Make working with "relational" or "labeled" data both easy and intuitive
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language.
- Developed at devel:languages:python:numeric
- Sources inherited from project openSUSE:Factory
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Source Files
Filename | Size | Changed |
---|---|---|
_constraints | 0000000163 163 Bytes | |
_multibuild | 0000000122 122 Bytes | |
_service | 0000000605 605 Bytes | |
pandas-2.1.3.tar.gz | 0049603896 47.3 MB | |
python-pandas.changes | 0000215149 210 KB | |
python-pandas.spec | 0000020536 20.1 KB |
Revision 58 (latest revision is 72)
Ana Guerrero (anag+factory)
accepted
request 1130126
from
Steve Kowalik (StevenK)
(revision 58)
- Update to 2.1.3: * Reverted deprecation of fill_method=None in DataFrame.pct_change(), Series.pct_change(), DataFrameGroupBy.pct_change(), and SeriesGroupBy.pct_change(); the values 'backfill', 'bfill', 'pad', and 'ffill' are still deprecated * Fixed regressions + Fixed infinite recursion from operations that return a new object on some DataFrame subclasses + Fixed regression in DataFrame.join() where result has missing values and dtype is arrow backed string + Fixed regression in rolling() where non-nanosecond index or on column would produce incorrect results + Fixed regression in DataFrame.resample() which was extrapolating back to origin when origin was outside its bounds + Fixed regression in DataFrame.sort_index() which was not sorting correctly when the index was a sliced MultiIndex + Fixed regression in DataFrameGroupBy.agg() and SeriesGroupBy.agg() where if the option compute.use_numba was set to True, groupby methods not supported by the numba engine would raise a TypeError + Fixed performance regression with wide DataFrames, typically involving methods where all columns were accessed individually + Fixed regression in merge_asof() raising TypeError for by with datetime and timedelta dtypes + Fixed regression in read_parquet() when reading a file with a string column consisting of more than 2 GB of string data and using the "string" dtype + Fixed regression in DataFrame.to_sql() not roundtripping datetime columns correctly for sqlite when using detect_types + Fixed regression in construction of certain DataFrame or Series subclasses
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