopia','Djibouti','Kenya','Eritrea','Somalia']\n", + "mm = cd.search_agg(countries)\n", + "```\n", + "The output mm is a python dict of #confirmed cases, #dead, and #recovered dataframes i.e `mm['dead']` is a data frame which contains countries as columns and date as rows.\n", + "\n", + "You can transform the `mm` dict of dataframes into single data frame by using the following code \n", + "\n", + "```python\n", + "for ix, ctype in enumerate(['confirmed', 'dead', 'recovered']):\n", + " df = mm[ctype].stack().reset_index()/\n", + " #print(df.head())\n", + " df = df.rename(columns={'level_0':'date','level_1':'country',0:ctype}) \n", + " if ix==0:\n", + " df['date'] = pd.to_datetime(df['date'])\n", + " dfall = df\n", + " else:\n", + " dfall[ctype] = df[ctype]\n", + "```\n", + "If you issue `print(dfall.head())` you should get something like this\n", + "```\n", + "date\t country\t confirmed\tdead\trecovered\n", + "0\t2020-01-22\tSudan\t 0\t 0\t 0\n", + "1\t2020-01-22\tEthiopia\t 0 0\t 0\n", + "2\t2020-01-22\tDjibouti\t 0\t 0\t 0\n", + "3\t2020-01-22\tKenya\t 0\t 0\t 0\n", + "4\t2020-01-22\tEritrea\t 0\t 0\t 0\n", + "```\n", + "\n", + "Once you have the data in the the right data frame, use groupby to get data per country, and use seaborn to do the plotting. Show plot for the all the three i.e. cases, deaths, and recoveries " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "rxyFGqvSSApe" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "0wvVpvC0aJfo" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "tenx_covid19_analysis.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/twitter_mining/twitter_mining.ipynb b/twitter_mining/twitter_mining.ipynb deleted file mode 100644 index a6460ee..0000000 --- a/twitter_mining/twitter_mining.ipynb +++ /dev/null @@ -1,635 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "rziXcUSBskS3" - }, - "outputs": [], - "source": [ - "#from google.colab import drive\n", - "#drive.mount('/content/drive')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Twitter Developer Account\n", - "In order to use Twitter’s API, we have to create a developer account on the Twitter apps site.\n", - " * Log in or make a Twitter account at https://apps.twitter.com/.\n", - " * Create a new app (button on the top right)\n", - " \n", - "\n", - "\n", - "Fill in the app creation page with a unique name, a website name (use a placeholder website if you don’t have one), and a project description. Accept the terms and conditions and proceed to the next page.\n", - "\n", - "Once your project has been created, click on the “Keys and Access Tokens” tab. You should now be able to see your consumer secret and consumer key.\n", - "\n", - "\n", - "\n", - "You’ll also need a pair of access tokens. Scroll down and request those tokens. The page should refresh, and you should now have an access token and access token secret.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import necessary modules" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "UI2fIQFxrNLB", - "run_control": { - "frozen": false, - "read_only": false - } - }, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "import json\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "3pf5Xapqrq0M" - }, - "outputs": [], - "source": [ - "#Import the necessary methods from tweepy library \n", - "\n", - "#install tweepy if you don't have it\n", - "#!pip install tweepy\n", - "import tweepy\n", - "from tweepy.streaming import StreamListener\n", - "from tweepy import OAuthHandler\n", - "from tweepy import Stream\n", - "\n", - "#sentiment analysis package\n", - "#!pip install textblob\n", - "from textblob import TextBlob\n", - "\n", - "#general