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Python Pandas – Can we use & Operator to find common columns between two DataFrames?
Yes, we can use the & operator to find the common columns between two DataFrames. At first, let us create two DataFrames −
# creating dataframe1
dataFrame1 = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],"Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],
})
print("Dataframe1...\n",dataFrame1)
# creating dataframe2
dataFrame2 = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],"Units_Sold": [ 100, 110, 150, 80, 200, 90]
})Get the common columns using the & operator −
res = dataFrame1.columns & dataFrame2.columns
Example
Following is the code −
import pandas as pd
# creating dataframe1
dataFrame1 = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],"Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],
})
print"Dataframe1...\n",dataFrame1
# creating dataframe2
dataFrame2 = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],"Units_Sold": [ 100, 110, 150, 80, 200, 90]
})
print"Dataframe2...\n",dataFrame2
# getting common columns using the & operator
res = dataFrame1.columns & dataFrame2.columns
print"\nCommon columns...\n",resOutput
This will produce the following output −
Dataframe1... Car Cubic_Capacity 0 BMW 2000 1 Lexus 1800 2 Tesla 1500 3 Mustang 2500 4 Mercedes 2200 5 Jaguar 3000 Dataframe2... Car Units_Sold 0 BMW 100 1 Lexus 110 2 Tesla 150 3 Mustang 80 4 Mercedes 200 5 Jaguar 90 Common columns... Index([u'Car'], dtype='object')
Updated on: 21-Sep-2021
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