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Python Pandas – Merge DataFrame with one-to-one relation
To merge Pandas DataFrame, use the merge() function. The one-to-one relation is implemented on both the DataFrames by setting under the “validate” parameter of the merge() function i.e. −
validate = “one-to-one” or validate = “1:1”
The one-to-many relation checks if merge keys are unique in both left and right dataset.
At first, let us create our 1st DataFrame −
dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],"Units": [100, 150, 110, 80, 110, 90] } )
Now, let us create our 2nd 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 one-to-one relation # import pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],"Units": [100, 150, 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 "one-to-one" in "validate" parameter mergedRes = pd.merge(dataFrame1, dataFrame2, validate ="one_to_one") print("\nMerged dataframe with one-to-one relation...\n", mergedRes)
Output
This will produce the following output −
DataFrame1 ... Car Units 0 BMW 100 1 Lexus 150 2 Audi 110 3 Mustang 80 4 Bentley 110 5 Jaguar 90 DataFrame2 ... Car Reg_Price 0 BMW 7000 1 Lexus 1500 2 Tesla 5000 3 Mustang 8000 4 Mercedes 9000 5 Jaguar 6000 Merged dataframe with one-to-one relation Car Units Reg_Price 0 BMW 100 7000 1 Lexus 150 1500 2 Mustang 80 8000 3 Jaguar 90 6000
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