Subset or Filter data with multiple conditions in pyspark

In order to subset or filter data with conditions in pyspark we will be using filter() function. filter() function  subsets or filters the data with single or multiple conditions in pyspark. Let’s get clarity with an example.

  • Subset or filter data with single condition
  • Subset or filter data with multiple conditions (multiple or condition in pyspark)
  • Subset or filter data with multiple conditions (multiple and condition in pyspark)
  • Subset or filter data with conditions using sql functions
  • Filter using Regular expression in pyspark
  • Filter starts with and ends with keyword in pyspark
  • Filter with null and non null values in pyspark
  • Filter with LIKE% and in operator in pyspark

We will be using dataframe df.

subset or filter the data in pyspark with condition 1

 

 

Subset or filter data with single condition in pyspark

Subset or filter data with single condition in pyspark can be done using filter() function with conditions inside the filter function.

## subset with single condition

df.filter(df.mathematics_score > 50).show()

The above filter function chosen mathematics_score greater than 50. So the dataframe is subsetted or filtered with mathematics_score greater than 50

subset or filter the data in pyspark with condition 2

 

 

Subset or filter data with multiple conditions in pyspark (multiple and)

Subset or filter data with multiple conditions can be done using filter() function, by passing the conditions inside the filter functions, here we have used and operators

## subset with multiple conditions with and conditions

df.filter('mathematics_score > 50 and science_score > 50').show()

The above filter function chosen mathematics_score greater than 50 and science_score greater than 50. So the result will be

subset or filter the data in pyspark with condition 3

 

 

Subset or filter data with multiple conditions in pyspark (multiple or)

Subset or filter data with multiple conditions can be done using filter function() with conditions inside the filter functions with either or / and operator

## subset with multiple conditions with or conditions

df.filter('mathematics_score > 50 or science_score > 50').show()

The above filter function chosen mathematics_score greater than 50 or science_score greater than 50. So the result will be

subset or filter the data in pyspark with condition 4

 

 

Subset or filter data with multiple conditions in pyspark  (multiple and spark sql)

Subset or filter data with multiple conditions can be done using filter() function, by passing the conditions inside the filter functions, here we have used & operators

## subset with multiple condition using sql.functions

import pyspark.sql.functions as f
df.filter((f.col('mathematics_score') > 50) & (f.col('science_score') > 50)).show()

The above filter function chosen mathematics_score greater than 50 and science_score greater than 50. So the result will be

subset or filter the data in pyspark with condition 5

 

 

Subset or filter data with multiple conditions in pyspark  (multiple or spark sql)

Subset or filter data with multiple conditions in pyspark can be done using filter function() and col() function along with conditions inside the filter functions with either or / and operator

## subset with multiple condition using sql.functions

import pyspark.sql.functions as f

df.filter((f.col('mathematics_score') > 60)| (f.col('science_score') > 60)).show()

The above filter function chosen mathematics_score greater than 60 or science_score greater than 60. So the result will be

subset or filter the data in pyspark with condition 6

 

 

Filter using Regex with column name like in pyspark:

colRegex() function with regular expression inside is used to select the column with regular expression.

## Filter using Regex with column name like

df.select(df.colRegex("`(mathe)+?.+`")).show()

the above code selects column with column name like mathe%

subset or filter the data in pyspark with condition 7

 

 

Filter column name contains in pyspark :

Returns rows where strings of a column contain a provided substring. In our example, filtering by rows which contain the substring “an” would be a good way to get all rows that contains “an”.

## Filter column name contains

df.filter(df.name.contains('an')).show()

So the resultant dataframe will be

subset or filter the data in pyspark with condition 8

 

 

Filter row with string starts with in pyspark :

Returns rows where strings of a row start with a provided substring. In our example, filtering by rows which starts with the substring “Em” is shown.

## Filter row with string starts with "Em"

df.filter(df.name.startswith('Em')).show()

So the resultant dataframe will be

subset or filter the data in pyspark with condition 11

 

 

Filter row with string ends with in pyspark :

Returns rows where strings of a row end with a provided substring. In our example, filtering by rows which ends with the substring “i” is shown.

## Filter row with string ends with "i"

df.filter(df.name.endswith('i')).show()

So the resultant dataframe will be

subset or filter the data in pyspark with condition 10

 

  • filter(df.name.isNull()): Returns rows where values in a provided column are null.
  • filter(df.name.isNotNull()):Returns rows where values in a provided column are not null.
  • filter(df.name.like(‘Em%’)).show() :  Performs a SQL-like query containing the LIKE clause.

subset or filter the data in pyspark with condition 11

 

  • filter(df.name.rlike(‘[A-Z]*vi$’)).show() : Performs a regexp filter.

subset or filter the data in pyspark with condition 12

 

 

  • filter(df.name.isin(‘Ravi’, ‘Manik’)).show() : Looks for rows where the string value of a column matches any of the provided strings exactly.

subset or filter the data in pyspark with condition 13

 


Other Related Topics:

 

Subset or Filter data with multiple conditions in pyspark                                                                                            Subset or Filter data with multiple conditions in pyspark