This cheat sheet will help you learn PySpark and write PySpark apps faster. Everything in here is fully functional PySpark code you can run or adapt to your programs.
These snippets are licensed under the CC0 1.0 Universal License. That means you can freely copy and adapt these code snippets and you don't need to give attribution or include any notices.
These snippets use DataFrames loaded from various data sources:
- "Auto MPG Data Set" available from the UCI Machine Learning Repository.
- customer_spend.csv, a generated time series dataset.
- date_examples.csv, a generated dataset with various date and time formats.
These snippets were tested against the Spark 3.1.2 API. This page was last updated 2021-09-03 14:20:31.
Make note of these helpful links:
- PySpark DataFrame Operations
- Built-in Spark SQL Functions
- MLlib Main Guide
- PySpark SQL Functions Source
If you find this guide helpful and want an easy way to run Spark, check out Oracle Cloud Infrastructure Data Flow, a fully-managed Spark service that lets you run Spark jobs at any scale with no administrative overhead. You can try Data Flow free.
- Accessing Data Sources
- Load a DataFrame from CSV
- Load a DataFrame from a Tab Separated Value (TSV) file
- Save a DataFrame in CSV format
- Load a DataFrame from Parquet
- Save a DataFrame in Parquet format
- Load a DataFrame from JSON Lines (jsonl) Formatted Data
- Save a DataFrame into a Hive catalog table
- Load a Hive catalog table into a DataFrame
- Load a CSV file from Amazon S3
- Load a CSV file from Oracle Cloud Infrastructure (OCI) Object Storage
- Read an Oracle DB table into a DataFrame using a Wallet
- Write a DataFrame to an Oracle DB table using a Wallet
- Read a Postgres table into a DataFrame
- Write a DataFrame to a Postgres table
- Data Handling Options
- Provide the schema when loading a DataFrame from CSV
- Save a DataFrame to CSV, overwriting existing data
- Save a DataFrame to CSV with a header
- Save a DataFrame in a single CSV file
- Save DataFrame as a dynamic partitioned table
- Overwrite specific partitions
- Load a CSV file with a money column into a DataFrame
- DataFrame Operations
- Add a new column to a DataFrame
- Modify a DataFrame column
- Add a column with multiple conditions
- Add a constant column
- Concatenate columns
- Drop a column
- Change a column name
- Change multiple column names
- Convert a DataFrame column to a Python list
- Convert a scalar query to a Python value
- Consume a DataFrame row-wise as Python dictionaries
- Select particular columns from a DataFrame
- Create an empty dataframe with a specified schema
- Create a constant dataframe
- Convert String to Double
- Convert String to Integer
- Get the size of a DataFrame
- Get a DataFrame's number of partitions
- Get data types of a DataFrame's columns
- Convert an RDD to Data Frame
- Print the contents of an RDD
- Print the contents of a DataFrame
- Process each row of a DataFrame
- DataFrame Map example
- DataFrame Flatmap example
- Create a custom UDF
- Transforming Data
- Sorting and Searching
- Filter a column using a condition
- Filter based on a specific column value
- Filter based on an IN list
- Filter based on a NOT IN list
- Filter values based on keys in another DataFrame
- Get Dataframe rows that match a substring
- Filter a Dataframe based on a custom substring search
- Filter based on a column's length
- Multiple filter conditions
- Sort DataFrame by a column
- Take the first N rows of a DataFrame
- Get distinct values of a column
- Remove duplicates
- Grouping
- count(*) on a particular column
- Group and sort
- Filter groups based on an aggregate value, equivalent to SQL HAVING clause
- Group by multiple columns
- Aggregate multiple columns
- Aggregate multiple columns with custom orderings
- Get the maximum of a column
- Sum a list of columns
- Sum a column
- Aggregate all numeric columns
- Count unique after grouping
- Count distinct values on all columns
- Group by then filter on the count
- Find the top N per row group (use N=1 for maximum)
- Group key/values into a list
- Compute a histogram
- Compute global percentiles
- Compute percentiles within a partition
- Compute percentiles after aggregating
- Filter rows with values below a target percentile
- Aggregate and rollup
- Aggregate and cube
- Joining DataFrames
- File Processing
- Handling Missing Data
- Dealing with Dates
- Unstructured Analytics
- Pandas
- Data Profiling
- Data Management
- Spark Streaming
- Time Series
- Machine Learning
- Save a model
- Load a model and use it for predictions
- A basic Linear Regression model
- A basic Random Forest Regression model
- A basic Random Forest Classification model
- Encode string variables before using a VectorAssembler
- Get feature importances of a trained model
- Automatically encode categorical variables
- Hyperparameter tuning
- Plot Hyperparameter tuning metrics
- A Random Forest Classification model with Hyperparameter Tuning
- Compute correlation matrix
- Performance
- Get the Spark version
- Cache a DataFrame
- Partition by a Column Value
- Range Partition a DataFrame
- Change Number of DataFrame Partitions
- Coalesce DataFrame partitions
- Set the number of shuffle partitions
- Sample a subset of a DataFrame
- Print Spark configuration properties
- Set Spark configuration properties
- Increase Spark driver/executor heap space
Loading data stored in filesystems or databases, and saving it.
# See https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/DataFrameReader.html
# for a list of supported options.
df = spark.read.format("csv").option("header", True).load("data/auto-mpg.csv")# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
# See https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/DataFrameReader.html
# for a list of supported options.
df = (
spark.read.format("csv")
.option("header", True)
.option("sep", "\t")
.load("data/auto-mpg.tsv")
)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
# See https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/DataFrameWriter.html
# for a list of supported options.
df.write.csv("output.csv")df = (
spark.read.format("parquet").load("data/auto-mpg.parquet")
)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
df.write.parquet("output.parquet")# JSON Lines / jsonl format uses one JSON document per line.
# If you have data with mostly regular structure this is better than nesting it in an array.
# See https://jsonlines.org/
df = spark.read.json("data/weblog.jsonl")# Code snippet result:
+----------+----------+--------+----------+----------+------+
| client| country| session| timestamp| uri| user|
+----------+----------+--------+----------+----------+------+
|{false,...|Bangladesh|55fa8213| 869196249|http://...|dde312|
|{true, ...| Niue|2fcd4a83|1031238717|http://...|9d00b9|
|{true, ...| Rwanda|013b996e| 628683372|http://...|1339d4|
|{false,...| Austria|07e8a71a|1043628668|https:/...|966312|
|{false,...| Belize|b23d05d8| 192738669|http://...|2af1e1|
|{false,...|Lao Peo...|d83dfbae|1066490444|http://...|844395|
|{false,...|French ...|e77dfaa2|1350920869|https:/...| null|
|{false,...|Turks a...|56664269| 280986223|http://...| null|
|{false,...| Ethiopia|628d6059| 881914195|https:/...|8ab45a|
|{false,...|Saint K...|85f9120c|1065114708|https:/...| null|
+----------+----------+--------+----------+----------+------+
only showing top 10 rows
df.write.mode("overwrite").saveAsTable("autompg")# Load the table previously saved.
df = spark.table("autompg")# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
import configparser
config = configparser.ConfigParser()
config.read(os.path.expanduser("~/.aws/credentials"))
access_key = config.get("default", "aws_access_key_id")
secret_key = config.get("default", "aws_secret_access_key")
# Requires compatible hadoop-aws and aws-java-sdk-bundle JARs.
spark.conf.set("fs.s3a.aws.credentials.provider", "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider")
spark.conf.set("fs.s3a.access.key", access_key)
spark.conf.set("fs.s3a.secret.key", secret_key)
df = spark.read.format("csv").option("header", True).load("s3a://cheatsheet111/auto-mpg.csv")import oci
oci_config = oci.config.from_file()
conf = spark.sparkContext.getConf()
conf.set("fs.oci.client.auth.tenantId", oci_config["tenancy"])
conf.set("fs.oci.client.auth.userId", oci_config["user"])
conf.set("fs.oci.client.auth.fingerprint", oci_config["fingerprint"])
conf.set("fs.oci.client.auth.pemfilepath", oci_config["key_file"])
conf.set(
"fs.oci.client.hostname",
"https://objectstorage.{0}.oraclecloud.com".format(oci_config["region"]),
)
PATH = "oci://<your_bucket>@<your_namespace/<your_path>"
df = spark.read.format("csv").option("header", True).load(PATH)# Key variables you need.
# Get the tnsname from tnsnames.ora.
# Wallet path should point to an extracted wallet file.
password = "my_password"
table = "source_table"
tnsname = "my_tns_name"
user = "ADMIN"
wallet_path = "/path/to/your/wallet"
properties = {
"driver": "oracle.jdbc.driver.OracleDriver",
"oracle.net.tns_admin": tnsname,
"password": password,
"user": user,
}
url = f"jdbc:oracle:thin:@{tnsname}?TNS_ADMIN={wallet_path}"
df = spark.read.jdbc(url=url, table=table, properties=properties)# Key variables you need.
# Get the tnsname from tnsnames.ora.
# Wallet path should point to an extracted wallet file.
password = "my_password"
table = "target_table"
tnsname = "my_tns_name"
user = "ADMIN"
wallet_path = "/path/to/your/wallet"
properties = {
"driver": "oracle.jdbc.driver.OracleDriver",
"oracle.net.tns_admin": tnsname,
"password": password,
"user": user,
}
url = f"jdbc:oracle:thin:@{tnsname}?TNS_ADMIN={wallet_path}"
# Possible modes are "Append", "Overwrite", "Ignore", "Error"
df.write.jdbc(url=url, table=table, mode="Append", properties=properties)# You need a compatible postgresql JDBC JAR.
pg_database = os.environ.get("PGDATABASE")
pg_host = os.environ.get("PGHOST")
pg_password = os.environ.get("PGPASSWORD")
pg_user = os.environ.get("PGUSER")
table = "test"
properties = {
"driver": "org.postgresql.Driver",
"user": pg_user,
"password": pg_password,
}
url = f"jdbc:postgresql://{pg_host}:5432/{pg_database}"
df = spark.read.jdbc(url=url, table=table, properties=properties)# You need a compatible postgresql JDBC JAR.
pg_database = os.environ.get("PGDATABASE")
pg_host = os.environ.get("PGHOST")
pg_password = os.environ.get("PGPASSWORD")
pg_user = os.environ.get("PGUSER")
table = "autompg"
properties = {
"driver": "org.postgresql.Driver",
"user": pg_user,
"password": pg_password,
}
url = f"jdbc:postgresql://{pg_host}:5432/{pg_database}"
df.write.jdbc(url=url, table=table, mode="Append", properties=properties)Special data handling scenarios.
# See https://spark.apache.org/docs/latest/api/python/_modules/pyspark/sql/types.html
# for a list of types.
from pyspark.sql.types import (
DoubleType,
IntegerType,
StringType,
StructField,
StructType,
)
schema = StructType(
[
StructField("mpg", DoubleType(), True),
StructField("cylinders", IntegerType(), True),
StructField("displacement", DoubleType(), True),
StructField("horsepower", DoubleType(), True),
StructField("weight", DoubleType(), True),
StructField("acceleration", DoubleType(), True),
StructField("modelyear", IntegerType(), True),
StructField("origin", IntegerType(), True),
StructField("carname", StringType(), True),
]
)
df = (
spark.read.format("csv")
.option("header", "true")
.schema(schema)
.load("data/auto-mpg.csv")
)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0|3504.0| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0|3693.0| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0|3436.0| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0|3433.0| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0|3449.0| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0|4341.0| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0|4354.0| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0|4312.0| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0|4425.0| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0|3850.0| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
df.write.mode("overwrite").csv("output.csv")# See https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/DataFrameWriter.html
# for a list of supported options.
df.coalesce(1).write.csv("header.csv", header="true")df.coalesce(1).write.csv("single.csv")spark.conf.set("spark.sql.sources.partitionOverwriteMode", "dynamic")
df.write.mode("append").partitionBy("modelyear").saveAsTable("autompg_partitioned")spark.conf.set("spark.sql.sources.partitionOverwriteMode", "dynamic")
your_dataframe.write.mode("overwrite").insertInto("your_table")from pyspark.sql.functions import udf
from pyspark.sql.types import DecimalType
from decimal import Decimal
# Load the text file.
df = (
spark.read.format("csv")
.option("header", True)
.load("data/customer_spend.csv")
)
# Convert with a hardcoded custom UDF.
money_udf = udf(lambda x: Decimal(x[1:].replace(",", "")), DecimalType(8, 4))
money1 = df.withColumn("spend_dollars", money_udf(df.spend_dollars))
# Convert with the money_parser library (much safer).
from money_parser import price_str
money_convert = udf(
lambda x: Decimal(price_str(x)) if x is not None else None,
DecimalType(8, 4),
)
money2 = df.withColumn("spend_dollars", money_convert(df.spend_dollars))# Code snippet result:
+----------+-----------+-------------+
| date|customer_id|spend_dollars|
+----------+-----------+-------------+
|2020-01-31| 0| 0.0700|
|2020-01-31| 1| 0.9800|
|2020-01-31| 2| 0.0600|
|2020-01-31| 3| 0.6500|
|2020-01-31| 4| 0.5700|
|2020-02-29| 0| 0.1000|
|2020-02-29| 2| 4.4000|
|2020-02-29| 3| 0.3900|
|2020-02-29| 4| 2.1300|
|2020-02-29| 5| 0.8200|
+----------+-----------+-------------+
only showing top 10 rows
Adding, removing and modifying DataFrame columns.
from pyspark.sql.functions import upper, lower
df = df.withColumn("upper", upper(df.carname)).withColumn(
"lower", lower(df.carname)
)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+----------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname| upper| lower|
+----+---------+------------+----------+------+------------+---------+------+----------+----------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|CHEVROL...|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|BUICK S...|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|PLYMOUT...|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|AMC REB...|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|FORD TO...|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|FORD GA...|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|CHEVROL...|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|PLYMOUT...|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|PONTIAC...|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|AMC AMB...|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+----------+----------+
only showing top 10 rows
from pyspark.sql.functions import col, concat, lit
df = df.withColumn("modelyear", concat(lit("19"), col("modelyear")))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 1970| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 1970| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 1970| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 1970| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 1970| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 1970| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 1970| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 1970| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 1970| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 1970| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col, when
df = df.withColumn(
"mpg_class",
when(col("mpg") <= 20, "low")
.when(col("mpg") <= 30, "mid")
.when(col("mpg") <= 40, "high")
.otherwise("very high"),
)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+---------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|mpg_class|
+----+---------+------------+----------+------+------------+---------+------+----------+---------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...| low|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...| low|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...| low|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...| low|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...| low|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...| low|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...| low|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...| low|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...| low|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...| low|
+----+---------+------------+----------+------+------------+---------+------+----------+---------+
only showing top 10 rows
from pyspark.sql.functions import lit
df = df.withColumn("one", lit(1))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+---+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|one|
+----+---------+------------+----------+------+------------+---------+------+----------+---+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...| 1|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...| 1|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...| 1|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...| 1|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...| 1|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...| 1|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...| 1|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...| 1|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...| 1|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...| 1|
+----+---------+------------+----------+------+------------+---------+------+----------+---+
only showing top 10 rows
from pyspark.sql.functions import concat, col, lit
df = df.withColumn(
"concatenated", concat(col("cylinders"), lit("_"), col("mpg"))
)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+------------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|concatenated|
+----+---------+------------+----------+------+------------+---------+------+----------+------------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...| 8_18.0|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...| 8_15.0|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...| 8_18.0|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...| 8_16.0|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...| 8_17.0|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...| 8_15.0|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...| 8_14.0|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...| 8_14.0|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...| 8_14.0|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...| 8_15.0|
+----+---------+------------+----------+------+------------+---------+------+----------+------------+
only showing top 10 rows
df = df.drop("horsepower")# Code snippet result:
+----+---------+------------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+------+------------+---------+------+----------+
only showing top 10 rows
df = df.withColumnRenamed("horsepower", "horses")# Code snippet result:
+----+---------+------------+------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horses|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+------+------+------------+---------+------+----------+
only showing top 10 rows
df = df.withColumnRenamed("horsepower", "horses").withColumnRenamed(
"modelyear", "year"
)# Code snippet result:
+----+---------+------------+------+------+------------+----+------+----------+
| mpg|cylinders|displacement|horses|weight|acceleration|year|origin| carname|
+----+---------+------------+------+------+------------+----+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+------+------+------------+----+------+----------+
only showing top 10 rows
names = df.select("carname").rdd.flatMap(lambda x: x).collect()# Code snippet result:
['chevrolet chevelle malibu', 'buick skylark 320', 'plymouth satellite', 'amc rebel sst', 'ford torino', 'ford galaxie 500', 'chevrolet impala', 'plymouth fury iii', 'pontiac catalina', 'amc ambassador dpl']
average = df.agg(dict(mpg="avg")).first()[0]
print(average)# Code snippet result:
23.514572864321615
first_three = df.limit(3)
for row in first_three.collect():
my_dict = row.asDict()# Code snippet result:
{'mpg': '18.0', 'cylinders': '8', 'displacement': '307.0', 'horsepower': '130.0', 'weight': '3504.', 'acceleration': '12.0', 'modelyear': '70', 'origin': '1', 'carname': 'chevrolet chevelle malibu'}
{'mpg': '15.0', 'cylinders': '8', 'displacement': '350.0', 'horsepower': '165.0', 'weight': '3693.', 'acceleration': '11.5', 'modelyear': '70', 'origin': '1', 'carname': 'buick skylark 320'}
{'mpg': '18.0', 'cylinders': '8', 'displacement': '318.0', 'horsepower': '150.0', 'weight': '3436.', 'acceleration': '11.0', 'modelyear': '70', 'origin': '1', 'carname': 'plymouth satellite'}
df = df.select(["mpg", "cylinders", "displacement"])# Code snippet result:
+----+---------+------------+
| mpg|cylinders|displacement|
+----+---------+------------+
|18.0| 8| 307.0|
|15.0| 8| 350.0|
|18.0| 8| 318.0|
|16.0| 8| 304.0|
|17.0| 8| 302.0|
|15.0| 8| 429.0|
|14.0| 8| 454.0|
|14.0| 8| 440.0|
|14.0| 8| 455.0|
|15.0| 8| 390.0|
+----+---------+------------+
only showing top 10 rows
from pyspark.sql.types import StructField, StructType, LongType, StringType
schema = StructType(
[
StructField("my_id", LongType(), True),
StructField("my_string", StringType(), True),
]
)
df = spark.createDataFrame([], schema)# Code snippet result:
+-----+---------+
|my_id|my_string|
+-----+---------+
+-----+---------+
import datetime
from pyspark.sql.types import (
StructField,
StructType,
LongType,
StringType,
TimestampType,
)
schema = StructType(
[
StructField("my_id", LongType(), True),
StructField("my_string", StringType(), True),
StructField("my_timestamp", TimestampType(), True),
]
)
df = spark.createDataFrame(
[
(1, "foo", datetime.datetime.strptime("2021-01-01", "%Y-%m-%d")),
(2, "bar", datetime.datetime.strptime("2021-01-02", "%Y-%m-%d")),
],
schema,
)# Code snippet result:
+-----+---------+------------+
|my_id|my_string|my_timestamp|
+-----+---------+------------+
| 1| foo| 2021-01...|
| 2| bar| 2021-01...|
+-----+---------+------------+
from pyspark.sql.functions import col
df = df.withColumn("horsepower", col("horsepower").cast("double"))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col
df = df.withColumn("horsepower", col("horsepower").cast("int"))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
print("{} rows".format(df.count()))
print("{} columns".format(len(df.columns)))# Code snippet result:
398 rows
9 columns
print("{} partition(s)".format(df.rdd.getNumPartitions()))# Code snippet result:
1 partition(s)
print(df.dtypes)# Code snippet result:
[('mpg', 'string'), ('cylinders', 'string'), ('displacement', 'string'), ('horsepower', 'string'), ('weight', 'string'), ('acceleration', 'string'), ('modelyear', 'string'), ('origin', 'string'), ('carname', 'string')]
from pyspark.sql import Row
row = Row("val")
df = rdd.map(row).toDF()# Code snippet result:
+----------+
| val|
+----------+
|mpg,cyl...|
|18.0,8,...|
|15.0,8,...|
|18.0,8,...|
|16.0,8,...|
|17.0,8,...|
|15.0,8,...|
|14.0,8,...|
|14.0,8,...|
|14.0,8,...|
+----------+
only showing top 10 rows
print(rdd.take(10))# Code snippet result:
['mpg,cylinders,displacement,horsepower,weight,acceleration,modelyear,origin,carname', '18.0,8,307.0,130.0,3504.,12.0,70,1,"chevrolet chevelle malibu"', '15.0,8,350.0,165.0,3693.,11.5,70,1,"buick skylark 320"', '18.0,8,318.0,150.0,3436.,11.0,70,1,"plymouth satellite"', '16.0,8,304.0,150.0,3433.,12.0,70,1,"amc rebel sst"', '17.0,8,302.0,140.0,3449.,10.5,70,1,"ford torino"', '15.0,8,429.0,198.0,4341.,10.0,70,1,"ford galaxie 500"', '14.0,8,454.0,220.0,4354.,9.0,70,1,"chevrolet impala"', '14.0,8,440.0,215.0,4312.,8.5,70,1,"plymouth fury iii"', '14.0,8,455.0,225.0,4425.,10.0,70,1,"pontiac catalina"']
df.show(10)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
import os
def foreach_function(row):
if row.horsepower is not None:
os.system("echo " + row.horsepower)
df.foreach(foreach_function)def map_function(row):
if row.horsepower is not None:
return [float(row.horsepower) * 10]
else:
return [None]
df = df.rdd.map(map_function).toDF()# Code snippet result:
+------+
| _1|
+------+
|1300.0|
|1650.0|
|1500.0|
|1500.0|
|1400.0|
|1980.0|
|2200.0|
|2150.0|
|2250.0|
|1900.0|
+------+
only showing top 10 rows
from pyspark.sql.types import Row
def flatmap_function(row):
if row.cylinders is not None:
return list(range(int(row.cylinders)))
else:
return [None]
rdd = df.rdd.flatMap(flatmap_function)
row = Row("val")
df = rdd.map(row).toDF()# Code snippet result:
+---+
|val|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
| 5|
| 6|
| 7|
| 0|
| 1|
+---+
only showing top 10 rows
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
first_word_udf = udf(lambda x: x.split()[0], StringType())
df = df.withColumn("manufacturer", first_word_udf(df.carname))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+------------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|manufacturer|
+----+---------+------------+----------+------+------------+---------+------+----------+------------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...| chevrolet|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...| buick|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...| plymouth|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...| amc|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...| ford|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...| ford|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...| chevrolet|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...| plymouth|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...| pontiac|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...| amc|
+----+---------+------------+----------+------+------------+---------+------+----------+------------+
only showing top 10 rows
Data conversions and other modifications.
from pyspark.sql.functions import col, regexp_extract
group = 0
df = (
df.withColumn(
"identifier", regexp_extract(col("carname"), "(\S?\d+)", group)
)
.drop("acceleration")
.drop("cylinders")
.drop("displacement")
.drop("modelyear")
.drop("mpg")
.drop("origin")
.drop("horsepower")
.drop("weight")
)# Code snippet result:
+----------+----------+
| carname|identifier|
+----------+----------+
|chevrol...| |
|buick s...| 320|
|plymout...| |
|amc reb...| |
|ford to...| |
|ford ga...| 500|
|chevrol...| |
|plymout...| |
|pontiac...| |
|amc amb...| |
+----------+----------+
only showing top 10 rows
df.fillna({"horsepower": 0})# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import avg
df.fillna({"horsepower": df.agg(avg("horsepower")).first()[0]})# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import coalesce
unmodified_columns = df.columns
unmodified_columns.remove("horsepower")
manufacturer_avg = df.groupBy("cylinders").agg({"horsepower": "avg"})
df = df.join(manufacturer_avg, "cylinders").select(
*unmodified_columns,
coalesce("horsepower", "avg(horsepower)").alias("horsepower"),
)# Code snippet result:
+----+---------+------------+------+------------+---------+------+----------+----------+
| mpg|cylinders|displacement|weight|acceleration|modelyear|origin| carname|horsepower|
+----+---------+------------+------+------------+---------+------+----------+----------+
|18.0| 8| 307.0| 3504.| 12.0| 70| 1|chevrol...| 130.0|
|15.0| 8| 350.0| 3693.| 11.5| 70| 1|buick s...| 165.0|
|18.0| 8| 318.0| 3436.| 11.0| 70| 1|plymout...| 150.0|
|16.0| 8| 304.0| 3433.| 12.0| 70| 1|amc reb...| 150.0|
|17.0| 8| 302.0| 3449.| 10.5| 70| 1|ford to...| 140.0|
|15.0| 8| 429.0| 4341.| 10.0| 70| 1|ford ga...| 198.0|
|14.0| 8| 454.0| 4354.| 9.0| 70| 1|chevrol...| 220.0|
|14.0| 8| 440.0| 4312.| 8.5| 70| 1|plymout...| 215.0|
|14.0| 8| 455.0| 4425.| 10.0| 70| 1|pontiac...| 225.0|
|15.0| 8| 390.0| 3850.| 8.5| 70| 1|amc amb...| 190.0|
+----+---------+------------+------+------------+---------+------+----------+----------+
only showing top 10 rows
from pyspark.sql.functions import col, json_tuple
source = spark.sparkContext.parallelize(
[["1", '{ "a" : 10, "b" : 11 }'], ["2", '{ "a" : 20, "b" : 21 }']]
).toDF(["id", "json"])
df = source.select("id", json_tuple(col("json"), "a", "b"))# Code snippet result:
+---+---+---+
| id| c0| c1|
+---+---+---+
| 1| 10| 11|
| 2| 20| 21|
+---+---+---+
from pyspark.sql.functions import col, json_tuple
source = spark.sparkContext.parallelize(
[["1", '{ "a" : 10, "b" : 11 }'], ["2", '{ "a" : 20, "b" : 21 }']]
).toDF(["id", "json"])
filtered = (
source.select("id", json_tuple(col("json"), "a", "b"))
.withColumnRenamed("c0", "a")
.withColumnRenamed("c1", "b")
.where(col("b") > 15)
)# Code snippet result:
+---+---+---+
| id| a| b|
+---+---+---+
| 2| 20| 21|
+---+---+---+
Filtering, sorting, removing duplicates and more.
from pyspark.sql.functions import col
filtered = df.filter(col("mpg") > "30")# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
| 9.0| 8| 304.0| 193.0| 4732.| 18.5| 70| 1| hi 1200d|
|30.0| 4| 79.00| 70.00| 2074.| 19.5| 71| 2|peugeot...|
|30.0| 4| 88.00| 76.00| 2065.| 14.5| 71| 2| fiat 124b|
|31.0| 4| 71.00| 65.00| 1773.| 19.0| 71| 3|toyota ...|
|35.0| 4| 72.00| 69.00| 1613.| 18.0| 71| 3|datsun ...|
|31.0| 4| 79.00| 67.00| 1950.| 19.0| 74| 3|datsun ...|
|32.0| 4| 71.00| 65.00| 1836.| 21.0| 74| 3|toyota ...|
|31.0| 4| 76.00| 52.00| 1649.| 16.5| 74| 3|toyota ...|
|32.0| 4| 83.00| 61.00| 2003.| 19.0| 74| 3|datsun 710|
|31.0| 4| 79.00| 67.00| 2000.| 16.0| 74| 2| fiat x1.9|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col
filtered = df.where(col("cylinders") == "8")# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col
filtered = df.where(col("cylinders").isin(["4", "6"]))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|24.0| 4| 113.0| 95.00| 2372.| 15.0| 70| 3|toyota ...|
|22.0| 6| 198.0| 95.00| 2833.| 15.5| 70| 1|plymout...|
|18.0| 6| 199.0| 97.00| 2774.| 15.5| 70| 1|amc hornet|
|21.0| 6| 200.0| 85.00| 2587.| 16.0| 70| 1|ford ma...|
|27.0| 4| 97.00| 88.00| 2130.| 14.5| 70| 3|datsun ...|
|26.0| 4| 97.00| 46.00| 1835.| 20.5| 70| 2|volkswa...|
|25.0| 4| 110.0| 87.00| 2672.| 17.5| 70| 2|peugeot...|
|24.0| 4| 107.0| 90.00| 2430.| 14.5| 70| 2|audi 10...|
|25.0| 4| 104.0| 95.00| 2375.| 17.5| 70| 2| saab 99e|
|26.0| 4| 121.0| 113.0| 2234.| 12.5| 70| 2| bmw 2002|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col
filtered = df.where(~col("cylinders").isin(["4", "6"]))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col
# Our DataFrame of keys to exclude.
exclude_keys = df.select(
(col("modelyear") + 1).alias("adjusted_year")
).distinct()
# The anti join returns only keys with no matches.
filtered = df.join(
exclude_keys, how="left_anti", on=df.modelyear == exclude_keys.adjusted_year
)
# Alternatively we can register a temporary table and use a SQL expression.
exclude_keys.registerTempTable("exclude_keys")
filtered = df.filter(
"modelyear not in ( select adjusted_year from exclude_keys )"
)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8.0| 307.0| 130.0|3504.0| 12.0| 70| 1|chevrol...|
|15.0| 8.0| 350.0| 165.0|3693.0| 11.5| 70| 1|buick s...|
|18.0| 8.0| 318.0| 150.0|3436.0| 11.0| 70| 1|plymout...|
|16.0| 8.0| 304.0| 150.0|3433.0| 12.0| 70| 1|amc reb...|
|17.0| 8.0| 302.0| 140.0|3449.0| 10.5| 70| 1|ford to...|
|15.0| 8.0| 429.0| 198.0|4341.0| 10.0| 70| 1|ford ga...|
|14.0| 8.0| 454.0| 220.0|4354.0| 9.0| 70| 1|chevrol...|
|14.0| 8.0| 440.0| 215.0|4312.0| 8.5| 70| 1|plymout...|
|14.0| 8.0| 455.0| 225.0|4425.0| 10.0| 70| 1|pontiac...|
|15.0| 8.0| 390.0| 190.0|3850.0| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
filtered = df.where(df.carname.contains("custom"))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|16.0| 6| 225.0| 105.0| 3439.| 15.5| 71| 1|plymout...|
|13.0| 8| 350.0| 155.0| 4502.| 13.5| 72| 1|buick l...|
|14.0| 8| 318.0| 150.0| 4077.| 14.0| 72| 1|plymout...|
|15.0| 8| 318.0| 150.0| 3777.| 12.5| 73| 1|dodge c...|
|12.0| 8| 455.0| 225.0| 4951.| 11.0| 73| 1|buick e...|
|16.0| 6| 250.0| 100.0| 3278.| 18.0| 73| 1|chevrol...|
|13.0| 8| 360.0| 170.0| 4654.| 13.0| 73| 1|plymout...|
|15.0| 8| 318.0| 150.0| 3399.| 11.0| 73| 1|dodge d...|
|14.0| 8| 318.0| 150.0| 4457.| 13.5| 74| 1|dodge c...|
|19.0| 6| 225.0| 95.00| 3264.| 16.0| 75| 1|plymout...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col
filtered = df.where(col("carname").like("%custom%"))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|16.0| 6| 225.0| 105.0| 3439.| 15.5| 71| 1|plymout...|
|13.0| 8| 350.0| 155.0| 4502.| 13.5| 72| 1|buick l...|
|14.0| 8| 318.0| 150.0| 4077.| 14.0| 72| 1|plymout...|
|15.0| 8| 318.0| 150.0| 3777.| 12.5| 73| 1|dodge c...|
|12.0| 8| 455.0| 225.0| 4951.| 11.0| 73| 1|buick e...|
|16.0| 6| 250.0| 100.0| 3278.| 18.0| 73| 1|chevrol...|
|13.0| 8| 360.0| 170.0| 4654.| 13.0| 73| 1|plymout...|
|15.0| 8| 318.0| 150.0| 3399.| 11.0| 73| 1|dodge d...|
|14.0| 8| 318.0| 150.0| 4457.| 13.5| 74| 1|dodge c...|
|19.0| 6| 225.0| 95.00| 3264.| 16.0| 75| 1|plymout...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col, length
filtered = df.where(length(col("carname")) < 12)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|18.0| 6| 199.0| 97.00| 2774.| 15.5| 70| 1|amc hornet|
|25.0| 4| 110.0| 87.00| 2672.| 17.5| 70| 2|peugeot...|
|24.0| 4| 107.0| 90.00| 2430.| 14.5| 70| 2|audi 10...|
|25.0| 4| 104.0| 95.00| 2375.| 17.5| 70| 2| saab 99e|
|26.0| 4| 121.0| 113.0| 2234.| 12.5| 70| 2| bmw 2002|
|21.0| 6| 199.0| 90.00| 2648.| 15.0| 70| 1|amc gre...|
|10.0| 8| 360.0| 215.0| 4615.| 14.0| 70| 1| ford f250|
|10.0| 8| 307.0| 200.0| 4376.| 15.0| 70| 1| chevy c20|
|11.0| 8| 318.0| 210.0| 4382.| 13.5| 70| 1|dodge d200|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col
or_conditions = df.filter((col("mpg") > "30") | (col("acceleration") < "10"))
and_conditions = df.filter((col("mpg") > "30") & (col("acceleration") < "13"))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|32.7| 6| 168.0| 132.0| 2910.| 11.4| 80| 3|datsun ...|
|30.0| 4| 135.0| 84.00| 2385.| 12.9| 81| 1|plymout...|
|32.0| 4| 135.0| 84.00| 2295.| 11.6| 82| 1|dodge r...|
+----+---------+------------+----------+------+------------+---------+------+----------+
from pyspark.sql.functions import col
ascending = df.orderBy("carname")
descending = df.orderBy(col("carname").desc())# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|31.9| 4| 89.00| 71.00| 1925.| 14.0| 79| 2|vw rabb...|
|44.3| 4| 90.00| 48.00| 2085.| 21.7| 80| 2|vw rabb...|
|29.0| 4| 90.00| 70.00| 1937.| 14.2| 76| 2| vw rabbit|
|41.5| 4| 98.00| 76.00| 2144.| 14.7| 80| 2| vw rabbit|
|44.0| 4| 97.00| 52.00| 2130.| 24.6| 82| 2| vw pickup|
|43.4| 4| 90.00| 48.00| 2335.| 23.7| 80| 2|vw dash...|
|30.7| 6| 145.0| 76.00| 3160.| 19.6| 81| 2|volvo d...|
|17.0| 6| 163.0| 125.0| 3140.| 13.6| 78| 2|volvo 2...|
|20.0| 4| 130.0| 102.0| 3150.| 15.7| 76| 2| volvo 245|
|22.0| 4| 121.0| 98.00| 2945.| 14.5| 75| 2|volvo 2...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
n = 10
reduced = df.limit(n)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
distinct = df.select("cylinders").distinct()# Code snippet result:
+---------+
|cylinders|
+---------+
| 3|
| 8|
| 5|
| 6|
| 4|
+---------+
filtered = df.dropDuplicates(["carname"])# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|24.0| 4| 107.0| 90.00| 2430.| 14.5| 70| 2|audi 10...|
|32.0| 4| 135.0| 84.00| 2295.| 11.6| 82| 1|dodge r...|
|24.5| 4| 151.0| 88.00| 2740.| 16.0| 77| 1|pontiac...|
|13.0| 8| 350.0| 145.0| 3988.| 13.0| 73| 1|chevrol...|
|15.0| 8| 350.0| 145.0| 4082.| 13.0| 73| 1|chevrol...|
|16.0| 8| 351.0| 149.0| 4335.| 14.5| 77| 1|ford th...|
|32.9| 4| 119.0| 100.0| 2615.| 14.8| 81| 3|datsun ...|
|34.1| 4| 86.00| 65.00| 1975.| 15.2| 79| 3|maxda g...|
|22.0| 4| 121.0| 98.00| 2945.| 14.5| 75| 2|volvo 2...|
|18.0| 6| 232.0| 100.0| 3288.| 15.5| 71| 1|amc mat...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
Group DataFrame data by key to perform aggregates like counting, sums, averages, etc.
from pyspark.sql.functions import desc
# No sorting.
grouped1 = df.groupBy("cylinders").count()
# With sorting.
grouped2 = df.groupBy("cylinders").count().orderBy(desc("count"))# Code snippet result:
+---------+-----+
|cylinders|count|
+---------+-----+
| 4| 204|
| 8| 103|
| 6| 84|
| 3| 4|
| 5| 3|
+---------+-----+
from pyspark.sql.functions import avg, desc
grouped = (
df.groupBy("cylinders")
.agg(avg("horsepower").alias("avg_horsepower"))
.orderBy(desc("avg_horsepower"))
)# Code snippet result:
+---------+--------------+
|cylinders|avg_horsepower|
+---------+--------------+
| 8| 158.300...|
| 6| 101.506...|
| 3| 99.25|
| 5| 82.3333...|
| 4| 78.2814...|
+---------+--------------+
from pyspark.sql.functions import col, desc
grouped = (
df.groupBy("cylinders")
.count()
.orderBy(desc("count"))
.filter(col("count") > 100)
)# Code snippet result:
+---------+-----+
|cylinders|count|
+---------+-----+
| 4| 204|
| 8| 103|
+---------+-----+
from pyspark.sql.functions import avg, desc
grouped = (
df.groupBy(["modelyear", "cylinders"])
.agg(avg("horsepower").alias("avg_horsepower"))
.orderBy(desc("avg_horsepower"))
)# Code snippet result:
+---------+---------+--------------+
|modelyear|cylinders|avg_horsepower|
+---------+---------+--------------+
| 70| 8| 183.666...|
| 73| 8| 170.0|
| 71| 8| 166.857...|
| 72| 8| 159.692...|
| 77| 8| 152.375|
| 76| 8| 146.333...|
| 74| 8| 146.0|
| 75| 8| 142.0|
| 78| 8| 135.5|
| 79| 8| 131.9|
+---------+---------+--------------+
only showing top 10 rows
expressions = dict(horsepower="avg", weight="max", displacement="max")
grouped = df.groupBy("modelyear").agg(expressions)# Code snippet result:
+---------+---------------+-----------+-----------------+
|modelyear|avg(horsepower)|max(weight)|max(displacement)|
+---------+---------------+-----------+-----------------+
| 73| 130.475| 4997.| 98.00|
| 71| 107.037...| 5140.| 98.00|
| 70| 147.827...| 4732.| 97.00|
| 75| 101.066...| 4668.| 97.00|
| 78| 99.6944...| 4080.| 98.00|
| 77| 105.071...| 4335.| 98.00|
| 82| 81.4666...| 3035.| 98.00|
| 81| 81.0357...| 3725.| 98.00|
| 79| 101.206...| 4360.| 98.00|
| 72| 120.178...| 4633.| 98.00|
+---------+---------------+-----------+-----------------+
only showing top 10 rows
from pyspark.sql.functions import asc, desc_nulls_last
expressions = dict(horsepower="avg", weight="max", displacement="max")
orderings = [
desc_nulls_last("max(displacement)"),
desc_nulls_last("avg(horsepower)"),
asc("max(weight)"),
]
grouped = df.groupBy("modelyear").agg(expressions).orderBy(*orderings)# Code snippet result:
+---------+---------------+-----------+-----------------+
|modelyear|avg(horsepower)|max(weight)|max(displacement)|
+---------+---------------+-----------+-----------------+
| 73| 130.475| 4997.| 98.00|
| 72| 120.178...| 4633.| 98.00|
| 71| 107.037...| 5140.| 98.00|
| 77| 105.071...| 4335.| 98.00|
| 79| 101.206...| 4360.| 98.00|
| 76| 101.117...| 4380.| 98.00|
| 78| 99.6944...| 4080.| 98.00|
| 74| 94.2307...| 4699.| 98.00|
| 82| 81.4666...| 3035.| 98.00|
| 81| 81.0357...| 3725.| 98.00|
+---------+---------------+-----------+-----------------+
only showing top 10 rows
from pyspark.sql.functions import col, max
grouped = df.select(max(col("horsepower")).alias("max_horsepower"))# Code snippet result:
+--------------+
|max_horsepower|
+--------------+
| 98.00|
+--------------+
exprs = {x: "sum" for x in ("weight", "cylinders", "mpg")}
summed = df.agg(exprs)# Code snippet result:
+----------+-----------+--------------+
| sum(mpg)|sum(weight)|sum(cylinders)|
+----------+-----------+--------------+
|9358.80...| 1182229.0| 2171.0|
+----------+-----------+--------------+
from pyspark.sql.functions import sum
grouped = df.groupBy("cylinders").agg(sum("weight").alias("total_weight"))# Code snippet result:
+---------+------------+
|cylinders|total_weight|
+---------+------------+
| 3| 9594.0|
| 8| 423816.0|
| 5| 9310.0|
| 6| 268651.0|
| 4| 470858.0|
+---------+------------+
numerics = set(["decimal", "double", "float", "integer", "long", "short"])
exprs = {x[0]: "sum" for x in df.dtypes if x[1] in numerics}
summed = df.agg(exprs)# Code snippet result:
+-----------+-----------------+--------------+----------+---------------+-----------------+
|sum(weight)|sum(acceleration)|sum(cylinders)| sum(mpg)|sum(horsepower)|sum(displacement)|
+-----------+-----------------+--------------+----------+---------------+-----------------+
| 1182229.0| 6196.09...| 2171.0|9358.80...| 40952.0| 76983.5|
+-----------+-----------------+--------------+----------+---------------+-----------------+
from pyspark.sql.functions import countDistinct
grouped = df.groupBy("cylinders").agg(countDistinct("mpg"))# Code snippet result:
+---------+----------+
|cylinders|count(mpg)|
+---------+----------+
| 3| 4|
| 8| 27|
| 5| 3|
| 6| 38|
| 4| 87|
+---------+----------+
from pyspark.sql.functions import countDistinct
grouped = df.agg(*(countDistinct(c) for c in df.columns))# Code snippet result:
+----------+----------------+-------------------+-----------------+-------------+-------------------+----------------+-------------+--------------+
|count(mpg)|count(cylinders)|count(displacement)|count(horsepower)|count(weight)|count(acceleration)|count(modelyear)|count(origin)|count(carname)|
+----------+----------------+-------------------+-----------------+-------------+-------------------+----------------+-------------+--------------+
| 129| 5| 82| 93| 351| 96| 13| 3| 305|
+----------+----------------+-------------------+-----------------+-------------+-------------------+----------------+-------------+--------------+
from pyspark.sql.functions import col
grouped = df.groupBy("cylinders").count().where(col("count") > 100)# Code snippet result:
