Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.
pandas is well suited for many different kinds of data:
The two primary data structures of pandas, Series(1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame provides everything that R’s data.frame
provides and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries.
Here are just a few of the things that pandas does well:
Many of these principles are here to address the shortcomings frequently experienced using other languages / scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis into a form suitable for plotting or tabular display. pandas is the ideal tool for all of these tasks.
Some other notes
Content of the Guide
What’s New
Installation
Contributing to pandas
Package overview
10 Minutes to pandas
Tutorials
Cookbook
Intro to Data Structures
Essential Basic Functionality
Working with Text Data
Options and Settings
Indexing and Selecting Data
MultiIndex / Advanced Indexing
Computational tools
Working with missing data
Group By: split-apply-combine
Merge, join, and concatenate
Reshaping and Pivot Tables
Time Series / Date functionality
Time Deltas
Categorical Data
Visualization
Styling
IO Tools (Text, CSV, HDF5, …)
Enhancing Performance
Sparse data structures
Frequently Asked Questions (FAQ)
rpy2 / R interface
pandas Ecosystem
Comparison with R / R libraries
Comparison with SQL
Comparison with SAS
Comparison with Stata
API Reference
Developer
Internals
Extending Pandas
Release Notes
Download the guide, or read it online, here.
DSC Resources
Posted 12 April 2021
© 2021 TechTarget, Inc.
Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Most popular articles
You need to be a member of Data Science Central to add comments!
Join Data Science Central