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: manning.com
Pandas Workout Book: 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 transcripts: talkpython.fm

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#503: The PyArrow Revolution

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