Python for Data Analysis
Python is where analysts go after SQL — to clean messy columns, reshape a frame, join datasets that do not live in the same database, and build a reproducible step between the raw extract and the dashboard. Pandas is the core library for that work, and these tutorials treat it as an analyst tool, not a software engineering exercise. Each guide is built around the API you will actually type in a notebook.
What you will learn
- ✓Select, filter and reshape a DataFrame without chained-indexing bugs
- ✓Group and aggregate with groupby().agg() and pivot_table()
- ✓Merge frames with the correct how and on, and verify the row count afterwards
- ✓Replace row-by-row loops with vectorised column operations
- ✓Clean nulls, duplicates and inconsistent strings before analysis
46 articles in this topic
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Time Series Analysis & Forecasting in Python: Trend, Seasonality & Moving Averages
Complete guide to time series analysis and forecasting in Python. Master datetime indexing, resampling, moving average smoothing, seasonal decomposition, and the Augmented Dickey-Fuller (ADF) stationarity test.
A/B Testing in Python: From Sample Size to p-Value Without the Ritual
Run A/B tests in Python the right way — simulate control vs variant, check SRM, run chi-square and t-tests, and read p-values correctly.
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Market Basket Analysis in Python: Support, Confidence, and What to Do With Them
Build a market basket analysis in Python — basket matrix, co-occurrence, lift scores, and a rule-based recommender from transaction logs.
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