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PySpark basics

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This article walks through simple examples to illustrate usage of PySpark. It assumes you understand fundamental Apache Spark concepts and are running commands in a Azure Databricks notebook connected to compute. You create DataFrames using sample data, perform basic transformations including row and column operations on this data, combine multiple DataFrames and aggregate this data, visualize this data, and then save it to a table or file.

Upload data

Some examples in this article use Databricks-provided sample data to demonstrate using DataFrames to load, transform, and save data. If you want to use your own data that is not yet in Databricks, you can upload it first and create a DataFrame from it. See Create or modify a table using file upload and Upload files to a Unity Catalog volume.

About Databricks sample data

Databricks provides sample data in the samples catalog and in the /databricks-datasets directory.

  • To access the sample data in the samples catalog, use the format samples.<schema-name>.<table-name>. This article uses tables in the samples.tpch schema, which contains data from a fictional business. The customer table contains information about customers, and orders contains information about orders placed by those customers.
  • Use dbutils.fs.ls to explore data in /databricks-datasets. Use Spark SQL or DataFrames to query data in this location using file paths. To learn more about Databricks-provided sample data, see Sample datasets.

Import data types

Many PySpark operations require that you use SQL functions or interact with native Spark types. You can either directly import only those functions and types that you need, or you can import the entire module.

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

# import select functions and types
from pyspark.sql.types import IntegerType, StringType
from pyspark.sql.functions import floor, round

Because some imported functions might override Python built-in functions, some users choose to import these modules using an alias. The following examples show a common alias used in Apache Spark code examples:

import pyspark.sql.types as T
import pyspark.sql.functions as F

For a comprehensive list of data types, see Spark Data Types.

For a comprehensive list of PySpark SQL functions, see Spark Functions.

Create a DataFrame

There are several ways to create a DataFrame. Usually you define a DataFrame against a data source such as a table or collection of files. Then as described in the Apache Spark fundamental concepts section, use an action, such as display, to trigger the transformations to execute. The display method outputs DataFrames.

Create a DataFrame with specified values

To create a DataFrame with specified values, use the createDataFrame method, where rows are expressed as a list of tuples:

df_children = spark.createDataFrame(
  data = [("Mikhail", 15), ("Zaky", 13), ("Zoya", 8)],
  schema = ['name', 'age'])
display(df_children)

Notice in the output that the data types of columns of df_children are automatically inferred. You can alternatively specify the types by adding a schema. Schemas are defined using the StructType which is made up of StructFields that specify the name, data type and a boolean flag indicating whether they contain a null value or not. You must import data types from pyspark.sql.types.

from pyspark.sql.types import StructType, StructField, StringType, IntegerType

df_children_with_schema = spark.createDataFrame(
  data = [("Mikhail", 15), ("Zaky", 13), ("Zoya", 8)],
  schema = StructType([
    StructField('name', StringType(), True),
    StructField('age', IntegerType(), True)
  ])
)
display(df_children_with_schema)

Create a DataFrame from a table in Unity Catalog

To create a DataFrame from a table in Unity Catalog, use the table method identifying the table using the format <catalog-name>.<schema-name>.<table-name>. Click on Catalog on the left navigation bar to use Catalog Explorer to navigate to your table. Click it, then select Copy table path to insert the table path into the notebook.

The following example loads the table samples.tpch.customer, but you can alternatively provide the path to your own table.

df_customer = spark.table('samples.tpch.customer')
display(df_customer)

Create a DataFrame from an uploaded file

To create a DataFrame from a file you uploaded to Unity Catalog volumes, use the read property. This method returns a DataFrameReader, which you can then use to read the appropriate format. Click on the catalog option on the small sidebar on the left and use the catalog browser to locate your file. Select it, then click Copy volume file path.

The example below reads from a *.csv file, but DataFrameReader supports uploading files in many other formats. See DataFrameReader methods.

# Assign this variable your full volume file path
volume_file_path = ""

df_csv = (spark.read
  .format("csv")
  .option("header", True)
  .option("inferSchema", True)
  .load(volume_file_path)
)
display(df_csv)

For more information about Unity Catalog volumes, see What are Unity Catalog volumes?.

Create a DataFrame from a JSON response

To create a DataFrame from a JSON response payload returned by a REST API, use the Python requests package to query and parse the response. You must import the package to use it. This example uses data from the United States Food and Drug Administration’s drug application database.

import requests

# Download data from URL
url = "https://api.fda.gov/drug/drugsfda.json?limit=100"
response = requests.get(url)

# Create the DataFrame
df_drugs = spark.createDataFrame(response.json()["results"])
display(df_drugs)

For information on working with JSON and other semi-structured data on Databricks, see Model semi-structured data.

Select a JSON field or object

To select a specific field or object from the converted JSON, use the [] notation. For example, to select the products field which itself is an array of products:

display(df_drugs.select(df_drugs["products"]))

You can also chain together method calls to traverse multiple fields. For example, to output the brand name of the first product in a drug application:

display(df_drugs.select(df_drugs["products"][0]["brand_name"]))

Create a DataFrame from a file

To demonstrate creating a DataFrame from a file, this example loads CSV data in the /databricks-datasets directory.

To navigate to the sample datasets, you can use the Databricks Utilties file system commands. The following example uses dbutils to list the datasets available in /databricks-datasets:

display(dbutils.fs.ls('/databricks-datasets'))

Alternatively, you can use %fs to access Databricks CLI file system commands, as shown in the following example:

%fs ls '/databricks-datasets'

To create a DataFrame from a file or directory of files, specify the path in the load method:

df_population = (spark.read
  .format("csv")
  .option("header", True)
  .option("inferSchema", True)
  .load("/databricks-datasets/samples/population-vs-price/data_geo.csv")
)
display(df_population)

Transform data with DataFrames

DataFrames make it easy to transform data using built-in methods to sort, filter and aggregate data. Many transformations are not specified as methods on DataFrames, but instead are provided in the spark.sql.functions package. See Databricks Spark SQL Functions.

Column operations

Spark provides many basic column operations:

Tip

To output all of the columns in a DataFrame, use columns, for example df_customer.columns.

Select columns

You can select specific columns using select and col. The col function is in the pyspark.sql.functions submodule.

from pyspark.sql.functions import col

df_customer.select(
  col("c_custkey"),
  col("c_acctbal")
)

You can also refer to a column using expr which takes an expression defined as a string:

from pyspark.sql.functions import expr

df_customer.select(
  expr("c_custkey"),
  expr("c_acctbal")
)

You can also use selectExpr, which accepts SQL expressions:

df_customer.selectExpr(
  "c_custkey as key",
  "round(c_acctbal) as account_rounded"
)

To select columns using a string literal, do the following:

df_customer.select(
  "c_custkey",
  "c_acctbal"
)

To explicitly select a column from a specific DataFrame, you can use the [] operator or the . operator. (The . operator cannot be used to select columns starting with an integer, or ones that contain a space or special character.) This can be especially helpful when you are joining DataFrames where some columns have the same name.

df_customer.select(
  df_customer["c_custkey"],
  df_customer["c_acctbal"]
)

df_customer.select(
  df_customer.c_custkey,
  df_customer.c_acctbal
)

Create columns

To create a new column, use the withColumn method. The following example creates a new column that contains a boolean value based on whether the customer account balance c_acctbal exceeds 1000:

df_customer_flag = df_customer.withColumn("balance_flag", col("c_acctbal") > 1000)

Rename columns

To rename a column, use the withColumnRenamed method, which accepts the existing and new column names:

df_customer_flag_renamed = df_customer_flag.withColumnRenamed("balance_flag", "balance_flag_renamed")

The alias method is especially helpful when you want to rename your columns as part of aggregations:

from pyspark.sql.functions import avg

df_segment_balance = df_customer.groupBy("c_mktsegment").agg(
    avg(df_customer["c_acctbal"]).alias("avg_account_balance")
)

display(df_segment_balance)

Cast column types

In some cases you may want to change the data type for one or more of the columns in your DataFrame. To do this, use the cast method to convert between column data types. The following example shows how to convert a column from an integer to string type, using the col method to reference a column:

from pyspark.sql.functions import col

df_casted = df_customer.withColumn("c_custkey", col("c_custkey").cast(StringType()))
print(type(df_casted))

