pandas 1.5.3+dfsg-5 source package in Ubuntu
Changelog
pandas (1.5.3+dfsg-5) unstable; urgency=medium * Backport the following from experimental: - Don't use numexpr for **, as it has different overflow behaviour. - Tests: don't use : in numexpr variable names. - Use tzdata-legacy where old-style timezone names are used. - Clean up after tests. - Tests: re-enable numba tests. -- Rebecca N. Palmer <email address hidden> Thu, 17 Aug 2023 20:11:19 +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 | Published | Component | Section |
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Downloads
File | Size | SHA-256 Checksum |
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pandas_1.5.3+dfsg-5.dsc | 4.8 KiB | fa03187a2dd13fd5af81ec336920e030177af8465ab85da3cd266654e0996b95 |
pandas_1.5.3+dfsg.orig.tar.xz | 8.6 MiB | 5c50f7c36d93ed1e6e41fdd6c1116def08dadbe64245365e3410009bcbb557f3 |
pandas_1.5.3+dfsg-5.debian.tar.xz | 70.5 KiB | e65fb9eac749dadfab12ddd8a2166ef9555bc546cc503b6c4f4c4e4c930e2a0a |
Available diffs
- diff from 1.5.3+dfsg-4 to 1.5.3+dfsg-5 (2.7 KiB)
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