You can use the describe() function to generate descriptive statistics for a pandas DataFrame.

This function uses the following basic syntax:

df.describe()

The following examples show how to use this syntax in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],
                   'points': [25, 12, 15, 14, 19, 23, 25, 29],
                   'assists': [5, 7, 7, 9, 12, 9, 9, 4],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})

#view DataFrame
df

	team	points	assists	rebounds
0	A	25	5	11
1	A	12	7	8
2	B	15	7	10
3	B	14	9	6
4	B	19	12	6
5	C	23	9	5
6	C	25	9	9
7	C	29	4	12

Example 1: Describe All Numeric Columns

By default, the describe() function only generates descriptive statistics for numeric columns in a pandas DataFrame:

#generate descriptive statistics for all numeric columns
df.describe()

	points	        assists	   rebounds
count	8.000000	8.00000	   8.000000
mean	20.250000	7.75000	   8.375000
std	6.158618	2.54951	   2.559994
min	12.000000	4.00000	   5.000000
25%	14.750000	6.50000	   6.000000
50%	21.000000	8.00000	   8.500000
75%	25.000000	9.00000	   10.250000
max	29.000000	12.00000   12.000000

Descriptive statistics are shown for the three numeric columns in the DataFrame.

Note: If there are missing values in any columns, pandas will automatically exclude these values when calculating the descriptive statistics.

Example 2: Describe All Columns

To calculate descriptive statistics for every column in the DataFrame, we can use the include=’all’ argument:

#generate descriptive statistics for all columns
df.describe(include='all')

	team	points	    assists	rebounds
count	8	8.000000    8.00000	8.000000
unique	3	NaN	    NaN	        NaN
top	B	NaN	    NaN	        NaN
freq	3	NaN	    NaN	        NaN
mean	NaN	20.250000   7.75000	8.375000
std	NaN	6.158618    2.54951	2.559994
min	NaN	12.000000   4.00000	5.000000
25%	NaN	14.750000   6.50000	6.000000
50%	NaN	21.000000   8.00000	8.500000
75%	NaN	25.000000   9.00000	10.250000
max	NaN	29.000000   12.00000	12.000000

Example 3: Describe Specific Columns

The following code shows how to calculate descriptive statistics for one specific column in the pandas DataFrame:

#calculate descriptive statistics for 'points' column only
df['points'].describe()

count     8.000000
mean     20.250000
std       6.158618
min      12.000000
25%      14.750000
50%      21.000000
75%      25.000000
max      29.000000
Name: points, dtype: float64

The following code shows how to calculate descriptive statistics for several specific columns:

#calculate descriptive statistics for 'points' and 'assists' columns only
df[['points', 'assists']].describe()

	points	assists
count	8.000000	8.00000
mean	20.250000	7.75000
std	6.158618	2.54951
min	12.000000	4.00000
25%	14.750000	6.50000
50%	21.000000	8.00000
75%	25.000000	9.00000
max	29.000000	12.00000

You can find the complete documentation for the describe() function .

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