Welcome to the tutorial “from-Excel-to-Pandas” for data analysis
Contents
Welcome to the tutorial “from-Excel-to-Pandas” for data analysis¶
This is a guide for data analysts that are fluent with Excel and want to modernize their analyses to Python and mainly using the Pandas library. This tutorial is NOT teaching programing in Python, as it is focused on the FUNCTIONs to implement in a sequence to achieve the result of an analysis. This simple multi-step flow of functions makes the analysis easier to understand and follow, and also to run the analyses in a consistent way in the future and across the organization.
The tutorial is based on a set of Jupyter notebooks demonstrating the various ways to implement the main functions that are available in Microsoft Excel with their Pandas equivalents. You can jump directly to a specific section by searching the Excel function name or read through the tutorial to learn the basic and advanced topics. The tutorial is organized in complexity order and the later chapters assume the knowledge that is covered in earlier ones.
Tip
Run the notebook in an interactive environment to better learn
“Tell me and I forget, teach me and I may remember, involve me and I learn.” - Xunzi
Main Chapters¶
Getting Started (if you want also to play with the code)
Installing locally (python, jyputer, git)
Using Amazon SageMaker
Using Google Colab
Using Binder
Loading Data
From Excel and CSV files
From HTML sources
From APIs
Table Summary
Statistics (describe, info, head)
Totals and Unique values
Adding Columns to Tables
Using built-in functions
Using custom functions
Grouping and Pivot Tables
Using pivot_table
Using groupby
Joining and Merging Tables and Lists
Using merge
Adding Charts
Using plot
Using plotly and seaborn
Extending plots with Matplotlib
Industries
Financial Analysis (Stocks, for example)
Manufacturing (Market Share, for example)
Logistics (Scheduling, for example)
Planning (Linear Programming, for example)