### Basics of Pandas python library

Basic Dataframes Operations

• Create Dataframe from a dictionary Sample Notebook
• Create Dataframe by inserting rows in an iterative way Sample Notebook
• Create Dataframe with randomly generated data Sample Notebook
• Create Dataframe from a csv file Sample Notebook
• Change Dataframe column names Sample Notebook
• Chose specific columns from a DataFrame Sample Notebook
• Delete drop columns or extract columns from Dataframe Sample Notebook

Manipulating Dataframe

• Iterate over a dataframe Sample Notebook
• Apply function to dataframe row wise Sample Notebook
• Apply function to a specific column of dataframe Sample Notebook
• Find and replace a value in Dataframe Sample Notebook

Split and Merge Dataframes

• Merge dataframes by columns using join Sample Notebook
• Merge dataframes by columns on index Sample Notebook
• Merge dataframe and split again Sample Notebook
• Group by a dataframe and iterate over grouped series Sample Notebook

Convert columns

• Get binary columns in a DataFrame Sample Notebook
• Convert categorical columns to integer columns using label encoding Sample Notebook
• Reduce high dimentionality from categorical column Sample Notebook
• Convert categorical column to one hot encoded column Sample Notebook

Split column

• Split column usiong a delimiter Sample Notebook
• Split a column using delimter and one hot encode the values Sample Notebook

Adding capabilities to pandas.DataFrame class

• Extend pandas.DataFrame class to store additional value(s). Sample Notebook