Python and Pandas for the Anaconda Jupyter Notebook

This video contains an introduction to the entire series. I introduce myself and the sequence of videos.

We will be using the Jupyter notebook throughout this series. This video guides you on installing the jupyter notebook.

This video shows you how to launch the Jupyter Notebook from the Anaconda Launcher and from command line.

This section covers the 2 main data structures in the Pandas Library, the Series and the DataFrame.

This video covers opening a local csv file into a DataFrame and doing some basic attribute analysis.

• Using read_csv to load a local csv file as a DataFrame

• Getting the column and row counts of a DataFrame using the shape attribute

• Getting the column datatypes using the dtypes attribute

This video covers selecting both columns and rows from a DataFrame.

• Selecting a single column from a DataFrame

• Selecting multiple columns from a DataFrame

• Selecting rows based on a text value in a DataFrame

• Selecting rows based on a numeric value in a DataFrame

This video covers adding both static and computed values to a DataFrame.

• Add a Static column to a DataFrame

• Add a Computed column to a DataFrame

• Add a Column to a DataFrame using a Lambda Function

• Add a Column to a DataFrame using a Function

• Export DataFrame to a new csv

This video covers data aggregation techniques using GroupBy and PivotTable.

• Calculate Sales by Associate using Group By

• Calculate Sales by Product using Group by

• Calculate Revenue by Product using Pivot_Table

• Flatten a Pivot using melt()

This video covers plotting data from a DataFrame.

• Prepare data for Plotting

• Create a pie Graph

• Create a bar Graph

This tutorial covers the many ways to create a DataFrame.

- Create an Empty DataFrame

- Create a DataFrame with Random Values using numpy random randint()

- Create a DataFrame from a Python Dictionary

- Create a DataFrame from a Python List

- Create a DataFrame from a local CSV file

- Create a DataFrame from an online CSV file

- Create a DataFrame from a local JSON file

- Create a DataFrame from an online JSON file

This section covers the many ways to filter/search a DataFrame.

• Filter DataFrame by column name

• Filter DataFrame rows by column values:equals a value

• Filter DataFrame rows by column values:doesn’t equal a value

• Filter DataFrame rows by column values is in a list of values

• Filter DataFrame rows by column values is not in a list of values

• Filter DataFrame rows by column values is null

• Filter DataFrame rows by column values is not null

• Filter DataFrame rows using query

• Filter DataFrame rows using iloc

• Filter DataFrame rows using loc

This tutorial will cover 12 different techniques to update a Pandas DataFrame

1. Add rows to a DataFrame using append()

2. Remove items from a DataFrame using drop()

3. Replace a value in a DataFrame using replace()

4. Add a single value to a new column of a DataFrame

5. Add a mean(),max(),median() column to a DataFrame

6. Update a column of a dataframe with applymap

7. Add a column to a dataframe using applymap

8. Add a column to a dataframe using map

9. Update a column to a dataframe with map

10. Add a column to a dataframe with apply

11. Fill null values using fillna()

12. Update the datatype of a dataframe using astype()

This video coves the applymap() function on all columns and a subset of columns of a DataFrame.

This tutorial covers applying the map function to Pandas DataFrame to create a new column or update an existing column.

Techniques to Export a DataFrame

1. Export a Dataframe to CSV

2. Export a Dataframe to Excel

3. Export a Dataframe to a Python Dictionary

4. Export a Dataframe to a json file

5. Export a Dataframe to a pickle file

6. Export a Dataframe to the clipboard for copy/paste

This lecture will cover 8 techniques to create a Pandas Series

1. Create an Empty Series

2. Create a Series with Random Values

3. Create a Series from a Python Dictionary

4. Create a Series from a Python List

5. Create a Series from an local CSV file

6. Create a Series from an online CSV file

7. Create a Series from a Local JSON file

8. Create a Series from a JSON file

8 Techniques to Analyze a Series

1. Get the name of a Series using name attribute

2. Get the row count of a Series using size attribute

3. Get the datatype of a Series using dtype attribute

4. Get all the Statistics of a Series using describe()

5. Get the MEAN of a Series using mean()

6. Return the first few rows of a Series using head()

7. Sort values in a Series using sort_values()

8. Sort indexes in a Series usisng sort_index()

9 Techniques to Search/Filter a Series.

1. Get the name of a Series using name attribute

2. Get the row count of a Series using size attribute

3. Get the datatype of a Series using dtype attribute

4. Get all the Statistics of a Series using describe()

5. Get the MEAN of a Series using mean()

6. Return the first few rows of a Series using head()

7. Sort values in a Series using sort_values()

8. Sort indexes in a Series usisng sort_index()

5 Techniques to Update a Series

1. Add items to a Series using append()

2. Replace items in a Series using replace()

3. Remove items of a Series using drop()

4. Fill null values using fillna()

5. Update a value in a Series by index and update()