A Coding Guide to Scaling Advanced Pandas Workflows with Modin
In this tutorial, we delve into Modin , a powerful drop-in replacement for Pandas that leverages parallel computing to speed up data workflows significantly. By importing modin.pandas as pd, we transform our pandas code into a distributed computation powerhouse. Our goal here is to understand how Modin performs across real-world data operations, such as groupby, joins, cleaning, and time series analysis, all while running on Google Colab. We benchmark each task against the standard Pandas library to see how much faster and more memory-efficient Modin can be. Copy Code Copied Use a different Browser !pip install "modin[ray]" -q import warnings warnings.filterwarnings('ignore') import numpy as np import pandas as pd import time import os from typing import Dict, Any import modin.pandas as mpd import ray ray.init(ignore_reinit_error=True, num_cpus=2) print(f"Ray initialized with {ray.cluster_resources()}") We begin by installing Modin with ...