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Python Pandas - Finding the uncommon rows between two DataFrames
To find the uncommon rows between two DataFrames, use the concat() method. Let us first import the required library with alias −
import pandas as pd
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": [1000, 1300, 1000, 800, 1100, 800]
}
)Finding uncommon rows between two DataFrames and concatenate the result −
print"\nUncommon rows between two DataFrames...\n",pd.concat([dataFrame1,dataFrame2]).drop_duplicates(keep=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": [1000, 1300, 1000, 800, 1100, 800]
}
)
print"\nDataFrame2 ...\n",dataFrame2
# finding uncommon rows between two DataFrames and concat the result
print"\nUncommon rows between two DataFrames...\n",pd.concat([dataFrame1,dataFrame2]).drop_duplicates(keep=False)
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 1000 1 Lexus 1300 2 Audi 1000 3 Tesla 800 4 Bentley 1100 5 Jaguar 800 Uncommon rows between two DataFrames... Car Reg_Price 1 Lexus 1500 2 Audi 1100 5 Jaguar 900 1 Lexus 1300 2 Audi 1000 5 Jaguar 800
Updated on: 15-Sep-2021
2K+ Views
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