The Apache Spark DataFrame API provides a rich set of functions (select columns, filter, join, aggregate, and so on) that allow you to solve common data analysis problems efficiently. DataFrames also allow you to intermix operations seamlessly with custom Python, R, Scala, and SQL code. In this tutorial module, you will learn how to:
We also provide a sample notebook that you can import to access and run all of the code examples included in the module.
The easiest way to start working with DataFrames is to use an example Azure Databricks dataset available in the
/databricks-datasets folder accessible within the Azure Databricks workspace. To access the file that compares city population versus median sale prices of homes, load the file
%python # Use the Spark CSV datasource with options specifying: # - First line of file is a header # - Automatically infer the schema of the data data = spark.read.csv("/databricks-datasets/samples/population-vs-price/data_geo.csv", header="true", inferSchema="true") data.cache() # Cache data for faster reuse data = data.dropna() # drop rows with missing values
Now that you have created the
data DataFrame, you can quickly access the data using standard Spark commands such as
take(). For example, you can use the command
data.take(10) to view the first ten rows of the
data DataFrame. Because this is a SQL notebook, the next few commands use the
%python magic command.
To view this data in a tabular format, you can use the Azure Databricks
display() command instead of exporting the data to a third-party tool.
Before you can issue SQL queries, you must save your
data DataFrame as a temporary table:
%python # Register table so it is accessible via SQL Context data.createOrReplaceTempView("data_geo")
Then, in a new cell, specify a SQL query to list the 2015 median sales price by state:
select `State Code`, `2015 median sales price` from data_geo
Or, query for population estimate in the state of Washington:
select City, `2014 Population estimate` from data_geo where `State Code` = 'WA';
An additional benefit of using the Azure Databricks
display() command is that you can quickly view this data with a number of embedded visualizations. Click the down arrow next to the to display a list of visualization types:
Then, select the Map icon to create a map visualization of the sale price SQL query from the previous section:
To run these code examples, visualizations, and more, import the Population versus Price notebook. For more DataFrame examples, see DataFrames and Datasets.
Apache Spark DataFrames notebook