You can use the following functions to calculate the mean, median, and mode of each numeric column in a pandas DataFrame:

print(df.mean(numeric_only=True)) print(df.median(numeric_only=True)) print(df.mode(numeric_only=True))

The following example shows how to use these functions in practice.

**Example: Calculate Mean, Median and Mode in Pandas**

Suppose we have the following pandas DataFrame that contains information about points scored by various basketball players in four different games:

import pandas as pd #create DataFrame df = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'], 'game1': [18, 22, 19, 14, 14, 11, 20, 28], 'game2': [5, 7, 7, 9, 12, 9, 9, 4], 'game3': [11, 8, 10, 6, 6, 5, 9, 12], 'game4': [9, 8, 10, 9, 14, 15, 10, 11]}) #view DataFrame print(df) player game1 game2 game3 game4 0 A 18 5 11 9 1 B 22 7 8 8 2 C 19 7 10 10 3 D 14 9 6 9 4 E 14 12 6 14 5 F 11 9 5 15 6 G 20 9 9 10 7 H 28 4 12 11

We can use the following syntax to calculate the **mean** value of each numeric column:

#calculate mean of each numeric column print(df.mean(numeric_only=True)) game1 18.250 game2 7.750 game3 8.375 game4 10.750 dtype: float64

From the output we can see:

- The mean value in the
**game1**column is**18.25**. - The mean value in the
**game2**column is**7.75**. - The mean value in the
**game3**column is**8.375**. - The mean value in the
**game4**column is**10.75**.

We can then use the following syntax to calculate the **median **value of each numeric column:

#calculate median of each numeric column print(df.median(numeric_only=True)) game1 18.5 game2 8.0 game3 8.5 game4 10.0 dtype: float64

From the output we can see:

- The median value in the
**game1**column is**18.5**. - The median value in the
**game2**column is**8**. - The median value in the
**game3**column is**8.5**. - The median value in the
**game4**column is**10**.

We can then use the following syntax to calculate the **mode** of each numeric column:

#calculate mode of each numeric column print(df.mode(numeric_only=True)) game1 game2 game3 game4 0 14.0 9.0 6.0 9 1 NaN NaN NaN 10

From the output we can see:

- The mode in the
**game1**column is**14**. - The mode in the
**game2**column is**9**. - The mode in the
**game3**column is**6**. - The mode in the
**game4**column is**9**and**10**

Note that the **game4** column had two modes since there were two values that occurred most frequently in that column.

**Note**: You can also use the describe() function in pandas to generate more descriptive statistics for each column.

**Additional Resources**

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

How to Calculate the Mean by Group in Pandas

How to Calculate the Median by Group in Pandas

How to Calculate Mode by Group in Pandas