From a0c5c0f04a5f254b776b9af78045ed93014da284 Mon Sep 17 00:00:00 2001 From: bhumanyug <47988176+bhumanyug@users.noreply.github.com> Date: Wed, 23 Oct 2019 17:58:16 +0530 Subject: [PATCH 1/2] added problem set 3 solutions... --- Lab3/1.py | 178 ++++ Lab3/2.py | 178 ++++ Lab3/Lab3Template.py | 97 ++ Lab3/landslide_data2_original.csv | 946 +++++++++++++++++ Lab3/winequality_red_original.csv | 1600 +++++++++++++++++++++++++++++ 5 files changed, 2999 insertions(+) create mode 100644 Lab3/1.py create mode 100644 Lab3/2.py create mode 100644 Lab3/Lab3Template.py create mode 100644 Lab3/landslide_data2_original.csv create mode 100644 Lab3/winequality_red_original.csv diff --git a/Lab3/1.py b/Lab3/1.py new file mode 100644 index 0000000..35d5d75 --- /dev/null +++ b/Lab3/1.py @@ -0,0 +1,178 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from sklearn import preprocessing + + +def read_data(path_to_file): + """ Returns Pandas dataframe for given csv file + + Parameters + ---------- + path_to_file: string + Given csv file + + Returns + ------- + pandas.Dataframe + """ + df = pd.read_csv(path_to_file) + return df + pass + + +def show_box_plot(attribute_name, dataframe): + """ Displays boxplot for atrribute + + Parameters + ---------- + attribute_name: string + Attribute selected + dataframe: pandas.Dataframe + DataFrame for the given dataset + Returns + ------- + None + """ + # dataframe.boxplot(column=[attribute_name]) + plt.boxplot(dataframe[attribute_name]) + plt.show() + + +def replace_outliers(dataframe): + """ Replaces the outliers in the given dataframe + + Parameters + ---------- + dataframe: pandas.Dataframe + DataFrame for the given dataset + Returns + ------- + pandas.Dataframe + """ + dataframe2 = dataframe.copy() + for i in dataframe2.columns: + col = dataframe2[i] + median = col.median() + Q1 = col.quantile(.25) + Q3 = col.quantile(.75) + IQR = Q3 - Q1 + dataframe2.loc[(col<(Q1-1.5*IQR)) | (col>(Q3+1.5*IQR)), i] = median + return dataframe2 + pass + + +def range(dataframe, attribute_name): + """ Gives Range of Selected Attribute + + Parameters + ---------- + attribute_name: string + Attribute selected + dataframe: pandas.Dataframe + DataFrame for the given dataset + Returns + ------- + pair(float,float) + """ + min = dataframe[attribute_name].min() + max = dataframe[attribute_name].max() + return (min, max) + pass + + +def min_max_normalization(dataframe, range=None): + """ Returns normalized pandas dataframe + + Parameters + ---------- + dataframe: pandas.Dataframe + Dataframe for the given dataset + range: pair(float,float) + Normalize between range + Returns + ------- + pandas.Dataframe + """ + dataframe_to_normalize = dataframe.iloc[:, 0:11] + dataframe_normalized = (dataframe_to_normalize-dataframe_to_normalize.min())/(dataframe_to_normalize.max()-dataframe_to_normalize.min()) + if range != None: + offset = range[0] + scaling_factor = range[1]-range[0] + dataframe_normalized = offset + dataframe_normalized*scaling_factor + dataframe_normalized = dataframe_normalized.join(dataframe['quality'], how="right") + return dataframe_normalized + pass + + +def standardize(dataframe): + """ Returns standardized pandas dataframe + + Parameters + ---------- + dataframe: pandas.Dataframe + Dataframe for the given dataset + Returns + ------- + pandas.Dataframe + """ + dataframe_to_standardize = dataframe.iloc[:, 0:11] + dataframe_standardize = (dataframe_to_standardize-dataframe_to_standardize.mean())/dataframe_to_standardize.std() + dataframe_standardize = dataframe_standardize.join(dataframe['quality'], how="right") + return dataframe_standardize + pass + + +def main(): + """ Main Function + Parameters + ---------- + + Returns + ------- + None + """ + path_to_file = "winequality_red_original.csv" + dataframe = read_data(path_to_file) + # dataframe2 = pd.DataFrame(dataframe) + # print("*****", dataframe['fixed_acidity'].median(), dataframe['fixed_acidity'].quantile(.25), dataframe['fixed_acidity'].quantile(.75)) + # show_box_plot('fixed_acidity', dataframe) + + # outlier correction + dataframe_corrected = replace_outliers(dataframe) + # print("*****", dataframe['fixed_acidity'].median(), dataframe['fixed_acidity'].quantile(.25), dataframe['fixed_acidity'].quantile(.75)) + # show_box_plot('fixed_acidity', dataframe_corrected) + + # plotting