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.. _getting_started:

===============
Getting started
===============

Installation
------------

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    .. grid-item-card:: Working with conda?
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        :columns: 12 12 6 6
        :padding: 3

        pandas is part of the `Anaconda <https://docs.continuum.io/anaconda/>`__
        distribution and can be installed with Anaconda or Miniconda:

        ++++++++++++++++++++++

        .. code-block:: bash

            conda install -c conda-forge pandas

    .. grid-item-card:: Prefer pip?
        :class-card: install-card
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        :padding: 3

        pandas can be installed via pip from `PyPI <https://pypi.org/project/pandas>`__.

        ++++

        .. code-block:: bash

            pip install pandas

    .. grid-item-card:: In-depth instructions?
        :class-card: install-card
        :columns: 12
        :padding: 3

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

        +++

        .. button-ref:: install
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            Learn more


.. _gentle_intro:

Intro to pandas
---------------

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                        What kind of data does pandas handle?
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:ref:`Straight to tutorial...<10min_tut_01_tableoriented>`

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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`.

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

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:ref:`To user guide <dsintro>`

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                        How do I read and write tabular data?
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:ref:`Straight to tutorial...<10min_tut_02_read_write>`

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

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                        How do I select a subset of a table?
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:ref:`Straight to tutorial...<10min_tut_03_subset>`

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

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                        How to create plots in pandas?
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:ref:`Straight to tutorial...<10min_tut_04_plotting>`

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pandas provides plotting for your data right out of the box with the power of Matplotlib. Simply pick the plot type (scatter, bar, boxplot,...)
corresponding to your data.

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                        How to create new columns derived from existing columns?
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:ref:`Straight to tutorial...<10min_tut_05_columns>`

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

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                        How to calculate summary statistics?
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:ref:`Straight to tutorial...<10min_tut_06_stats>`

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Basic statistics (mean, median, min, max, counts...) are easily calculable across data frames. These, or even 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.

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

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Change the structure of your data table in a variety of ways. You can use :func:`~pandas.melt` to reshape your data from a wide format to a long and tidy one. Use :func:`~pandas.pivot`
 to go from long to wide format. With aggregations built-in, a pivot table can be created with a single command.

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                        How to combine data from multiple tables?
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:ref:`Straight to tutorial...<10min_tut_08_combine>`

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Multiple tables can be concatenated column wise or row wise with pandas' database-like join and merge operations.

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

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Data sets often contain more than just numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it.

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.. _comingfrom:

Coming from...
--------------

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

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        The `R programming language <https://www.r-project.org/>`__ provides a
        ``data.frame`` data structure as well as packages like
        `tidyverse <https://www.tidyverse.org>`__ which use and extend ``data.frame``
        for convenient data handling functionalities similar to pandas.

        +++

        .. button-ref:: compare_with_r
            :ref-type: ref
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            Learn more

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        Already familiar with ``SELECT``, ``GROUP BY``, ``JOIN``, etc.?
        Many SQL manipulations have equivalents in pandas.

        +++

        .. button-ref:: compare_with_sql
            :ref-type: ref
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            Learn more

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        The ``data set`` included in the `STATA <https://en.wikipedia.org/wiki/Stata>`__
        statistical software suite corresponds to the pandas ``DataFrame``.
        Many of the operations known from STATA have an equivalent in pandas.

        +++

        .. button-ref:: compare_with_stata
            :ref-type: ref
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            Learn more

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        Users of `Excel <https://en.wikipedia.org/wiki/Microsoft_Excel>`__
        or other spreadsheet programs will find that many of the concepts are
        transferrable to pandas.

        +++

        .. button-ref:: compare_with_spreadsheets
            :ref-type: ref
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            Learn more

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        `SAS <https://en.wikipedia.org/wiki/SAS_(software)>`__, the statistical software suite,
        uses the ``data set`` structure, which closely corresponds pandas' ``DataFrame``.
        Also SAS vectorized operations such as filtering or string processing operations
        have similar functions in pandas.

        +++

        .. button-ref:: compare_with_sas
            :ref-type: ref
            :click-parent:
            :color: secondary
            :expand:

            Learn more

Tutorials
---------

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

You can also reference the pandas `cheat sheet <https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf>`_
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`.


.. If you update this toctree, also update the manual toctree in the
   main index.rst.template

.. toctree::
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    install
    overview
    intro_tutorials/index
    comparison/index
    tutorials