Conditional operation on Pandas DataFrame columns

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.

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# importing pandas as pd
import pandas as pd
  
# Create the dataframe
df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
                   'Product':['Umbrella', 'Matress', 'Badminton', 'Shuttle'],
                   'Last Price':[1200, 1500, 1600, 352],
                   'Updated Price':[1250, 1450, 1550, 400],
                   'Discount':[10, 10, 10, 10]})
  
# Print the dataframe
print(df)

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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.

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# Check if the updated price is available or not
if '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 Dataframe
print(df)

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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.

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# importing pandas as pd
import pandas as pd
  
# Create the dataframe
df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
                   'Product':['Umbrella', 'Matress', 'Badminton', 'Shuttle'],
                   'Last Price':[1200, 1500, 1600, 352],
                   'Discount':[10, 10, 10, 10]})
  
# Print the dataframe
print(df)

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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.

filter_none

edit
close

play_arrow

link
brightness_4
code

# Check if the updated price is available or not
if '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 Dataframe
print(df)

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Output :

 

Solution #2: We can use Python’s issubset() function to check if the desired columns are present in the set or not.

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# importing pandas as pd
import pandas as pd
  
# Create the dataframe
df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
                   'Product':['Umbrella', 'Matress', 'Badminton', 'Shuttle'],
                   'Last Price':[1200, 1500, 1600, 352],
                   'Updated Price':[1250, 1450, 1550, 400],
                   'Discount':[10, 10, 10, 10]})
  
# Print the dataframe
print(df)

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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.

filter_none

edit
close

play_arrow

link
brightness_4
code

# Check if the updated price is available or not
if {'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 Dataframe
print(df)

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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.

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas as pd
import pandas as pd
  
# Create the dataframe
df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
                   'Product':['Umbrella', 'Matress', 'Badminton', 'Shuttle'],
                   'Last Price':[1200, 1500, 1600, 352],
                   'Discount':[10, 10, 10, 10]})
  
# Print the dataframe
print(df)

chevron_right


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.

filter_none

edit
close

play_arrow

link
brightness_4
code

# Check if the updated price is available or not
if {'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 Dataframe
print(df)

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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.



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