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Data Wrangling and Automation

Introduction

Working with Spreadsheets

Access Excel Tables with Python
SpreadSheet Munging Strategies in Python - Small Multiples
SpreadSheet Munging Strategies in Python - Meaningful Formats
SpreadSheet Munging Strategies in Python - Pivot Tables - Simple Unpivoting
SpreadSheet Munging Strategies in Python - Pivot Tables - Complex Unpivoting

Pydatatable

Replicating .SD in Python Datatable
Data Wrangling with Python Datatable - Conditional Statements
Data Wrangling with Python Datatable - Row-wise Transformations
Data Wrangling with Python Datatable - Select Columns by Data Type
Data Wrangling with Python Datatable - Replicate Pandas’ Map Function
Filtering Rows in Datatable
Selecting and Grouping Data with Python Datatable
Data Wrangling with Python Datatable - Selecting Columns
Extract DataFrame from Compressed Data into Python Datatable
Data Wrangling with Python Datatable - Transformations Within a GroupBy
Data Wrangling - Aggregation on Multiple Columns

CSVs

Read Multiple CSV Files into one Frame in Python

Pandas

Dplyr’s across: Replicating within Pandas
Fast and Efficient Inequality Joins in Pandas
Pivot_longer : Reshape Data in Pandas Efficiently and with Ease from Wide to Long
Extract DataFrame from Compressed Data into Pandas

Web Scraping

Easy Web Scraping with Python

Polars

Dplyr’s across: Replicating within Polars
Reshape Data in Polars Efficiently from Wide to Long Form - Part I
Reshape Data in Polars Efficiently from Wide to Long Form - Part II
Reshape Data in Polars Efficiently from Wide to Long Form - Part III

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By Samuel Oranyeli

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