How to get the mean of columns that contains numeric values of a dataframe in Pandas Python?

Sometimes, you may need to calculate the mean values of specific columns or all columns containing numeric data in a pandas DataFrame. The mean() function automatically identifies and computes the mean for numeric columns only.

The term mean refers to finding the sum of all values and dividing it by the total number of values in the dataset (also called the arithmetic average).

Basic Example

Let's create a DataFrame with mixed data types and calculate the mean of numeric columns ?

import pandas as pd

# Create a DataFrame with mixed data types
data = {
    'Name': ['Tom', 'Jane', 'Vin', 'Eve', 'Will'],
    'Age': [45, 67, 89, 12, 23],
    'Salary': [8.79, 23.24, 31.98, 78.56, 90.20]
}

df = pd.DataFrame(data)
print("The DataFrame is:")
print(df)
print("\nThe mean of numeric columns:")
print(df.mean())

The DataFrame is:
   Name  Age  Salary
0   Tom   45    8.79
1  Jane   67   23.24
2   Vin   89   31.98
3   Eve   12   78.56
4  Will   23   90.20

The mean of numeric columns:
Age       47.200
Salary    46.554
dtype: float64

Getting Mean of Specific Columns

You can calculate the mean of specific numeric columns by selecting them first ?

import pandas as pd

data = {
    'Name': ['Tom', 'Jane', 'Vin', 'Eve', 'Will'],
    'Age': [45, 67, 89, 12, 23],
    'Salary': [8.79, 23.24, 31.98, 78.56, 90.20],
    'Experience': [5, 10, 15, 2, 8]
}

df = pd.DataFrame(data)

# Mean of a single column
print("Mean age:", df['Age'].mean())

# Mean of multiple specific columns
print("\nMean of Age and Salary:")
print(df[['Age', 'Salary']].mean())

Mean age: 47.2

Mean of Age and Salary:
Age       47.200
Salary    46.554
dtype: float64

Handling Missing Values

The mean() function automatically excludes NaN values from calculations ?

import pandas as pd
import numpy as np

data = {
    'A': [1, 2, np.nan, 4, 5],
    'B': [10, 20, 30, np.nan, 50],
    'C': ['x', 'y', 'z', 'w', 'v']
}

df = pd.DataFrame(data)
print("DataFrame with missing values:")
print(df)
print("\nMean (excludes NaN):")
print(df.mean())

DataFrame with missing values:
     A     B  C
0  1.0  10.0  x
1  2.0  20.0  y
2  NaN  30.0  z
3  4.0   NaN  w
4  5.0  50.0  v

Mean (excludes NaN):
A    3.0
B    27.5
dtype: float64

Key Features

Conclusion

The mean() function in pandas automatically identifies numeric columns and calculates their arithmetic mean while excluding NaN values. Use column selection to calculate means for specific columns when needed.