text pre-processor\n", - "#!pip install nltk\n", - "import nltk\n", - "from nltk.corpus import stopwords\n", - "\n", - "#tweet pre-processor \n", - "#!pip install tweet-preprocessor\n", - "import preprocessor as ppr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Starting code\n", - "Below we define some starting codes (python classes and function) to illustrate and assist on how to fetch data from twitter and analyse them. \n", - "\n", - "### **Your task is**\n", - "1. Go through the code and understand it. Know what each function does\n", - "2. If you find error, fix it. Ask for help in the slack channel if you find serious mistake\n", - "3. Extend the code such that it will be useful for topics you choose to analyse\n", - "4. Make nice plots and share your finding (e.g. insight on the main covid19 twitter converstions about your country)\n", - "5. Submit what ever you managed to do by Wednesday morning. But you should keep using what you build to write blogs, share on facebook, etc. \n", - "\n", - "\n", - "[Reference used to build some of the functions here](https://towardsdatascience.com/extracting-twitter-data-pre-processing-and-sentiment-analysis-using-python-3-0-7192bd8b47cf)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "class tweetsearch():\n", - " '''\n", - " This is a basic class to search and download twitter data.\n", - " You can build up on it to extend the functionalities for more \n", - " sophisticated analysis.\n", - " \n", - " '''\n", - " def __init__(self,cols=None,auth=None):\n", - " #\n", - " if not cols is None:\n", - " self.cols = cols\n", - " else:\n", - " self.cols = ['id', 'created_at', 'source', 'original_text','clean_text', \n", - " 'sentiment','polarity','subjectivity', 'lang',\n", - " 'favorite_count', 'retweet_count', 'original_author', \n", - " 'possibly_sensitive', 'hashtags',\n", - " 'user_mentions', 'place', 'place_coord_boundaries']\n", - " \n", - " if auth is None:\n", - " #Variables that contains the user credentials to access Twitter API \n", - " consumer_key = os.environ.get('TWITTER_API_KEY')\n", - " consumer_secret = os.environ.get('TWITTER_API_SECRET')\n", - " access_token = os.environ.get('TWITTER_ACCESS_TOKEN')\n", - " access_token_secret = os.environ.get('TWITTER_ACCESS_TOKEN_SECRET')\n", - "\n", - "\n", - " #This handles Twitter authetification and the connection to Twitter \n", - " #Streaming API\n", - " auth = OAuthHandler(consumer_key, consumer_secret)\n", - " auth.set_access_token(access_token, access_token_secret)\n", - " \n", - "\n", - " # \n", - " self.auth = auth\n", - " self.api = tweepy.API(auth) \n", - " \n", - "\n", - " def clean_tweets(self,twitter_text):\n", - "\n", - " #use pre processor\n", - " tweet = p.clean(twitter_text)\n", - "\n", - " #HappyEmoticons\n", - " emoticons_happy = set([\n", - " ':-)', ':)', ';)', ':o)', ':]', ':3', ':c)', ':>', '=]', '8)', '=)', ':}',\n", - " ':^)', ':-D', ':D', '8-D', '8D', 'x-D', 'xD', 'X-D', 'XD', '=-D', '=D',\n", - " '=-3', '=3', ':-))', \":'-)\", \":')\", ':*', ':^*', '>:P', ':-P', ':P', 'X-P',\n", - " 'x-p', 'xp', 'XP', ':-p', ':p', '=p', ':-b', ':b', '>:)', '>;)', '>:-)',\n", - " '<3'\n", - " ])\n", - "\n", - " # Sad Emoticons\n", - " emoticons_sad = set([\n", - " ':L', ':-/', '>:/', ':S', '>:[', ':@', ':-(', ':[', ':-||', '=L', ':<',\n", - " ':-[', ':-<', '=\\\\', '=/', '>:(', ':(', '>.