Python Pandas – Merge DataFrame with many-to-one relation

To merge Pandas DataFrame, use the merge() function. The many-to-one relation is implemented on both the DataFrames by setting under the “validate” parameter of the merge() function i.e. −

validate = “many-to-one”
or
validate = “m:1”

The many-to-one relation checks if merge keys are unique in right dataset.

At first, let us create our 1^st DataFrame −

dataFrame1 = pd.DataFrame(
   {
      "Car": ['BMW', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],"Units": [100, 110, 80, 110, 90]
   }
)

Now, let us create our 2^nd DataFrame −

dataFrame2 = pd.DataFrame(
   {
      "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],"Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000]
   }
)

Example

Following is the code −

#
# Merge Pandas DataFrame with many-to-one relation
#

import pandas as pd

# Create DataFrame1
dataFrame1 = pd.DataFrame(
   {
      "Car": ['BMW', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],"Units": [100, 110, 80, 110, 90]
   }
)

print("DataFrame1 ...\n",dataFrame1)

# Create DataFrame2
dataFrame2 = pd.DataFrame(
   {
      "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],"Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000]

   }
)

print("\nDataFrame2 ...\n",dataFrame2)

# merge DataFrames with "many-to-one" in "validate" parameter
mergedRes = pd.merge(dataFrame1, dataFrame2, validate ="many_to_one")
print("\nMerged dataframe with many-to-one relation...\n", mergedRes)

Output

This will produce the following output −

DataFrame1 ...
       Car   Units
0      BMW     100
1     Audi     110
2  Mustang      80
3  Bentley     110
4   Jaguar      90

DataFrame1 ...
        Car   Reg_Price
0       BMW        7000
1     Lexus        1500
2     Tesla        5000
3   Mustang        8000
4  Mercedes        9000
5    Jaguar        6000

Merged dataframe with many-to-one relation...
       Car   Units   Reg_Price
0      BMW     100        7000
1  Mustang      80        8000
2   Jaguar      90        6000