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",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')