```
In [1]: import pandas as pd
```

- Titanic data
This tutorial uses the Titanic data set, stored as CSV. The data consists of the following data columns:

PassengerId: Id of every passenger.

Survived: This feature have value 0 and 1. 0 for not survived and 1 for survived.

Pclass: There are 3 classes: Class 1, Class 2 and Class 3.

Name: Name of passenger.

Sex: Gender of passenger.

Age: Age of passenger.

SibSp: Indication that passenger have siblings and spouse.

Parch: Whether a passenger is alone or have family.

Ticket: Ticket number of passenger.

Fare: Indicating the fare.

Cabin: The cabin of passenger.

Embarked: The embarked category.

In [2]: titanic = pd.read_csv("data/titanic.csv") In [3]: titanic.head() Out[3]: PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male ... 0 373450 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() Out[5]: Age 28.0000 Fare 14.4542 dtype: float64

The statistic applied to multiple columns of a

`DataFrame`

(the selection of two columns return 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 first tutorial tutorial?

```
In [6]: titanic[["Age", "Fare"]].describe()
Out[6]:
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"],
...: }
...: )
...:
Out[7]:
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
```

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() Out[8]: Age Sex 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. 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:

```
In [9]: titanic.groupby("Sex").mean()
Out[9]:
PassengerId Survived Pclass Age SibSp Parch Fare
Sex
female 431.028662 0.742038 2.159236 27.915709 0.694268 0.649682 44.479818
male 454.147314 0.188908 2.389948 30.726645 0.429809 0.235702 25.523893
```

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()
Out[10]:
Sex
female 27.915709
male 30.726645
Name: Age, dtype: float64
```

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
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() Out[11]: 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.

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() Out[12]: 3 491 1 216 2 184 Name: Pclass, 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()
Out[13]:
Pclass
1 216
2 184
3 491
Name: Pclass, dtype: int64
```

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.

The user guide has a dedicated section on `value_counts`

, see page on 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

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