By default, the describe() function in pandas calculates descriptive statistics for all numeric variables in a DataFrame.

However, you can use the following methods to calculate descriptive statistics for as well:

Method 1: Calculate Descriptive Statistics for Categorical Variables

df.describe(include='object')

This method will calculate count, unique, top and freq for each categorical variable in a DataFrame.

Method 2: Calculate Categorical Descriptive Statistics for All Variables

df.astype('object').describe()

This method will calculate count, unique, top and freq for every variable in a DataFrame.

The following examples show how to use each method with the following pandas DataFrame that contains information about various basketball players:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
                   'points': [18, 22, 19, 14, 14, 11, 20, 28],
                   'assists': [5, 7, 7, 9, 12, 9, 9, 4],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})

#view DataFrame
print(df)

  team  points  assists  rebounds
0    A      18        5        11
1    B      22        7         8
2    C      19        7        10
3    D      14        9         6
4    E      14       12         6
5    F      11        9         5
6    G      20        9         9
7    H      28        4        12

Example 1: Calculate Descriptive Statistics for Categorical Variables

We can use the following syntax to calculate descriptive statistics for each categorical variable in the DataFrame:

#calculate descriptive statistics for categorical variables only
df.describe(include='object')

team
count	8
unique	8
top	A
freq	1

The output shows various descriptive statistics for the only categorical variable (team) in the DataFrame.

Here’s how to interpret the output:

• count: There are 8 values in the team column.
• unique: There are 8 unique values in the team column.
• top: The “top” value (i.e. highest in the alphabet) is A.
• freq: This top value occurs 1 time.

Example 2: Calculate Categorical Descriptive Statistics for All Variables

#calculate categorical descriptive statistics for all variables
df.astype('object').describe()

        team	points	assists	 rebounds
count	8	8	8	 8
unique	8	7	5	 7
top	A	14	9	 6
freq	1	2	3	 2

The output shows count, unique, top and freq for every variable in the DataFrame, including the numeric variables.

The following tutorials explain how to perform other common operations in pandas: