To find the means of the columns in a DataFrame, or the average value of a Series in pandas, the easiest way is to use the pandas mean() function.


You can also use the numpy mean() function.


When working with data, many times we want to calculate summary statistics to understand our data better. One such statistic is the mean, or the average of a number.

Finding the mean of a column, or the mean for all columns or rows in a DataFrame using pandas is easy. We can use the pandas mean() function to find the average value of a column of numbers, or a DataFrame.

Let’s say we have the following DataFrame.

df = pd.DataFrame({'Age': [43,23,71,49,52,37], 

# Output: 
   Age  Test_Score
0   43          90
1   23          87
2   71          92
3   49          96
4   52          84
5   37          79

To get the means for all columns, we can call the pandas mean() function.


# Output:
Age           45.833333
Test_Score    88.000000
dtype: float64

If we only want to get the mean of one column, we can do this using the pandas mean() function in the following Python code:


# Output:

Using numpy mean to Calculate Averages in pandas DataFrame

We can also use the numpy mean() function to calculate the mean value of the numbers in a column in a pandas DataFrame.

To get the average of the numbers in the column “Test_Score”, we can use the numpy mean() function in the following Python code:


# Output:

As you can see above, this is the same value we received from the pandas mean() function.

Hopefully this article has been helpful for you to understand how to find the mean value of numbers in a Series or DataFrame in pandas.

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Last Update: February 26, 2024