+---------+-----+
|cylinders|count|
+---------+-----+
| 8| 103|
| 4| 204|
+---------+-----+
from pyspark.sql.functions import col, row_number
from pyspark.sql.window import Window
# To get the maximum per group, set n=1.
n = 5
w = Window().partitionBy("cylinders").orderBy(col("horsepower").desc())
result = (
df.withColumn("horsepower", col("horsepower").cast("double"))
.withColumn("rn", row_number().over(w))
.where(col("rn") <= n)
.select("*")
)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+---+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname| rn|
+----+---------+------------+----------+------+------------+---------+------+----------+---+
|21.5| 3| 80.00| 110.0| 2720.| 13.5| 77| 3|mazda rx-4| 1|
|23.7| 3| 70.00| 100.0| 2420.| 12.5| 80| 3|mazda r...| 2|
|19.0| 3| 70.00| 97.0| 2330.| 13.5| 72| 3|mazda r...| 3|
|18.0| 3| 70.00| 90.0| 2124.| 13.5| 73| 3| maxda rx3| 4|
|16.0| 8| 400.0| 230.0| 4278.| 9.50| 73| 1|pontiac...| 1|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...| 2|
|14.0| 8| 455.0| 225.0| 3086.| 10.0| 70| 1|buick e...| 3|
|12.0| 8| 455.0| 225.0| 4951.| 11.0| 73| 1|buick e...| 4|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...| 5|
|20.3| 5| 131.0| 103.0| 2830.| 15.9| 78| 2| audi 5000| 1|
+----+---------+------------+----------+------+------------+---------+------+----------+---+
only showing top 10 rows
from pyspark.sql.functions import col, collect_list
collected = df.groupBy("cylinders").agg(
collect_list(col("carname")).alias("models")
)# Code snippet result:
+---------+----------+
|cylinders| models|
+---------+----------+
| 3|[mazda ...|
| 8|[chevro...|
| 5|[audi 5...|
| 6|[plymou...|
| 4|[toyota...|
+---------+----------+
from pyspark.sql.functions import col
# Target column must be numeric.
df = df.withColumn("horsepower", col("horsepower").cast("double"))
# N is the number of bins.
N = 11
histogram = df.select("horsepower").rdd.flatMap(lambda x: x).histogram(N)
print(histogram)# Code snippet result:
([46.0, 62.72727272727273, 79.45454545454545, 96.18181818181819, 112.9090909090909, 129.63636363636363, 146.36363636363637, 163.0909090909091, 179.8181818181818, 196.54545454545453, 213.27272727272725, 230.0], [23, 89, 102, 65, 17, 27, 32, 15, 9, 5, 8])
from pyspark.sql.functions import col, ntile
from pyspark.sql.window import Window
w = Window().orderBy(col("mpg").desc())
result = df.withColumn("ntile4", ntile(4).over(w))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|ntile4|
+----+---------+------------+----------+------+------------+---------+------+----------+------+
|46.6| 4.0| 86.0| 65.0|2110.0| 17.9| 80| 3| mazda glc| 1|
|44.6| 4.0| 91.0| 67.0|1850.0| 13.8| 80| 3|honda c...| 1|
|44.3| 4.0| 90.0| 48.0|2085.0| 21.7| 80| 2|vw rabb...| 1|
|44.0| 4.0| 97.0| 52.0|2130.0| 24.6| 82| 2| vw pickup| 1|
|43.4| 4.0| 90.0| 48.0|2335.0| 23.7| 80| 2|vw dash...| 1|
|43.1| 4.0| 90.0| 48.0|1985.0| 21.5| 78| 2|volkswa...| 1|
|41.5| 4.0| 98.0| 76.0|2144.0| 14.7| 80| 2| vw rabbit| 1|
|40.9| 4.0| 85.0| null|1835.0| 17.3| 80| 2|renault...| 1|
|40.8| 4.0| 85.0| 65.0|2110.0| 19.2| 80| 3|datsun 210| 1|
|39.4| 4.0| 85.0| 70.0|2070.0| 18.6| 78| 3|datsun ...| 1|
+----+---------+------------+----------+------+------------+---------+------+----------+------+
only showing top 10 rows
from pyspark.sql.functions import col, ntile
from pyspark.sql.window import Window
w = Window().partitionBy("cylinders").orderBy(col("mpg").desc())
result = df.withColumn("ntile4", ntile(4).over(w))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|ntile4|
+----+---------+------------+----------+------+------------+---------+------+----------+------+
|26.6| 8.0| 350.0| 105.0|3725.0| 19.0| 81| 1|oldsmob...| 1|
|23.9| 8.0| 260.0| 90.0|3420.0| 22.2| 79| 1|oldsmob...| 1|
|23.0| 8.0| 350.0| 125.0|3900.0| 17.4| 79| 1|cadilla...| 1|
|20.2| 8.0| 302.0| 139.0|3570.0| 12.8| 78| 1|mercury...| 1|
|20.0| 8.0| 262.0| 110.0|3221.0| 13.5| 75| 1|chevrol...| 1|
|19.9| 8.0| 260.0| 110.0|3365.0| 15.5| 78| 1|oldsmob...| 1|
|19.4| 8.0| 318.0| 140.0|3735.0| 13.2| 78| 1|dodge d...| 1|
|19.2| 8.0| 305.0| 145.0|3425.0| 13.2| 78| 1|chevrol...| 1|
|19.2| 8.0| 267.0| 125.0|3605.0| 15.0| 79| 1|chevrol...| 1|
|18.5| 8.0| 360.0| 150.0|3940.0| 13.0| 79| 1|chrysle...| 1|
+----+---------+------------+----------+------+------------+---------+------+----------+------+
only showing top 10 rows
from pyspark.sql.functions import col, ntile
from pyspark.sql.window import Window
grouped = df.groupBy("modelyear").count()
w = Window().orderBy(col("count").desc())
result = grouped.withColumn("ntile4", ntile(4).over(w))# Code snippet result:
+---------+-----+------+
|modelyear|count|ntile4|
+---------+-----+------+
| 73| 40| 1|
| 78| 36| 1|
| 76| 34| 1|
| 82| 31| 1|
| 75| 30| 2|
| 81| 29| 2|
| 80| 29| 2|
| 70| 29| 3|
| 79| 29| 3|
| 72| 28| 3|
+---------+-----+------+
only showing top 10 rows
from pyspark.sql.functions import col, lit
import pyspark.sql.functions as F
target_percentile = df.agg(
F.expr("percentile(mpg, 0.9)").alias("target_percentile")
).first()[0]
result = df.filter(col("mpg") > lit(target_percentile))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|35.0| 4.0| 72.0| 69.0|1613.0| 18.0| 71| 3|datsun ...|
|36.0| 4.0| 79.0| 58.0|1825.0| 18.6| 77| 2|renault...|
|43.1| 4.0| 90.0| 48.0|1985.0| 21.5| 78| 2|volkswa...|
|36.1| 4.0| 98.0| 66.0|1800.0| 14.4| 78| 1|ford fi...|
|39.4| 4.0| 85.0| 70.0|2070.0| 18.6| 78| 3|datsun ...|
|36.1| 4.0| 91.0| 60.0|1800.0| 16.4| 78| 3|honda c...|
|35.7| 4.0| 98.0| 80.0|1915.0| 14.4| 79| 1|dodge c...|
|34.5| 4.0| 105.0| 70.0|2150.0| 14.9| 79| 1|plymout...|
|37.3| 4.0| 91.0| 69.0|2130.0| 14.7| 79| 2|fiat st...|
|41.5| 4.0| 98.0| 76.0|2144.0| 14.7| 80| 2| vw rabbit|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import avg, col, count, desc
subset = df.filter(col("modelyear") > 79)
grouped = (
subset.rollup("modelyear", "cylinders")
.agg(
avg("horsepower").alias("avg_horsepower"),
count("modelyear").alias("count"),
)
.orderBy(desc("modelyear"), desc("cylinders"))
)# Code snippet result:
+---------+---------+--------------+-----+
|modelyear|cylinders|avg_horsepower|count|
+---------+---------+--------------+-----+
| 82| 6.0| 102.333...| 3|
| 82| 4.0| 79.1481...| 28|
| 82| null| 81.4666...| 31|
| 81| 8.0| 105.0| 1|
| 81| 6.0| 100.714...| 7|
| 81| 4.0| 72.95| 21|
| 81| null| 81.0357...| 29|
| 80| 6.0| 111.0| 2|
| 80| 5.0| 67.0| 1|
| 80| 4.0| 74.0434...| 25|
| 80| 3.0| 100.0| 1|
| 80| null| 77.4814...| 29|
| null| null| 80.0588...| 89|
+---------+---------+--------------+-----+
from pyspark.sql.functions import avg, col, count, desc
subset = df.filter(col("modelyear") > 79)
grouped = (
subset.cube("modelyear", "cylinders")
.agg(
avg("horsepower").alias("avg_horsepower"),
count("modelyear").alias("count"),
)
.orderBy(desc("modelyear"), desc("cylinders"))
)# Code snippet result:
+---------+---------+--------------+-----+
|modelyear|cylinders|avg_horsepower|count|
+---------+---------+--------------+-----+
| 82| 6.0| 102.333...| 3|
| 82| 4.0| 79.1481...| 28|
| 82| null| 81.4666...| 31|
| 81| 8.0| 105.0| 1|
| 81| 6.0| 100.714...| 7|
| 81| 4.0| 72.95| 21|
| 81| null| 81.0357...| 29|
| 80| 6.0| 111.0| 2|
| 80| 5.0| 67.0| 1|
| 80| 4.0| 74.0434...| 25|
| 80| 3.0| 100.0| 1|
| 80| null| 77.4814...| 29|
| null| 8.0| 105.0| 1|
| null| 6.0| 102.833...| 12|
| null| 5.0| 67.0| 1|
| null| 4.0| 75.7| 74|
| null| 3.0| 100.0| 1|
| null| null| 80.0588...| 89|
+---------+---------+--------------+-----+
Joining and stacking DataFrames.
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
# Load a list of manufacturer / country pairs.
countries = (
spark.read.format("csv")
.option("header", True)
.load("data/manufacturers.csv")
)
# Add a manufacturers column, to join with the manufacturers list.
first_word_udf = udf(lambda x: x.split()[0], StringType())
df = df.withColumn("manufacturer", first_word_udf(df.carname))
# The actual join.
joined = df.join(countries, "manufacturer")# Code snippet result:
+------------+----+---------+------------+----------+------+------------+---------+------+----------+-------+
|manufacturer| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|country|
+------------+----+---------+------------+----------+------+------------+---------+------+----------+-------+
| chevrolet|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...| us|
| buick|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...| us|
| plymouth|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...| us|
| amc|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...| us|
| ford|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...| us|
| ford|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...| us|
| chevrolet|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...| us|
| plymouth|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...| us|
| pontiac|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...| us|
| amc|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...| us|
+------------+----+---------+------------+----------+------+------------+---------+------+----------+-------+
only showing top 10 rows
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
# Load a list of manufacturer / country pairs.
countries = (
spark.read.format("csv")
.option("header", True)
.load("data/manufacturers.csv")
)
# Add a manufacturers column, to join with the manufacturers list.
first_word_udf = udf(lambda x: x.split()[0], StringType())
df = df.withColumn("manufacturer", first_word_udf(df.carname))
# The actual join.
joined = df.join(countries, df.manufacturer == countries.manufacturer)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+------------+------------+-------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|manufacturer|manufacturer|country|
+----+---------+------------+----------+------+------------+---------+------+----------+------------+------------+-------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...| chevrolet| chevrolet| us|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...| buick| buick| us|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...| plymouth| plymouth| us|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...| amc| amc| us|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...| ford| ford| us|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...| ford| ford| us|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...| chevrolet| chevrolet| us|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...| plymouth| plymouth| us|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...| pontiac| pontiac| us|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...| amc| amc| us|
+----+---------+------------+----------+------+------------+---------+------+----------+------------+------------+-------+
only showing top 10 rows
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
# Load a list of manufacturer / country pairs.
countries = (
spark.read.format("csv")
.option("header", True)
.load("data/manufacturers.csv")
)
# Add a manufacturers column, to join with the manufacturers list.
first_word_udf = udf(lambda x: x.split()[0], StringType())
df = df.withColumn("manufacturer", first_word_udf(df.carname))
