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:

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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:

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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Tutorials on getting statistics for pandas dataframe values –

- Pandas – Get Mean of one or more Columns
- Pandas – Get Standard Deviation of one or more Columns
- Pandas – Get Median of One or More Columns
- Get correlation between columns of Pandas DataFrame
- Cumulative Sum of Column in Pandas DataFrame
- Pandas – Count Missing Values in Each Column
- Get Rolling Window estimates in Pandas
- Get the number of rows in a Pandas DataFrame
- Pandas – Count of Unique Values in Each Column