You can use the following methods to compare columns in two different pandas DataFrames:

Method 1: Count Matching Values Between Columns

df1['my_column'].isin(df2['my_column']).value_counts()

Method 2: Display Matching Values Between Columns

pd.merge(df1, df2, on=['my_column'], how='inner')

The following examples show how to use each method with the following pandas DataFrames:

import numpy as np
import pandas as pd

#create first DataFrame
df1 = pd.DataFrame({'team': ['Mavs', 'Rockets', 'Spurs', 'Heat', 'Nets'],
                    'points': [22, 30, 15, 17, 14]})

#view DataFrame
print(df1)

      team  points
0     Mavs      22
1  Rockets      30
2    Spurs      15
3     Heat      17
4     Nets      14

#create second DataFrame
df2 = pd.DataFrame({'team': ['Mavs', 'Thunder', 'Spurs', 'Nets', 'Cavs'],
                    'points': [25, 40, 31, 32, 22]})

#view DataFrame
print(df2)

      team  points
0     Mavs      25
1  Thunder      40
2    Spurs      31
3     Nets      32
4     Cavs      22

Example 1: Count Matching Values Between Columns

The following code shows how to count the number of matching values between the team columns in each DataFrame:

#count matching values in team columns
df1['team'].isin(df2['team']).value_counts()

True     3
False    2
Name: team, dtype: int64

We can see that the two DataFrames have 3 team names in common and 2 team names that are different.

Example 2: Display Matching Values Between Columns

The following code shows how to display the actual matching values between the team columns in each DataFrame:

#display matching values between team columns
pd.merge(df1, df2, on=['team'], how='inner')

	team	points_x  points_y
0	Mavs	22	  25
1	Spurs	15	  31
2	Nets	14	  32

From the output we can see that the two DataFrames have the following values in common in the team columns:

• Mavs
• Spurs
• Nets

Related:

The following tutorials explain how to perform other common tasks in pandas: