Kung Fu Data Wrangling in Python with Pandas

Learn essential data wrangling techniques using Python's Pandas library in this 48-minute tutorial. Explore key concepts like data frames, CSV reading, filtering methods, and joining tables through practical examples using the Adventureworks dataset. Discover how to efficiently manipulate data with chaining methods, handle missing values, create pivot tables, and export results. Compare Pandas to R and SQL while gaining hands-on experience with Jupyter Notebook. Master powerful data manipulation tools including iloc, loc, filtering, sorting, grouping, and merging to enhance your data analysis skills.

Introduction
What is Python
What is pandas
What is numpy
What are data frames
Pandas vs R
Pandas vs SQL
Demo
Jupiter Notebook
Adventureworks Play
PD
Read CSV
DataFrame
Description
Numerics
Internet Sales
Chaining Methods
Filtering Methods
Performant
Ilog
Iloka
Row Subscript
Set Index
DropEqualFalse
Loke
Range
Index Access
Filter Access
Update Data
Export Data
Reset Index
Sort
Save
Pickle
GroupBy
PivotTable
Joining
Adding a new table
Merge statement
Left join
Concatenation
Missing Values
Explanation
Pandas Journey
References