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 here.
Additional Resources
The following tutorials explain how to perform other common functions in pandas:
Pandas: How to Find Unique Values in a Column
Pandas: How to Find the Difference Between Two Rows
Pandas: How to Count Missing Values in DataFrame