Work with data in a Spark dataframe

Completed

In the previous unit, you learned how to connect to a data source, load data into a dataframe, and optionally save the dataframe to a lakehouse as a file or table. Now let's explore the dataframe in a little more detail.

Natively, Spark uses a data structure called a resilient distributed dataset (RDD); but while you can write code that works directly with RDDs, the most commonly used data structure for working with structured data in Spark is the dataframe, which is provided as part of the Spark SQL library. Dataframes in Spark are similar to those in the ubiquitous Pandas Python library, but optimized to work in Spark's distributed processing environment.

Note

In addition to the Dataframe API, Spark SQL provides a strongly-typed Dataset API that is supported in Java and Scala. We'll focus on the Dataframe API in this module.

Loading data into a dataframe

Let's explore a hypothetical example to see how you can use a dataframe to work with data. Suppose you have the following data in a comma-delimited text file named products.csv in the Files/data folder in your lakehouse:

ProductID,ProductName,Category,ListPrice
771,"Mountain-100 Silver, 38",Mountain Bikes,3399.9900
772,"Mountain-100 Silver, 42",Mountain Bikes,3399.9900
773,"Mountain-100 Silver, 44",Mountain Bikes,3399.9900
...

Inferring a schema

In a Spark notebook, you could use the following PySpark code to load the file data into a dataframe and display the first 10 rows:

%%pyspark
df = spark.read.load('Files/data/products.csv',
    format='csv',
    header=True
)
display(df.limit(10))

As you learned previously, the %%pyspark line at the beginning is called a magic, and tells Spark that the language used in this cell is PySpark. In most cases, PySpark is the default language; and we'll generally stick to it in the examples in this module. However, for completeness, here's the equivalent Scala code for the products data example:

%%spark
val df = spark.read.format("csv").option("header", "true").load("Files/data/products.csv")
display(df.limit(10))

The magic %%spark is used to specify Spark within a Scala code cell. Notice that the Scala implementation of the dataframe behaves similarly to the PySpark version.

Both of these code samples would produce output like this:

ProductID	ProductName	Category	ListPrice
771	Mountain-100 Silver, 38	Mountain Bikes	3399.9900
772	Mountain-100 Silver, 42	Mountain Bikes	3399.9900
773	Mountain-100 Silver, 44	Mountain Bikes	3399.9900
...	...	...	...

Specifying an explicit schema

In the previous example, the first row of the CSV file contained the column names, and Spark was able to infer the data type of each column from the data it contains. You can also specify an explicit schema for the data, which is useful when the column names aren't included in the data file, like this CSV example:

771,"Mountain-100 Silver, 38",Mountain Bikes,3399.9900
772,"Mountain-100 Silver, 42",Mountain Bikes,3399.9900
773,"Mountain-100 Silver, 44",Mountain Bikes,3399.9900
...

The following PySpark example shows how to specify a schema for the dataframe to be loaded from a file named product-data.csv in this format:

from pyspark.sql.types import *
from pyspark.sql.functions import *

productSchema = StructType([
    StructField("ProductID", IntegerType()),
    StructField("ProductName", StringType()),
    StructField("Category", StringType()),
    StructField("ListPrice", FloatType())
    ])

df = spark.read.load('Files/data/product-data.csv',
    format='csv',
    schema=productSchema,
    header=False)
display(df.limit(10))

The results would once again be similar to:

ProductID	ProductName	Category	ListPrice
771	Mountain-100 Silver, 38	Mountain Bikes	3399.9900
772	Mountain-100 Silver, 42	Mountain Bikes	3399.9900
773	Mountain-100 Silver, 44	Mountain Bikes	3399.9900
...	...	...	...

Tip

Specifying an explicit schema also improves performance!

Filtering and grouping dataframes

You can use the methods of the Dataframe class to filter, sort, group, and otherwise manipulate the data it contains. For example, the following code example uses the select method to retrieve the ProductID and ListPrice columns from the df dataframe containing product data in the previous example:

pricelist_df = df.select("ProductID", "ListPrice")

The results from this code example would look something like this:

ProductID	ListPrice
771	3399.9900
772	3399.9900
773	3399.9900
...	...

In common with most data manipulation methods, select returns a new dataframe object.

Tip

Selecting a subset of columns from a dataframe is a common operation, which can also be achieved by using the following shorter syntax:

pricelist_df = df["ProductID", "ListPrice"]

You can "chain" methods together to perform a series of manipulations that results in a transformed dataframe. For example, this example code chains the select and where methods to create a new dataframe containing the ProductName and ListPrice columns for products with a category of Mountain Bikes or Road Bikes:

bikes_df = df.select("ProductName", "Category", "ListPrice").where((df["Category"]=="Mountain Bikes") | (df["Category"]=="Road Bikes"))
display(bikes_df)

The results from this code example would look something like this:

ProductName	Category	ListPrice
Mountain-100 Silver, 38	Mountain Bikes	3399.9900
Road-750 Black, 52	Road Bikes	539.9900
...	...	...

To group and aggregate data, you can use the groupBy method and aggregate functions. For example, the following PySpark code counts the number of products for each category:

counts_df = df.select("ProductID", "Category").groupBy("Category").count()
display(counts_df)

The results from this code example would look something like this:

Category	count
Headsets	3
Wheels	14
Mountain Bikes	32
...	...