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Changelog polars/ py-polars

[future] Polars v0.13

[future] py-polars 0.7.0

  • feature

    • [python] add parquet compression
    • [python] implicitly download raw files from the web in read_parquet, read_csv.
    • [python | rust] methods for local peak finding in numerical series
    • [python | rust] faster query optimization due to local memory arena's.
    • [rust] reduce default compile time by making less features default.

  • bug fix

    • [python] support file buffers for reading and writing csv and parquet
    • [python | rust] fix csv-parser: allow new-line character in a string field
    • [python | rust] don't let predicate-pushdown pass shift | sort operation to maintain correctness.

py-polars 0.6.7

  • performance
    • [python | rust] use mimalloc global allocator
    • [python | rust] undo performance regression on large number of threads
  • bug fix
    • [python | rust] fix accidental over-allocation in csv-parser
    • [python] support agg (dictionary aggregation) for downsample

py-polars 0.6.6

  • performance
    • [python | rust] categorical type groupby keys (use size hint)
    • [python | rust] remove indirection layer in vector hasher
    • [python | rust] improve performance of null array creation
  • bug fix
    • [python] implement set_with_mask for Boolean type
    • [python | rust] don't panic (instead return null) in dataframe aggregation std and var
  • other
    • [rust] internal refactors

py-polars 0.6.5

  • bug fix
    • [python] fix various pyarrow related bugs

py-polars 0.6.4

  • feature
    • [python] render html tables
  • performance
    • [python] default to pyarrow for parquet reading
    • [python | rust] use u32 instead of usize in groupby and join to increase cache coherence and reduce memory pressure.

Polars v0.12

  • Lot's of bug fixes

Polars v0.10 / v0.11

  • CSV Read IO

    • Parallel csv reader

  • Sample DataFrames/ Series

  • Performance increase in take kernel

  • Performance increase in ChunkedArray builders

  • Join operation on multiple columns.

  • ~3.5 x performance increase in groupby operations (measured on db-benchmark), due to embarrassingly parallel grouping and better branch prediction (tight loops).

  • Performance increase on join operation due to better branch prediction.

  • Categorical datatype and global string cache (BETA).

  • Lazy

    • Lot's of bug fixes in optimizer.
    • Parallel execution of Physical plan
    • Partition window function
    • More simplify expression optimizations.
    • Caching
    • Alpha release of Aggregate pushdown optimization.

  • Start of general Object type in ChunkedArray/DataFrames/Series