Pandas is at a the core of virtually all data science done in Python, that is virtually all data science. Since it's beginning, Pandas has been based upon numpy. But changes are afoot to update those internals and you can now optionally use PyArrow. PyArrow comes with a ton of benefits including it's columnar format which makes answering analytical questions faster, support for a range of high performance file formats, inter-machine data streaming, faster file IO and more. Reuven Lerner is here to give us the low-down on the PyArrow revolution.

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Links from the show

Reuven: github.com/reuven

Apache Arrow: github.com

Parquet: parquet.apache.org

Feather format: arrow.apache.org

Python Workout Book (45% off with code talkpython45): manning.com

Pandas Workout Book (45% off with code talkpython45): manning.com

Pandas: pandas.pydata.org

PyArrow CSV docs: arrow.apache.org

Future string inference in Pandas: pandas.pydata.org

Pandas NA/nullable dtypes: pandas.pydata.org

Pandas `.iloc` indexing: pandas.pydata.org

DuckDB: duckdb.org

Pandas user guide: pandas.pydata.org

Pandas GitHub issues: github.com

Watch this episode on YouTube: youtube.com

Episode #503 deep-dive: talkpython.fm/503

Episode transcripts: talkpython.fm

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