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PySpark SQL Functions | regexp_extract method

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PySpark SQL Functions
schedule Jul 1, 2022
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PySpark SQL Functions' regexp_extract(~) method extracts a substring using regular expression.

Parameters

1. str | string or Column

The column whose substrings will be extracted.

2. pattern | string or Regex

The regular expression pattern used for substring extraction.

3. idx | int

The group from which to extract values. Consult the examples below for clarification.

Return Value

A new PySpark Column.

Examples

Consider the following PySpark DataFrame:

df = spark.createDataFrame([['id_20_30', 10], ['id_40_50', 30]], ['id', 'age'])
df.show()
+--------+---+
| id|age|
+--------+---+
|id_20_30| 10|
|id_40_50| 30|
+--------+---+

Extracting a specific substring

To extract the first number in each id value, use regexp_extract(~) like so:

from pyspark.sql import functions as F
df.select(F.regexp_extract('id', '(\d+)', 1)).show()
+----------------------------+
|regexp_extract(id, (\d+), 1)|
+----------------------------+
| 20|
| 40|
+----------------------------+

Here, the regular expression (\d+) matches one or more digits (20 and 40 in this case). We set the third argument value as 1 to indicate that we are interested in extracting the first matched group - this argument is useful when we capture multiple groups.

Extracting the n-th captured substring

We can use multiple (~) capture groups for regexp_extract(~) like so:

from pyspark.sql import functions as F
df.select(F.regexp_extract('id', '(\d+)_(\d+)', 2)).show()
+----------------------------------+
|regexp_extract(id, (\d+)_(\d+), 2)|
+----------------------------------+
| 30|
| 50|
+----------------------------------+

Here, we set the third argument value to 2 to indicate that we are interested in extracting the values captured by the second group.

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Published by Isshin Inada
Edited by 0 others
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