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