In this tutorial, we will look at how to calculate the average for each row in a pandas dataframe with the help of some examples.

## How to find the mean row wise in Pandas?

To get the average for each row in a pandas dataframe, use the pandas dataframe `mean()`

function with `axis=1`

. The following is the syntax:

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# get mean for each row df.mean(axis=1)

It returns the mean for each row with `axis=1`

. Note that the pandas mean() function calculates the mean for columns and not rows by default. Thus, make sure to pass 1 to the `axis`

parameter if you want the get the average for each row.

## Examples

Let’s look at some examples of using the above syntax. First, we will create a dataframe that we will be using throughout this tutorial.

import pandas as pd # create a pandas dataframe scores_df = pd.DataFrame({ 'Name': ['Sam', 'Soniya', 'Neeraj'], 'Maths': [49, 81, 83], 'History': [88, 70, 76], 'Science': [61, 76, 90] }) # display the dataframe print(scores_df)

Output:

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Name Maths History Science 0 Sam 49 88 61 1 Soniya 81 70 76 2 Neeraj 83 76 90

We created a dataframe with three rows, each storing the scores of a student in the subjects – Maths, History, and Science.

### 1. Average for each row in the dataframe

To get the mean for each row in the dataframe, apply the pandas dataframe mean() function with axis=1. For example, let’s find the average score for each of the students in the dataframe scores_df

# get mean for each row print(scores_df.mean(axis=1))

Output:

0 66.000000 1 75.666667 2 83.000000 dtype: float64

We get the mean for each row as a pandas series.

Let’s add a new column to the scores_df dataframe representing the mean scores for each student.

# add new column with average score of each student scores_df['Average Score'] = scores_df.mean(axis=1) # display the dataframe print(scores_df)

Output:

Name Maths History Science Average Score 0 Sam 49 88 61 66.000000 1 Soniya 81 70 76 75.666667 2 Neeraj 83 76 90 83.000000

### 2. Average if the row contains NaN values

By default, the pandas mean() function doesn’t take into account the NA values when computing the average. To demonstrate this, let’s create a scores dataframe with some missing values.

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import numpy as np # dataframe with some misssing values scores_df = pd.DataFrame({ 'Name': ['Sam', 'Soniya', 'Neeraj'], 'Maths': [49, np.nan, 83], 'History': [np.nan, 70, 76], 'Science': [61, np.nan, 90] }) # display the dataframe print(scores_df)

Output:

Name Maths History Science 0 Sam 49.0 NaN 61.0 1 Soniya NaN 70.0 NaN 2 Neeraj 83.0 76.0 90.0

Now let’s see how the result will look like when getting the average for each row.

# add new column with average score of each student scores_df['Average Score'] = scores_df.mean(axis=1) # display the dataframe print(scores_df)

Output:

Name Maths History Science Average Score 0 Sam 49.0 NaN 61.0 55.0 1 Soniya NaN 70.0 NaN 70.0 2 Neeraj 83.0 76.0 90.0 83.0

You can see that the average value for each row doesn’t take the NaN values into account.

If you want to include the NaN values when calculating the average, pass `skipna=False`

to the pandas mean() function.

# add new column with average score of each student scores_df['Average Score'] = scores_df.mean(axis=1, skipna=False) # display the dataframe print(scores_df)

Output:

Name Maths History Science Average Score 0 Sam 49.0 NaN 61.0 NaN 1 Soniya NaN 70.0 NaN NaN 2 Neeraj 83.0 76.0 90.0 83.0

We get a NaN in the average if any of the values in the row is NaN.

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

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 numpy version 1.18.5 and pandas version 1.0.5

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