PYTHON

lambda function dataframe

# importing pandas library
import pandas as pd
   
# creating and initializing a list
values= [['Rohan',455],['Elvish',250],['Deepak',495],
         ['Soni',400],['Radhika',350],['Vansh',450]] 
  
# creating a pandas dataframe
df = pd.DataFrame(values,columns=['Name','Total_Marks'])
  
# Applying lambda function to find 
# percentage of 'Total_Marks' column 
# using df.assign()
df = df.assign(Percentage = lambda x: (x['Total_Marks'] /500 * 100))
  
# displaying the data frame
df

lambda function dataframe

# importing pandas library
import pandas as pd
  
# creating and initializing a nested list
values_list = [[15, 2.5, 100], [20, 4.5, 50], [25, 5.2, 80],
               [45, 5.8, 48], [40, 6.3, 70], [41, 6.4, 90],
               [51, 2.3, 111]]
  
# creating a pandas dataframe
df = pd.DataFrame(values_list, columns=['Field_1', 'Field_2', 'Field_3'])
  
# Applying lambda function to find 
# the product of 3 columns using 
# df.assign()
df = df.assign(Product=lambda x: (x['Field_1'] * x['Field_2'] * x['Field_3']))
  
# printing dataframe
df

panda lambda function returns dataframe

df = df.assign(total_cost=lambda x: TotalCost(x['Distance'], m, c))

panda lambda function returns dataframe

df.eval('total_cost = @m * Distance + @c', inplace=True)