Remove columns

To remove columns, you can omit columns during a select or select(*) except or you can use the drop method:

df_customer_flag_renamed.drop("balance_flag_renamed")

You can also drop multiple columns at once:

df_customer_flag_renamed.drop("c_phone", "balance_flag_renamed")

Row operations

Spark provides many basic row operations:

Filter rows

To filter rows, use the filter or where method on a DataFrame to return only certain rows. To identify a column to filter on, use the col method or an expression that evaluates to a column.

from pyspark.sql.functions import col

df_that_one_customer = df_customer.filter(col("c_custkey") == 412449)

To filter on multiple conditions, use logical operators. For example, & and | enable you to AND and OR conditions, respectively. The following example filters rows where the c_nationkey is equal to 20 and c_acctbal is greater than 1000.

df_customer.filter((col("c_nationkey") == 20) & (col("c_acctbal") > 1000))

df_filtered_customer = df_customer.filter((col("c_custkey") == 412446) | (col("c_custkey") == 412447))

Remove duplicate rows

To de-duplicate rows, use distinct, which returns only the unique rows.

df_unique = df_customer.distinct()

Handle null values

To handle null values, drop rows that contain null values using the na.drop method. This method lets you specify if you want to drop rows containing any null values or all null values.

To drop any null values use either of the following examples.

df_customer_no_nulls = df_customer.na.drop()
df_customer_no_nulls = df_customer.na.drop("any")

If instead you want to only filter out rows that contain all null values use the following:

df_customer_no_nulls = df_customer.na.drop("all")

You can apply this for a subset of columns by specifying this, as shown below:

df_customer_no_nulls = df_customer.na.drop("all", subset=["c_acctbal", "c_custkey"])

To fill in missing values, use the fill method. You can choose to apply this to all columns or a subset of columns. In the example below account balances that have a null value for their account balance c_acctbal are filled with 0.

df_customer_filled = df_customer.na.fill("0", subset=["c_acctbal"])

To replace strings with other values, use the replace method. In the example below, any empty address strings are replaced with the word UNKNOWN:

df_customer_phone_filled = df_customer.na.replace([""], ["UNKNOWN"], subset=["c_phone"])

Append rows

To append rows you need to use the union method to create a new DataFrame. In the following example, the DataFrame df_that_one_customer created previously and df_filtered_customer are combined, which returns a DataFrame with three customers:

df_appended_rows = df_that_one_customer.union(df_filtered_customer)

display(df_appended_rows)

Note

You can also combine DataFrames by writing them to a table and then appending new rows. For production workloads, incremental processing of data sources to a target table can drastically reduce latency and compute costs as data grows in size. See Ingest data into a Databricks lakehouse.

Sort rows

Important

Sorting can be expensive at scale, and if you store sorted data and reload the data with Spark, order is not guaranteed. Make sure you are intentional in your use of sorting.

To sort rows by one or more columns, use the sort or orderBy method. By default these methods sort in ascending order:

df_customer.orderBy(col("c_acctbal"))

To filter in descending order, use desc:

df_customer.sort(col("c_custkey").desc())

The following example shows how to sort on two columns:

df_sorted = df_customer.orderBy(col("c_acctbal").desc(), col("c_custkey").asc())
df_sorted = df_customer.sort(col("c_acctbal").desc(), col("c_custkey").asc())

To limit the number of rows to return once the DataFrame is sorted, use the limit method. The following example displays only the top 10 results:

display(df_sorted.limit(10))

Join DataFrames

To join two or more DataFrames, use the join method. You can specify how you would like the DataFrames to be joined in the how (the join type) and on (on which columns to base the join) parameters. Common join types include:

  • inner: This is the join type default, which returns a DataFrame that keeps only the rows where there is a match for the on parameter across the DataFrames.
  • left: This keeps all rows of the first specified DataFrame and only rows from the second specified DataFrame that have a match with the first.
  • outer: An outer join keeps all rows from both DataFrames regardless of match.

