Merge Python Pandas dataframe with a common column and set NaN for unmatched values

To merge two Pandas DataFrame with common column, use the merge() function and set the ON parameter as the column name. To set NaN for unmatched values, use the “how” parameter and set it left or right. That would mean, merging left or right.

At first, let us import the pandas library with an alias −

import pandas as pd

Let us create DataFrame1 −

dataFrame1 = pd.DataFrame(
   {
      "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],
      "Units": [100, 150, 110, 80, 110, 90]
   }
)

Let us create DataFrame2 −

dataFrame2 = pd.DataFrame(
   {
      "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],
      "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000]

   }
)

Now, merge DataFrames with common column Car. The left" “displays all the values of the left DataFrame and sets NaN for unmatched values from 2nd DataFrame −

mergedRes = pd.merge(dataFrame1, dataFrame2, on ='Car', how ="left")

Example

Following is the 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', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],
      "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000]

   }
)

print"\nDataFrame2 ...\n",dataFrame2

# merge DataFrames with common column Car and "left" sets NaN for unmatched values from second DataFrame
mergedRes = pd.merge(dataFrame1, dataFrame2, on ='Car', how ="left")
print"\nMerged data frame with common column...\n", mergedRes

Output

Following is the code −

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     Tesla      5000
3   Mustang      8000
4  Mercedes      9000
5    Jaguar      6000

Merged data frame with common column...
       Car  Units Reg_Price
0      BMW    100    7000.0
1    Lexus    150    1500.0
2     Audi    110       NaN
3  Mustang     80    8000.0
4  Bentley    110       NaN
5   Jaguar     90    6000.0