How to Use Pandas apply() inplace?

The apply() function in pandas is used to apply a custom function to the data frame or series. The apply() function can be used to perform transformations, computation, and other operations on the data. The apply() function returns a new Data frame or series by default. We can also modify the dataframe or series by using the inplace parameter of the apply() function. In this article, we will understand how we can use apply() function inplace with the help of examples.

Syntax of apply() Function

df.apply(func, axis=0)

Here, df is the dataframe on which we need to apply the function func. The axis parameter is used to set the applied function to the row(axis=0) or to the column(axis=1). The func can be a built-in function or a custom function.

Using apply() Method Without inplace Parameter

When we do not use the inplace parameter with apply() function, it returns a new Data Frame or series leaving the original dataframe unchanged.

Example

In the below example, we have a DataFrame with a 'Name' column. We define a custom function add_prefix() that adds the prefix 'Mr.' to each name. By using apply() on the 'Name' column, we obtain a new Series new_df with the modified names.

import pandas as pd

# Create a DataFrame
data = {'Name': ['John', 'Emily', 'James', 'Emma'],
        'Age': [28, 32, 25, 29]}
df = pd.DataFrame(data)

# Function to add a prefix to names
def add_prefix(name):
    return 'Mr. ' + name

# Apply the function to the 'Name' column
new_df = df['Name'].apply(add_prefix)

# Output the new DataFrame
print(new_df)

Output

0     Mr. John
1    Mr. Emily
2    Mr. James
3     Mr. Emma
Name: Name, dtype: object

Using apply() Method inplace

By passing the inplace parameter to the apply() method we can modify the original dataframe or series in-place. We need to specify the inplace parameter as True.

Example

In the below example, we first create a DataFrame with 'Name' and 'Age' columns. Then, a custom function add_prefix() is defined to add the prefix 'Mr.' to each name. The apply() function is applied to the 'Name' column with the add_prefix() function, and the inplace=True parameter is set to modify the original DataFrame directly. Finally, the modified DataFrame is printed, showing the names in the 'Name' column with the added prefix.

import pandas as pd

# Create a DataFrame
data = {'Name': ['John', 'Emily', 'James', 'Emma'],
        'Age': [28, 32, 25, 29]}
df = pd.DataFrame(data)

# Function to add a prefix to names
def add_prefix(name):
    return 'Mr. ' + name

# Apply the function to the 'Name' column in-place
df['Name'].apply(add_prefix, inplace=True)

# Output the modified DataFrame
print(df)

Output

       Name  Age
0   Mr. John   28
1  Mr. Emily   32
2  Mr. James   25
3   Mr. Emma   29

Using apply Function with Multiple Columns

We can implement apply the () function to multiple columns by using the axis parameter. If we pass axis=1, the apply method is applied column-wise and if the axis parameter is passed as axis=0, the apply() method is applied row-wise.

Example

In the below example, we have a DataFrame with three columns: 'Name', 'Age', and 'Salary'. The process_data() function is defined to add the prefix 'Mr.' to the 'Name' column and double the values in the 'Salary' column. By applying this function to the specified columns using apply() with axis=1, we modify the original DataFrame df in place.

import pandas as pd

# Create a DataFrame
data = {'Name': ['John', 'Emily', 'James', 'Emma'],
        'Age': [28, 32, 25, 29],
        'Salary': [50000, 60000, 45000, 55000]}
df = pd.DataFrame(data)

# Function to add a prefix to names and double the salary
def process_data(row):
    row['Name'] = 'Mr. ' + row['Name']
    row['Salary'] *= 2
    return row

# Apply the function to multiple columns
df[['Name', 'Salary']] = df[['Name', 'Salary']].apply(process_data, axis=1)

# Output the modified DataFrame
print(df)

Output

        Name  Age  Salary
0   Mr. John   28  100000
1  Mr. Emily   32  120000
2  Mr. James   25   90000
3   Mr. Emma   29  110000

When to Use apply() Method inplace

The apply() method with the inplace parameter set to `True` is mostly used when we want to modify the original dataframe or series without creating a new object. This will help in saving memory and improve performance especially when working with large datasets.

Sometimes using apply() method with an inplace parameter set to True, modifying the original data can be risky as it can lead to unexpected results or loss of data if not handled carefully. It is usually recommended to take the backup of the original data before modifying the data by using apply() method.

Conclusion

In this article, we discussed how we can use the Pandas apply() method inplace and apply a custom function using it to a data frame column or series. By default the apply() method returns a new dataframe or series with the applied function. If we want to modify the original data frame we need to specify the inplace parameter as true.