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Merge Pandas DataFrame with a common column
To merge two Pandas DataFrame with common column, use the merge() function and set the ON parameter as the column name.
At first, let us import the pandas library with an alias −
import pandas as pd
Let us create the 1st DataFrame −
dataFrame1 = pd.DataFrame(
{
"Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],"Units": [100, 150, 110, 80, 110, 90]
}
)Next, create the 2nd DataFrame −
dataFrame2 = pd.DataFrame(
{
"Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Mercedes', 'Jaguar'],"Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000]
}
)Now, merge the two DataFrames with a column column “Car” −
mergedRes = pd.merge(dataFrame1, dataFrame2, on ='Car')
Example
Following is the complete code −
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', 'Audi', 'Mustang', 'Mercedes', 'Jaguar'],"Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000]
}
)
print"\nDataFrame2 ...\n",dataFrame2
# merge DataFrames with common column Car
mergedRes = pd.merge(dataFrame1, dataFrame2, on ='Car')
print"\nMerged data frame with common column...\n", mergedResOutput
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 Audi 5000 3 Mustang 8000 4 Mercedes 9000 5 Jaguar 6000 Merged data frame with common column... Car Units Reg_Price 0 BMW 100 7000 1 Lexus 150 1500 2 Audi 110 5000 3 Mustang 80 8000 4 Jaguar 90 6000
Updated on: 22-Sep-2021
11K+ Views
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