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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 1st DataFrame −
dataFrame1 = pd.DataFrame(
{
"Car": ['BMW', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],"Units": [100, 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 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
Updated on: 29-Sep-2021
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