Pandas vs Pandasql: What are the differences?
Developers describe Pandas as "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. On the other hand, Pandasql is detailed as "Make python speak SQL". pandasql allows you to query pandas DataFrames using SQL syntax. It works similarly to sqldf in R. pandasql seeks to provide a more familiar way of manipulating and cleaning data for people new to Python or pandas.
Pandas belongs to "Data Science Tools" category of the tech stack, while Pandasql can be primarily classified under "Database Tools".
Pandas and Pandasql are both open source tools. It seems that Pandas with 20.2K GitHub stars and 8K forks on GitHub has more adoption than Pandasql with 738 GitHub stars and 109 GitHub forks.
What is Pandas?
What is Pandasql?
Need advice about which tool to choose?Ask the StackShare community!
Why do developers choose Pandasql?
What are the cons of using Pandas?
What are the cons of using Pandasql?
What companies use Pandasql?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Pandasql?
Sign up to get full access to all the tool integrationsMake informed product decisions
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