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Pandas – Get Mean of one or more Columns

In this tutorial, we will look at how to get the mean of one or more columns in a pandas dataframe.

How to calculate the mean of pandas column?

You can use the pandas series mean() function to get the mean of a single column or the pandas dataframe mean() function to get the mean of all numerical columns in the dataframe. The following is the syntax:

# mean of single column
df['Col'].mean()
# mean of all numerical columns in dataframe
df.mean()

Let’s create a sample dataframe that we will be using throughout this tutorial to demonstrate the usage of the methods and syntax mentioned.

import pandas as pd

# create a dataframe
df = pd.DataFrame({
    'sepal_length': [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0],
    'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4],
    'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5],
    'petal_width': [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2],
    'sepices': ['setosa']*8
})
# display the dataframe
print(df)

Output:

   sepal_length  sepal_width  petal_length  petal_width sepices
0          5.1          3.5           1.4          0.2  setosa
1          4.9          3.0           1.4          0.2  setosa
2          4.7          3.2           1.3          0.2  setosa
3          4.6          3.1           1.5          0.2  setosa
4          5.0          3.6           1.4          0.2  setosa
5          5.4          3.9           1.7          0.4  setosa
6          4.6          3.4           1.4          0.3  setosa
7          5.0          3.4           1.5          0.2  setosa

The sample dataframe is taken form a section of the Iris dataset. This sample has petal and sepal dimensions of eight data points of the “Setosa” species.

Mean of a single column

First, let’s see how to get the mean of a single dataframe column.

You can use the pandas series mean() function to get the mean of individual columns (which essentially are pandas series). For example, let’s get the mean of the “sepal_length” column in the above dataframe.

# mean of sepal_length column
print(df['sepal_length'].mean())

Output:

4.9125

You see that we get the mean of all values in the “sepal_length” column as the scaler value 4.9125.

Mean of more than one columns

To get the mean of multiple columns together, first, create a dataframe with the columns you want to calculate the mean for and then apply the pandas dataframe mean() function. For example, let’s get the mean of the columns “petal_length” and “petal_width” 

# mean of more than one columns
print(df[['petal_length', 'petal_width']].mean())

Output:

petal_length    1.4500
petal_width     0.2375
dtype: float64

We get the result as a pandas series. Here, we first created a subset of the dataframe “df” with only the columns “petal_length” and “petal_width” and then applied the mean function.

Mean of all the columns

To get the mean of all the columns, use the same method as above but this time on the entire dataframe. Let’s use this function on the dataframe “df” created above.

# mean of all the columns
print(df.mean())

Output:

sepal_length    4.9125
sepal_width     3.3875
petal_length    1.4500
petal_width     0.2375
dtype: float64

You can see that we get the mean of all the numerical columns present in the dataframe.

Note that you can also use the pandas describe() function to look at statistics including the mean of columns in the dataframe.

# get dataframe statistics
df.describe()

Output:

Dataframe statistics including mean from the describe() function.

For more on the pandas dataframe mean() function, refer to its documention.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5


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Author

  • Piyush Raj

    Piyush is a data professional passionate about using data to understand things better and make informed decisions. In the past, he's worked as a Data Scientist for ZS and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.

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