pandas

Powerful data structures for data analysis, time series,and statistics

These details have not been verified by PyPI

Project links

Homepage

Development Status
- 4 - Beta
Environment
- Console
Intended Audience
- Science/Research
Operating System
- OS Independent
Programming Language
- Cython
- Python
- Python :: 2
- Python :: 3
Topic
- Scientific/Engineering

Project description

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.

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

The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame provides everything that R’s data.frame provides and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries.

Here are just a few of the things that pandas does well:

Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data

Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects

Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations

Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data

Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects

Intelligent label-based slicing, fancy indexing, and subsetting of large data sets

Intuitive merging and joining data sets

Flexible reshaping and pivoting of data sets

Hierarchical labeling of axes (possible to have multiple labels per tick)

Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format

Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.

Many of these principles are here to address the shortcomings frequently experienced using other languages / scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis into a form suitable for plotting or tabular display. pandas is the ideal tool for all of these tasks.

Note

Windows binaries built against NumPy 1.6.1

Project details

These details have not been verified by PyPI

Project links

Homepage

Development Status
- 4 - Beta
Environment
- Console
Intended Audience
- Science/Research
Operating System
- OS Independent
Programming Language
- Cython
- Python
- Python :: 2
- Python :: 3
Topic
- Scientific/Engineering

Release history Release notifications | RSS feed

2.2.3

Sep 20, 2024

2.2.2

Apr 10, 2024

2.2.1

Feb 23, 2024

2.2.0

Jan 20, 2024

2.2.0rc0 pre-release

Dec 22, 2023

2.1.4

Dec 8, 2023

2.1.3

Nov 10, 2023

2.1.2

Oct 26, 2023

2.1.1

Sep 20, 2023

2.1.0

Aug 30, 2023

2.1.0rc0 pre-release

Aug 11, 2023

2.0.3

Jun 28, 2023

2.0.2

May 29, 2023

2.0.1

Apr 24, 2023

2.0.0

Apr 3, 2023

2.0.0rc1 pre-release

Mar 16, 2023

2.0.0rc0 pre-release

Feb 20, 2023

1.5.3

Jan 19, 2023

1.5.2

Nov 23, 2022

1.5.1

Oct 19, 2022

1.5.0

Sep 19, 2022

1.5.0rc0 pre-release

Aug 24, 2022

1.4.4

Aug 31, 2022

1.4.3

Jun 23, 2022

1.4.2

Apr 2, 2022

1.4.1

Feb 12, 2022

1.4.0

Jan 22, 2022

1.4.0rc0 pre-release

Jan 6, 2022

1.3.5

Dec 12, 2021

1.3.4

Oct 17, 2021

1.3.3

Sep 12, 2021

1.3.2

Aug 15, 2021

1.3.1

Jul 25, 2021

1.3.0

Jul 2, 2021

1.2.5

Jun 22, 2021

1.2.4

Apr 12, 2021

1.2.3

Mar 2, 2021

1.2.2

Feb 9, 2021

1.2.1

Jan 20, 2021

1.2.0

Dec 26, 2020

1.1.5

Dec 7, 2020

1.1.4

Oct 30, 2020

1.1.3

Oct 5, 2020

1.1.2

Sep 8, 2020

1.1.1

Aug 20, 2020

1.1.0

Jul 28, 2020

1.0.5

Jun 18, 2020

1.0.4

May 29, 2020

1.0.3

Mar 18, 2020

1.0.2

Mar 15, 2020

1.0.1

Feb 5, 2020

1.0.0

Jan 30, 2020

0.25.3

Nov 1, 2019

0.25.2

Oct 19, 2019

0.25.1

Aug 22, 2019

0.25.0

Jul 19, 2019

0.24.2

Mar 14, 2019

0.24.1

Feb 4, 2019

0.24.0

Jan 25, 2019

0.23.4

Aug 4, 2018

0.23.3

Jul 7, 2018

0.23.2

Jul 6, 2018

0.23.1

Jun 13, 2018

0.23.0

May 16, 2018

0.22.0

Dec 31, 2017

0.21.1

Dec 13, 2017

0.21.0

Oct 28, 2017

0.20.3

Jul 7, 2017

0.20.2

Jun 5, 2017

0.20.1

May 5, 2017

0.20.0

May 5, 2017

0.19.2

Dec 24, 2016

0.19.1

Nov 3, 2016

0.19.0

Oct 2, 2016

0.18.1

May 5, 2016

0.18.0

Mar 12, 2016

0.17.1

Nov 20, 2015

0.17.0

Oct 9, 2015

0.16.2

Jun 13, 2015

0.16.1

May 11, 2015

0.16.0

Mar 22, 2015

0.15.2

Dec 11, 2014

0.15.1

Nov 8, 2014

0.15.0

Oct 19, 2014

0.14.1

Jul 10, 2014

0.14.0

May 30, 2014

0.13.1

Feb 9, 2014

0.13.0

Jan 14, 2014

0.12.0

Jul 24, 2013

0.11.0

Apr 23, 2013

This version

0.10.1

Jan 22, 2013

0.10.0

Dec 17, 2012

0.9.1

Nov 15, 2012

0.9.0

Oct 8, 2012

0.8.1

Jul 22, 2012

0.8.0

Jun 29, 2012

0.7.3

Apr 12, 2012

0.7.2

Mar 16, 2012

0.7.1

Feb 29, 2012

0.7.0

Feb 9, 2012

0.6.1

Dec 13, 2011

0.6.0

Nov 26, 2011

0.5.0

Oct 25, 2011

0.4.3

Oct 9, 2011

0.4.2

Oct 3, 2011

0.4.1

Sep 26, 2011

0.4.0

Sep 12, 2011

0.3.0

Feb 20, 2011

0.2

May 18, 2010

0.1

Dec 25, 2009

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