Apply Functions in Python pandas – Apply(), Applymap(), pipe()

To Apply our own function or some other library’s function, pandas provide three important functions namely pipe(), apply() and applymap().  These Functions are discussed below.

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

Table wise Function Application: pipe()

Pipe() function performs the custom operation for the entire dataframe. In below example we will using pipe() Function to add value 2 to the entire dataframe

import pandas as pd
import numpy as np
import math

# own function
def adder(adder1,adder2):
   return adder1+adder2

#Create a Dictionary of series
d = {'Score_Math':pd.Series([66,57,75,44,31,67,85,33,42,62,51,47]),
   'Score_Science':pd.Series([89,87,67,55,47,72,76,79,44,92,93,69])}

df = pd.DataFrame(d)
print df
print df.pipe(adder,2)

output will be

Original dataframe:

Dataframe with value 2 added:

Row or Column Wise Function Application: apply()

apply() function performs the custom operation for either row wise or column wise . In below example we will be using apply() Function to find the mean of values across rows and mean of values across columns

Create Dataframe

import pandas as pd
import numpy as np
import math


#Create a DataFrame
d = {'Score_Math':pd.Series([66,57,75,44,31,67,85,33,42,62,51,47]),
   'Score_Science':pd.Series([89,87,67,55,47,72,76,79,44,92,93,69])}

df = pd.DataFrame(d)
print df

resultant dataframe will be

Row wise Function in python pandas : Apply()

apply() Function to find the mean of values across rows

#row wise mean

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

so the output will be

Column wise Function in python pandas : Apply()

apply() Function to find the mean of values across columns

#column wise meanprint 

df.apply(np.mean,axis=0)

so the output will be

Element wise Function Application in python pandas: applymap()

applymap() Function performs the specified operation for all the elements the dataframe. we will be using the same dataframe to depict example of applymap() Function. We will be multiplying the all the elements of dataframe by 2 as shown below

Example1: applymap() Function in python

import pandas as pd
import numpy as np
import math


# applymap() Function
print df.applymap(lambda x:x*2)

so the output will be

Example2: applymap() Function in python

We will be finding the square root of all the elements of dataframe with applymap() function as shown below

import math

#applymap() Function to find the sqrt
print df.applymap(lambda x:math.sqrt(x))

so the output will be