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Getting started

Installation

Working with conda?

pandas is part of the Anaconda distribution and can be installed with Anaconda or Miniconda:

conda install pandas

Prefer pip?

pandas can be installed via pip from PyPI.

pip install pandas

In-depth instructions?

Installing a specific version? Installing from source? Check the advanced installation page.

:ref:`Learn more <install>`

Intro to pandas

What kind of data does pandas handle?

:ref:`Straight to tutorial...<10min_tut_01_tableoriented>`

When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean and process your data. In pandas, a data table is called a :class:`DataFrame`.

:ref:`To introduction tutorial <10min_tut_01_tableoriented>`

:ref:`To user guide <dsintro>`

How do I read and write tabular data?

:ref:`Straight to tutorial...<10min_tut_02_read_write>`

pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). Importing data from each of these data sources is provided by function with the prefix read_*. Similarly, the to_* methods are used to store data.

:ref:`To introduction tutorial <10min_tut_02_read_write>`

:ref:`To user guide <io>`

How do I select a subset of a table?

:ref:`Straight to tutorial...<10min_tut_03_subset>`

Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the data you need are available in pandas.

:ref:`To introduction tutorial <10min_tut_03_subset>`

:ref:`To user guide <indexing>`

How to create plots in pandas?

:ref:`Straight to tutorial...<10min_tut_04_plotting>`

pandas provides plotting your data out of the box, using the power of Matplotlib. You can pick the plot type (scatter, bar, boxplot,...) corresponding to your data.

:ref:`To introduction tutorial <10min_tut_04_plotting>`

:ref:`To user guide <visualization>`

How to create new columns derived from existing columns?

:ref:`Straight to tutorial...<10min_tut_05_columns>`

There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a :class:`DataFrame` based on existing data in other columns is straightforward.

:ref:`To introduction tutorial <10min_tut_05_columns>`

:ref:`To user guide <basics.dataframe.sel_add_del>`

How to calculate summary statistics?

:ref:`Straight to tutorial...<10min_tut_06_stats>`

Basic statistics (mean, median, min, max, counts...) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data or grouped by categories. The latter is also known as the split-apply-combine approach.

:ref:`To introduction tutorial <10min_tut_06_stats>`

:ref:`To user guide <groupby>`

How to reshape the layout of tables?

:ref:`Straight to tutorial...<10min_tut_07_reshape>`

Change the structure of your data table in multiple ways. You can :func:`~pandas.melt` your data table from wide to long/tidy form or :func:`~pandas.pivot` from long to wide format. With aggregations built-in, a pivot table is created with a sinlge command.

:ref:`To introduction tutorial <10min_tut_07_reshape>`

:ref:`To user guide <reshaping>`

How to combine data from multiple tables?

:ref:`Straight to tutorial...<10min_tut_08_combine>`

Multiple tables can be concatenated both column wise as row wise and database-like join/merge operations are provided to combine multiple tables of data.

:ref:`To introduction tutorial <10min_tut_08_combine>`

:ref:`To user guide <merging>`

How to handle time series data?

:ref:`Straight to tutorial...<10min_tut_09_timeseries>`

pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data.

:ref:`To introduction tutorial <10min_tut_09_timeseries>`

:ref:`To user guide <timeseries>`

How to manipulate textual data?

:ref:`Straight to tutorial...<10min_tut_10_text>`

Data sets do not only contain numerical data. pandas provides a wide range of functions to cleaning textual data and extract useful information from it.

:ref:`To introduction tutorial <10min_tut_10_text>`

:ref:`To user guide <timeseries>`

Coming from...

Are you familiar with other software for manipulating tablular data? Learn the pandas-equivalent operations compared to software you already know:

The R programming language provides the dataframe data structure and multiple packages, such as tidyverse use and extend data.frames for convenient data handling functionalities similar to pandas.

:ref:`Learn more <compare_with_r>`

Already familiar to SELECT, GROUP BY, JOIN, etc.? Most of these SQL manipulations do have equivalents in pandas.

:ref:`Learn more <compare_with_sql>`

The data set included in the STATA statistical software suite corresponds to the pandas dataframe. Many of the operations known from STATA have an equivalent in pandas.

:ref:`Learn more <compare_with_stata>`

The SAS statistical software suite also provides the data set corresponding to the pandas dataframe. Also SAS vectorized operations, filtering, string processing operations, and more have similar functions in pandas.

:ref:`Learn more <compare_with_sas>`

Tutorials

For a quick overview of pandas functionality, see :ref:`10 Minutes to pandas<10min>`.

You can also reference the pandas cheat sheet for a succinct guide for manipulating data with pandas.

The community produces a wide variety of tutorials available online. Some of the material is enlisted in the community contributed :ref:`communitytutorials`.

.. toctree::
    :maxdepth: 2
    :hidden:

    install
    overview
    intro_tutorials/index
    comparison/index
    tutorials