Note
Estimated Reading Time: 10 minutes
You can follow along this tutorial in a Jupyter notebook
here.
Quick Start Guide#To install the most recent stable release for Modin run the following:
For further instructions on how to install Modin with conda or for specific platforms or engines, see our detailed installation guide.
Modin acts as a drop-in replacement for pandas so you simply have to replace the import of pandas with the import of Modin as follows to speed up your pandas workflows:
# import pandas as pd import modin.pandas as pdSummary#
Hopefully, this tutorial demonstrated how Modin delivers significant speedup on pandas operations without the need for any extra effort. Throughout example, we moved from working with 100MBs of data to 20GBs of data all without having to change anything or manually optimize our code to achieve the level of scalable performance that Modin provides.
Note that in this quickstart example, we’ve only shown read_csv, concat, apply, but these are not the only pandas operations that Modin optimizes for. In fact, Modin covers more than 90% of the pandas API, yielding considerable speedups for many common operations.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4