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",res
Output
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')