Pandas vs PySpark: What are the differences?
What is Pandas? High-performance, easy-to-use data structures and data analysis tools for the Python programming language. Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
What is PySpark? The Python API for Spark. It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
Pandas and PySpark can be categorized as "Data Science" tools.
Pandas is an open source tool with 20.7K GitHub stars and 8.16K GitHub forks. Here's a link to Pandas's open source repository on GitHub.
Instacart, Twilio SendGrid, and Sighten are some of the popular companies that use Pandas, whereas PySpark is used by Repro, Autolist, and Shuttl. Pandas has a broader approval, being mentioned in 110 company stacks & 341 developers stacks; compared to PySpark, which is listed in 8 company stacks and 6 developer stacks.
What is Pandas?
What is PySpark?
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Why do developers choose PySpark?
What are the cons of using Pandas?
What are the cons of using PySpark?
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Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead