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{{ header }}

.. ipython:: python

    import pandas as pd
Data used for this tutorial: 
  • .. ipython:: python

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

How to calculate summary statistics

Aggregating statistics

  • What is the average age of the Titanic passengers?

    .. ipython:: python

    titanic["Age"].mean()

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.

  • 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.

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"],
        }
    )
To user guide

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

Aggregating statistics grouped by category

  • 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.

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. 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 by passing numeric_only=True:

.. ipython:: python

    titanic.groupby("Sex").mean(numeric_only=True)

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()

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.

  • 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.

To user guide

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

Count number of records by category

  • 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.

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.

To user guide

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

REMEMBER

  • 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.
To user guide

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