In [1]: import pandas as pd
Data used for this tutorial:
  • This tutorial uses the Titanic data set, stored as CSV. The data consists of the following data columns:

    • PassengerId: Id of every passenger.

    • Survived: Indication whether passenger survived. 0 for yes and 1 for no.

    • Pclass: One out of the 3 ticket classes: Class 1, Class 2 and Class 3.

    • Name: Name of passenger.

    • Sex: Gender of passenger.

    • Age: Age of passenger in years.

    • SibSp: Number of siblings or spouses aboard.

    • Parch: Number of parents or children aboard.

    • Ticket: Ticket number of passenger.

    • Fare: Indicating the fare.

    • Cabin: Cabin number of passenger.

    • Embarked: Port of embarkation.

    To raw data
    In [2]: titanic = pd.read_csv("data/titanic.csv")
    In [3]: titanic.head()
       PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked
    0            1         0       3  ...   7.2500   NaN         S
    1            2         1       1  ...  71.2833   C85         C
    2            3         1       3  ...   7.9250   NaN         S
    3            4         1       1  ...  53.1000  C123         S
    4            5         0       3  ...   8.0500   NaN         S
    [5 rows x 12 columns]

How to calculate summary statistics#

Aggregating statistics#

  • What is the average age of the Titanic passengers?

    In [4]: titanic["Age"].mean()
    Out[4]: 29.69911764705882

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?

    In [5]: titanic[["Age", "Fare"]].median()
    Age     28.0000
    Fare    14.4542
    dtype: float64

    The statistic applied to multiple columns of a DataFrame (the selection of two columns returns a DataFrame, see the subset data tutorial) 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 first tutorial?

In [6]: titanic[["Age", "Fare"]].describe()
              Age        Fare
count  714.000000  891.000000
mean    29.699118   32.204208
std     14.526497   49.693429
min      0.420000    0.000000
25%     20.125000    7.910400
50%     28.000000   14.454200
75%     38.000000   31.000000
max     80.000000  512.329200

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

In [7]: titanic.agg(
   ...:     {
   ...:         "Age": ["min", "max", "median", "skew"],
   ...:         "Fare": ["min", "max", "median", "mean"],
   ...:     }
   ...: )
              Age        Fare
min      0.420000    0.000000
max     80.000000  512.329200
median  28.000000   14.454200
skew     0.389108         NaN
mean          NaN   32.204208
To user guide

Details about descriptive statistics are provided in the user guide section on descriptive statistics.

Aggregating statistics grouped by category#

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

    In [8]: titanic[["Sex", "Age"]].groupby("Sex").mean()
    female  27.915709
    male    30.726645

    As our interest is the average age for each gender, a subselection on these two columns is made first: titanic[["Sex", "Age"]]. Next, the 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:

In [9]: titanic.groupby("Sex").mean(numeric_only=True)
        PassengerId  Survived    Pclass  ...     SibSp     Parch       Fare
Sex                                      ...                               
female   431.028662  0.742038  2.159236  ...  0.694268  0.649682  44.479818
male     454.147314  0.188908  2.389948  ...  0.429809  0.235702  25.523893

[2 rows x 7 columns]

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:

In [10]: titanic.groupby("Sex")["Age"].mean()
female    27.915709
male      30.726645
Name: Age, dtype: float64


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 Categorical data section.

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

    In [11]: titanic.groupby(["Sex", "Pclass"])["Fare"].mean()
    Sex     Pclass
    female  1         106.125798
            2          21.970121
            3          16.118810
    male    1          67.226127
            2          19.741782
            3          12.661633
    Name: Fare, dtype: float64

    Grouping can be done by multiple columns at the same time. Provide the column names as a list to the groupby() method.

To user guide

A full description on the split-apply-combine approach is provided in the user guide section on groupby operations.

Count number of records by category#

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

    In [12]: titanic["Pclass"].value_counts()
    3    491
    1    216
    2    184
    Name: count, dtype: int64

    The 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:

In [13]: titanic.groupby("Pclass")["Pclass"].count()
1    216
2    184
3    491
Name: Pclass, dtype: int64


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 discretization.


  • 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 groupby operations.