# The actual join.
joined = df.join(
countries,
(df.manufacturer == countries.manufacturer)
| (df.mpg == countries.manufacturer),
)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+------------+------------+-------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|manufacturer|manufacturer|country|
+----+---------+------------+----------+------+------------+---------+------+----------+------------+------------+-------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...| chevrolet| chevrolet| us|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...| buick| buick| us|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...| plymouth| plymouth| us|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...| amc| amc| us|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...| ford| ford| us|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...| ford| ford| us|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...| chevrolet| chevrolet| us|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...| plymouth| plymouth| us|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...| pontiac| pontiac| us|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...| amc| amc| us|
+----+---------+------------+----------+------+------------+---------+------+----------+------------+------------+-------+
only showing top 10 rows
# Inner join on one column.
joined = df.join(df, "carname")
# Left (outer) join.
joined = df.join(df, "carname", "left")
# Left anti (not in) join.
joined = df.join(df, "carname", "left_anti")
# Right (outer) join.
joined = df.join(df, "carname", "right")
# Full join.
joined = df.join(df, "carname", "full")
# Cross join.
joined = df.crossJoin(df)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
df1 = spark.read.format("csv").option("header", True).load("data/part1.csv")
df2 = spark.read.format("csv").option("header", True).load("data/part2.csv")
df = df1.union(df2)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
# Approach 1: Use a list.
df = (
spark.read.format("csv")
.option("header", True)
.load(["data/part1.csv", "data/part2.csv"])
)
# Approach 2: Use a wildcard.
df = spark.read.format("csv").option("header", True).load("data/part*.csv")# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|20.0| 6| 225.0| 100.0| 3651.| 17.7| 76| 1|dodge a...|
|18.0| 6| 250.0| 78.00| 3574.| 21.0| 76| 1|ford gr...|
|18.5| 6| 250.0| 110.0| 3645.| 16.2| 76| 1|pontiac...|
|17.5| 6| 258.0| 95.00| 3193.| 17.8| 76| 1|amc pac...|
|29.5| 4| 97.00| 71.00| 1825.| 12.2| 76| 2|volkswa...|
|32.0| 4| 85.00| 70.00| 1990.| 17.0| 76| 3|datsun ...|
|28.0| 4| 97.00| 75.00| 2155.| 16.4| 76| 3|toyota ...|
|26.5| 4| 140.0| 72.00| 2565.| 13.6| 76| 1|ford pinto|
|20.0| 4| 130.0| 102.0| 3150.| 15.7| 76| 2| volvo 245|
|13.0| 8| 318.0| 150.0| 3940.| 13.2| 76| 1|plymout...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col
reduced = df.subtract(df.where(col("mpg") < "25"))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|33.5| 4| 85.00| 70.00| 1945.| 16.8| 77| 3|datsun ...|
|37.0| 4| 91.00| 68.00| 2025.| 18.2| 82| 3|mazda g...|
|26.0| 4| 116.0| 75.00| 2246.| 14.0| 74| 2|fiat 12...|
|31.0| 4| 112.0| 85.00| 2575.| 16.2| 82| 1|pontiac...|
|29.0| 4| 90.00| 70.00| 1937.| 14.0| 75| 2|volkswa...|
|37.2| 4| 86.00| 65.00| 2019.| 16.4| 80| 3|datsun 310|
|26.0| 4| 98.00| 90.00| 2265.| 15.5| 73| 2|fiat 12...|
|40.9| 4| 85.00| null| 1835.| 17.3| 80| 2|renault...|
|35.1| 4| 81.00| 60.00| 1760.| 16.1| 81| 3|honda c...|
|32.8| 4| 78.00| 52.00| 1985.| 19.4| 78| 3|mazda g...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
Loading File Metadata and Processing Files.
from pyspark.sql.types import (
StructField,
StructType,
LongType,
StringType,
TimestampType,
)
import datetime
import glob
import os
# Simple: Use glob and only file names.
files = [[x] for x in glob.glob("/etc/*")]
df = spark.createDataFrame(files)
# Advanced: Use os.walk and extended attributes.
target_path = "/etc"
entries = []
walker = os.walk(target_path)
for root, dirs, files in walker:
for file in files:
full_path = os.path.join(root, file)
try:
stat_info = os.stat(full_path)
entries.append(
[
file,
full_path,
stat_info.st_size,
datetime.datetime.fromtimestamp(stat_info.st_mtime),
]
)
except:
pass
schema = StructType(
[
StructField("file", StringType(), False),
StructField("path", StringType(), False),
StructField("size", LongType(), False),
StructField("mtime", TimestampType(), False),
]
)
df = spark.createDataFrame(entries, schema)# Code snippet result:
+----------+----------+-----+----------+
| file| path| size| mtime|
+----------+----------+-----+----------+
| manpaths|/etc/ma...| 36|2020-06...|
| rc.common|/etc/rc...| 1560|2020-06...|
|auto_ma...|/etc/au...| 195|2020-08...|
| csh.login|/etc/cs...| 121|2020-06...|
|syslog....|/etc/sy...| 133|2020-12...|
|krb5.ke...|/etc/kr...| 1946|2021-08...|
| nanorc|/etc/na...| 11|2020-06...|
|csh.logout|/etc/cs...| 39|2020-06...|
|aliases.db|/etc/al...|16384|2020-06...|
|bashrc_...|/etc/ba...| 9192|2020-06...|
+----------+----------+-----+----------+
only showing top 10 rows
from pyspark.sql.types import (
StructField,
StructType,
LongType,
StringType,
TimestampType,
)
# Requires an object_store_client object.
# See https://oracle-cloud-infrastructure-python-sdk.readthedocs.io/en/latest/api/object_storage/client/oci.object_storage.ObjectStorageClient.html
input_bucket = "oow_2019_dataflow_lab"
raw_inputs = object_store_client.list_objects(
object_store_client.get_namespace().data,
input_bucket,
fields="size,md5,timeModified",
)
files = [
[x.name, x.size, x.time_modified, x.md5] for x in raw_inputs.data.objects
]
schema = StructType(
[
StructField("name", StringType(), False),
StructField("size", LongType(), True),
StructField("modified", TimestampType(), True),
StructField("md5", StringType(), True),
]
)
df = spark.createDataFrame(files, schema)# Code snippet result:
+----------+--------+----------+----------+
| name| size| modified| md5|
+----------+--------+----------+----------+
|sharedc...| 58|2021-01...|TY0HU7h...|
|usercon...|32006919|2021-01...|igX2QgX...|
|usercon...| 4307|2021-01...|NYXxVUc...|
|usercon...|71183217|2021-01...|HlBkZ/l...|
|usercon...|16906538|2021-01...|K8qMDXT...|
|usercon...| 4774|2021-01...|cXnXiq3...|
|usercon...| 303|2021-01...|5wgh5PJ...|
|usercon...| 1634|2021-01...|3Nqbf6K...|
|usercon...| 2611|2021-01...|B8XLwDe...|
|usercon...| 2017366|2021-01...|XyKoSOA...|
+----------+--------+----------+----------+
only showing top 10 rows
from PIL import Image
import glob
def resize_an_image(row):
width, height = 128, 128
file_name = row._1
new_name = file_name.replace(".png", ".resized.png")
img = Image.open(file_name)
img = img.resize((width, height), Image.ANTIALIAS)
img.save(new_name)
files = [[x] for x in glob.glob("data/resize_image?.png")]
df = spark.createDataFrame(files)
df.foreach(resize_an_image)Dealing with NULLs and NaNs in DataFrames.
from pyspark.sql.functions import col
filtered = df.where(col("horsepower").isNull())
filtered = df.where(col("horsepower").isNotNull())# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
# thresh controls the number of nulls before the row gets dropped.
# subset controls the columns to consider.
df = df.na.drop(thresh=2, subset=("horsepower",))from pyspark.sql.functions import col, count, isnan, when
result = df.select([count(when(isnan(c), c)).alias(c) for c in df.columns])
result = df.select(
[count(when(col(c).isNull(), c)).alias(c) for c in df.columns]
)# Code snippet result:
+---+---------+------------+----------+------+------------+---------+------+-------+
|mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin|carname|
+---+---------+------------+----------+------+------------+---------+------+-------+
| 0| 0| 0| 6| 0| 0| 0| 0| 0|
+---+---------+------------+----------+------+------------+---------+------+-------+
Parsing and processing dates and times.
from pyspark.sql.functions import col
df = spark.sparkContext.parallelize([["2021-01-01"], ["2022-01-01"]]).toDF(
["date_col"]
)
df = df.withColumn("date_col", col("date_col").cast("date"))# Code snippet result:
+----------+
| date_col|
+----------+
|2021-01-01|
|2022-01-01|
+----------+
from pyspark.sql.functions import col, to_date
df = spark.sparkContext.parallelize([["20210101"], ["20220101"]]).toDF(
["date_col"]
)
df = df.withColumn("date_col", to_date(col("date_col"), "yyyyddMM"))# Code snippet result:
+----------+
| date_col|
+----------+
|2021-01-01|
|2022-01-01|
+----------+
from pyspark.sql.functions import col, last_day
df = spark.sparkContext.parallelize([["2020-01-01"], ["1712-02-10"]]).toDF(
["date_col"]
)
df = df.withColumn("date_col", col("date_col").cast("date")).withColumn(
"last_day", last_day(col("date_col"))
)# Code snippet result:
+----------+----------+
| date_col| last_day|
+----------+----------+
|2020-01-01|2020-01-31|
|1712-02-10|1712-02-29|
+----------+----------+
from pyspark.sql.functions import col, from_unixtime
df = spark.sparkContext.parallelize([["1590183026"], ["2000000000"]]).toDF(
["ts_col"]
)
df = df.withColumn("date_col", from_unixtime(col("ts_col")))# Code snippet result:
+----------+----------+
| ts_col| date_col|
+----------+----------+
|1590183026|2020-05...|
|2000000000|2033-05...|
+----------+----------+
from pyspark.sql.functions import udf
from pyspark.sql.types import TimestampType
import dateparser
# Use the dateparser module to convert many formats into timestamps.
date_convert = udf(
lambda x: dateparser.parse(x) if x is not None else None, TimestampType()
)
df = (
spark.read.format("csv")
.option("header", True)
.load("data/date_examples.csv")
)
df = df.withColumn("parsed", date_convert(df.date))# Code snippet result:
+----------+----------+
| date| parsed|
+----------+----------+
|2012-01...|2012-01...|
|2012-01...|2011-12...|
|2012-01...|2012-01...|
|2012-01...|2012-01...|
|2012-01...|2011-12...|
|2012-01...|2012-01...|
|01-01-2...|2012-01...|
|01-01-2...|2011-12...|
|01-01-2...|2012-01...|
|01-01-2...|2012-01...|
+----------+----------+
only showing top 10 rows
Analyzing unstructured data like JSON, XML, etc.
from pyspark.sql.functions import col
# Load JSONl into a DataFrame. Schema is inferred automatically.
base = spark.read.json("data/financial.jsonl")
# Extract interesting fields. Alias keeps columns readable.
target_json_fields = [
col("symbol").alias("symbol"),
col("quoteType.longName").alias("longName"),
col("price.marketCap.raw").alias("marketCap"),
col("summaryDetail.previousClose.raw").alias("previousClose"),
col("summaryDetail.fiftyTwoWeekHigh.raw").alias("fiftyTwoWeekHigh"),
col("summaryDetail.fiftyTwoWeekLow.raw").alias("fiftyTwoWeekLow"),
col("summaryDetail.trailingPE.raw").alias("trailingPE"),
]
df = base.select(target_json_fields)# Code snippet result:
+------+----------+----------+-------------+----------------+---------------+----------+
|symbol| longName| marketCap|previousClose|fiftyTwoWeekHigh|fiftyTwoWeekLow|trailingPE|
+------+----------+----------+-------------+----------------+---------------+----------+
| ACLS|Axcelis...| 747277888| 22.58| 31.5| 12.99| 21.616652|
| AMSC|America...| 403171712| 14.69| 18.5| 4.4| null|
| ATH|Athene ...|6210204672| 33.31| 50.43| 13.37| 13.404612|
| BLDR|Builder...|3659266048| 31.69| 34.69| 9.0| 17.721876|
| BRC|Brady C...|2069810944| 41.27| 59.11| 33.0| 18.997612|
| CATC|Cambrid...| 437452224| 64.43| 82.0| 44.2| 14.195144|
| CBSH|Commerc...|6501258752| 58.54| 71.92| 45.51| 22.01284|
| CFFI|C&F Fin...| 113067632| 29.18| 57.61| 28.0| 6.614728|
| CFFN|Capitol...|1613282304| 11.28| 14.57| 8.75| 22.937624|
| COHR|Coheren...|2668193024| 112.97| 178.08| 78.21| null|
+------+----------+----------+-------------+----------------+---------------+----------+
only showing top 10 rows
from pyspark.sql.functions import col, from_json, schema_of_json
# quote/escape options needed when loading CSV containing JSON.
base = (
spark.read.format("csv")
.option("header", True)
.option("quote", '"')
.option("escape", '"')
.load("data/financial.csv")
)
# Infer JSON schema from one entry in the DataFrame.
sample_json_document = base.select("financial_data").first()[0]
schema = schema_of_json(sample_json_document)
# Parse using this schema.
parsed = base.withColumn("parsed", from_json("financial_data", schema))
