How to check the schema of PySpark DataFrame?

In this article, we are going to check the schema of pyspark dataframe. We are going to use the below Dataframe for demonstration.

Method 1: Using df.schema

Schema is used to return the columns along with the type.

Syntax: dataframe.schema

Where, dataframe is the input dataframe

Code:

Python3

# importing module
import pyspark

# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession

# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()

# list  of employee data with 5 row values
data = [["1", "sravan", "company 1"],
        ["2", "ojaswi", "company 2"],
        ["3", "bobby", "company 3"],
        ["4", "rohith", "company 2"],
        ["5", "gnanesh", "company 1"]]

# specify column names
columns = ['Employee ID', 'Employee NAME', 'Company Name']

# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)

# display dataframe columns
dataframe.schema

Output:

StructType(List(StructField(Employee ID,StringType,true),
StructField(Employee NAME,StringType,true),
StructField(Company Name,StringType,true)))

Method 2: Using schema.fields

It is used to return the names of the columns

Syntax: dataframe.schema.fields

where dataframe is the dataframe name

Code:

Python3

# importing module
import pyspark

# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession

# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()

# list  of employee data with 5 row values
data = [["1", "sravan", "company 1"],
        ["2", "ojaswi", "company 2"],
        ["3", "bobby", "company 3"],
        ["4", "rohith", "company 2"],
        ["5", "gnanesh", "company 1"]]

# specify column names
columns = ['Employee ID', 'Employee NAME', 'Company Name']

# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)

# display dataframe columns
dataframe.schema.fields

Output:

[StructField(Employee ID,StringType,true),
StructField(Employee NAME,StringType,true),
StructField(Company Name,StringType,true)]

Method 3: Using printSchema()

It is used to return the schema with column names

Syntax: dataframe.printSchema()

where dataframe is the input pyspark dataframe

Python3

# importing module
import pyspark

# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession

# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()

# list  of employee data with 5 row values
data = [["1", "sravan", "company 1"],
        ["2", "ojaswi", "company 2"],
        ["3", "bobby", "company 3"],
        ["4", "rohith", "company 2"],
        ["5", "gnanesh", "company 1"]]

# specify column names
columns = ['Employee ID', 'Employee NAME', 'Company Name']

# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)

# display dataframe columns
dataframe.printSchema()

Output:

root
 |-- Employee ID: string (nullable = true)
 |-- Employee NAME: string (nullable = true)
 |-- Company Name: string (nullable = true)