*20*

You can use the following syntax to add a new column to a pandas DataFrame that shows if each row exists in another DataFrame:

#merge two DataFrames on specific columns all_df = pd.merge(df1, df2, on=['column1', 'column2'], how='left', indicator='exists') #drop unwanted columns all_df = all_df.drop('column3', axis=1) #add column that shows if each row in one DataFrame exists in another all_df['exists'] = np.where(all_df.exists == 'both', True, False)

The following example shows how to use this syntax in practice.

**Example: Check if Row in One Pandas DataFrame Exist in Another**

Suppose we have the following two pandas DataFrames:

import pandas as pd #create first DataFrame df1 = pd.DataFrame({'team' : ['A', 'B', 'C', 'D', 'E'], 'points' : [12, 15, 22, 29, 24]}) print(df1) team points 0 A 12 1 B 15 2 C 22 3 D 29 4 E 24 #create second DataFrame df2 = pd.DataFrame({'team' : ['A', 'D', 'F', 'G', 'H'], 'points' : [12, 29, 15, 19, 10], 'assists' : [4, 7, 7, 10, 12]}) print(df2) team points assists 0 A 12 4 1 D 29 7 2 F 15 7 3 G 19 10 4 H 10 12

We can use the following syntax to add a column called **exists** to the first DataFrame that shows if each value in the **team** and **points** column of each row exists in the second DataFrame:

**import numpy as np
#merge two dataFrames and add indicator column
all_df = pd.merge(df1, df2, on=['team', 'points'], how='left', indicator='exists')
#drop assists columns
all_df = all_df.drop('assists', axis=1)
#add column to show if each row in first DataFrame exists in second
all_df['exists'] = np.where(all_df.exists == 'both', True, False)
#view updated DataFrame
print (all_df)
team points exists
0 A 12 True
1 B 15 False
2 C 22 False
3 D 29 True
4 E 24 False
**

The new **exists** column shows if each value in the **team** and **points** column of each row exists in the second DataFrame.

From the output we can see:

- A Team value of
**A**and points value of**12**does exist in the second DataFrame. - A Team value of
**B**and points value of**15**does not exist in the second DataFrame. - A Team value of
**C**and points value of**22**does not exist in the second DataFrame. - A Team value of
**D**and points value of**29**does exist in the second DataFrame. - A Team value of
**E**and points value of**24**does not exist in the second DataFrame.

Also note that you can specify values other than True and False in the **exists** column by changing the values in the NumPy **where()** function.

For example, you could instead use â€˜existsâ€™ and â€˜not existsâ€™ as follows:

**#add column to show if each row in first DataFrame exists in second
all_df['exists'] = np.where(all_df.exists == 'both', 'exists', 'not exists')
#view updated DataFrame
print (all_df)
team points exists
0 A 12 exists
1 B 15 not exists
2 C 22 not exists
3 D 29 exists
4 E 24 not exists
**

Notice that the values in the **exists** column have been changed.

**Additional Resources**

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

Pandas: Add Column from One DataFrame to Another

Pandas: Get Rows Which Are Not in Another DataFrame

Pandas: How to Check if Multiple Columns are Equal