Why learn Pandas & NumPy?
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The default toolkit for tabular data in Python.
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Used by analysts, scientists, and ML engineers daily.
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Replaces spreadsheets for any non-trivial dataset.
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Foundation for everything from EDA to ML feature engineering.
What you can build with Pandas & NumPy
Cleaning and exploring datasets ETL and data pipelines Feature engineering for ML Reporting and ad-hoc analysis
Pandas & NumPy tutorials
19 articles · page 1 of 1Hand-written tutorials, ordered as a recommended learning path.
- 01 What Is Pandas? A clear introduction to pandas — what DataFrames and Series are, why analysts and ML engineers live in it, how to install it, and a tiny first end-to-end example.
- 02 DataFrames Basics A practical guide to the daily DataFrame moves — read_csv and read_json, head and info, column selection, loc vs iloc, boolean filtering, sorting, and value_counts.
- 03 groupby & merge A practical guide to combining and summarising DataFrames — groupby with named aggregations, multi-column aggregates, the four merge styles, and stacking with concat.
- 04 Pandas Cheat Sheet A practical Pandas cheat sheet covering 50 essential DataFrame operations including selection, filtering, grouping, merging, and data cleaning with copy-paste examples.
- 05 Pandas Plotting Create effective charts directly from Pandas DataFrames with plot(), including line, bar, histogram, scatter, box plots, and customization tips.
- 06 read_csv Options Master pandas read_csv with practical examples for every important parameter including dtypes, parsing dates, handling missing data, chunked reading, and encoding.
- 07 apply vs Vectorize When to reach for .apply and when vectorized operations win. A practical comparison with benchmarks, mental models, and the patterns that keep Pandas code both readable and fast.
- 08 Categorical Data Use Pandas Categorical dtype to cut memory, speed up groupby, and encode ordered categories cleanly with practical conversion and pitfall notes.
- 09 Data Cleaning Hands-on pandas patterns for cleaning messy real-world data, covering missing values, types, duplicates, strings, and a reliable cleaning pipeline.
- 10 GroupBy Master pandas groupby with single and multi-column aggregations, named outputs, transform, and filter for clean analytical pipelines.
- 11 Merge/Join Pick the right pandas combining function with clear examples of inner, outer, left, right joins, concat, and the gotchas that cause silent bugs.
- 12 Performance Practical pandas speedups: vectorization, dtype tuning, categorical columns, eval/query, and chunked I/O patterns that turn slow scripts into responsive pipelines.
- 13 MultiIndex A practical guide to Pandas MultiIndex: when to use it, how it really works, and the slicing, stacking, and groupby patterns that make hierarchical data manageable.
- 14 Pivot Tables Learn how to use pandas pivot_table to summarize, reshape, and aggregate data with multiple indexes, columns, and custom aggregation functions in real workflows.
- 15 String Methods A practical tour of the Pandas .str accessor: cleaning text, extracting patterns, splitting and joining, dealing with missing values, and writing string code that stays fast.
- 16 Time Series Work with datetime indexes, resampling, rolling windows, lag features, and timezone gotchas to analyze time series cleanly in pandas.
- 17 Window Functions Use Pandas rolling, expanding, and ewm window functions to compute moving averages, running totals, and time-aware aggregations with clear examples.
- 18 Window Functions Master Pandas window functions: rolling averages, expanding cumulative stats, exponential weighting, groupby + rolling, and custom window operations.
- 19 Performance Tips Speed up Pandas code with vectorization, categorical dtypes, chunked reading, eval/query, PyArrow backend, and memory profiling techniques.