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[py39]= Data structures for time series, statistics (3.9)
9 HOMEPAGE= https://pandas.pydata.org
10 CONTACT= Python_Automaton[python@ironwolf.systems]
13 SITES[main]= PYPI/p/pandas
14 DISTFILE[1]= pandas-1.5.0.tar.gz:main
19 OPTIONS_AVAILABLE= PY310 PY39
20 OPTIONS_STANDARD= none
21 VOPTS[py310]= PY310=ON PY39=OFF
22 VOPTS[py39]= PY310=OFF PY39=ON
24 BUILD_DEPENDS= python-Cython:single:python_used
28 DISTNAME= pandas-1.5.0
34 [PY39].BUILDRUN_DEPENDS_ON= python-python-dateutil:single:py39
35 python-pytz:single:py39
36 python-numpy:single:py39
37 [PY39].USES_ON= python:py39,sutools
39 [PY310].BUILDRUN_DEPENDS_ON= python-python-dateutil:single:py310
40 python-pytz:single:py310
41 python-numpy:single:py310
42 [PY310].USES_ON= python:py310,sutools
44 [FILE:3351:descriptions/desc.single]
46 **pandas** is a Python package that provides fast, flexible, and expressive
48 structures designed to make working with structured (tabular,
50 potentially heterogeneous) and time series data both easy and intuitive. It
51 aims to be the fundamental high-level building block for doing practical,
52 **real world** data analysis in Python. Additionally, it has the broader
54 of becoming **the most powerful and flexible open source data analysis /
55 manipulation tool available in any language**. It is already well on its
59 pandas is well suited for many different kinds of data:
61 - Tabular data with heterogeneously-typed columns, as in an SQL table or
63 - Ordered and unordered (not necessarily fixed-frequency) time series
65 - Arbitrary matrix data (homogeneously typed or heterogeneous) with row
68 - Any other form of observational / statistical data sets. The data
70 need not be labeled at all to be placed into a pandas data structure
72 The two primary data structures of pandas, Series (1-dimensional) and
74 (2-dimensional), handle the vast majority of typical use cases in finance,
75 statistics, social science, and many areas of engineering. For R users,
76 DataFrame provides everything that R's ``data.frame`` provides and much
77 more. pandas is built on top of [NumPy] and is
78 intended to integrate well within a scientific computing environment with
80 other 3rd party libraries.
82 Here are just a few of the things that pandas does well:
84 - Easy handling of **missing data** (represented as NaN) in floating
86 well as non-floating point data
87 - Size mutability: columns can be **inserted and deleted** from DataFrame
89 higher dimensional objects
90 - Automatic and explicit **data alignment**: objects can be explicitly
91 aligned to a set of labels, or the user can simply ignore the labels
93 let `Series`, `DataFrame`, etc. automatically align the data for you in
95 - Powerful, flexible **group by** functionality to perform
96 split-apply-combine operations on data sets, for both aggregating and
98 - Make it **easy to convert** ragged, differently-indexed data in other
99 Python and NumPy data structures into DataFrame objects
100 - Intelligent label-based **slicing**, **fancy indexing**, and
103 - Intuitive **merging** and **joining** data sets
104 - Flexible **reshaping** and pivoting of data sets
105 - **Hierarchical** labeling of axes (possible to have multiple labels per
107 - Robust IO tools for loading data from **flat files** (CSV and
109 Excel files, databases, and saving / loading data from the ultrafast
112 - **Time series**-specific functionality: date range generation and
114 conversion, moving window statistics, date shifting and lagging.
116 Many of these principles are here to address the shortcomings frequently
117 experienced using other languages / scientific research environments. For
119 scientists, working with data is typically divided into multiple stages:
120 munging and cleaning data, analyzing / modeling it, then organizing the
122 of the analysis into a form suitable for plotting or tabular display.
124 the ideal tool for all of these tasks.
128 3ee61b881d2f64dd90c356eb4a4a4de75376586cd3c9341c6c0fcaae18d52977 5191537 pandas-1.5.0.tar.gz