# Extract interesting fields.
target_json_fields = [
col("parsed.symbol").alias("symbol"),
col("parsed.quoteType.longName").alias("longName"),
col("parsed.price.marketCap.raw").alias("marketCap"),
col("parsed.summaryDetail.previousClose.raw").alias("previousClose"),
col("parsed.summaryDetail.fiftyTwoWeekHigh.raw").alias("fiftyTwoWeekHigh"),
col("parsed.summaryDetail.fiftyTwoWeekLow.raw").alias("fiftyTwoWeekLow"),
col("parsed.summaryDetail.trailingPE.raw").alias("trailingPE"),
]
df = parsed.select(target_json_fields)# Code snippet result:
+------+----------+----------+-------------+----------------+---------------+----------+
|symbol| longName| marketCap|previousClose|fiftyTwoWeekHigh|fiftyTwoWeekLow|trailingPE|
+------+----------+----------+-------------+----------------+---------------+----------+
| ACLS|Axcelis...| 747277888| 22.58| 31.5| 12.99| 21.616652|
| AMSC|America...| 403171712| 14.69| 18.5| 4.4| null|
| ATH|Athene ...|6210204672| 33.31| 50.43| 13.37| 13.404612|
| BLDR|Builder...|3659266048| null| null| null| null|
| BRC|Brady C...|2069810944| null| null| null| null|
| CATC|Cambrid...| 437452224| null| null| null| null|
| CBSH|Commerc...|6501258752| null| null| null| null|
| CFFI|C&F Fin...| 113067632| null| null| null| null|
| CFFN|Capitol...|1613282304| null| null| null| null|
| COHR|Coheren...|2668193024| 112.97| 178.08| 78.21| null|
+------+----------+----------+-------------+----------------+---------------+----------+
only showing top 10 rows
from pyspark.sql.functions import col, explode
base = spark.read.json("data/financial.jsonl")
# Analyze balance sheet data, which is held in an array of complex types.
target_json_fields = [
col("symbol").alias("symbol"),
col("balanceSheetHistoryQuarterly.balanceSheetStatements").alias(
"balanceSheetStatements"
),
]
selected = base.select(target_json_fields)
# Select a few fields from the balance sheet statement data.
target_json_fields = [
col("symbol").alias("symbol"),
col("col.endDate.fmt").alias("endDate"),
col("col.cash.raw").alias("cash"),
col("col.totalAssets.raw").alias("totalAssets"),
col("col.totalLiab.raw").alias("totalLiab"),
]
# Balance sheet data is in an array, use explode to generate one row per entry.
df = selected.select("symbol", explode("balanceSheetStatements")).select(
target_json_fields
)# Code snippet result:
+------+----------+----------+-----------+----------+
|symbol| endDate| cash|totalAssets| totalLiab|
+------+----------+----------+-----------+----------+
| ACLS|2020-06-30| 190340000| 588564000| 143540000|
| ACLS|2020-03-31| 174745000| 562573000| 135401000|
| ACLS|2019-12-31| 139881000| 548094000| 128667000|
| ACLS|2019-09-30| 155317000| 530477000| 122630000|
| AMSC|2020-06-30| 20709000| 109670000| 40251000|
| AMSC|2020-03-31| 24699000| 124109000| 51890000|
| AMSC|2019-12-31| 25481000| 123491000| 46303000|
| AMSC|2019-09-30| 52829000| 117443000| 40757000|
| ATH|2020-06-30|6240000000| 1832410...|1676020...|
| ATH|2020-03-31|5419000000| 1421790...|1316490...|
+------+----------+----------+-----------+----------+
only showing top 10 rows
Using Python's Pandas library to augment Spark. Some operations require the pyarrow library.
pandas_df = df.toPandas()# Code snippet result:
mpg cylinders displacement ... modelyear origin carname
0 18.0 8 307.0 ... 70 1 chevrolet chevelle malibu
1 15.0 8 350.0 ... 70 1 buick skylark 320
2 18.0 8 318.0 ... 70 1 plymouth satellite
3 16.0 8 304.0 ... 70 1 amc rebel sst
4 17.0 8 302.0 ... 70 1 ford torino
.. ... ... ... ... ... ... ...
393 27.0 4 140.0 ... 82 1 ford mustang gl
394 44.0 4 97.00 ... 82 2 vw pickup
395 32.0 4 135.0 ... 82 1 dodge rampage
396 28.0 4 120.0 ... 82 1 ford ranger
397 31.0 4 119.0 ... 82 1 chevy s-10
[398 rows x 9 columns]
# This code converts everything to strings.
# If you want to preserve types, see https://gist.github.com/tonyfraser/79a255aa8a9d765bd5cf8bd13597171e
from pyspark.sql.types import StructField, StructType, StringType
schema = StructType(
[StructField(name, StringType(), True) for name in pandas_df.columns]
)
df = spark.createDataFrame(pandas_df, schema)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
N = 10
pdf = df.limit(N).toPandas()# Code snippet result:
mpg cylinders displacement ... modelyear origin carname
0 18.0 8 307.0 ... 70 1 chevrolet chevelle malibu
1 15.0 8 350.0 ... 70 1 buick skylark 320
2 18.0 8 318.0 ... 70 1 plymouth satellite
3 16.0 8 304.0 ... 70 1 amc rebel sst
4 17.0 8 302.0 ... 70 1 ford torino
5 15.0 8 429.0 ... 70 1 ford galaxie 500
6 14.0 8 454.0 ... 70 1 chevrolet impala
7 14.0 8 440.0 ... 70 1 plymouth fury iii
8 14.0 8 455.0 ... 70 1 pontiac catalina
9 15.0 8 390.0 ... 70 1 amc ambassador dpl
[10 rows x 9 columns]
from pyspark.sql.functions import pandas_udf
from pandas import DataFrame
@pandas_udf("double")
def mean_udaf(pdf: DataFrame) -> float:
return pdf.mean()
df = df.groupby("cylinders").agg(mean_udaf(df["mpg"]))# Code snippet result:
+---------+--------------+
|cylinders|mean_udaf(mpg)|
+---------+--------------+
| 8.0| 14.9631...|
| 4.0| 29.2867...|
| 3.0| 20.55|
| 6.0| 19.9857...|
| 5.0| 27.3666...|
+---------+--------------+
def rescale(pdf):
minv = pdf.horsepower.min()
maxv = pdf.horsepower.max() - minv
return pdf.assign(horsepower=(pdf.horsepower - minv) / maxv * 100)
df = df.groupby("cylinders").applyInPandas(rescale, df.schema)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8.0| 307.0|28.5714...|3504.0| 12.0| 70| 1|chevrol...|
|15.0| 8.0| 350.0|53.5714...|3693.0| 11.5| 70| 1|buick s...|
|18.0| 8.0| 318.0|42.8571...|3436.0| 11.0| 70| 1|plymout...|
|16.0| 8.0| 304.0|42.8571...|3433.0| 12.0| 70| 1|amc reb...|
|17.0| 8.0| 302.0|35.7142...|3449.0| 10.5| 70| 1|ford to...|
|15.0| 8.0| 429.0|77.1428...|4341.0| 10.0| 70| 1|ford ga...|
|14.0| 8.0| 454.0|92.8571...|4354.0| 9.0| 70| 1|chevrol...|
|14.0| 8.0| 440.0|89.2857...|4312.0| 8.5| 70| 1|plymout...|
|14.0| 8.0| 455.0|96.4285...|4425.0| 10.0| 70| 1|pontiac...|
|15.0| 8.0| 390.0|71.4285...|3850.0| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
Extracting key statistics out of a body of data.
from pyspark.sql.functions import col, count, when
result = df.select(
[count(when(col(c).isNull(), c)).alias(c) for c in df.columns]
)# Code snippet result:
+---+---------+------------+----------+------+------------+---------+------+-------+
|mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin|carname|
+---+---------+------------+----------+------+------------+---------+------+-------+
| 0| 0| 0| 6| 0| 0| 0| 0| 0|
+---+---------+------------+----------+------+------------+---------+------+-------+
numerics = set(["decimal", "double", "float", "integer", "long", "short"])
exprs = {x[0]: "avg" for x in df.dtypes if x[1] in numerics}
result = df.agg(exprs)# Code snippet result:
+-----------+-----------------+--------------+----------+---------------+-----------------+
|avg(weight)|avg(acceleration)|avg(cylinders)| avg(mpg)|avg(horsepower)|avg(displacement)|
+-----------+-----------------+--------------+----------+---------------+-----------------+
| 2970.42...| 15.5680...| 5.45477...|23.5145...| 104.469...| 193.425...|
+-----------+-----------------+--------------+----------+---------------+-----------------+
numerics = set(["decimal", "double", "float", "integer", "long", "short"])
exprs = {x[0]: "min" for x in df.dtypes if x[1] in numerics}
result = df.agg(exprs)# Code snippet result:
+-----------+-----------------+--------------+--------+---------------+-----------------+
|min(weight)|min(acceleration)|min(cylinders)|min(mpg)|min(horsepower)|min(displacement)|
+-----------+-----------------+--------------+--------+---------------+-----------------+
| 1613.0| 8.0| 3.0| 9.0| 46.0| 68.0|
+-----------+-----------------+--------------+--------+---------------+-----------------+
numerics = set(["decimal", "double", "float", "integer", "long", "short"])
exprs = {x[0]: "max" for x in df.dtypes if x[1] in numerics}
result = df.agg(exprs)# Code snippet result:
+-----------+-----------------+--------------+--------+---------------+-----------------+
|max(weight)|max(acceleration)|max(cylinders)|max(mpg)|max(horsepower)|max(displacement)|
+-----------+-----------------+--------------+--------+---------------+-----------------+
| 5140.0| 24.8| 8.0| 46.6| 230.0| 455.0|
+-----------+-----------------+--------------+--------+---------------+-----------------+
import pyspark.sql.functions as F
numerics = set(["decimal", "double", "float", "integer", "long", "short"])
aggregates = []
for name, dtype in df.dtypes:
if dtype not in numerics:
continue
aggregates.append(
F.expr("percentile({}, 0.5)".format(name)).alias(
"{}_median".format(name)
)
)
profiled = df.agg(*aggregates)# Code snippet result:
+----------+----------------+-------------------+-----------------+-------------+-------------------+
|mpg_median|cylinders_median|displacement_median|horsepower_median|weight_median|acceleration_median|
+----------+----------------+-------------------+-----------------+-------------+-------------------+
| 23.0| 4.0| 148.5| 93.5| 2803.5| 15.5|
+----------+----------------+-------------------+-----------------+-------------+-------------------+
# This approach uses the Median Absolute Deviation.
# Outliers are based on variances in a single numeric column.
# Tune outlier sensitivity using z_score_threshold.
from pyspark.sql.functions import col, sqrt
target_column = "mpg"
z_score_threshold = 2
# Compute the median of the target column.
target_df = df.select(target_column)
target_df.registerTempTable("target_column")
profiled = sqlContext.sql(
f"select percentile({target_column}, 0.5) as median from target_column"
)
# Compute deviations.
deviations = target_df.crossJoin(profiled).withColumn(
"deviation", sqrt((target_df[target_column] - profiled["median"]) ** 2)
)
deviations.registerTempTable("deviations")
# The Median Absolute Deviation
mad = sqlContext.sql("select percentile(deviation, 0.5) as mad from deviations")
# Add a modified z score to the original DataFrame.
df = (
df.crossJoin(mad)
.crossJoin(profiled)
.withColumn(
"zscore",
0.6745
* sqrt((df[target_column] - profiled["median"]) ** 2)
/ mad["mad"],
)
)
df_outliers = df.where(col("zscore") > z_score_threshold)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+---+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|mad|median| zscore|
+----+---------+------------+----------+------+------------+---------+------+----------+---+------+----------+
|43.1| 4.0| 90.0| 48.0|1985.0| 21.5| 78| 2|volkswa...|6.0| 23.0|2.25957...|
|41.5| 4.0| 98.0| 76.0|2144.0| 14.7| 80| 2| vw rabbit|6.0| 23.0|2.07970...|
|46.6| 4.0| 86.0| 65.0|2110.0| 17.9| 80| 3| mazda glc|6.0| 23.0|2.65303...|
|40.8| 4.0| 85.0| 65.0|2110.0| 19.2| 80| 3|datsun 210|6.0| 23.0|2.00101...|
|44.3| 4.0| 90.0| 48.0|2085.0| 21.7| 80| 2|vw rabb...|6.0| 23.0|2.39447...|
|43.4| 4.0| 90.0| 48.0|2335.0| 23.7| 80| 2|vw dash...|6.0| 23.0| 2.2933|
|44.6| 4.0| 91.0| 67.0|1850.0| 13.8| 80| 3|honda c...|6.0| 23.0| 2.4282|
|40.9| 4.0| 85.0| null|1835.0| 17.3| 80| 2|renault...|6.0| 23.0|2.01225...|
|44.0| 4.0| 97.0| 52.0|2130.0| 24.6| 82| 2| vw pickup|6.0| 23.0| 2.36075|
+----+---------+------------+----------+------+------------+---------+------+----------+---+------+----------+
Upserts, updates and deletes on data.
