Python Pandas

Pandas is how Python people actually work with data. Six tutorials take you through DataFrames, selecting data with loc and iloc, cleaning missing values, groupby and pivot tables, and merging datasets. There is also an honest comparison with Polars and Dask so you know when to switch.

Pandas Project: Clean a Messy Real-World Dataset

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This pandas project takes a genuinely messy dataset and turns it into numbers you can trust. You will audit the mess first, fix it in clear stages (types, dates, duplicates, categories, outliers), validate the result, then answer three real business questions with groupby and merge. By the end you have a notebook you can put on GitHub and point a hiring manager at. “Most of data science is janitor work. The model is the easy part, the cleaning is the ... Read More

Pandas Merge, Join, Concat: Combining DataFrames

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You have customer data in one table and order data in another. To answer a real question, you need them side by side. This pandas merge guide shows you exactly which tool to reach for: merge for SQL (Structured Query Language) style joins (inner, left, right, outer), concat for stacking tables, and join for combining on the index. “Pick the right join and the answer falls out in one line. Pick the wrong one and you spend the afternoon wondering ... Read More

Python: Pandas vs Polars vs Dask, DataFrames Compared

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You loaded a big CSV, hit run, and watched your Pandas script crawl while a single CPU core did all the work and your RAM crept toward full. That slow moment is exactly what the pandas vs polars question is about. This guide puts Pandas, Polars, and Dask side by side with real tested output, so you can see where each one shines and pick the right DataFrame library without guessing. “Pick the tool that fits the data, not the ... Read More