PySpark - col()

In this PySpark tutorial, we will discuss how to use col() method on PySpark DataFrame. 

Introduction:

DataFrame in PySpark is an two dimensional data structure that will store data in two dimensional format. One dimension refers to a row and second dimension refers to a column, So It will store the data in rows and columns.

Let's install pyspark module before going to this. The command to install any module in python is "pip".

Syntax:

pip install module_name

Installing PySpark:

pip install pyspark

Steps to create dataframe in PySpark:

1. Import the below modules

      import pyspark
      from pyspark.sql import SparkSession

2. Create spark app named tutorialsinhand using getOrCreate() method

     Syntax:

     spark = SparkSession.builder.appName('tutorialsinhand').getOrCreate()

3. Create list of values for dataframe

4. Pass this list to createDataFrame() method to create pyspark dataframe

    Syntax:
    spark.createDataFrame(list of values)

col stands for column.

Before using this method, we have to import this from pyspark.sql.functions module.

Syntax:

from pyspark.sql.functions import col

col() is used to select columns from the PySpark dataframe.

In this scenario, we will select columns using col() function through select() method and display the values in the column/s.

Syntax:

dataframe.select(col("column_name"),...............)

where, column_name is the column to be displayed.

Example:

In this example, we are selecting rows from rollno and marks columns.

# import the below modules
import pyspark
from pyspark.sql import SparkSession

# create an app
spark = SparkSession.builder.appName('tutorialsinhand').getOrCreate()

#create a  list of data
values = [{'rollno': 1, 'student name': 'Gottumukkala Sravan','marks': 98},

        {'rollno': 2, 'student name': 'Gottumukkala Bobby','marks': 89},
        {'rollno': 3, 'student name': 'Lavu Ojaswi','marks': 90},

        {'rollno': 4, 'student name': 'Lavu Gnanesh','marks': 78},

        {'rollno': 5, 'student name': 'Chennupati Rohith','marks': 100}]


# create the dataframe from the values
data = spark.createDataFrame(values)

#display dataframe
data.show()


#import col function
from pyspark.sql.functions import col

#select rollno and marks column
data.select(col("rollno"),col("marks")).collect()

Output:

Finally, we are displaying rows from rollno and marks column through collect() method.

+-----+------+-------------------+
|marks|rollno|       student name|
+-----+------+-------------------+
|   98|     1|Gottumukkala Sravan|
|   89|     2| Gottumukkala Bobby|
|   90|     3|        Lavu Ojaswi|
|   78|     4|       Lavu Gnanesh|
|  100|     5|  Chennupati Rohith|
+-----+------+-------------------+

[Row(rollno=1, marks=98),
 Row(rollno=2, marks=89),
 Row(rollno=3, marks=90),
 Row(rollno=4, marks=78),
 Row(rollno=5, marks=100)]