from pyspark.sql.functions import expr
output_path = "delta_tests"
# Currently you have to save/reload to convert from table to DataFrame.
df.write.mode("overwrite").format("delta").save(output_path)
dt = DeltaTable.forPath(spark, output_path)
# Run a SQL update operation.
dt.update(
condition=expr("carname like 'Volks%'"), set={"carname": expr("carname")}
)
# Convert back to a DataFrame.
df = dt.toDF()# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
from pyspark.sql.functions import col, expr
# Save the original data.
output_path = "delta_tests"
df.write.mode("overwrite").format("delta").save(output_path)
# Load data that corrects some car names.
corrected_df = (
spark.read.format("csv")
.option("header", True)
.load("data/auto-mpg-fixed.csv")
)
# Merge the corrected data in.
dt = DeltaTable.forPath(spark, output_path)
ret = (
dt.alias("original")
.merge(
corrected_df.alias("corrected"),
"original.modelyear = corrected.modelyear and original.weight = corrected.weight and original.acceleration = corrected.acceleration",
)
.whenMatchedUpdate(
condition=expr("original.carname <> corrected.carname"),
set={"carname": col("corrected.carname")},
)
.whenNotMatchedInsertAll()
.execute()
)
# Show select table history.
history = dt.history().select("version operation operationMetrics".split())# Code snippet result:
+-------+---------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|version|operation|operationMetrics |
+-------+---------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|17 |MERGE |{numTargetRowsCopied -> 373, numTargetRowsDeleted -> 0, numTargetFilesAdded -> 175, executionTimeMs -> 10798, numTargetRowsInserted -> 0, scanTimeMs -> 5350, numTargetRowsUpdated -> 25, numOutputRows -> 398, numSourceRows -> 398, numTargetFilesRemoved -> 1, rewriteTimeMs -> 5443}|
|16 |WRITE |{numFiles -> 1, numOutputBytes -> 11582, numOutputRows -> 398} |
|15 |WRITE |{numFiles -> 1, numOutputBytes -> 11582, numOutputRows -> 398} |
|14 |MERGE |{numTargetRowsCopied -> 373, numTargetRowsDeleted -> 0, numTargetFilesAdded -> 175, executionTimeMs -> 7199, numTargetRowsInserted -> 0, scanTimeMs -> 3172, numTargetRowsUpdated -> 25, numOutputRows -> 398, numSourceRows -> 398, numTargetFilesRemoved -> 1, rewriteTimeMs -> 4023} |
|13 |WRITE |{numFiles -> 1, numOutputBytes -> 11582, numOutputRows -> 398} |
|12 |WRITE |{numFiles -> 1, numOutputBytes -> 11582, numOutputRows -> 398} |
|11 |MERGE |{numTargetRowsCopied -> 373, numTargetRowsDeleted -> 0, numTargetFilesAdded -> 175, executionTimeMs -> 8465, numTargetRowsInserted -> 0, scanTimeMs -> 4395, numTargetRowsUpdated -> 25, numOutputRows -> 398, numSourceRows -> 398, numTargetFilesRemoved -> 1, rewriteTimeMs -> 4066} |
|10 |WRITE |{numFiles -> 1, numOutputBytes -> 11582, numOutputRows -> 398} |
|9 |WRITE |{numFiles -> 1, numOutputBytes -> 11582, numOutputRows -> 398} |
|8 |MERGE |{numTargetRowsCopied -> 373, numTargetRowsDeleted -> 0, numTargetFilesAdded -> 175, executionTimeMs -> 6766, numTargetRowsInserted -> 0, scanTimeMs -> 3084, numTargetRowsUpdated -> 25, numOutputRows -> 398, numSourceRows -> 398, numTargetFilesRemoved -> 1, rewriteTimeMs -> 3679} |
+-------+---------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
only showing top 10 rows
Spark Streaming (Focuses on Structured Streaming).
options = {
"kafka.sasl.jaas.config": 'org.apache.kafka.common.security.plain.PlainLoginModule required username="USERNAME" password="PASSWORD";',
"kafka.sasl.mechanism": "PLAIN",
"kafka.security.protocol": "SASL_SSL",
"kafka.bootstrap.servers": "server:9092",
"group.id": "my_group",
"subscribe": "my_topic",
}
df = spark.readStream.format("kafka").options(**options).load()from pyspark.sql.functions import avg, count, current_timestamp, window
from pyspark.sql.types import (
StructField,
StructType,
DoubleType,
IntegerType,
StringType,
)
input_location = "streaming/input"
schema = StructType(
[
StructField("mpg", DoubleType(), True),
StructField("cylinders", IntegerType(), True),
StructField("displacement", DoubleType(), True),
StructField("horsepower", DoubleType(), True),
StructField("weight", DoubleType(), True),
StructField("acceleration", DoubleType(), True),
StructField("modelyear", IntegerType(), True),
StructField("origin", IntegerType(), True),
StructField("carname", StringType(), True),
StructField("manufacturer", StringType(), True),
]
)
df = spark.readStream.csv(path=input_location, schema=schema).withColumn(
"timestamp", current_timestamp()
)
aggregated = (
df.groupBy(window(df.timestamp, "1 minute"), "manufacturer")
.agg(
avg("horsepower").alias("avg_horsepower"),
avg("timestamp").alias("avg_timestamp"),
count("modelyear").alias("count"),
)
.coalesce(10)
)
summary = aggregated.orderBy("window", "manufacturer")
query = (
summary.writeStream.outputMode("complete")
.format("console")
.option("truncate", False)
.start()
)
query.awaitTermination()# Code snippet result:
-------------------------------------------
Batch: 0
-------------------------------------------
+------------------------------------------+------------+--------------+----------------+-----+
|window |manufacturer|avg_horsepower|avg_timestamp |count|
+------------------------------------------+------------+--------------+----------------+-----+
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|amc |131.75 |1.616243250178E9|4 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|chevrolet |175.0 |1.616243250178E9|4 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|datsun |88.0 |1.616243250178E9|1 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|dodge |190.0 |1.616243250178E9|2 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|ford |159.5 |1.616243250178E9|4 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|plymouth |155.0 |1.616243250178E9|4 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|pontiac |225.0 |1.616243250178E9|1 |
+------------------------------------------+------------+--------------+----------------+-----+
-------------------------------------------
Batch: 1
-------------------------------------------
+------------------------------------------+------------+------------------+--------------------+-----+
|window |manufacturer|avg_horsepower |avg_timestamp |count|
+------------------------------------------+------------+------------------+--------------------+-----+
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|amc |119.57142857142857|1.6162432596022856E9|7 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|chevrolet |140.875 |1.6162432611729999E9|8 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|datsun |81.66666666666667 |1.616243264838E9 |3 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|dodge |186.66666666666666|1.6162432575080001E9|3 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|ford |142.125 |1.6162432623946667E9|9 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|plymouth |135.0 |1.6162432596022856E9|7 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|pontiac |168.75 |1.6162432666704998E9|4 |
+------------------------------------------+------------+------------------+--------------------+-----+
-------------------------------------------
Batch: 2
-------------------------------------------
+------------------------------------------+------------+------------------+--------------------+-----+
|window |manufacturer|avg_horsepower |avg_timestamp |count|
+------------------------------------------+------------+------------------+--------------------+-----+
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|amc |119.57142857142857|1.6162432596022856E9|7 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|chevrolet |140.875 |1.6162432611729999E9|8 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|datsun |81.66666666666667 |1.616243264838E9 |3 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|dodge |186.66666666666666|1.6162432575080001E9|3 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|ford |142.125 |1.6162432623946667E9|9 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|plymouth |135.0 |1.6162432596022856E9|7 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|pontiac |168.75 |1.6162432666704998E9|4 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|amc |150.0 |1.616243297163E9 |2 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|chevrolet |128.33333333333334|1.616243297163E9 |3 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|datsun |92.0 |1.616243297163E9 |1 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|dodge |80.0 |1.616243297163E9 |2 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|ford |116.25 |1.616243297163E9 |4 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|plymouth |150.0 |1.616243297163E9 |2 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|pontiac |175.0 |1.616243297163E9 |1 |
+------------------------------------------+------------+------------------+--------------------+-----+
-------------------------------------------
Batch: 3
-------------------------------------------
+------------------------------------------+------------+------------------+--------------------+-----+
|window |manufacturer|avg_horsepower |avg_timestamp |count|
+------------------------------------------+------------+------------------+--------------------+-----+
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|amc |119.57142857142857|1.6162432596022856E9|7 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|chevrolet |140.875 |1.6162432611729999E9|8 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|datsun |81.66666666666667 |1.616243264838E9 |3 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|dodge |186.66666666666666|1.6162432575080001E9|3 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|ford |142.125 |1.6162432623946667E9|9 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|plymouth |135.0 |1.6162432596022856E9|7 |
|{2021-03-20 05:27:00, 2021-03-20 05:28:00}|pontiac |168.75 |1.6162432666704998E9|4 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|amc |137.5 |1.616243313837E9 |6 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|chevrolet |127.44444444444444|1.6162433138370001E9|9 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|datsun |93.0 |1.6162433096685E9 |2 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|dodge |115.0 |1.6162433096685E9 |4 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|ford |122.22222222222223|1.6162433110579998E9|9 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|plymouth |136.66666666666666|1.616243313837E9 |6 |
|{2021-03-20 05:28:00, 2021-03-20 05:29:00}|pontiac |202.5 |1.6162433096685E9 |2 |
+------------------------------------------+------------+------------------+--------------------+-----+
from pyspark.sql.functions import avg, count, desc
from pyspark.sql.types import (
StructField,
StructType,
DoubleType,
IntegerType,
StringType,
)
input_location = "streaming/input"
schema = StructType(
[
StructField("mpg", DoubleType(), True),
StructField("cylinders", IntegerType(), True),
StructField("displacement", DoubleType(), True),
StructField("horsepower", DoubleType(), True),
StructField("weight", DoubleType(), True),
StructField("acceleration", DoubleType(), True),
StructField("modelyear", IntegerType(), True),
StructField("origin", IntegerType(), True),
StructField("carname", StringType(), True),
StructField("manufacturer", StringType(), True),
]
)
df = spark.readStream.csv(path=input_location, schema=schema)
summary = (
df.groupBy("modelyear")
.agg(
avg("horsepower").alias("avg_horsepower"),
count("modelyear").alias("count"),
)
.orderBy(desc("modelyear"))
.coalesce(10)
)
query = summary.writeStream.outputMode("complete").format("console").start()
query.awaitTermination()# Code snippet result:
-------------------------------------------
Batch: 0
-------------------------------------------
+---------+------------------+-----+
|modelyear| avg_horsepower|count|
+---------+------------------+-----+
| 70|147.82758620689654| 29|
+---------+------------------+-----+
-------------------------------------------
Batch: 1
-------------------------------------------
+---------+------------------+-----+
|modelyear| avg_horsepower|count|
+---------+------------------+-----+
| 71|107.03703703703704| 28|
| 70|147.82758620689654| 29|
+---------+------------------+-----+
-------------------------------------------
Batch: 2
-------------------------------------------
+---------+------------------+-----+
|modelyear| avg_horsepower|count|
+---------+------------------+-----+
| 72|120.17857142857143| 28|
| 71|107.03703703703704| 28|
| 70|147.82758620689654| 29|
+---------+------------------+-----+
from pyspark.sql.functions import current_timestamp
df = df.withColumn("timestamp", current_timestamp())# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname| timestamp|
+----+---------+------------+----------+------+------------+---------+------+----------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|2021-09...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|2021-09...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|2021-09...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|2021-09...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|2021-09...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|2021-09...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|2021-09...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|2021-09...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|2021-09...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|2021-09...|
+----+---------+------------+----------+------+------------+---------+------+----------+----------+
only showing top 10 rows
Techniques for dealing with time series data.
from pyspark.sql.functions import coalesce, lit
# Use distinct values of customer and date from the dataset itself.
# In general it's safer to use known reference tables for IDs and dates.
filled = df.join(
df.select("customer_id").distinct().crossJoin(df.select("date").distinct()),
["date", "customer_id"],
"right",
).select("date", "customer_id", coalesce("spend_dollars", lit(0)))# Code snippet result:
+----------+-----------+--------------------------+
| date|customer_id|coalesce(spend_dollars, 0)|
+----------+-----------+--------------------------+
|2022-07-31| 31| 16.4400|
|2020-04-30| 31| 0.0000|
|2022-01-31| 31| 25.1100|
|2021-09-30| 31| 2.1400|
|2021-05-31| 31| 34.3900|
|2022-02-28| 31| 59.7800|
|2021-07-31| 31| 8.2700|
|2020-07-31| 31| 0.0000|
|2021-04-30| 31| 20.7000|
|2021-10-31| 31| 62.1500|
+----------+-----------+--------------------------+
only showing top 10 rows
from pyspark.sql.functions import first
from pyspark.sql.window import Window
w = Window().partitionBy("customer_id").orderBy("date")
df = df.withColumn("first_seen", first("date").over(w))# Code snippet result:
+----------+-----------+-------------+----------+
| date|customer_id|spend_dollars|first_seen|
+----------+-----------+-------------+----------+
|2020-09-30| 31| 33.4400|2020-09-30|
|2020-10-31| 31| 24.8000|2020-09-30|
|2020-11-30| 31| 111.1600|2020-09-30|
|2020-12-31| 31| 22.3800|2020-09-30|
|2021-01-31| 31| 31.0600|2020-09-30|
|2021-02-28| 31| 22.4300|2020-09-30|
|2021-04-30| 31| 20.7000|2020-09-30|
|2021-05-31| 31| 34.3900|2020-09-30|
|2021-06-30| 31| 11.0800|2020-09-30|
|2021-07-31| 31| 8.2700|2020-09-30|
+----------+-----------+-------------+----------+
only showing top 10 rows
from pyspark.sql.functions import sum
from pyspark.sql.window import Window
w = (
Window()
.partitionBy("customer_id")
.orderBy("date")
.rangeBetween(Window.unboundedPreceding, 0)
)
df = df.withColumn("running_sum", sum("spend_dollars").over(w))# Code snippet result:
