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Ways to apply an if condition in Pandas DataFrame

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  • Difficulty Level : Hard
  • Last Updated : 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: 

 


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