PySpark: How to Filter Rows Using LIKE Operator

You can use the following syntax to filter a PySpark DataFrame using a LIKE operator:


This particular example filters the DataFrame to only show rows where the string in the team column has a pattern like “avs” somewhere in the string.

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

Example: How to Filter Using LIKE Operator in PySpark

Suppose we have the following PySpark DataFrame that contains information about points scored by various basketball players:

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()

#define data
data = [['Mavs', 18], 
        ['Nets', 33], 
        ['Lakers', 12], 
        ['Mavs', 15], 
        ['Cavs', 19],
        ['Wizards', 24],
        ['Cavs', 28],
        ['Nets', 40],
        ['Mavs', 24],
        ['Spurs', 13]] 
#define column names
columns = ['team', 'points'] 
#create dataframe using data and column names
df = spark.createDataFrame(data, columns) 
#view dataframe

|   team|points|
|   Mavs|    18|
|   Nets|    33|
| Lakers|    12|
|   Mavs|    15|
|   Cavs|    19|
|Wizards|    24|
|   Cavs|    28|
|   Nets|    40|
|   Mavs|    24|
|  Spurs|    13|

We can use the following syntax to filter the DataFrame to only contain rows where the team column contains the pattern “avs” somewhere in the string:

#filter DataFrame where team column contains pattern like 'avs'

|Mavs|    18|
|Mavs|    15|
|Cavs|    19|
|Cavs|    28|
|Mavs|    24|

Notice that each of the rows in the resulting DataFrame contain “avs” in the team column.

Note: You can find the complete documentation for the PySpark like function here.

Additional Resources

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

PySpark: How to Use “OR” Operator
PySpark: How to Use “AND” Operator
PySpark: How to Use “NOT IN” Operator

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