pandas 1.3.5+dfsg-4 source package in Ubuntu
Changelog
pandas (1.3.5+dfsg-4) unstable; urgency=medium * Temporarily skip numba tests. (Closes: #1008179) -- Rebecca N. Palmer <email address hidden> Fri, 25 Mar 2022 20:57:26 +0000
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- 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 |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| pandas_1.3.5+dfsg-4.dsc | 4.3 KiB | 9c3b5b7ecef5a17c6cd5dc05429555374f51847df26b9ef1fec2988ccae5e9ca |
| pandas_1.3.5+dfsg.orig.tar.xz | 7.8 MiB | f4e0716afbae3ec09e869d28fd4d8dfc05b1faaa8f7b545d24effd105ca0b3ea |
| pandas_1.3.5+dfsg-4.debian.tar.xz | 64.8 KiB | 887f22ca39e51219c5f4898cdd50fbde8c9df24338ad5ee2f0016220b1c615ec |
Available diffs
- diff from 1.3.5+dfsg-3 to 1.3.5+dfsg-4 (648 bytes)
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
