#Pandas

Combining & Reshaping DataFrames in Pandas Four operations cover virtually every real-world need for combining and reshaping DataFrames: merge(), concat(), melt(), and pivot(). Knowing when to reach for each — and what pitfalls to avoid — saves …

We are excited to share the materials for our data engineering and exploratory data analysis (EDA) session: Advanced Data Manipulation & EDA with Pandas: Preprocessing for Machine Learning. This session serves as the critical bridge from raw data …

Advanced Deduplication Strategies in Polars: Beyond Sort and Drop Deduplication is one of the most common yet performance-critical operations in data engineering. Whether you are building Change Data Capture (CDC) pipelines, cleaning telemetry …

Advanced pandas GroupBy and Window Functions Dataset Setup All examples use a retail sales dataset with transactions across stores, regions, and product categories. import pandas as pd import numpy as np np.random.seed(42) dates = …

pandas groupby() — A Practical Guide What is groupby()? groupby() splits a DataFrame into buckets based on one or more column values, lets you run a function on each bucket independently, then stitches all the results back together into one …

Pandas 101: Data Manipulation in Python Pandas is the most popular Python library for data manipulation and analysis. It provides high-performance, easy-to-use data structures and data analysis tools. Core Data Structures Series A one-dimensional …

From Pandas to Polars: A Paradigm Shift in DataFrame Processing Welcome to the first installment of our Polars blog series! If you’ve spent years mastering Pandas and are curious about what makes Polars the talk of the data community, this post …

Filtering Large DataFrames in PySpark: isin vs Broadcast Join A Practical Guide for Developers Coming from Pandas Executive Summary In pandas, filtering a large DataFrame using values from a small one is trivial — you use .isin() or .merge() and …

Python Logging Module — A Brief Course for Pandas & PySpark Users Why Logging Instead of print() print() is fine for quick experiments but terrible for production code because it has no level, no timestamp, no module name, and no way to turn it …

PySpark Datetime Cheatsheet — For Pandas Users All examples use: from pyspark.sql import functions as F import pandas as pd 1. Casting Strings to Dates / Timestamps Pandas df["CreatedDate"] = pd.to_datetime(df["CreatedDate"]) …