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