Fetch Common Rows Between Two DataFrames in Python Pandas Using Concat

To fetch the common rows between two DataFrames, use the concat() function. 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]
}
)

Finding common rows between two DataFrames with concat() −

dfRes = pd.concat([dataFrame1, dataFrame2])

Reset index −

dfRes = dfRes.reset_index(drop=True)

Groupby columns −

dfGroup = dfRes.groupby(list(dfRes.columns))

Getting the length of each row to calculate the count. If count is greater than 1, that would mean common rows −

res = [k[0] for k in dfGroup.groups.values() if len(k) > 1]

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
dfRes = pd.concat([dataFrame1, dataFrame2])

# reset index
dfRes = dfRes.reset_index(drop=True)

# groupby columns
dfGroup = dfRes.groupby(list(dfRes.columns))

# length of each row to calculate the count
# if count is greater than 1, that would mean common rows
res = [k[0] for k in dfGroup.groups.values() if len(k) > 1]

print"\nCommon rows...\n",dfRes.reindex(res)

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...
       Car   Reg_Price
3    Tesla         800
1    Lexus        1500
4  Bentley        1100