You can use the following methods to use LIKE (similar to SQL) inside a pandas query() function to find rows that contain a particular pattern:

Method 1: Find Rows that Contain One Pattern

df.query('my_column.str.contains("pattern1")')

Method 2: Find Rows that Contain One of Several Patterns

df.query('my_column.str.contains("pattern1|pattern2")')

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

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['Cavs', 'Heat', 'Mavs', 'Mavs', 'Nets',
                            'Heat', 'Cavs', 'Jazz', 'Jazz', 'Hawks'],
                   'points': [3, 3, 4, 5, 4, 7, 8, 7, 12, 14],
                   'rebounds': [15, 14, 14, 10, 8, 14, 13, 9, 5, 4]})

#view DataFrame
print(df)

    team  points  rebounds
0   Cavs       3        15
1   Heat       3        14
2   Mavs       4        14
3   Mavs       5        10
4   Nets       4         8
5   Heat       7        14
6   Cavs       8        13
7   Jazz       7         9
8   Jazz      12         5
9  Hawks      14         4

Example 1: Find Rows that Contain One Pattern

The following code shows how to use the query() function to find all rows in the DataFrame that contain “avs” in the team column:

df.query('team.str.contains("avs")')

        team	points	rebounds
0	Cavs	3	15
2	Mavs	4	14
3	Mavs	5	10
6	Cavs	8	13

Each row that is returned contains “avs” somewhere in the team column.

Also note that this syntax is case-sensitive.

Thus, if we used “AVS” instead then we would not receive any results because no row contains uppercase “AVS” in the team column.

Example 2: Find Rows that Contain One of Several Patterns

The following code shows how to use the query() function to find all rows in the DataFrame that contain “avs” or “eat” in the team column:

df.query('team.str.contains("avs|eat")')

        team	points	rebounds
0	Cavs	3	15
1	Heat	3	14
2	Mavs	4	14
3	Mavs	5	10
5	Heat	7	14
6	Cavs	8	13

Each row that is returned contains either “avs” or “eat” somewhere in the team column.

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