How to find the common rows between two DataFrames with merge()

Overview

A DataFrame is a two-dimensional data structure in which data is aligned in rows and columns. In this Answer, we'll learn how to find the common rows between two DataFrames using the `merge()` function in Python pandas.

Syntax

df1.merge(df2, how = 'inner' ,indicator=False)

In the syntax above, df1 and df2 are two DataFrames.

We pass inner as a value to the how parameter to get the common rows between two DataFrames. This operation is similar to InnerJoin in SQL.

Example

import pandas as pd
#data frame 1
classA = pd.DataFrame(
   {
      "Student": ['John', 'Lexi', 'Augustin', 'Jane', 'Kate'],
      "Age": [18, 17, 19, 17, 18]
   }
)
#data frame 2
classB = pd.DataFrame(
   {
      "Student": ['John', 'Lexi', 'Bob', 'karl', 'Kate', 'Jane'],
      "Age": [18, 17, 16, 19, 18, 20]
   }
)
#get uncommon rows
print(classA.merge(classB, how = 'inner' ,indicator=False))

Explanation

In the above code snippet,

  • Line 1: We import the pandas module, which contains methods to create DataFrames and modify them.
  • Lines 4–8 and 13–19: We create two DataFrames that represent two classes of students. These data frames contain students' names and their ages.
  • Line 21: We get the common students between the two classes classA and classB by merging both DataFrames using the merge() method and passing inner as the value to the how parameter.

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