1 # Buildsheet autogenerated by ravenadm tool -- Do not edit.
3 NAMEBASE= python-pandas
7 SDESC[py310]= Data structures for time series, statistics (3.10)
8 SDESC[v11]= Data structures for time series, statistics (3.11)
9 HOMEPAGE= https://pandas.pydata.org
10 CONTACT= Python_Automaton[python@ironwolf.systems]
13 SITES[main]= PYPI/p/pandas
14 DISTFILE[1]= pandas-2.0.3.tar.gz:main
19 OPTIONS_AVAILABLE= PY310 PY311
20 OPTIONS_STANDARD= none
21 VOPTS[py310]= PY310=ON PY311=OFF
22 VOPTS[v11]= PY310=OFF PY311=ON
24 BUILD_DEPENDS= python-Cython:single:python_used
28 DISTNAME= pandas-2.0.3
34 [PY310].BROKEN_ON= configparser.NoSectionError: No section: 'versioneer'
35 [PY310].BUILDRUN_DEPENDS_ON= python-python-dateutil:single:py310
36 python-pytz:single:py310
37 python-numpy:single:py310
38 python-versioneer:single:py310
39 [PY310].USES_ON= python:py310,sutools
41 [PY311].BUILDRUN_DEPENDS_ON= python-python-dateutil:single:v11
42 python-pytz:single:v11
43 python-numpy:single:v11
44 python-versioneer:single:v11
45 [PY311].USES_ON= python:v11,sutools
47 [FILE:3351:descriptions/desc.single]
49 **pandas** is a Python package that provides fast, flexible, and expressive
51 structures designed to make working with structured (tabular,
53 potentially heterogeneous) and time series data both easy and intuitive. It
54 aims to be the fundamental high-level building block for doing practical,
55 **real world** data analysis in Python. Additionally, it has the broader
57 of becoming **the most powerful and flexible open source data analysis /
58 manipulation tool available in any language**. It is already well on its
62 pandas is well suited for many different kinds of data:
64 - Tabular data with heterogeneously-typed columns, as in an SQL table or
66 - Ordered and unordered (not necessarily fixed-frequency) time series
68 - Arbitrary matrix data (homogeneously typed or heterogeneous) with row
71 - Any other form of observational / statistical data sets. The data
73 need not be labeled at all to be placed into a pandas data structure
75 The two primary data structures of pandas, Series (1-dimensional) and
77 (2-dimensional), handle the vast majority of typical use cases in finance,
78 statistics, social science, and many areas of engineering. For R users,
79 DataFrame provides everything that R's ``data.frame`` provides and much
80 more. pandas is built on top of [NumPy] and is
81 intended to integrate well within a scientific computing environment with
83 other 3rd party libraries.
85 Here are just a few of the things that pandas does well:
87 - Easy handling of **missing data** (represented as NaN) in floating
89 well as non-floating point data
90 - Size mutability: columns can be **inserted and deleted** from DataFrame
92 higher dimensional objects
93 - Automatic and explicit **data alignment**: objects can be explicitly
94 aligned to a set of labels, or the user can simply ignore the labels
96 let `Series`, `DataFrame`, etc. automatically align the data for you in
98 - Powerful, flexible **group by** functionality to perform
99 split-apply-combine operations on data sets, for both aggregating and
101 - Make it **easy to convert** ragged, differently-indexed data in other
102 Python and NumPy data structures into DataFrame objects
103 - Intelligent label-based **slicing**, **fancy indexing**, and
106 - Intuitive **merging** and **joining** data sets
107 - Flexible **reshaping** and pivoting of data sets
108 - **Hierarchical** labeling of axes (possible to have multiple labels per
110 - Robust IO tools for loading data from **flat files** (CSV and
112 Excel files, databases, and saving / loading data from the ultrafast
115 - **Time series**-specific functionality: date range generation and
117 conversion, moving window statistics, date shifting and lagging.
119 Many of these principles are here to address the shortcomings frequently
120 experienced using other languages / scientific research environments. For
122 scientists, working with data is typically divided into multiple stages:
123 munging and cleaning data, analyzing / modeling it, then organizing the
125 of the analysis into a form suitable for plotting or tabular display.
127 the ideal tool for all of these tasks.
131 c02f372a88e0d17f36d3093a644c73cfc1788e876a7c4bcb4020a77512e2043c 5284455 pandas-2.0.3.tar.gz