How to apply a function to a column in a Pandas DataFrame?

To apply a function to a column in a Pandas DataFrame, you can use the apply() method. Here’s an example:

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) # create a DataFrame with columns A and B

# define a function to add 1 to each value in the 'B' column
def add_one(x):
    return x + 1

df['B'] = df['B'].apply(add_one) # apply the 'add_one' function to the 'B' column
print(df)

Output:

   A  B
0  1  5
1  2  6
2  3  7

In the example above, the add_one function takes a single input and returns the input plus one. The apply() method applies the add_one function to each value in the ‘B’ column of the DataFrame and returns a new DataFrame with the modified values. The modified DataFrame is then stored back into the ‘B’ column of the original DataFrame.

You can also apply a function to multiple columns of a DataFrame by passing a list of column names to the apply() method:

def multiply_by_two(x):
    return x * 2

df[['A', 'B']] = df[['A', 'B']].apply(multiply_by_two) # apply the 'multiply_by_two' function to columns 'A' and 'B'
print(df)

Output:

   A   B
0  2  10
1  4  12
2  6  14

In this example, the multiply_by_two function multiplies its input by two. The apply() method applies the multiply_by_two function to each value in columns ‘A’ and ‘B’ of the DataFrame and stores the modified values back into the same columns of the original DataFrame.

Overall, applying a function to a column or multiple columns of a Pandas DataFrame is easy using the apply() method.