How to apply a function with arguments to a pandas DataFrame ?

The following tutorial demonstrates how to apply a function with arguments on a pandas DataFrame. Example:

Pandas apply()

The DataFrame apply() function allows you to quickly and easily apply operations or transformations to a given DataFrame on a row-by-row or column-by-column basis.

When using the apply() method with arguments, you can specify the axis that should be used for iteration (i.e., rows or columns). This is done by passing in either 0 (for rows) or 1 (for columns) as the first argument. For example, if you want to apply an operation across all the columns of a DataFrame, you could use the following syntax:

DataFrame.apply(operation, axis=1)

Here a simple example of how to apply a function to a dataframe without arguments

import pandas as pd

def myFunction(x):
    return 2 * x + 1

df = pd.DataFrame({'Col1': [3, 7], 'Col2': [1, 2]})

print(df)

returns

  Col1  Col2
0     3     1
1     7     2

Then if we apply the function called "myFunction"

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

print(df_new)

we will get:

   Col1  Col2
0     7     3
1    15     5

Apply a function with arguments

In addition, it has the ability to accept arguments, making it even more flexible and useful for a variety of tasks.

Using positional arguments

def myFunction_02(x, a, b):
    return a * x + b

df_new = df.apply(myFunction_02, a=2, b=1)

print(df_new)

Output

   Col1  Col2
0     7     3
1    15     5

Another example: Applying the function for a given column:

df_new = df.copy()
df_new['Col2'] = df['Col2'].apply(myFunction_02, a=2, b=1)

print(df_new)

Output

   Col1  Col2
0     3     3
1     7     5

Other approach:

def myFunction_02(x, a, b, c):
    return a * x[c] + b

df_new = df.apply(myFunction_02, a=2, b=1, c='Col2', axis=1)

print(df_new)

Output

0    3
1    5
dtype: int64

Using keyword arguments

def myFunction_03(x, a, b):
    return a * x + b

df_new = df.apply(myFunction_03, args=(2, 1))

print(df_new)

Output

   Col1  Col2
0     7     3
1    15     5

Using positional and keyword arguments

def myFunction_04(x, a, b, c):
    return a * x + b - c

df_new = df.apply(myFunction_04, args=(2, 1), c=100)

print(df_new)

Output

Col1  Col2
0   -93   -97
1   -85   -95

References

Related Questions