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In Pandas, you can transform and manipulate columns and DataFrames using methods apply()such as transform()and . The desired transformation is passed to these methods as a function argument. Each method has its own subtle differences and effects. This article will show you how to apply a function to a column or an entire DataFrame.

Pandas apply()and transform()methods

apply()Both the and transform()methods operate on individual columns and entire DataFrames. apply()The method applies a function along the specified axis. It passes the columns as a DataFrame to the custom function, while transform()the method passes a single column as a Pandas Seriesto the custom function.

apply()The output of the method is received in the form of DataFrameor depending on the input Series, while transform()the method Seriesreceives in the form of . The syntax of both the apply()and transform()methods is similar to:

Dataframe.apply(customFunction, axis=0)
Dataframe.transform(customFunction, axis=0)

The parameters correspond to

• customFunction: SeriesFunction to be applied to a DataFrame or .
• axis: 0 refers to the row, 1 refers to the column, and the function needs to be applied to the row or column.

apply()Applying functions to Pandas DataFrame columns using

Now that we have the basics down, let's get our hands dirty and learn how apply()to apply a function to a DataFrame column using the method.

We will use the following DataFrame example.

import pandas as pd
import numpy as np

df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["A", "B", "C"])
print(df)

The sample code to apply a function to the entire DataFrame is shown below.

import pandas as pd
import numpy as np

df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["A", "B", "C"])
print(df)


def add_2(x):
    return x + 2


df = df.apply(add_2)
print(df)

Output:

   A  B  C
0  1  2  3
1  4  5  6
2  7  8  9
   A   B   C
0  3   4   5
1  6   7   8
2  9  10  11

As shown above, functions can be applied to an entire DataFrame.

Apply a function to a single column

Let's look at what happens when a function is applied along a single column.

import pandas as pd
import numpy as np

df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["A", "B", "C"])
print(df)


def add_2(x):
    return x + 2


df["A"] = df["A"].apply(add_2)
print(df)

# or #

df["A"].transform(add_2)
print(df)

Output:

   A  B  C
0  1  2  3
1  4  5  6
2  7  8  9
   A  B  C
0  3  2  3
1  6  5  6
2  9  8  9

transform()Apply a function to a Pandas DataFrame column using

Let's see how to transform()apply a function to a DataFrame column using the method. We will use the same DataFrame example as above.

The sample code for applying a function to an entire DataFrame is shown below.

import pandas as pd
import numpy as np

df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["A", "B", "C"])
print(df)


def add_2(x):
    return x + 2


df = df.transform(add_2)
print(df)

Output:

   A  B  C
0  1  2  3
1  4  5  6
2  7  8  9
   A   B   C
0  3   4   5
1  6   7   8
2  9  10  11

As shown above, functions can be applied to an entire DataFrame.

Apply a function to a single column

Let's look at what happens when a function is applied along a single column.

import pandas as pd
import numpy as np

df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["A", "B", "C"])
print(df)


def add_2(x):
    return x + 2


df["A"] = df["A"].transform(add_2)
print(df)

Output:

   A  B  C
0  1  2  3
1  4  5  6
2  7  8  9
   A  B  C
0  3  2  3
1  6  5  6
2  9  8  9