You can use the following methods to check if a column exists in a pandas DataFrame:

Method 1: Check if One Column Exists

'column1' in df.columns

This will return True if ‘column1’ exists in the DataFrame, otherwise it will return False.

Method 2: Check if Multiple Columns Exist

{'column1', 'column2'}.issubset(df.columns)

This will return True if ‘column1’ and ‘column2’ exists in the DataFrame, otherwise it will return False.

The following examples shows how to use each method in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
                   'points': [18, 22, 19, 14, 14, 11, 20, 28],
                   'assists': [5, 7, 7, 9, 12, 9, 9, 4],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})

#view DataFrame
print(df)

  team  points  assists  rebounds
0    A      18        5        11
1    B      22        7         8
2    C      19        7        10
3    D      14        9         6
4    E      14       12         6
5    F      11        9         5
6    G      20        9         9
7    H      28        4        12

Example 1: Check if One Column Exists

We can use the following code to see if the column ‘team’ exists in the DataFrame:

#check if 'team' column exists in DataFrame
'team' in df.columns

True

The column ‘team’ does exist in the DataFrame, so pandas returns a value of True.

We can also use an if statement to perform some operation if the column ‘team’ exists:

#if 'team' exists, create new column called 'team_name'
if 'team' in df.columns:
    df['team_name'] = df['team']
    
#view updated DataFrame
print(df)

  team  points  assists  rebounds team_name
0    A      18        5        11         A
1    B      22        7         8         B
2    C      19        7        10         C
3    D      14        9         6         D
4    E      14       12         6         E
5    F      11        9         5         F
6    G      20        9         9         G
7    H      28        4        12         H

Example 2: Check if Multiple Columns Exist

We can use the following code to see if the columns ‘team’ and ‘player’ exist in the DataFrame:

#check if 'team' and 'player' columns both exist in DataFrame
{'team', 'player'}.issubset(df.columns)

False

The column ‘team’ exists in the DataFrame but ‘player’ does not, so pandas returns a value of False.

We could also use the following code to see if both ‘points’ and ‘assists’ exist in the DataFrame:

#check if 'points' and 'assists' columns both exist in DataFrame
{'points', 'assists'}.issubset(df.columns)

True

Both columns exist, so pandas returns a value of True.

We can then use an if statement to perform some operation if ‘points’ and ‘assists’ both exist:

#if both exist, create new column called 'total' that finds sum of points and assists
if {'points', 'assists'}.issubset(df.columns):
    df['total'] = df['points'] + df['assists']
    
#view updated DataFrame
print(df)

     team   points assists rebounds  total
0	A	18	 5	 11	23
1	B	22	 7	  8	29
2	C	19	 7	 10	26
3	D	14	 9	  6	23
4	E	14	12	  6	26
5	F	11	 9	  5	20
6	G	20	 9	  9	29
7	H	28	 4	 12	32

Since ‘points’ and ‘assists’ both exist in the DataFrame, pandas went ahead and created a new column called ‘total’ that shows the sum of the ‘points’ and ‘assists’ columns.

Additional Resources

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