.. _10min_tut_06_stats:

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.. ipython:: python

    import pandas as pd

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                Data used for this tutorial:
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.. include:: includes/titanic.rst

.. ipython:: python

    titanic = pd.read_csv("data/titanic.csv")
    titanic.head()

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How to calculate summary statistics?
------------------------------------

Aggregating statistics
~~~~~~~~~~~~~~~~~~~~~~

.. image:: ../../_static/schemas/06_aggregate.svg
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What is the average age of the Titanic passengers?

.. ipython:: python

    titanic["Age"].mean()

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Different statistics are available and can be applied to columns with
numerical data. Operations in general exclude missing data and operate
across rows by default.

.. image:: ../../_static/schemas/06_reduction.svg
   :align: center

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What is the median age and ticket fare price of the Titanic passengers?

.. ipython:: python

    titanic[["Age", "Fare"]].median()

The statistic applied to multiple columns of a ``DataFrame`` (the selection of two columns
returns a ``DataFrame``, see the :ref:`subset data tutorial <10min_tut_03_subset>`) is calculated for each numeric column.

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The aggregating statistic can be calculated for multiple columns at the
same time. Remember the ``describe`` function from the :ref:`first tutorial <10min_tut_01_tableoriented>`?

.. ipython:: python

    titanic[["Age", "Fare"]].describe()

Instead of the predefined statistics, specific combinations of
aggregating statistics for given columns can be defined using the
:func:`DataFrame.agg` method:

.. ipython:: python

    titanic.agg(
        {
            "Age": ["min", "max", "median", "skew"],
            "Fare": ["min", "max", "median", "mean"],
        }
    )

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        <span class="badge badge-info">To user guide</span>

Details about descriptive statistics are provided in the user guide section on :ref:`descriptive statistics <basics.stats>`.

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Aggregating statistics grouped by category
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. image:: ../../_static/schemas/06_groupby.svg
   :align: center

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What is the average age for male versus female Titanic passengers?

.. ipython:: python

    titanic[["Sex", "Age"]].groupby("Sex").mean()

As our interest is the average age for each gender, a subselection on
these two columns is made first: ``titanic[["Sex", "Age"]]``. Next, the
:meth:`~DataFrame.groupby` method is applied on the ``Sex`` column to make a group per
category. The average age *for each gender* is calculated and
returned.

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Calculating a given statistic (e.g. ``mean`` age) *for each category in
a column* (e.g. male/female in the ``Sex`` column) is a common pattern.
The ``groupby`` method is used to support this type of operations. More
general, this fits in the more general ``split-apply-combine`` pattern:

-  **Split** the data into groups
-  **Apply** a function to each group independently
-  **Combine** the results into a data structure

The apply and combine steps are typically done together in pandas.

In the previous example, we explicitly selected the 2 columns first. If
not, the ``mean`` method is applied to each column containing numerical
columns:

.. ipython:: python

    titanic.groupby("Sex").mean()

It does not make much sense to get the average value of the ``Pclass``.
If we are only interested in the average age for each gender, the
selection of columns (rectangular brackets ``[]`` as usual) is supported
on the grouped data as well:

.. ipython:: python

    titanic.groupby("Sex")["Age"].mean()

.. image:: ../../_static/schemas/06_groupby_select_detail.svg
   :align: center

.. note::
    The ``Pclass`` column contains numerical data but actually
    represents 3 categories (or factors) with respectively the labels ‘1’,
    ‘2’ and ‘3’. Calculating statistics on these does not make much sense.
    Therefore, pandas provides a ``Categorical`` data type to handle this
    type of data. More information is provided in the user guide
    :ref:`categorical` section.

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What is the mean ticket fare price for each of the sex and cabin class combinations?

.. ipython:: python

    titanic.groupby(["Sex", "Pclass"])["Fare"].mean()

Grouping can be done by multiple columns at the same time. Provide the
column names as a list to the :meth:`~DataFrame.groupby` method.

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    <div class="d-flex flex-row gs-torefguide">
        <span class="badge badge-info">To user guide</span>

A full description on the split-apply-combine approach is provided in the user guide section on :ref:`groupby operations <groupby>`.

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Count number of records by category
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. image:: ../../_static/schemas/06_valuecounts.svg
   :align: center

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What is the number of passengers in each of the cabin classes?

.. ipython:: python

    titanic["Pclass"].value_counts()

The :meth:`~Series.value_counts` method counts the number of records for each
category in a column.

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The function is a shortcut, as it is actually a groupby operation in combination with counting of the number of records
within each group:

.. ipython:: python

    titanic.groupby("Pclass")["Pclass"].count()

.. note::
    Both ``size`` and ``count`` can be used in combination with
    ``groupby``. Whereas ``size`` includes ``NaN`` values and just provides
    the number of rows (size of the table), ``count`` excludes the missing
    values. In the ``value_counts`` method, use the ``dropna`` argument to
    include or exclude the ``NaN`` values.

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The user guide has a dedicated section on ``value_counts`` , see the page on :ref:`discretization <basics.discretization>`.

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    <div class="shadow gs-callout gs-callout-remember">
        <h4>REMEMBER</h4>

-  Aggregation statistics can be calculated on entire columns or rows.
-  ``groupby`` provides the power of the *split-apply-combine* pattern.
-  ``value_counts`` is a convenient shortcut to count the number of
   entries in each category of a variable.

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   </div>

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    <div class="d-flex flex-row gs-torefguide">
        <span class="badge badge-info">To user guide</span>

A full description on the split-apply-combine approach is provided in the user guide pages about :ref:`groupby operations <groupby>`.

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   </div>