box plots with and without outlier correction + for i in dataframe.columns: + plt.boxplot([dataframe[i], dataframe_corrected[i]]) + plt.title(i) + plt.show() + + for i in dataframe_corrected.columns: + Range = range(dataframe, i) + print("Range of {} : {}".format(i, Range)) + + print("***** min_max_normalization : range(0, 1) *****") + print(min_max_normalization(dataframe)) + print("***** min_max_normalization : range(0, 20) *****") + print(min_max_normalization(dataframe, range=(0, 20))) + print(min_max_normalization(dataframe, range=(0, 20)).max()) + print("***** standardization *****") + print(standardize(dataframe)) + + x = dataframe.values + StandardScaler = preprocessing.StandardScaler() + x_scaled1 = StandardScaler.fit_transform(x) + df_scaled1 = pd.DataFrame(x_scaled1) + print(df_scaled1) + + MinMaxScaler = preprocessing.MinMaxScaler() + x_scaled2 = MinMaxScaler.fit_transform(x) + df_scaled2 = pd.DataFrame(x_scaled2) + print(df_scaled2) + + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Lab3/2.py b/Lab3/2.py new file mode 100644 index 0000000..36380ad --- /dev/null +++ b/Lab3/2.py @@ -0,0 +1,178 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from sklearn import preprocessing + + +def read_data(path_to_file): + """ Returns Pandas dataframe for given csv file + + Parameters + ---------- + path_to_file: string + Given csv file + + Returns + ------- + pandas.Dataframe + """ + df = pd.read_csv(path_to_file) + return df + pass + + +def show_box_plot(attribute_name, dataframe): + """ Displays boxplot for atrribute + + Parameters + ---------- + attribute_name: string + Attribute selected + dataframe: pandas.Dataframe + DataFrame for the given dataset + Returns + ------- + None + """ + # dataframe.boxplot(column=[attribute_name]) + plt.boxplot(dataframe[attribute_name]) + plt.show() + + +def replace_outliers(dataframe): + """ Replaces the outliers in the given dataframe + + Parameters + ---------- + dataframe: pandas.Dataframe + DataFrame for the given dataset + Returns + ------- + pandas.Dataframe + """ + dataframe2 = dataframe.copy() + for i in dataframe2.columns: + col = dataframe2[i] + median = col.median() + Q1 = col.quantile(.25) + Q3 = col.quantile(.75) + IQR = Q3 - Q1 + dataframe2.loc[(col<(Q1-1.5*IQR)) | (col>(Q3+1.5*IQR)), i] = median + return dataframe2 + pass + + +def range(dataframe, attribute_name): + """ Gives Range of Selected Attribute + + Parameters + ---------- + attribute_name: string + Attribute selected + dataframe: pandas.Dataframe + DataFrame for the given dataset + Returns + ------- + pair(float,float) + """ + min = dataframe[attribute_name].min() + max = dataframe[attribute_name].max() + return (min, max) + pass + + +def min_max_normalization(dataframe, range=None): + """ Returns normalized pandas dataframe + + Parameters + ---------- + dataframe: pandas.Dataframe + Dataframe for the given dataset + range: pair(float,float) + Normalize between range + Returns + ------- + pandas.Dataframe + """ + dataframe_to_normalize = dataframe.copy() + dataframe_normalized = (dataframe_to_normalize-dataframe_to_normalize.min())/(dataframe_to_normalize.max()-dataframe_to_normalize.min()) + if range != None: + offset = range[0] + scaling_factor = range[1]-range[0] + dataframe_normalized = offset + dataframe_normalized*scaling_factor + return dataframe_normalized + pass + + +def standardize(dataframe): + """ Returns standardized pandas dataframe + + Parameters + ---------- + dataframe: pandas.Dataframe + Dataframe for the given dataset + Returns + ------- + pandas.Dataframe + """ + dataframe_to_standardize = dataframe.copy() + dataframe_standardize = (dataframe_to_standardize-dataframe_to_standardize.mean())/dataframe_to_standardize.std() + return dataframe_standardize + pass + + +def main(): + """ Main Function + Parameters + ---------- + + Returns + ------- + None + """ + path_to_file = "landslide_data2_original.csv" + dataframe = read_data(path_to_file).iloc[:, 2:] + # dataframe2 = pd.DataFrame(dataframe) + # print("*****", dataframe['fixed_acidity'].median(), dataframe['fixed_acidity'].quantile(.25), dataframe['fixed_acidity'].quantile(.75)) + # show_box_plot('fixed_acidity', dataframe) + + # outlier correction + dataframe_corrected = replace_outliers(dataframe) + # print("*****", dataframe['fixed_acidity'].median(), dataframe['fixed_acidity'].quantile(.25), dataframe['fixed_acidity'].quantile(.75)) + # show_box_plot('fixed_acidity', dataframe_corrected) + + # plotting box plots with and without outlier correction + for i in dataframe.columns: + plt.boxplot([dataframe[i], dataframe_corrected[i]]) + plt.title(i) + plt.show() + + for i in dataframe_corrected.columns: + Range = range(dataframe, i) + print("Range of {} : {}".format(i, Range)) + + print("***** min_max_normalization : range(0, 1) *****") + print(min_max_normalization(dataframe_corrected)) + print("***** min_max_normalization : range(0, 20) *****") + print(min_max_normalization(dataframe_corrected, range=(0, 20))) + print(min_max_normalization(dataframe_corrected, range=(0, 20)).max()) + print("***** standardization *****") + print("###BEFORE###", dataframe_corrected.mean(), dataframe_corrected.std()) + print(standardize(dataframe_corrected)) + print("###AFTER###", standardize(dataframe_corrected).mean(), standardize(dataframe_corrected).std()) + + x = dataframe.values + StandardScaler = preprocessing.StandardScaler() + x_scaled1 = StandardScaler.fit_transform(x) + df_scaled1 = pd.DataFrame(x_scaled1) + print(df_scaled1) + + MinMaxScaler = preprocessing.MinMaxScaler() + x_scaled2 = MinMaxScaler.fit_transform(x) + df_scaled2 = pd.DataFrame(x_scaled2) + print(df_scaled2) + + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Lab3/Lab3Template.py b/Lab3/Lab3Template.py new file mode 100644 index 0000000..a0789f1 --- /dev/null +++ b/Lab3/Lab3Template.py @@ -0,0 +1,97 @@ +import numpy as np +import pandas as pd +def read_data(path_to_file): + """ Returns Pandas dataframe for given csv file + + Parameters + ---------- + path_to_file: string + Given csv file + + Returns + ------- + pandas.Dataframe + """ + pass +def show_box_plot(attribute_name,dataframe): + """ Displays boxplot for atrribute + + Parameters + ---------- + attribute_name: string + Attribute selected + dataframe: pandas.Dataframe + DataFrame for the given dataset + Returns + ------- + None + """ + pass +def replace_outliers(dataframe): + """ Replaces the outliers in the given dataframe + + Parameters + ---------- + dataframe: pandas.Dataframe + DataFrame for the given dataset + Returns + ------- + pandas.Dataframe + """ + pass +def range(dataframe,attribute_name): + """ Gives Range of Selected Attribute + + Parameters + ---------- + attribute_name: string + Attribute selected + dataframe: pandas.Dataframe + DataFrame for the given dataset + Returns + ------- + pair(float,float) + """ + pass +def min_max_normalization(dataframe,range=None): + """ Returns normalized pandas dataframe + + Parameters + ---------- + dataframe: pandas.Dataframe + Dataframe for the given dataset + range: pair(float,float) + Normalize between range + Returns + ------- + pandas.Dataframe + """ + pass +def standardize(dataframe): + """ Returns standardized pandas dataframe + + Parameters + ---------- + dataframe: pandas.Dataframe + Dataframe for the given dataset + Returns + ------- + pandas.Dataframe + """ + pass + +def main(): + """ Main Function + Parameters + ---------- + + Returns + ------- + None + """ + path_to_file="Your_File_Path" + dataframe=read_data(path_to_file) + return + +if __name__=="__main__": + main() \ No newline at end of file diff --git a/Lab3/landslide_data2_original.csv b/Lab3/landslide_data2_original.csv new file mode 100644 index 0000000..10130f0 --- /dev/null +++ b/Lab3/landslide_data2_original.csv @@ -0,0 +1,946 @@ +dates,stationid,dispx,dispy,dispz,temperature,humidity,rain +08-07-2018,t10,0,0,0,25.46875,82.1875,6.75 +09-07-2018,t10,0,0,0,26.19298,83.14912,1761.75 +10-07-2018,t10,0,0,0,25.17021,85.34043,652.5 +11-07-2018,t10,-0.00283509,0.0037210556,0.0049614075,24.29851,87.68657,963 +12-07-2018,t10,-0.00283509,0.0037210556,0.0049614075,24.06923,87.64615,254.25 +13-07-2018,t10,-0.00283509,0.0037210556,0.0049614075,21.20779,95.94805,339.75 +15-07-2018,t10,-0.00283509,-0.0203772094,-0.1226176425,23.48571,96.17143,38.25 +18-07-2018,t10,-0.00283509,-0.0203772094,-0.1226176425,21.79487,98.58974,29.25 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@@ -0,0 +1,2 @@ +# Data_Science +This repo is to maintain solutions of problem sets of elementry data science practise questions...