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wesm/pydata-book

★ 24,586Jupyter NotebookAudience · generalComplexity · 2/5QuietSetup · easy

TLDR

Interactive Jupyter Notebooks and code examples for learning data analysis in Python, covering NumPy, pandas, visualization, and time series.

Mindmap

mindmap
  root((repo))
    What it does
      Data analysis tutorials
      Interactive notebooks
      Book companion code
    Topics covered
      Python basics
      NumPy arrays
      Data cleaning
      Visualization
      Time series
    Tech stack
      Python
      Jupyter Notebooks
      pandas
      NumPy
      matplotlib
    Use cases
      Learn data analysis
      Practice with code
      Reference while reading
    Audience
      Beginners
      Data learners
      Python students

Things people build with this

USE CASE 1

Work through interactive notebooks while reading the Python for Data Analysis book to learn data manipulation with pandas and NumPy.

USE CASE 2

Practice loading, cleaning, and reshaping real-world datasets using runnable code examples.

USE CASE 3

Create visualizations and analyze time series data by experimenting with the provided matplotlib and pandas examples.

USE CASE 4

Reference working code snippets for common data analysis tasks like joining tables and handling missing values.

Tech stack

PythonJupyter NotebookpandasNumPymatplotlib

Getting it running

Difficulty · easy Time to first run · 5min
License could not be detected automatically. Check the repository's LICENSE file before use.

In plain English

This repository contains the companion code and Jupyter Notebooks for the book "Python for Data Analysis, 3rd Edition" by Wes McKinney, published by O'Reilly Media. Wes McKinney is the creator of pandas, the most widely used Python library for working with structured data. The notebooks cover data analysis from the ground up using Python. Topics include Python language basics, working with NumPy arrays (a library for numerical computing), loading and cleaning real-world datasets, reshaping and joining data tables, creating visualizations, analyzing time series data, and an introduction to modeling. Each chapter of the book has a corresponding interactive notebook where you can run and experiment with the code. You would use this repository as a hands-on companion while reading the book, or as a free reference for learning data analysis in Python. The book content itself is also freely available on the author's website. The tech stack is Python, with Jupyter Notebooks as the interactive environment, and libraries including pandas, NumPy, and matplotlib.

Copy-paste prompts

Prompt 1
Show me how to load a CSV file and clean missing values using the code from chapter 7 of this repository.
Prompt 2
How do I reshape and pivot a pandas DataFrame? Walk me through the examples in the data wrangling notebooks.
Prompt 3
I want to create a time series plot with matplotlib. Which notebook in this repo has examples I can adapt?
Prompt 4
Explain the NumPy array operations shown in chapter 4 of these notebooks and how they differ from Python lists.
Prompt 5
Help me understand the data joining and merging examples in the pandas section of these Jupyter Notebooks.
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