Suppose you have an online store. The price of the products is updated frequently. While calculating the final price on the product, you check if the updated price is available or not. If not available then you use the last price available. Solution #1: We can use conditional expression to check if the column is present or not. If it is not present then we calculate the price using the alternative column.
Python3
# importing pandas as pdimport pandas as pd# Create the dataframedf = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Product':['Umbrella', 'Mattress', 'Badminton', 'Shuttle'], 'Last Price':[1200, 1500, 1600, 352], 'Updated Price':[1250, 1450, 1550, 400], 'Discount':[10, 10, 10, 10]})# Print the dataframeprint(df) |
Output : 
Now we will check if the updated price is available or not. If not available then we will apply the discount of 10% on the ‘Last Price’ column to calculate the final price.
Python3
# Check if the updated price is available or notif 'Updated Price' in df.columns: df['Final cost'] = df['Updated Price'] - (df['Updated Price']*0.1)else : df['Final cost'] = df['Last Price'] - (df['Last Price']*0.1)# Print the Dataframeprint(df) |
Output : 
As we can see in the output, as the ‘Update Price’ column was available, so the ‘Final Cost’ has been calculated on the updated price. Now let’s consider a scenario when the ‘Updated Price’ is not available.
Python3
# importing pandas as pdimport pandas as pd# Create the dataframedf = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Product':['Umbrella', 'Mattress', 'Badminton', 'Shuttle'], 'Last Price':[1200, 1500, 1600, 352], 'Discount':[10, 10, 10, 10]})# Print the dataframeprint(df) |
Output : 
Now we will check if the updated price is available or not. If not available then we will apply the discount of 10% on the ‘Last Price’ column to calculate the final price.
Python3
# Check if the updated price is available or notif 'Updated Price' in df.columns: df['Final cost'] = df['Updated Price'] - (df['Updated Price']*0.1)else : df['Final cost'] = df['Last Price'] - (df['Last Price']*0.1)# Print the Dataframeprint(df) |
Output : 
Solution #2: We can use Python’s issubset() function to check if the desired columns are present in the set or not.
Python3
# importing pandas as pdimport pandas as pd# Create the dataframedf = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Product':['Umbrella', 'Mattress', 'Badminton', 'Shuttle'], 'Last Price':[1200, 1500, 1600, 352], 'Updated Price':[1250, 1450, 1550, 400], 'Discount':[10, 10, 10, 10]})# Print the dataframeprint(df) |
Output : Now we will check if the updated price is available or not. If not available then we will apply the discount of 10% on the ‘Last Price’ column to calculate the final price.
Python3
# Check if the updated price is available or notif {'Updated Price', 'Discount'}.issubset(df.columns): df['Final cost'] = df['Updated Price'] - (df['Updated Price']*0.1)elif {'Last Price', 'Discount'}.issubset(df.columns): df['Final cost'] = df['Last Price'] - (df['Last Price']*0.1)# Print the Dataframeprint(df) |
Output : As we can see in the output, as the ‘Update Price’ column was available, so the ‘Final Cost’ has been calculated on the updated price. Now let’s consider a scenario when the ‘Updated Price’ is not available.
Python3
# importing pandas as pdimport pandas as pd# Create the dataframedf = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Product':['Umbrella', 'Mattress', 'Badminton', 'Shuttle'], 'Last Price':[1200, 1500, 1600, 352], 'Discount':[10, 10, 10, 10]})# Print the dataframeprint(df) |
Output : Now we will check if the updated price is available or not. If not available then we will apply the discount of 10% on the ‘Last Price’ column to calculate the final price.
Python3
# Check if the updated price is available or notif {'Updated Price', 'Discount'}.issubset(df.columns): df['Final cost'] = df['Updated Price'] - (df['Updated Price']*0.1)elif {'Last Price', 'Discount'}.issubset(df.columns): df['Final cost'] = df['Last Price'] - (df['Last Price']*0.1)# Print the Dataframeprint(df) |
Output : As we can see in the output, as the ‘Update Price’ column was not available, so the ‘Final Cost’ has been calculated on the basis of last price.
