Ways to apply an if condition in Pandas DataFrame

07 Oct, 2021
Let’s discuss the different ways of applying If condition to a data frame in pandas.
1) Applying IF condition on Numbers Let us create a Pandas DataFrame that has 5 numbers (say from 51 to 55). Let us apply IF conditions for the following situation. If the particular number is equal or lower than 53, then assign the value of ‘True’. Otherwise, if the number is greater than 53, then assign the value of ‘False’.
Syntax:
df.loc[df[‘column name’] condition, ‘new column name’] = ‘value if condition is met’  
Example:  
Python3
from pandas import DataFrame numbers = {'mynumbers': [51, 52, 53, 54, 55]}df = DataFrame(numbers, columns =['mynumbers']) df.loc[df['mynumbers'] <= 53, '<= 53'] = 'True'df.loc[df['mynumbers'] > 53, '<= 53'] = 'False' df
Output: 
2) Applying IF condition with lambda Let us create a Pandas DataFrame that has 5 numbers (say from 51 to 55). Let us apply IF conditions for the following situation. If the particular number is equal or lower than 53, then assign the value of ‘True’. Otherwise, if the number is greater than 53, then assign the value of ‘False’.
Syntax: 
df[‘new column name’] = df[‘column name’].apply(lambda x: ‘value if condition is met’ if x condition else ‘value if condition is not met’)  
Example:  
Python3
from pandas import DataFrame numbers = {'mynumbers': [51, 52, 53, 54, 55]}df = DataFrame(numbers, columns =['mynumbers']) df['<= 53'] = df['mynumbers'].apply(lambda x: 'True' if x <= 53 else 'False') print (df)
Output: 
3) Applying IF condition on strings We will deal with the DataFrame that contains only strings with 5 names: Hanah, Ria, Jay, Bholu, Sachin. The conditions are: If the name is equal to ‘Ria, ’ then assign the value of ‘Found’. Otherwise, if the name is not ‘Ria, ’ then assign the value of ‘Not Found’.
Example:  
Python3
from pandas import DataFrame names = {'First_name': ['Hanah', 'Ria', 'Jay', 'Bholu', 'Sachin']}df = DataFrame(names, columns =['First_name']) df.loc[df['First_name'] == 'Ria', 'Status'] = 'Found' df.loc[df['First_name'] != 'Ria', 'Status'] = 'Not Found' print (df)
Output: 
4) Applying IF condition on strings using lambda We will deal with the DataFrame that contains only strings with 5 names: Hanah, Ria, Jay, Bholu, Sachin. The conditions are: If the name is equal to ‘Ria, ’ then assign the value of ‘Found’. Otherwise, if the name is not ‘Ria, ’ then assign the value of ‘Not Found’. But this time we will deal with it using lambdas.
Example  
Python3
from pandas import DataFrame names = {'First_name': ['Hanah', 'Ria', 'Jay', 'Bholu', 'Sachin']}df = DataFrame(names, columns =['First_name']) df['Status'] = df['First_name'].apply(lambda x: 'Found' if x == 'Ria' else 'Not Found') print (df)
Output: 
5) Applying IF condition with OR We will deal with the DataFrame that contains only strings with 5 names: Hanah, Ria, Jay, Bholu, Sachin. The conditions are: If the name is equal to “Ria”, or “Jay” then assign the value of ‘Found’. Otherwise, if the name is not “Ria” or “Jay” then assign the value of ‘Not Found’. 
Example  
Python3
from pandas import DataFrame names = {'First_name': ['Hanah', 'Ria', 'Jay', 'Bholu', 'Sachin']}df = DataFrame(names, columns =['First_name']) df.loc[(df['First_name'] == 'Ria') | (df['First_name'] == 'Jay'), 'Status'] = 'Found' df.loc[(df['First_name'] != 'Ria') & (df['First_name'] != 'Jay'), 'Status'] = 'Not Found'  print (df)
Output: 
 
abhishek0719kadiyan
Python pandas-dataFrame
Python-pandas
Python
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