To find the common rows between two DataFrames with merge(), use the parameter “how” as “inner” since it works like SQL Inner Join and this is what we want to achieve.
Let us create DataFrame1 with two columns −
dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Reg_Price": [1000, 1500, 1100, 800, 1100, 900] } )
Create DataFrame2 with two columns −
dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Reg_Price": [1200, 1500, 1000, 800, 1100, 1000] } )
Let us now find the common rows −
dataFrame1.merge(dataFrame2, how = 'inner' ,indicator=False)
Example
Following is the code −
import pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Reg_Price": [1000, 1500, 1100, 800, 1100, 900] } ) print"DataFrame1 ...\n",dataFrame1 # Create DataFrame2 dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Reg_Price": [1200, 1500, 1000, 800, 1100, 1000] } ) print"\nDataFrame2 ...\n",dataFrame2 # finding common rows between two DataFrames resData = dataFrame1.merge(dataFrame2, how = 'inner' ,indicator=False) print"\nCommon rows between two DataFrames...\n",resData
Output
This will produce the following output −
DataFrame1 ... Car Reg_Price 0 BMW 1000 1 Lexus 1500 2 Audi 1100 3 Tesla 800 4 Bentley 1100 5 Jaguar 900 DataFrame2 ... Car Reg_Price 0 BMW 1200 1 Lexus 1500 2 Audi 1000 3 Tesla 800 4 Bentley 1100 5 Jaguar 1000 Common rows between two DataFrames... Car Reg_Price 0 Lexus 1500 1 Tesla 800 2 Bentley 1100