- Python and Data Analysis - excellent guide for data anaylsis with Python - basic Python, data wrangling, data visualization and more. Part of Chris Albon's excellent website
- Python for Data Analysis - a blog with a detailed guide for data analysis with Python from A (Python basics) to Z (building predictive models in Python).
- Data Exploration in Python - using NumPy, Pandas and Matplotlib.
- Python - Data Science Tutorial - another tutorial from Tutorialspoint.com.
- Pandas Official Documentation - Pandas has a pretty good documentation. Here you can read about different Pandas functions and methods. Also check out 10 Minutes to Pandas and the tutorials and Pandas Cookbook.
- Pandas API - all of Pandas functions, classes, methods and attribues.
- Indexing and Selecting Data
- Merge, Join, and Concatenate
- Group By: split-apply-combine
- Working with Missing Data
- Reshaping and Pivot Tables
- Time Series / Date Functionality
- Time Deltas
- Categorical Data
- Pandas Official Cheat Sheet
- Introduction to Pandas
- A Quick Introduction to the “Pandas” Python Library - (shamelessly promoting my content) a tutorial for Pandas I wrote.
- Pandas Tutorial: Aggregation and Grouping
- Pandas Tutorial: Important Data Formatting Methods (merge, sort, reset_index, fillna)
- Learn Pandas - a BitBucket repo with Pandas lessons, cookbooks and tools.
- Pandas Jupyter Notebooks - IPython Notebook(s) demonstrating pandas functionality. From Data Science IPython Notebooks.
- Python Pandas Tutorial - a very detailed Pandas tutorial from Tutorialspoint.com.
- List of Resources for Learning Data Analysis with Pandas - from Data School.
- 20 Pandas Functions That Will Boost Your Data Analysis Process
- "Minimally Sufficient Pandas" - a blog post that argues that only a small subset of the library is sufficient to complete nearly all of the data analysis tasks that one will encounter. This minimally sufficient subset of the library will benefit both beginners and professionals using Pandas.
- Data Manipulation with Pandas - from the Python Data Science Handbook.
- Data School's Pandas Series - if you prefer learning from videos. A handy video series geared towards beginners with thorough explanations. Might be a little dated as it's from 2016.
- 10-minute Tour of Pandas - by Wes McKinney.
- Pandas From The Ground Up - PyCon 2015 - a video lecture on Pandas by Brandon Rhodes.
- 101 Pandas Exercises for Data Analysis - 101 python pandas exercises are designed to challenge your logical muscle and to help internalize data manipulation with python’s favorite package for data analysis. The questions are of 3 levels of difficulties with L1 being the easiest to L3 being the hardest.
- Pandas Exercises on w3resource
- Pandas Exercises - practice your pandas skills: a repo with exercises to practice Pandas.
- 100 Pandas Puzzles - 100 data puzzles for pandas, ranging from short and simple to super tricky.
- Introduction to Exploratory Data Analysis - a GitHub repo of a workshop and lesson on Exploratory Data Analysis from Ritika Bhasker's GitHub.
- EDA Tutorial
- What is a Data Detective? How to go Deeper With Your Data
- Basic Statistics in Python: Descriptive Statistics
- Basic Statistics in Python: Probability
- Chris Albon's Statistics Tutorials
- NumPy Tutorial - official NumPy tutorial.
- NumPy and Scipy Documentation - official NumPy and SciPy documentation.
- Introduction to NumPy - from the Python Data Science Handbook.
- NumPy Tutorial - a very detailed NumPy tutorial.
- A Quick Introduction to the NumPy Library - another shameless promotion of my content: a quick intro to NumPy.
- NumPy Jupyter Notebooks - IPython Notebook(s) demonstrating NumPy functionality. From Data Science IPython Notebooks.
- NumPy Introduction - w3schools
- Python for Data Analysis - materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney.
- Python Data Science Handbook - this website contains the full text of the Python Data Science Handbook by Jake VanderPlas.
- The content is also available on a GitHub Repo in the form of Jupyter notebooks.