Coursera Learner working on a presentation with Coursera logo and
Coursera Learner working on a presentation with Coursera logo and

Pandas.apply enable the clients to pass a capacity and apply it on each and every estimation of the Pandas arrangement. It comes as a colossal improvement for the pandas library as this capacity isolates information as indicated by the conditions required because of which it is productively utilized in information science and AI. 


pip install pandas

Import the Pandas module into the python record utilizing the accompanying directions on the terminal:

import pandas as pd

s = pd.read_csv(“stock.csv”, squeeze=True)


s.apply(func, convert_dtype=True, args=())


func: .apply takes a function and applies it to all values of pandas series.
convert_dtype: Convert dtype as per the function’s operation.
args=(): Additional arguments to pass to function instead of series.
Return Type: Pandas Series after applied function/operation.

Example #1:

The following example passes a function and checks the value of each element in series and returns low, normal or High accordingly.

import pandas as pd 

# reading csv 

s = pd.read_csv(“stock.csv”, squeeze = True) 

# defining function to check price 

def fun(num): 

    if num<200: 

        return “Low”

    elif num>= 200 and num<400: 

        return “Normal”


        return “High”

# passing function to apply and storing returned series in new 

new = s.apply(fun) 

# printing first 3 element 


# printing elements somewhere near the middle of series 

print(new[1400], new[1500], new[1600]) 

# printing last 3 elements 



Example 2

In the following example, a temporary anonymous function is made in .apply itself using lambda. It adds 5 to each value in series and returns a new series.

import pandas as pd 

s = pd.read_csv(“stock.csv”, squeeze = True) 

# adding 5 to each value 

new = s.apply(lambda num : num + 5) 

# printing first 5 elements of old and new series 

print(s.head(), ‘\n’, new.head()) 

# printing last 5 elements of old and new series 

print(‘\n\n’, s.tail(), ‘\n’, new.tail()) 


0    50.12

1    54.10

2    54.65

3    52.38

4    52.95

Name: Stock Price, dtype: float64 

0    55.12

1    59.10

2    59.65

3    57.38

4    57.95

Name: Stock Price, dtype: float64

3007    772.88

3008    771.07

3009    773.18

3010    771.61

3011    782.22

Name: Stock Price, dtype: float64

3007    777.88

3008    776.07

3009    778.18

3010    776.61

3011    787.22

Name: Stock Price, dtype: float64


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