Ubuntu
pandas package

pandas 2.1.4+dfsg-4 source package in Ubuntu

Changelog

pandas (2.1.4+dfsg-4) unstable; urgency=medium

  * Tests: shorten ignoredtests to avoid timeout.
  * Temporarily skip numba tests (workaround for #1033907).
  * Update transition Breaks.

 -- Rebecca N. Palmer <email address hidden>  Fri, 09 Feb 2024 20:48:14 +0000

Upload details

Uploaded by:
Debian Science Team
Uploaded to:
Sid
Original maintainer:
Debian Science Team
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

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Downloads

File Size SHA-256 Checksum
pandas_2.1.4+dfsg-4.dsc 4.9 KiB 588fdbb1e70961f1812180b05c68e9cdc33acfc14ba3f5c7ce48544e044e6e0b
pandas_2.1.4+dfsg.orig.tar.xz 10.6 MiB b516a6f52b8be6ae5461666143f0c9f9013761c26cc6109ffc7253e0b3119502
pandas_2.1.4+dfsg-4.debian.tar.xz 75.9 KiB 72a323e82d8d4a44d2e893f4a1cf6e451281055619cab84cd3c44b3880675e3a

Available diffs

No changes file available.

Binary packages built by this source

python-pandas-doc: data structures for "relational" or "labeled" data - documentation

 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. pandas is well suited for many different kinds of
 data:
 .
  - Tabular data with heterogeneously-typed columns, as in an SQL
    table or Excel spreadsheet
  - Ordered and unordered (not necessarily fixed-frequency) time
    series data.
  - Arbitrary matrix data (homogeneously typed or heterogeneous) with
    row and column labels
  - Any other form of observational / statistical data sets. The data
    actually need not be labeled at all to be placed into a pandas
    data structure
 .
 This package contains the documentation.

python3-pandas: data structures for "relational" or "labeled" data

 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. pandas is well suited for many different kinds of
 data:
 .
  - Tabular data with heterogeneously-typed columns, as in an SQL
    table or Excel spreadsheet
  - Ordered and unordered (not necessarily fixed-frequency) time
    series data.
  - Arbitrary matrix data (homogeneously typed or heterogeneous) with
    row and column labels
  - Any other form of observational / statistical data sets. The data
    actually need not be labeled at all to be placed into a pandas
    data structure
 .
 This package contains the Python 3 version.

python3-pandas-lib: low-level implementations and bindings for pandas

 This is a low-level package for python3-pandas providing
 architecture-dependent extensions.
 .
 Users should not need to install it directly.

python3-pandas-lib-dbgsym: debug symbols for python3-pandas-lib