Pandas vs R — cheat sheet
This is a Python/Pandas vs R cheatsheet for a quick reference for switching between both. The post contains equivalent operations between Pandas and R. The post includes the most used operations needed on a daily baisis for data analysis.
Full article: Pandas vs R — cheat sheet
Have in mind that some examples might differ due to different indexing or updates.
If you want to contribute feel free to suggest changes or additions on GitHub: pandas_r_cheatsheet.csv
Main Differences: R and Pandas
Pandas and R are both popular tools/languages for data analysis, manipulation and statistics. Some key differences between them:
Indexing
One big difference between R and Pandas is indexing:
- R — 1 based
* Indexing from zero in R
* Package ‘index0’
* Pandas — 0 based
Syntax
- R syntax is tailored for statistical analysis. It uses functions and operators that are well-suited for data manipulation, statistics and visualization.
- Pandas uses Python syntax, which is more general-purpose. It leverages Python’s data structures like DataFrames and Series for data manipulation. Pandas also use the indexing, slicing and other Python techniques.
Data Structures
R — uses data structures like:
- Vectors
- Lists
- Matrices
- Dataframes
Pandas — Intro to data structures
- DataFrames
- Series
DataFrames are the primary data structure for data analysis in R and Pandas.
Package Ecosystem
Both offer mature package systems with a wide variety of packages related to data analysis and visualization.
- R has a vast repository of packages on CRAN (Comprehensive R Archive Network) dedicated to statistics, data analysis, and visualization.
- Pandas is part of the Python ecosystem, which has a broader range of packages for various purposes beyond data analysis.
Community
- R has a strong community of experienced statisticians and data analysts, and there are numerous resources and documentation available for R users.
- Pandas benefits from the larger Python community, which offers extensive resources and documentation for data analysis and programming in general. People from different scientific areas join Python and Pandas communities to solve everyday problems.
