Calculate the Average for each Row in a Pandas DataFrame

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# Calculate the Average for each Row in a Pandas DataFrame

To calculate the average for each row in a Pandas DataFrame:

Call the mean() method on the DataFrame.
Set the axis argument to 1 to calculate the average for each row.

main.py

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import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [30, 40, 50],
    'C': [50, 60, 70]
})

print(df)

df['row_average'] = df.mean(axis=1)

print('-' * 50)

print(df['row_average'])

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    A   B   C
0  10  30  50
1  20  40  60
2  30  50  70
--------------------------------------------------
0    30.0
1    40.0
2    50.0
Name: row_average, dtype: float64

calculate the average for each row in pandas dataframe

The DataFrame.mean() method returns the mean of the values over the specified axis.

By default, the axis argument is set to 0, which means that the column average is calculated.

We set the axis argument to 1 to compute the mean along the rows' axis.

main.py

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df['row_average'] = df.mean(axis=1)

When running the code sample you might get a warning that "A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead".

If you get the warning, try to use the DataFrame.assign() method when adding the row average column to the DataFrame.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [30, 40, 50],
    'C': [50, 60, 70]
})

print(df)

df = df.assign(row_average=df.mean(axis=1))

print('-' * 50)

print(df['row_average'])

The code for this article is available on GitHub

Running the code sample produces the same output.

shell

Copied!
    A   B   C
0  10  30  50
1  20  40  60
2  30  50  70
--------------------------------------------------
0    30.0
1    40.0
2    50.0
Name: row_average, dtype: float64

calculate row average using dataframe assign

# Calculate the Row Average in a Pandas DataFrame using `df.iloc`

If you only want to calculate the row average for specific columns, use the df.iloc position-based indexer.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [30, 40, 50],
    'C': [50, 60, 70]
})

print(df)

df['row_average'] = df.iloc[:, 0:2].mean(axis=1)

print('-' * 50)

print(df['row_average'])

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    A   B   C
0  10  30  50
1  20  40  60
2  30  50  70
--------------------------------------------------
0    20.0
1    30.0
2    40.0
Name: row_average, dtype: float64

calculate row average using df iloc

We used the df.iloc indexer to calculate the row average for the A and B columns.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [30, 40, 50],
    'C': [50, 60, 70]
})

print(df)

print('-' * 50)

print(df.iloc[:, 0:2])

Running the code sample produces the following output.

shell

Copied!
    A   B   C
0  10  30  50
1  20  40  60
2  30  50  70
--------------------------------------------------
    A   B
0  10  30
1  20  40
2  30  50

Once you select the specific columns, you just have to call the mean() method with the axis argument set to 1.

main.py

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df['row_average'] = df.iloc[:, 0:2].mean(axis=1)

You can also use the df.iloc position-based indexer to specify individual columns by index before calculating the row average.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [30, 40, 50],
    'C': [50, 60, 70]
})

print(df)

df['row_average'] = df.iloc[:, [1, 2]].mean(axis=1)

print('-' * 50)

print(df['row_average'])

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    A   B   C
0  10  30  50
1  20  40  60
2  30  50  70
--------------------------------------------------
0    40.0
1    50.0
2    60.0
Name: row_average, dtype: float64

get row average for specific columns in pandas dataframe

We calculated the row average for the columns with indexes 1 and 2.

Note that Python indices are zero-based, so the first index is 0 and the last is len(columns) - 1.

# Calculating the row average based on the column names in a Pandas DataFrame

You can use the DataFrame.loc label-based indexer to calculate the row average based on specific column names.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [30, 40, 50],
    'C': [50, 60, 70]
})

print(df)

df['row_average'] = df.loc[:, ['B', 'C']].mean(axis=1)

print('-' * 50)

print(df['row_average'])

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    A   B   C
0  10  30  50
1  20  40  60
2  30  50  70
--------------------------------------------------
0    40.0
1    50.0
2    60.0
Name: row_average, dtype: float64

calculate row average based on column names

The df.loc indexer is label-based, so it enables you to select specific columns based on their name.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [30, 40, 50],
    'C': [50, 60, 70]
})

print(df)

print('-' * 50)

print(df.loc[:, ['B', 'C']])

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    A   B   C
0  10  30  50
1  20  40  60
2  30  50  70
--------------------------------------------------
    B   C
0  30  50
1  40  60
2  50  70

select specific columns by name before calculating row average

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Calculate the Average for each Row in a Pandas DataFrame

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