PySpark Select Columns From DataFrame

In PySpark, select() function is used to select single, multiple, column by index, all columns from the list and the nested columns from a DataFrame, PySpark select() is a transformation function hence it returns a new DataFrame with the selected columns.

Select a Single & Multiple Columns from PySpark
Select All Columns From List
Select Columns By Index
Select a Nested Column
Other Ways to Select Columns

Create a Dataframe.


import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('AIStudyLabs.com').getOrCreate()

# Data
data = [("James","Smith","USA","CA"),
    ("Michael","Rose","USA","NY"),
    ("Robert","Williams","USA","CA"),
    ("Maria","Jones","USA","FL")
  ]

# Column names
columns = ["firstname","lastname","country","state"]

# Create DataFrame
df = spark.createDataFrame(data = data, schema = columns)
df.show(truncate=False)

1. Select Single & Multiple Columns From PySpark

You can select the single or multiple columns of the DataFrame by passing the column names you wanted to select to the select() function. Since DataFrame is immutable, this creates a new DataFrame with selected columns. show() function is used to show the Dataframe contents.

Below are ways to select single, multiple or all columns.


# Select columns by different ways
df.select("firstname","lastname").show()
df.select(df.firstname,df.lastname).show()
df.select(df["firstname"],df["lastname"]).show()

# By using col() function
from pyspark.sql.functions import col
df.select(col("firstname"),col("lastname")).show()

# Select columns by regular expression
df.select(df.colRegex("`^.*name*`")).show()

2. Select All Columns From List

Sometimes you may need to select all DataFrame columns from a Python list. In the below example, we have all columns in the columns list object.


# Select All columns from List
df.select(*columns).show()

# Select All columns
df.select([col for col in df.columns]).show()
df.select("*").show()

3. Select Columns by Index

Using a python list features, you can select the columns by index.


#Selects first 3 columns and top 3 rows
df.select(df.columns[:3]).show(3)

#Selects columns 2 to 4  and top 3 rows
df.select(df.columns[2:4]).show(3)

4. Select Nested Struct Columns from PySpark

If you have a nested struct (StructType) column on PySpark DataFrame, you need to use an explicit column qualifier in order to select. If you are new to PySpark and you have not learned StructType yet, I would recommend skipping the rest of the section or first Understand PySpark StructType before you proceed.

First, let's create a new DataFrame with a struct type.


# Create DataFrame with nested columns
data = [
        (("James",None,"Smith"),"OH","M"),
        (("Anna","Rose",""),"NY","F"),
        (("Julia","","Williams"),"OH","F"),
        (("Maria","Anne","Jones"),"NY","M"),
        (("Jen","Mary","Brown"),"NY","M"),
        (("Mike","Mary","Williams"),"OH","M")
        ]

from pyspark.sql.types import StructType,StructField, StringType        
schema = StructType([
    StructField('name', StructType([
         StructField('firstname', StringType(), True),
         StructField('middlename', StringType(), True),
         StructField('lastname', StringType(), True)
         ])),
     StructField('state', StringType(), True),
     StructField('gender', StringType(), True)
     ])
df2 = spark.createDataFrame(data = data, schema = schema)
df2.printSchema()
df2.show(truncate=False) # shows all columns

Produces the following schema output. If you observe, the column name is of a struct type, containing the columns firstname, middlename, and lastname.


# output:
root
 |-- name: struct (nullable = true)
 |    |-- firstname: string (nullable = true)
 |    |-- middlename: string (nullable = true)
 |    |-- lastname: string (nullable = true)
 |-- state: string (nullable = true)
 |-- gender: string (nullable = true)

+----------------------+-----+------+
|name                  |state|gender|
+----------------------+-----+------+
|[James,, Smith]       |OH   |M     |
|[Anna, Rose, ]        |NY   |F     |
|[Julia, , Williams]   |OH   |F     |
|[Maria, Anne, Jones]  |NY   |M     |
|[Jen, Mary, Brown]    |NY   |M     |
|[Mike, Mary, Williams]|OH   |M     |
+----------------------+-----+------+

To select the struct column, just specify the column name in the select().


# Select struct column
df2.select("name").show(truncate=False)

This returns the struct column name as is.


# Output:
+----------------------+
|name                  |
+----------------------+
|[James,, Smith]       |
|[Anna, Rose, ]        |
|[Julia, , Williams]   |
|[Maria, Anne, Jones]  |
|[Jen, Mary, Brown]    |
|[Mike, Mary, Williams]|
+----------------------+

In order to select the specific column from a nested struct, you need to explicitly qualify the nested struct column name.


# Select child columns
df2.select("name.firstname","name.lastname").show(truncate=False)

This results the firstname and lastname as two seperate columns from the name struct column.


# Output:
+---------+--------+
|firstname|lastname|
+---------+--------+
|James    |Smith   |
|Anna     |        |
|Julia    |Williams|
|Maria    |Jones   |
|Jen      |Brown   |
|Mike     |Williams|
+---------+--------+

To get all columns from the struct column, specify name.*


# Select all child columns
df2.select("name.*").show(truncate=False)

Output:


# Output
+---------+----------+--------+
|firstname|middlename|lastname|
+---------+----------+--------+
|James    |null      |Smith   |
|Anna     |Rose      |        |
|Julia    |          |Williams|
|Maria    |Anne      |Jones   |
|Jen      |Mary      |Brown   |
|Mike     |Mary      |Williams|
+---------+----------+--------+

5. Complete Example


import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('AIStudyLabs.com').getOrCreate()

data = [("James","Smith","USA","CA"),
    ("Michael","Rose","USA","NY"),
    ("Robert","Williams","USA","CA"),
    ("Maria","Jones","USA","FL")
  ]

columns = ["firstname","lastname","country","state"]
df = spark.createDataFrame(data = data, schema = columns)
df.show(truncate=False)

df.select("firstname").show()

df.select("firstname","lastname").show()

#Using Dataframe object name
df.select(df.firstname,df.lastname).show()

# Using col function
from pyspark.sql.functions import col
df.select(col("firstname"),col("lastname")).show()

data = [(("James",None,"Smith"),"OH","M"),
        (("Anna","Rose",""),"NY","F"),
        (("Julia","","Williams"),"OH","F"),
        (("Maria","Anne","Jones"),"NY","M"),
        (("Jen","Mary","Brown"),"NY","M"),
        (("Mike","Mary","Williams"),"OH","M")
        ]

from pyspark.sql.types import StructType,StructField, StringType        
schema = StructType([
    StructField('name', StructType([
         StructField('firstname', StringType(), True),
         StructField('middlename', StringType(), True),
         StructField('lastname', StringType(), True)
         ])),
     StructField('state', StringType(), True),
     StructField('gender', StringType(), True)
     ])

df2 = spark.createDataFrame(data = data, schema = schema)
df2.printSchema()
df2.show(truncate=False) # shows all columns

df2.select("name").show(truncate=False)
df2.select("name.firstname","name.lastname").show(truncate=False)
df2.select("name.*").show(truncate=False)

This example is also available at PySpark github project.

6. Conclusion

In this article, you have learned select() is a transformation function of the DataFrame and is used to select single, multiple columns, select all columns from the list, select by index, and finally select nested struct columns, you have also learned how to select nested elements from the DataFrame.

Happy Learning !!