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This article will show you how to apply functions to multiple columns in a Pandas DataFrame. In all the example codes, we will use the following same DataFrame.

import pandas as pd
import numpy as np

df = pd.DataFrame(
    [[5, 6, 7, 8], [1, 9, 12, 14], [4, 8, 10, 6]], columns=["a", "b", "c", "d"]
)

Output:

   a  b   c   d
0  5  6   7   8
1  1  9  12  14
2  4  8  10   6

apply()Applying functions to columns in Pandas using

apply()The method allows to apply a function to the entire DataFrame, either across columns or across rows. We axisset the parameter to 0 for rows and 1 for columns.

In the following example, we will use the function defined previously to increment the values ​​of the example DataFrame.

import pandas as pd
import numpy as np

df = pd.DataFrame(
    [[5, 6, 7, 8], [1, 9, 12, 14], [4, 8, 10, 6]], columns=["a", "b", "c", "d"]
)


def x(a):
    return a + 1


df_new = df.apply(x, axis=1)

print("The original dataframe:")
print(df)
print("The new dataframe:")
print(df_new)

Output:

The original dataframe:
   a  b   c   d
0  5  6   7   8
1  1  9  12  14
2  4  8  10   6
The new dataframe:
   a   b   c   d
0  6   7   8   9
1  2  10  13  15
2  5   9  11   7

We can also apply a function to multiple columns as shown below.

import pandas as pd
import numpy as np

df = pd.DataFrame(
    [[5, 6, 7, 8], [1, 9, 12, 14], [4, 8, 10, 6]], columns=["a", "b", "c", "d"]
)

print("The original dataframe:")
print(df)


def func(x):
    return x[0] + x[1]


df["e"] = df.apply(func, axis=1)

print("The new dataframe:")
print(df)

Output:

The original dataframe:
   a  b   c   d
0  5  6   7   8
1  1  9  12  14
2  4  8  10   6
The new dataframe:
   a  b   c   d   e
0  5  6   7   8  11
1  1  9  12  14  10
2  4  8  10   6  12

The newly added ecolumn is the sum of the data in athe and bcolumns. The DataFrame itself is a hidden parameter passed to the function. Columns can be accessed using the index as in the above example, or by using the column name as shown below.

import pandas as pd
import numpy as np

df = pd.DataFrame(
    [[5, 6, 7, 8], [1, 9, 12, 14], [4, 8, 10, 6]], columns=["a", "b", "c", "d"]
)

print("The original dataframe:")
print(df)

df["e"] = df.apply(lambda x: x.a + x.b, axis=1)

print("The new dataframe:")
print(df)

It performs the same operation as in the above example. We have used a function here lambda. x.aand x.brefers to the columns aand in the DataFrame b.