Ubuntu
pandas package

pandas 1.3.5+dfsg-5 source package in Ubuntu

Changelog

pandas (1.3.5+dfsg-5) unstable; urgency=medium

  * Fix FTBFS with Sphinx 5.  (Closes: #1013375)

 -- Rebecca N. Palmer <email address hidden>  Sun, 26 Jun 2022 15:06:07 +0100

Upload details

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

See full publishing history Publishing

Series Pocket Published Component Section
Kinetic release universe python

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File Size SHA-256 Checksum
pandas_1.3.5+dfsg-5.dsc 4.3 KiB 66d07b34736d6234d8bb1f900f89164029af751828ae784b2785d1b777664ef9
pandas_1.3.5+dfsg.orig.tar.xz 7.8 MiB f4e0716afbae3ec09e869d28fd4d8dfc05b1faaa8f7b545d24effd105ca0b3ea
pandas_1.3.5+dfsg-5.debian.tar.xz 65.2 KiB e670a621df89654254180f0fb415d581454ef3686ea103ca8c5fc786201db9cd

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