Merge pull request #161 from LaunchCodeEducation/eda-with-pands-audit

Typos, among some calrity issues fixed for all reading material

Author: jwoolbright23

content/eda-with-pandas/reading/conditional-formatting/_index.md

@@ -5,7 +5,7 @@ draft = false
 weight = 5
 +++
 
-When exploring data, using pandas you can also apply conditional formatting similar to how you did using spreadsheets. Say, for instance, you only want to display related to a specific city, state, movie genre, or name, you can do so!
+When exploring data, using pandas you can also apply conditional formatting similar to how you did using spreadsheets. Say, for instance, you only want to display data related to a specific city, state, movie genre, or name, you can do so!
 
 We will begin by identifying rows based on a condition using one column of data.
 

content/eda-with-pandas/reading/dataframes-with-pandas/_index.md

@@ -7,7 +7,7 @@ weight = 3
 
 A pandas **DataFrame** is the second type of class that is capable of handling data.
 
-Similar to a spreadsheet, a DataFrame can be visualzed as having multiple columns and rows associated with the data inside. The data within can be of any type.
+Similar to a spreadsheet, a DataFrame can be visualized as having multiple columns and rows associated with the data inside. The data within can be of any type.
 
 A DataFrame can also be considered a collection or assortment of Series. Similar to a Series there are multiple ways that a DataFrame can be created:
 1. Using a multi-dimensional list, dictionary, or tuple
@@ -66,7 +66,7 @@ One thing to note about lists when they are added into a DataFrame is that each
 import pandas as pd
 
 # Create a pandas DataFrame by providing a dictionary
-movie_dictionary_dataframe = pd.DataFrame(movies = {'Name': ["Interstellar", "Pride and Prejudice", "Inception", "Barbie"],'Release': [2014, 2005, 2010, 2003]})
+movie_dictionary_dataframe = pd.DataFrame({'Name': ["Interstellar", "Pride and Prejudice", "Inception", "Barbie"],'Release': [2014, 2005, 2010, 2003]})
 
 # Create a pandas DataFrame from a pre-existing dictionary
 movies = {'Name': ["Interstellar", "Pride and Prejudice", "Inception", "Barbie"],'Release': [2014, 2005, 2010, 2003]}
@@ -131,7 +131,7 @@ movies_genres_dataframe = pd.concat([movies, genres], axis=1) # axis 1 specifies
 the `axis` parameter specifies whether the data will be joined or combined along the *row* or *column*. Take a look at the table below. If you do not specify `axis=1` it will default to `axis=0`.
 
 | Axis | Represents | Use Case |
-|---|---|---|---|
+|---|---|---|
 | 0 (default) | **Row** | Operations performed **across rows** |
 | 1 | **Column** | Operations performed **down each column** |
 {{% /notice %}}

content/eda-with-pandas/reading/exploring-data-with-pandas/_index.md

@@ -7,7 +7,7 @@ weight = 1
 
 Exploratory Data Analysis or EDA as you already know is a critical step when beginning your analysis work. Similar to the EDA work with spreadsheets we will do the same with Python and **pandas** in order to accomplish the following:
 
-1. Form a hypotheses about what is the underlying forces effecting your data.
+1. Form a hypothesis about what is the underlying forces effecting your data.
 1. Challenge previous assumptions that may have been made when discussing the business issue.
 1. Guide you on what tools and techniques you should use when working with that dataset.
 
@@ -55,7 +55,7 @@ import pandas as pd
 
 ## NumPy
 
-The NumPy library will be used in conjuction with pandas so that we can perform mathematical operations on some of our datasets. As we explore our data and in later chapters, begin cleaning and manipulating data we will use the tools it provides to make our life easier.
+The NumPy library will be used in conjunction with pandas so that we can perform mathematical operations on some of our datasets. As we explore our data and in later chapters, begin cleaning and manipulating data we will use the tools it provides to make our life easier.
 
 {{% notice blue Note "rocket" %}}
 Once NumPy is installed, it can be imported into your workspace in the following way:

content/eda-with-pandas/reading/numpy-intro/_index.md

@@ -7,7 +7,7 @@ weight = 2
 
 NumPy, or Numerical Python, provides additional flexibility in working with data than standard Python lists. The foundational element of NumPy are arrays, and the main structure found in NumPy is the `ndarray`.
 
-Arrays can hold a collection of the same data type and can be one-dimesional, also called a vector, or multi-dimensional, such as a matrix. The benefits of NumPy arrays are efficient element access and data manipulation / transformation.
+Arrays can hold a collection of the same data type and can be one-dimensional, also called a vector, or multi-dimensional, such as a matrix. The benefits of NumPy arrays are efficient element access and data manipulation / transformation.
 
