You can use the following basic syntax to apply a function to every row in a pandas DataFrame:

df['new_col'] = df.apply(lambda x: some function, axis=1)

This syntax applies a function to each row in a pandas DataFrame and returns the results in a new column.

The following example shows how to use this syntax in practice.

**Example: Apply Function to Every Row in DataFrame**

Suppose we have the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'A': [5, 4, 7, 9, 12, 9, 9, 4], 'B': [10, 8, 10, 6, 6, 5, 9, 12]}) #view DataFrame print(df) A B 0 5 10 1 4 8 2 7 10 3 9 6 4 12 6 5 9 5 6 9 9 7 4 12

Now suppose we would like to apply a function that multiplies the values in column A and column B and then divides by 2.

We can use the following syntax to apply this function to each row in the DataFrame:

#create new column by applying function to each row in DataFrame df['z'] = df.apply(lambda x: x['A'] * x['B'] / 2, axis=1) #view updated DataFrame print(df) A B z 0 5 10 25.0 1 4 8 16.0 2 7 10 35.0 3 9 6 27.0 4 12 6 36.0 5 9 5 22.5 6 9 9 40.5 7 4 12 24.0

Column z displays the results of the function.

For example:

- First row: A * B / 2 = 5 * 10 / 2 =
**25** - Second row: A * B / 2 = 4 * 8 / 2 =
**16** - Third row: A * B / 2 = 7 * 10 / 2 =
**35**

And so on.

You can use similar syntax with **lambda** to apply any function you’d like to every row in a pandas DataFrame.

**Additional Resources**

The following tutorials explain how to perform other common operations in pandas:

How to Apply Function to Pandas Groupby

How to Perform a GroupBy Sum in Pandas

How to Use Groupby and Plot in Pandas