| University Libraries
Almost all tutorials on using Python for data assume experience with Python or enough programming knowledge and experience to be able to follow along with a very quick review.
If you want a single course for people with no prior experience, this is the best option:
Otherwise, start with any general tutorial and make sure that you learn about the following before following a Panda's-specific tutorial below.
Pandas - Python for Data Analysis, provides R-like tools for data frames.
Python for Data Analysis by
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the Jupyter notebook and IPython shell for exploratory computing Learn basic and advanced features in NumPy Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
Wes McKinney is the author of pandas – a Python library for data analysis. This book covers basic usage of pandas and NumPy for data wrangling and analysis and serves as a good companion to the pandas documentation.