DataFrames, data cleaning, groupby, merges, time series, and the patterns that make pandas fast on real datasets. This is the pandas course for engineers building AI systems — not generic data science tutorials.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on pandas you can find — even without producing a single minute of custom video.
This course focuses on the pandas patterns you need when building AI pipelines — data cleaning before fine-tuning, feature engineering for ML, and moving data between pandas and databases efficiently.
Most pandas courses ignore performance until it's too late. This one explains vectorized operations, why apply() is slow, and when to reach for Polars or DuckDB — starting on day 2.
Instead of re-explaining the pandas API, this course links to the official pandas documentation, the user guide sections that matter most, and the best performance benchmarks.
Each day is one major pandas capability. Read the explanation, run the code examples in a Jupyter notebook, and understand a new layer of the library.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
Creating DataFrames from dicts, CSVs, and databases. Indexing with .loc, .iloc, and boolean masks. The difference between a view and a copy (and why it causes bugs). Data types and dtypes.
Missing values (isnull, fillna, dropna), duplicate detection, string operations with .str, type conversion, and the real-world patterns for cleaning messy CSV data before it hits your AI pipeline.
The split-apply-combine pattern, custom aggregation functions, pivot_table vs crosstab, multi-level groupby, and transforming grouped results back into the original shape with transform().
merge() vs concat() vs join(), all four join types (inner/left/right/outer), merging on multiple keys, handling duplicate columns after joins, and debugging mismatched join keys.
DatetimeIndex, resampling, rolling windows, and time-zone handling. Performance patterns: vectorization vs apply, Categoricals, chunked reading. Connecting pandas to scikit-learn, torch Datasets, and LLM fine-tuning pipelines.
Instead of shooting our own videos, we link to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Foundational pandas tutorials covering DataFrame creation, indexing, and the core data manipulation operations.
The split-apply-combine pattern, custom aggregation, and transform — the most powerful and commonly misunderstood pandas feature.
Real-world data cleaning walkthroughs — handling nulls, string cleaning, type coercion, and duplicate detection on messy datasets.
When pandas is fast enough and when Polars or DuckDB is the right tool — benchmark comparisons and migration strategies.
Connecting pandas DataFrames to scikit-learn pipelines, feature engineering workflows, and the pandas patterns ML practitioners use every day.
The best way to deepen understanding is to read the canonical open-source implementations. Clone them, trace the code, understand how the concepts in this course get applied in production.
The official pandas repository. The /doc/source/user_guide directory has the most detailed reference for every feature covered in this course.
The Rust-based DataFrame library that outperforms pandas on large datasets. Read the examples to understand when to graduate from pandas.
In-process SQL analytics engine that reads parquet, CSV, and pandas DataFrames natively. Often faster than pandas for analytical queries.
100 pandas exercises organized by difficulty. The best way to test your understanding after each day of this course.
Pandas is the primary data manipulation tool in every ML pipeline. If you're building models, fine-tuning LLMs, or creating feature stores, this course covers the pandas you'll use every day.
Your API returns data, your analytics need aggregations, your ETL pipelines need cleaning. Pandas is the tool for all of it, and this course gets you proficient fast.
Coming from Excel or SQL? Pandas is the bridge. This course explains the DataFrame model in terms that map cleanly to spreadsheet and SQL concepts you already know.
The 2-day in-person Precision AI Academy bootcamp covers AI engineering in depth — hands-on, with practitioners who build AI systems for a living. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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