<', \":'-(\", \":'(\", ':\\\\', ':-c',\n", - " ':c', ':{', '>:\\\\', ';('\n", - " ])\n", - "\n", - " #Emoji patterns\n", - " emoji_pattern = re.compile(\"[\"\n", - " u\"\\U0001F600-\\U0001F64F\" # emoticons\n", - " u\"\\U0001F300-\\U0001F5FF\" # symbols & pictographs\n", - " u\"\\U0001F680-\\U0001F6FF\" # transport & map symbols\n", - " u\"\\U0001F1E0-\\U0001F1FF\" # flags (iOS)\n", - " u\"\\U00002702-\\U000027B0\"\n", - " u\"\\U000024C2-\\U0001F251\"\n", - " \"]+\", flags=re.UNICODE)\n", - "\n", - " #combine sad and happy emoticons\n", - " emoticons = emoticons_happy.union(emoticons_sad)\n", - "\n", - " stop_words = set(stopwords.words('english'))\n", - " word_tokens = word_tokenize(tweet)\n", - " #after tweepy preprocessing the colon symbol left remain after \n", - " #removing mentions\n", - " tweet = re.sub(r':', '', tweet)\n", - " tweet = re.sub(r'‚Ķ', '', tweet)\n", - "\n", - " #replace consecutive non-ASCII characters with a space\n", - " tweet = re.sub(r'[^\\x00-\\x7F]+',' ', tweet)\n", - "\n", - " #remove emojis from tweet\n", - " tweet = emoji_pattern.sub(r'', tweet)\n", - "\n", - " #filter using NLTK library append it to a string\n", - " filtered_tweet = [w for w in word_tokens if not w in stop_words]\n", - "\n", - " #looping through conditions\n", - " filtered_tweet = [] \n", - " for w in word_tokens:\n", - " #check tokens against stop words , emoticons and punctuations\n", - " if w not in stop_words and w not in emoticons and w not in string.punctuation:\n", - " filtered_tweet.append(w)\n", - "\n", - " return ' '.join(filtered_tweet) \n", - "\n", - " def get_tweets(self, keyword, csvfile=None):\n", - " \n", - " \n", - " df = pd.DataFrame(columns=self.cols)\n", - " \n", - " if not csvfile is None:\n", - " #If the file exists, then read the existing data from the CSV file.\n", - " if os.path.exists(csvfile):\n", - " df = pd.read_csv(csvfile, header=0)\n", - " \n", - "\n", - " #page attribute in tweepy.cursor and iteration\n", - " for page in tweepy.Cursor(api.search, q=keyword,count=200, include_rts=False):\n", - "\n", - "\n", - " for status in page:\n", - " \n", - " new_entry = []\n", - " status = status._json\n", - " \n", - " #filter by language\n", - " if status['lang'] != 'en':\n", - " continue\n", - "\n", - " \n", - " #if this tweet is a retweet update retweet count\n", - " if status['created_at'] in df['created_at'].values:\n", - " i = df.loc[df['created_at'] == status['created_at']].index[0]\n", - " #\n", - " cond1 = status['favorite_count'] != df.at[i, 'favorite_count']\n", - " cond2 = status['retweet_count'] != df.at[i, 'retweet_count']\n", - " if cond1 or cond2:\n", - " df.at[i, 'favorite_count'] = status['favorite_count']\n", - " df.at[i, 'retweet_count'] = status['retweet_count']\n", - " continue\n", - "\n", - " #calculate sentiment\n", - " blob = TextBlob(filtered_tweet)\n", - " Sentiment = blob.sentiment \n", - " polarity = Sentiment.polarity\n", - " subjectivity = Sentiment.subjectivity\n", - "\n", - " new_entry += [status['id'], status['created_at'],\n", - " status['source'], status['text'],filtered_tweet, \n", - " Sentiment,polarity,subjectivity, status['lang'],\n", - " status['favorite_count'], status['retweet_count']]\n", - "\n", - " new_entry.append(status['user']['screen_name'])\n", - "\n", - " try:\n", - " is_sensitive = status['possibly_sensitive']\n", - " except KeyError:\n", - " is_sensitive = None\n", - "\n", - " new_entry.append(is_sensitive)\n", - "\n", - " hashtags = \", \".join([hashtag_item['text'] for \\\n", - " hashtag_item in status['entities']['hashtags']])\n", - " new_entry.append(hashtags) #append the hashtags\n", - "\n", - " #\n", - " mentions = \", \".join([mention['screen_name'] for \\\n", - " mention in status['entities']['user_mentions']])\n", - " new_entry.append(mentions) #append the user mentions\n", - "\n", - " try:\n", - " xyz = status['place']['bounding_box']['coordinates']\n", - " coordinates = [coord for loc in xyz for coord in loc]\n", - " except