For detailed information on joins, see Work with joins on Azure Databricks. For a list of joins supported in PySpark, see DataFrame joins.

The following example returns a single DataFrame where each row of the orders DataFrame is joined with the corresponding row from the customers DataFrame. An inner join is used, as the expectation is that every order corresponds to exactly one customer.

df_customer = spark.table('samples.tpch.customer')
df_order = spark.table('samples.tpch.orders')

df_joined = df_order.join(
  df_customer,
  on = df_order["o_custkey"] == df_customer["c_custkey"],
  how = "inner"
)

display(df_joined)

To join on multiple conditions, use boolean operators such as & and | to specify AND and OR, respectively. The following example adds an additional condition, filtering to just the rows that have o_totalprice greater than 500,000:

df_customer = spark.table('samples.tpch.customer')
df_order = spark.table('samples.tpch.orders')

df_complex_joined = df_order.join(
  df_customer,
  on = ((df_order["o_custkey"] == df_customer["c_custkey"]) & (df_order["o_totalprice"] > 500000)),
  how = "inner"
)

display(df_complex_joined)

Aggregate data

To aggregate data in a DataFrame, similar to a GROUP BY in SQL, use the groupBy method to specify columns to group by and the agg method to specify aggregations. Import common aggregations including avg, sum, max, and min from pyspark.sql.functions. The following example shows the average customer balance by market segment:

from pyspark.sql.functions import avg

# group by one column
df_segment_balance = df_customer.groupBy("c_mktsegment").agg(
    avg(df_customer["c_acctbal"])
)

display(df_segment_balance)

from pyspark.sql.functions import avg

# group by two columns
df_segment_nation_balance = df_customer.groupBy("c_mktsegment", "c_nationkey").agg(
    avg(df_customer["c_acctbal"])
)

display(df_segment_nation_balance)

Some aggregations are actions, which means that they trigger computations. In this case you do not need to use other actions to output results.

To count rows in a DataFrame, use the count method:

df_customer.count()

Chaining calls

Methods that transform DataFrames return DataFrames, and Spark does not act on transformations until actions are called. This lazy evaluation means you can chain multiple methods for convenience and readability. The following example shows how to chain filtering, aggregation and ordering:

from pyspark.sql.functions import count

df_chained = (
    df_order.filter(col("o_orderstatus") == "F")
    .groupBy(col("o_orderpriority"))
    .agg(count(col("o_orderkey")).alias("n_orders"))
    .sort(col("n_orders").desc())
)

display(df_chained)

Visualize your DataFrame

To visualize a DataFrame in a notebook, click the + sign next to table on the top left of the DataFrame, then select Visualization to add one or more charts based on your DataFrame. For details on visualizations, see Visualizations in Databricks notebooks.

display(df_order)

To perform additional visualizations, Databricks recommends using pandas API for Spark. The .pandas_api() allows you to cast to the corresponding pandas API for a Spark DataFrame. For more information, see Pandas API on Spark.

Save your data

Once you have transformed your data, you can save it using the DataFrameWriter methods. A complete list of these methods can be found in DataFrameWriter. The following sections show how to save your DataFrame as a table and as a collection of data files.

Save your DataFrame as a table

To save your DataFrame as a table in Unity Catalog, use the write.saveAsTable method and specify the path in the format <catalog-name>.<schema-name>.<table-name>.

df_joined.write.saveAsTable(f"{catalog_name}.{schema_name}.{table_name}")

Write your DataFrame as CSV

To write your DataFrame to *.csv format, use the write.csv method, specifying the format and options. By default if data exists at the specified path the write operation fails. You can specify one of the following modes to take a different action:

  • overwrite overwrites all existing data in the target path with the DataFrame contents.
  • append appends contents of the DataFrame to data in the target path.
  • ignore silently fails the write if data exists in the target path.

The following example demonstrates overwriting data with DataFrame contents as CSV files:

# Assign this variable your file path
file_path = ""

(df_joined.write
  .format("csv")
  .mode("overwrite")
  .write(file_path)
)

Next steps

To leverage more Spark capabilities on Databricks, see: