# Average for each row in Pandas Dataframe

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.

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:

```# 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.

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:

```     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.

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```

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.

```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