Get Mean of Numeric Columns in DataFrame using Pandas

Sometimes, it may be required to get the mean values of a specific column or mean values of all columns that contains numerical values. This is where the mean() function can be used.

The term ‘mean’ refers to finding the sum of all values and dividing it by the total number of values in the dataset.

Let us see a demonstration of the same −

Example

 Live Demo

import pandas as pd
my_data = {'Name':pd.Series(['Tom','Jane','Vin','Eve','Will']),
'Age':pd.Series([45, 67, 89, 12, 23]),
'value':pd.Series([8.79,23.24,31.98,78.56,90.20])
}
print("The dataframe is :")
my_df = pd.DataFrame(my_data)
print(my_df)
print("The mean is :")
print(my_df.mean())

Output

The dataframe is :
   Name Age  value
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 is :
Age    47.200
value  46.554
dtype: float64

Explanation

• The required libraries are imported, and given alias names for ease of use.

• Dictionary of series consisting of key and value is created, wherein a value is actually a series data structure.

• This dictionary is later passed as a parameter to the ‘Dataframe’ function present in the ‘pandas’ library

• The dataframe is printed on the console.

• We are looking at computing the mean of all columns that contain numeric values in them.

• The ‘mean’ function is called on the dataframe using the dot operator.

• The mean of numeric columns is printed on the console.