Python Pandas - Function Application

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Pandas provides powerful methods to apply custom or library functions to DataFrame and Series objects. Depending on whether you want to apply a function to the entire DataFrame, row- or column-wise, or element-wise, Pandas offers several methods to achieve these tasks.

In this tutorial, we will explore three essential methods for function application in Pandas −

• Table wise Function Application: pipe()
• Row or Column Wise Function Application: apply()
• Element wise Function Application: map()

Let's dive into each method and see how they can be utilized effectively.

Table-wise Function Application

The pipe() function allows you to apply chainable functions that expect a DataFrame or Series as input. This method is useful for performing custom operations on the entire DataFrame in a clean and readable manner.

Example: Applying a Custom Function to the Entire DataFrame

Here is the example that demonstrates how you can add a value to all elements in the DataFrame using the pipe() function.

import pandas as pd
import numpy as np

def adder(ele1,ele2):
   return ele1+ele2

df = pd.DataFrame(np.random.randn(5,3),columns=['col1','col2','col3'])
print('Original DataFrame:\n', df)

df.pipe(adder,2)
print('Modified DataFrame:\n', df)

Its output is as follows −

Original DataFrame:
        col1      col2      col3
0  2.349190  1.908931 -0.121444
1  1.306488 -0.946431  0.308926
2 -0.235694 -0.720602  1.089614
3  0.960508 -1.273928  0.943044
4 -1.180202 -0.959529  0.464541

Modified DataFrame:
        col1      col2      col3
0  2.349190  1.908931 -0.121444
1  1.306488 -0.946431  0.308926
2 -0.235694 -0.720602  1.089614
3  0.960508 -1.273928  0.943044
4 -1.180202 -0.959529  0.464541

Row or Column Wise Function Application

The apply() function is versatile and allows you to apply a function along the axes of a DataFrame. By default, it applies the function column-wise, but you can specify row-wise application using the axis parameter.

Example: Applying a Function Column-wise

This example applies a function to the DataFrame columns. Here the np.mean() function calculates the mean of each column.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), columns=['col1', 'col2', 'col3'])
print('Original DataFrame:\n', df)

result = df.apply(np.mean)
print('Result:\n',result)

Its output is as follows −

Original DataFrame:
        col1      col2      col3
0 -0.024666  0.058480  0.658520
1 -0.040997  1.253245 -1.242394
2  1.073832 -1.039897  0.840698
3  0.248157 -1.985475  0.310767
4 -0.973393 -1.002330 -0.890125

Result:
 col1    0.056587
col2   -0.543195
col3   -0.064507
dtype: float64

By passing value 1 to the axis parameter, operations can be performed row wise.

Example: Applying a Function Row-wise

This function applies the np.mean() function to the rows of the pandas DataFrame.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), columns=['col1', 'col2', 'col3'])
print('Original DataFrame:\n', df)

result = df.apply(np.mean, axis=1)
print('Result:\n',result)

Its output is as follows −

Original DataFrame:
        col1      col2      col3
0  0.069495 -1.228534 -1.431796
1  0.468724  0.497217 -0.270103
2 -0.754304  0.053360 -1.298396
3  0.762669 -2.181029 -2.067756
4  0.129679  0.131104  1.010851

Result:
 0   -0.863612
1    0.231946
2   -0.666446
3   -1.162039
4    0.423878
dtype: float64  

Example: Applying a Lambda Function

The following example applies the lambda function to the DataFrame elements using the apply() method.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), columns=['col1', 'col2', 'col3'])
print('Original DataFrame:\n', df)

result = df.apply(lambda x: x.max() - x.min())
print('Result:\n',result)

Its output is as follows −

Original DataFrame:
        col1      col2      col3
0 -1.143522  0.413272  0.633881
1  0.200806 -0.050024  0.108580
2 -2.147704 -0.400682 -1.191469
3  2.342222 -2.398639  0.063151
4 -1.071437  1.895879 -0.916805

Result:
 col1    4.489926
col2    4.294518
col3    1.825350
dtype: float64

Element Wise Function Application

When you need to apply a function to each element individually, you can use map() function. These methods are particularly useful when the function cannot be vectorized.

Example: Using map() Function

The following example demonstrates how to use the map() function for applying a custom function to the elements of the DataFrame object.

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(5,3),columns=['col1','col2','col3'])

# My custom function
df['col1'].map(lambda x:x*100)
print(df.apply(np.mean))

Its output is as follows −

col1    0.480742
col2    0.454185
col3    0.266563
dtype: float64