TypeError:\n", - " coordinates = None\n", - " #\n", - " new_entry.append(coordinates)\n", - "\n", - " try:\n", - " location = status['user']['location']\n", - " except TypeError:\n", - " location = ''\n", - " #\n", - " new_entry.append(location)\n", - "\n", - " #now append a row to the dataframe\n", - " single_tweet_df = pd.DataFrame([new_entry], columns=self.cols)\n", - " df = df.append(single_tweet_df, ignore_index=True)\n", - "\n", - " if not csvfile is None:\n", - " #save it to file\n", - " df.to_csv(csvfile, columns=self.cols, index=False, encoding=\"utf-8\")\n", - " \n", - " return df\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Search twitter and fetch data example" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "covid_keywords = '#COVID19 OR #COVID19Africa' #hashtag based search\n", - "tweets_file = 'data/ethiopia_covid19_23june2020.json'\n", - "\n", - "#get data on keywords\n", - "ts = tweetsearch()\n", - "df = ts.get_tweets(covid_keywords, csvfile=tweets_file) #you saved the " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stream data and save it to file\n", - "In the above we saw how to search and fetch data, below we will see how we will stream data from twitter. Make sure you understand the difference between search and stream features of twitter api.\n", - "\n", - "### **SAME TASK AS ABOVE**\n" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "r6lcy009rX_e" - }, - "outputs": [], - "source": [ - "#This is a basic listener that writes received tweets to file.\n", - "class StdOutListener(StreamListener):\n", - "\n", - " def __init__(self,fhandle, stop_at = 1000):\n", - " self.tweet_counter = 0\n", - " self.stop_at = stop_at\n", - " self.fhandle = fhandle\n", - " \n", - " \n", - " def on_data(self, data):\n", - " self.fhandle.write(data)\n", - " \n", - " #stop if enough tweets are obtained\n", - " self.tweet_counter += 1 \n", - " if self.tweet_counter < self.stop_at: \n", - " return True\n", - " else:\n", - " print('Max number of tweets reached: #tweets = ' + str(self.tweet_counter))\n", - " return False\n", - "\n", - " def on_error(self, status):\n", - " print (status)\n", - "\n", - "def stream_tweet_data(filename='data/tweets.json',\n", - " keywords=['COVID19Africa','COVID19Ethiopia'],\n", - " is_async=False):\n", - " # tweet topics to use as a filter. The tweets downloaded\n", - " # will have one of the topics in their text or hashtag \n", - "\n", - " print('saving data to file: ',filename)\n", - "\n", - " #print the tweet topics \n", - " print('TweetKeywords are: ',keywords)\n", - " print('For testing case, please interupt the downloading process using ctrl+x after about 5 mins ')\n", - " print('To keep streaming in the background, pass is_async=True')\n", - "\n", - " #Variables that contains the user credentials to access Twitter API \n", - " consumer_key = os.environ.get('TWITTER_API_KEY')\n", - " consumer_secret = os.environ.get('TWITTER_API_SECRET')\n", - " access_token = os.environ.get('TWITTER_ACCESS_TOKEN')\n", - " access_token_secret = os.environ.get('TWITTER_ACCESS_TOKEN_SECRET')\n", - "\n", - " #open file \n", - " fhandle=open(filename,'w')\n", - "\n", - " #This handles Twitter authetification and the connection to Twitter Streaming API\n", - " l = StdOutListener(fhandle)\n", - " auth = OAuthHandler(consumer_key, consumer_secret)\n", - " auth.set_access_token(access_token, access_token_secret)\n", - "\n", - " stream = Stream(auth, l)\n", - "\n", - " #This line filter Twitter Streams to capture data by the keywords: first argument to this code\n", - " stream.filter(track=keywords,is_async=is_async)\n", - "\n", - " return