 ### The basics of NumPy arrays
 
@@ -26,7 +26,7 @@ print(array1)
 [8 5 3 2 1 1]
 ```
 
-Multi-dimensional arrays can be created applying the same method using mutiple nested lists, such as the following two-dimensional array (or matrix)
+Multi-dimensional arrays can be created applying the same method using multiple nested lists, such as the following two-dimensional array (or matrix)
 
 ```python {linenos=table}
 # import numpy as np
@@ -45,7 +45,7 @@ print(array2)
 
 ### Basic array operations
 
-Many functions can be performed on arrays, such as determing attributes of the array, sorting, spliting or combining arrays, and, of course, numerical operations.
+Many functions can be performed on arrays, such as determining attributes of the array, sorting, spliting or combining arrays, and, of course, numerical operations.
 
 We will introduce a brief selection of the broad array (no pun intended) of functions which provide useful insights into NumPy arrays.
 
@@ -118,7 +118,7 @@ print(array5)
  [3 7 9]]
 ```
 
-Lastly for this introduction, arthimetic functions can be performed on an array as a whole, or for a specific dimension.
+Lastly for this introduction, arithmetic functions can be performed on an array as a whole, or for a specific dimension.
 
 ```python {linenos=table}
 # import numpy as np

content/eda-with-pandas/reading/pandas-functions/_index.md

@@ -29,14 +29,14 @@ Two functions below that may stick out to you might be the `.info()` and the `.d
 | `.fillna(desired_value)` | Fills null values in a DataFrame with the specified value. |
 | `.groupby('column_name')` | Groups a DataFrame by the unique values in the specified column. |
 | `.value_counts()` | Computes the value counts for each element in a DataFrame. |
-| `.shape()` | Returns a tuple representing the dimensions of your data structure |
+| `.shape` | Returns a tuple representing the dimensions of your data structure |
 | `.loc[]` | Method used to locate rows or columns based on label-indexing |
 | `.iloc[]` | Method used to locate rows or columns based on index positions `(0, 1, 2 ,3 ,etc..)` |
 
 {{% notice blue Note "rocket" %}}
-When using the `.shape()` function for a DataFrame it will show the number of rows and columns `(rows, columns)`. 
+When using the `.shape` attribute for a DataFrame it will show the number of rows and columns `(rows, columns)`.
 
-When using the `.shape()` function for a Series it will only return the number of rows and the column will be empty `(rows, )`
+When using the `.shape` attribute for a Series it will only return the number of rows and the column will be empty `(rows, )`
 {{% /notice %}}
 
 ## Statistics with pandas
@@ -87,7 +87,7 @@ How do we view only the first 13 rows of a DataFrame?
 {{% /notice %}}
 
 {{% notice green Question "rocket" %}}
-Which pandas function will prin the number of records, three quartiles, mean, standard deviation, minimum and maximum values of a DataFrame?
+Which pandas function will print the number of records, three quartiles, mean, standard deviation, minimum and maximum values of a DataFrame?
 
 <!-- Solution: .describe() -->
 {{% /notice %}}

content/eda-with-pandas/reading/series-with-pandas/_index.md

@@ -170,7 +170,7 @@ What does index-labeling default to if none are provided?
 {{% /notice %}}
 
 {{% notice green Question "rocket" %}}
-What type of data is a pandas Series capable of holidng?
+What type of data is a pandas Series capable of holding?
 
 <!-- Solution: Any type -->
 {{% /notice %}}

content/eda-with-pandas/reading/visuals-with-pandas/_index.md

@@ -6,7 +6,7 @@ weight = 4
 hidden = true
 +++
 
-The `pandas` library works in conjuction and is able to integrate other libraries very easily. One of those libraries is **Matplotlib**, which is a library used to visalize the data that you are working with.
+The `pandas` library works in conjuction and is able to integrate other libraries very easily. One of those libraries is **Matplotlib**, which is a library used to visualize the data that you are working with.
 
 ## Installation
 
@@ -22,7 +22,7 @@ import matplotlib as mpl
 ```
 
 ```python
-# best practice when is to import matplotlib.pyplot as plt
+# best practice is to import matplotlib.pyplot as plt
 import matplotlib.pyplot as plt
 ```
 
@@ -31,7 +31,7 @@ import matplotlib.pyplot as plt
 
 When importing matplotlib you are also importing `pyplot` as well. You could reference pyplot as such: `mpl.pyplot.function_here()`
 
-Often times pyplot is used to generate 2-D graphics and since it is referenced often importing it separately and assigning it it's own alias is helpful and may save some time!
+Often times pyplot is used to generate 2-D graphics and since it is referenced often importing it separately and assigning it its own alias is helpful and may save some time!
 {{% /notice %}}
 
 ## Example
@@ -54,7 +54,7 @@ plt.show()
 ```
 
 {{% notice blue Note "rocket" %}}
-When using tools like Jupyter Notebooks you may not need to include the `plt.show()` line of code shown on line 11 in the above code block. 
+When using tools like Jupyter Notebooks you may not need to include the `plt.show()` line of code shown in the above code block.
 
 The `plt.show()` when used within a terminal emulator environment will create a separate pop-out of the graph. Tools like Jupyter Notebooks will include the visual within the notebook itself, eliminating the need for the pop out window.
 {{% /notice %}}