Python Pandas - Function Application

To apply your own or another library’s functions to Pandas objects, you should be aware of the three important methods. The methods have been discussed below. The appropriate method to use depends on whether your function expects to operate on an entire DataFrame, row- or column-wise, or element wise.

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

Table-wise Function Application:

Custom operations can be performed by passing the function and the appropriate number of parameters as pipe arguments. Thus, operation is performed on the whole DataFrame.

For example, add a value 2 to all the elements in the DataFrame. Then,

adder function:

The adder function adds two numeric values as parameters and returns the sum.

def adder(ele1,ele2):

   return ele1+ele2

We will now use the custom function to conduct operation on the DataFrame.

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

df.pipe(adder,2)

Let’s see the full program −

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' ])

df. pipe( adder, 2 )

print df. apply( np. mean)

Its output is as follows −

        col1       col2       col3

0   2.176704   2.219691   1.509360

1   2.222378   2.422167   3.953921

2   2.241096   1.135424   2.696432

3   2.355763   0.376672   1.182570

4   2.308743   2.714767   2.130288

Row or Column Wise Function Application:

Arbitrary functions can be applied along the axes of a DataFrame or Panel using the apply()  method, which, like the descriptive statistics methods, takes an optional axis argument. By default, the operation performs column wise, taking each column as an array-like.

Example 1:

import pandas as pd

import numpy as np

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

df. apply( np. mean)

print df. apply( np. mean)

Its output is as follows −

col1   -0.288022

col2    1.044839

col3   -0.187009

dtype: float64

By passing axis  parameter, operations can be performed row wise.

Example 2:

import pandas as pd

import numpy as np

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

df. apply( np. mean, axis= 1 )

print df. apply( np. mean)

Its output is as follows −

col1    0.034093

col2   -0.152672

col3   -0.229728

dtype: float64  

Example 3:

import pandas as pd

import numpy as np

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

df. apply( lambda x: x. max() - x. min())

print df. apply( np. mean)

Its output is as follows −

col1   -0.167413

col2   -0.370495

col3   -0.707631

dtype: float64

Element Wise Function Application:

Not all functions can be vectorized (neither the NumPy arrays which return another array nor any value), the methods applymap()  on DataFrame and analogously map()  on Series accept any Python function taking a single value and returning a single value.

Example 1:

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

Example 2:

import pandas as pd

import numpy as np

# My custom function

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

df. applymap( lambda x: x* 100 )

print df. apply( np. mean)

Its output is as follows −

col1    0.395263

col2    0.204418

col3   -0.795188

dtype: float64