None\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use case of the above code" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "F8tcPcSMrNLL", - "outputId": "d7abd9c2-065c-40e8-f71b-e808d985c364" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "saving data to file: data/covid19_23june2020.json\n", - "TweetKeywords are: ['covid19', '#COVID19Africa']\n", - "For testing case, please interupt the downloading process using ctrl+x after about 5 mins \n", - "To keep streaming in the background, pass is_async=True\n", - "Max number of tweets reached: #tweets = 1000\n" - ] - } - ], - "source": [ - "tweets_file = 'data/covid19_23june2020.json'\n", - "stream_tweet_data(filename=tweets_file,keywords=['covid19','#COVID19Africa']) #\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Filter twitter data and do basic analysis\n", - "**Extend it to gain more insight**" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "F8tcPcSMrNLL", - "outputId": "d7abd9c2-065c-40e8-f71b-e808d985c364" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "saved numbers of tweets: 998\n" - ] - } - ], - "source": [ - "tweets_data = []\n", - "for line in open(tweets_file, \"r\"):\n", - " try:\n", - " tweet = json.loads(line)\n", - " x=tweet['text']\n", - " tweets_data.append(tweet)\n", - " except:\n", - " continue\n", - "\n", - "\n", - "print('saved numbers of tweets: ', len(tweets_data))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "hlFKyGnYrNLX" - }, - "outputs": [], - "source": [ - "tweets = pd.DataFrame(columns=['text','lang','country'])\n", - "\n", - "tweets['text'] = list(map(lambda tweet: tweet['text'], tweets_data))\n", - "tweets['lang'] = list(map(lambda tweet: tweet['lang'], tweets_data))\n", - "tweets['country'] = list(map(lambda tweet: tweet['place']['country'] if tweet['place'] != None else None, \n", - " tweets_data))\n", - "\n", - "\n", - "tweets_by_lang = tweets['lang'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "aEPPoCBtrNLd", - "outputId": "bfe0cee1-814d-4f30-f47d-205218b3adc9" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.tick_params(axis='x', labelsize=15)\n", - "ax.tick_params(axis='y', labelsize=10)\n", - "ax.set_xlabel('Languages', fontsize=15)\n", - "ax.set_ylabel('Number of tweets' , fontsize=15)\n", - "ax.set_title('Top 5 languages', fontsize=15, fontweight='bold')\n", - "tweets_by_lang[:5].plot(ax=ax, kind='bar', color='red')\n", - "\n", - "tweets_by_country = tweets['country'].value_counts()\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.tick_params(axis='x', labelsize=15)\n", - "ax.tick_params(axis='y', labelsize=10)\n", - "ax.set_xlabel('Countries', fontsize=15)\n", - "ax.set_ylabel('Number of tweets' , fontsize=15)\n", - "ax.set_title('Top 5 countries', fontsize=15, fontweight='bold')\n", - "tweets_by_country[:5].plot(ax=ax, kind='bar', color='blue')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "IQYFg6t5rNLj" - }, - "source": [ - "# Hashtag histogram. \n", - "\n", - "## Please write code that will help you answer the following questions\n", - " 1) What is the most used hashtag?\n", - " \n", - " 2) What is the most used referenced username?\n", - " \n", - " 3) What is the most retweeted tweet?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "p0VUwYR8rNLk" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "vistweet.ipynb", - "provenance": [] - }, - "hide_input": false, - "kernelspec": { - "display_name": "Python [conda env:.conda-10x]", - "language": "python", - "name": "conda-env-.conda-10x-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - }, - "latex_envs": { - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 0 - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}