#503: The PyArrow Revolution

Talk Python To Me

Michael Kennedy에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Michael Kennedy 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

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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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