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

PythonServer Side ProgrammingProgramming

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
raja
Updated on 29-Sep-2021 07:09:45

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