+----------+-----------+-------------+-----------+
| date|customer_id|spend_dollars|running_sum|
+----------+-----------+-------------+-----------+
|2020-09-30| 31| 33.4400| 33.4400|
|2020-10-31| 31| 24.8000| 58.2400|
|2020-11-30| 31| 111.1600| 169.4000|
|2020-12-31| 31| 22.3800| 191.7800|
|2021-01-31| 31| 31.0600| 222.8400|
|2021-02-28| 31| 22.4300| 245.2700|
|2021-04-30| 31| 20.7000| 265.9700|
|2021-05-31| 31| 34.3900| 300.3600|
|2021-06-30| 31| 11.0800| 311.4400|
|2021-07-31| 31| 8.2700| 319.7100|
+----------+-----------+-------------+-----------+
only showing top 10 rows
from pyspark.sql.functions import sum, year
from pyspark.sql.window import Window
# Add an additional partition clause for the sub-period.
w = (
Window()
.partitionBy(["customer_id", year("date")])
.orderBy("date")
.rangeBetween(Window.unboundedPreceding, 0)
)
df = df.withColumn("running_sum", sum("spend_dollars").over(w))# Code snippet result:
+----------+-----------+-------------+-----------+
| date|customer_id|spend_dollars|running_sum|
+----------+-----------+-------------+-----------+
|2021-01-31| 26| 13.1900| 13.1900|
|2021-02-28| 26| 41.0800| 54.2700|
|2021-03-31| 26| 152.3100| 206.5800|
|2021-05-31| 26| 16.1600| 222.7400|
|2021-07-31| 26| 44.8800| 267.6200|
|2021-09-30| 26| 44.0800| 311.7000|
|2021-10-31| 26| 69.2700| 380.9700|
|2021-11-30| 26| 53.1100| 434.0800|
|2021-12-31| 26| 7.2300| 441.3100|
|2021-01-31| 31| 31.0600| 31.0600|
+----------+-----------+-------------+-----------+
only showing top 10 rows
from pyspark.sql.functions import avg
from pyspark.sql.window import Window
w = (
Window()
.partitionBy("customer_id")
.orderBy("date")
.rangeBetween(Window.unboundedPreceding, 0)
)
df = df.withColumn("running_avg", avg("spend_dollars").over(w))# Code snippet result:
+----------+-----------+-------------+-----------+
| date|customer_id|spend_dollars|running_avg|
+----------+-----------+-------------+-----------+
|2020-09-30| 31| 33.4400| 33.4400...|
|2020-10-31| 31| 24.8000| 29.1200...|
|2020-11-30| 31| 111.1600| 56.4666...|
|2020-12-31| 31| 22.3800| 47.9450...|
|2021-01-31| 31| 31.0600| 44.5680...|
|2021-02-28| 31| 22.4300| 40.8783...|
|2021-04-30| 31| 20.7000| 37.9957...|
|2021-05-31| 31| 34.3900| 37.5450...|
|2021-06-30| 31| 11.0800| 34.6044...|
|2021-07-31| 31| 8.2700| 31.9710...|
+----------+-----------+-------------+-----------+
only showing top 10 rows
from pyspark.sql.functions import avg, year
from pyspark.sql.window import Window
# Add an additional partition clause for the sub-period.
w = (
Window()
.partitionBy(["customer_id", year("date")])
.orderBy("date")
.rangeBetween(Window.unboundedPreceding, 0)
)
df = df.withColumn("running_avg", avg("spend_dollars").over(w))# Code snippet result:
+----------+-----------+-------------+-----------+
| date|customer_id|spend_dollars|running_avg|
+----------+-----------+-------------+-----------+
|2021-01-31| 26| 13.1900| 13.1900...|
|2021-02-28| 26| 41.0800| 27.1350...|
|2021-03-31| 26| 152.3100| 68.8600...|
|2021-05-31| 26| 16.1600| 55.6850...|
|2021-07-31| 26| 44.8800| 53.5240...|
|2021-09-30| 26| 44.0800| 51.9500...|
|2021-10-31| 26| 69.2700| 54.4242...|
|2021-11-30| 26| 53.1100| 54.2600...|
|2021-12-31| 26| 7.2300| 49.0344...|
|2021-01-31| 31| 31.0600| 31.0600...|
+----------+-----------+-------------+-----------+
only showing top 10 rows
Machine Learning
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import RandomForestRegressor
vectorAssembler = VectorAssembler(
inputCols=[
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
],
outputCol="features",
handleInvalid="skip",
)
assembled = vectorAssembler.transform(df)
# Random test/train split.
train_df, test_df = assembled.randomSplit([0.7, 0.3])
# Define the model.
rf = RandomForestRegressor(
numTrees=50,
featuresCol="features",
labelCol="mpg",
)
# Train the model.
rf_model = rf.fit(train_df)
rf_model.write().overwrite().save("rf_regression.model")from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import RandomForestRegressionModel
# Model type and assembled features need to agree with the trained model.
rf_model = RandomForestRegressionModel.load("rf_regression.model")
vectorAssembler = VectorAssembler(
inputCols=[
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
],
outputCol="features",
handleInvalid="skip",
)
assembled = vectorAssembler.transform(df)
predictions = rf_model.transform(assembled).select(
"carname", "mpg", "prediction"
)# Code snippet result:
+----------+----+----------+
| carname| mpg|prediction|
+----------+----+----------+
|chevrol...|18.0|17.2741...|
|buick s...|15.0|15.1640...|
|plymout...|18.0|16.3667...|
|amc reb...|16.0|16.2606...|
|ford to...|17.0|17.2151...|
|ford ga...|15.0|14.3816...|
|chevrol...|14.0|14.3816...|
|plymout...|14.0|14.3816...|
|pontiac...|14.0|13.4232...|
|amc amb...|15.0|14.6402...|
+----------+----+----------+
only showing top 10 rows
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
vectorAssembler = VectorAssembler(
inputCols=[
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
],
outputCol="features",
handleInvalid="skip",
)
assembled = vectorAssembler.transform(df)
assembled = assembled.select(["features", "mpg", "carname"])
# Random test/train split.
train_df, test_df = assembled.randomSplit([0.7, 0.3])
# Define the model.
lr = LinearRegression(
featuresCol="features",
labelCol="mpg",
maxIter=10,
regParam=0.3,
elasticNetParam=0.8,
)
# Train the model.
lr_model = lr.fit(train_df)
# Stats for training.
print(
"RMSE={} r2={}".format(
lr_model.summary.rootMeanSquaredError, lr_model.summary.r2
)
)
# Make predictions.
predictions = lr_model.transform(test_df)# Code snippet result:
+----------+----+----------+----------+
| features| mpg| carname|prediction|
+----------+----+----------+----------+
|[4.0,71...|31.0|toyota ...|31.3498...|
|[4.0,76...|31.0|toyota ...|32.1648...|
|[4.0,83...|32.0|datsun 710|30.4888...|
|[4.0,85...|29.0|chevrol...|30.6520...|
|[4.0,85...|31.8|datsun 210|30.2627...|
|[4.0,85...|33.5|datsun ...|30.3609...|
|[4.0,85...|39.4|datsun ...|29.9131...|
|[4.0,86...|39.0|plymout...|30.8017...|
|[4.0,86...|37.2|datsun 310|30.2518...|
|[4.0,90...|43.1|volkswa...|30.8952...|
+----------+----+----------+----------+
only showing top 10 rows
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import RandomForestRegressor
from pyspark.ml.evaluation import RegressionEvaluator
vectorAssembler = VectorAssembler(
inputCols=[
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
],
outputCol="features",
handleInvalid="skip",
)
assembled = vectorAssembler.transform(df)
assembled = assembled.select(["features", "mpg", "carname"])
# Random test/train split.
train_df, test_df = assembled.randomSplit([0.7, 0.3])
# Define the model.
rf = RandomForestRegressor(
numTrees=20,
featuresCol="features",
labelCol="mpg",
)
# Train the model.
rf_model = rf.fit(train_df)
# Make predictions.
predictions = rf_model.transform(test_df)
# Evaluate the model.
r2 = RegressionEvaluator(
labelCol="mpg", predictionCol="prediction", metricName="r2"
).evaluate(predictions)
rmse = RegressionEvaluator(
labelCol="mpg", predictionCol="prediction", metricName="rmse"
).evaluate(predictions)
print("RMSE={} r2={}".format(rmse, r2))# Code snippet result:
+----------+----+----------+----------+
| features| mpg| carname|prediction|
+----------+----+----------+----------+
|[3.0,70...|19.0|mazda r...|24.8344...|
|[4.0,71...|31.0|toyota ...|32.8749...|
|[4.0,71...|32.0|toyota ...|33.2013...|
|[4.0,78...|32.8|mazda g...|33.2214...|
|[4.0,79...|39.1|toyota ...|33.2573...|
|[4.0,81...|35.1|honda c...|34.5568...|
|[4.0,83...|32.0|datsun 710|34.5682...|
|[4.0,86...|37.2|datsun 310|34.1947...|
|[4.0,86...|46.6| mazda glc|34.7690...|
|[4.0,89...|38.1|toyota ...|34.0990...|
+----------+----+----------+----------+
only showing top 10 rows
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.feature import VectorAssembler
label_column = "cover_type"
vectorAssembler = VectorAssembler(
inputCols=df.columns,
outputCol="features",
handleInvalid="skip",
)
assembled = vectorAssembler.transform(df)
# Random test/train split.
train_df, test_df = assembled.randomSplit([0.7, 0.3])
# Define the model.
rf = RandomForestClassifier(
numTrees=50,
featuresCol="features",
labelCol=label_column,
)
# Train the model.
rf_model = rf.fit(train_df)
# Make predictions.
predictions = rf_model.transform(test_df)
# Select (prediction, true label) and compute test error
evaluator = MulticlassClassificationEvaluator(
labelCol=label_column, predictionCol="prediction", metricName="accuracy"
)
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))
results = predictions.select([label_column, "prediction"])# Code snippet result:
+----------+----------+
|cover_type|prediction|
+----------+----------+
| 3| 3.0|
| 3| 3.0|
| 6| 6.0|
| 3| 3.0|
| 3| 3.0|
| 3| 3.0|
| 6| 6.0|
| 6| 6.0|
| 6| 3.0|
| 6| 6.0|
+----------+----------+
only showing top 10 rows
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml.regression import RandomForestRegressor
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
# Add manufacturer name we will use as a string column.
first_word_udf = udf(lambda x: x.split()[0], StringType())
df = df.withColumn("manufacturer", first_word_udf(df.carname))
# Strings must be indexed or we will get:
# pyspark.sql.utils.IllegalArgumentException: Data type string of column manufacturer is not supported.
#
# We also encode outside of the main pipeline or else we risk getting:
# Caused by: org.apache.spark.SparkException: Unseen label: XXX. To handle unseen labels, set Param handleInvalid to keep.
#
# This is because training data is selected randomly and may not have all possible categories.
manufacturer_encoded = StringIndexer(
inputCol="manufacturer", outputCol="manufacturer_encoded"
)
encoded_df = manufacturer_encoded.fit(df).transform(df)
# Set up our main ML pipeline.
columns_to_assemble = [
"manufacturer_encoded",
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
]
vector_assembler = VectorAssembler(
inputCols=columns_to_assemble,
outputCol="features",
handleInvalid="skip",
)
# Random test/train split.
train_df, test_df = encoded_df.randomSplit([0.7, 0.3])
# Define the model.
rf = RandomForestRegressor(
numTrees=20,
featuresCol="features",
labelCol="mpg",
)
# Run the pipeline.
pipeline = Pipeline(stages=[vector_assembler, rf])
model = pipeline.fit(train_df)
# Make predictions.
predictions = model.transform(test_df).select("carname", "mpg", "prediction")
# Select (prediction, true label) and compute test error
rmse = RegressionEvaluator(
labelCol="mpg", predictionCol="prediction", metricName="rmse"
).evaluate(predictions)
print("RMSE={}".format(rmse))# Code snippet result:
+----------+----+----------+
| carname| mpg|prediction|
+----------+----+----------+
|chevrol...|10.0|13.6711...|
|oldsmob...|12.0|13.0878...|
|dodge m...|12.0|13.5042...|
| ford f108|13.0|16.1286...|
|ford gr...|13.0|14.4684...|
|chevrol...|13.0|16.1248...|
|dodge d100|13.0|15.0328...|
|chevrol...|13.0|14.8465...|
|chevrol...|13.0|13.6027...|
|ford co...|13.0|13.3417...|
+----------+----+----------+
only showing top 10 rows
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml.regression import RandomForestRegressor
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
# Add manufacturer name we will use as a string column.
first_word_udf = udf(lambda x: x.split()[0], StringType())
df = df.withColumn("manufacturer", first_word_udf(df.carname))
manufacturer_encoded = StringIndexer(
inputCol="manufacturer", outputCol="manufacturer_encoded"
)
encoded_df = manufacturer_encoded.fit(df).transform(df)
# Set up our main ML pipeline.
columns_to_assemble = [
"manufacturer_encoded",
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
]
vector_assembler = VectorAssembler(
inputCols=columns_to_assemble,
outputCol="features",
handleInvalid="skip",
)
# Random test/train split.
train_df, test_df = encoded_df.randomSplit([0.7, 0.3])
# Define the model.
rf = RandomForestRegressor(
numTrees=20,
featuresCol="features",
labelCol="mpg",
)
# Run the pipeline.
pipeline = Pipeline(stages=[vector_assembler, rf])
model = pipeline.fit(train_df)
# Make predictions.
predictions = model.transform(test_df).select("carname", "mpg", "prediction")
# Get feature importances.
real_model = model.stages[1]
for feature, importance in zip(
columns_to_assemble, real_model.featureImportances
):
print("{} contributes {:0.3f}%".format(feature, importance * 100))# Code snippet result:
manufacturer_encoded contributes 5.589%
cylinders contributes 8.920%
displacement contributes 27.820%
horsepower contributes 23.499%
weight contributes 31.229%
acceleration contributes 2.944%
from pyspark.ml.feature import VectorAssembler, VectorIndexer
from pyspark.ml.regression import RandomForestRegressor
from pyspark.sql.functions import countDistinct
# Remove non-numeric columns.
df = df.drop("carname")
# Profile this DataFrame to get a good value for maxCategories.
grouped = df.agg(*(countDistinct(c) for c in df.columns))
grouped.show()
# Assemble all columns except mpg into a vector.
feature_columns = list(df.columns)
feature_columns.remove("mpg")
vector_assembler = VectorAssembler(
inputCols=feature_columns,
outputCol="features",
handleInvalid="skip",
)
assembled = vector_assembler.transform(df)
# From profiling the dataset, 15 is a good value for max categories.
indexer = VectorIndexer(
inputCol="features", outputCol="indexed", maxCategories=15
)
indexed = indexer.fit(assembled).transform(assembled)
# Build and train the model.
train_df, test_df = indexed.randomSplit([0.7, 0.3])
rf = RandomForestRegressor(
numTrees=50,
featuresCol="features",
labelCol="mpg",
)
rf_model = rf.fit(train_df)
# Get feature importances.
for feature, importance in zip(feature_columns, rf_model.featureImportances):
print("{} contributes {:0.3f}%".format(feature, importance * 100))
# Make predictions.
predictions = rf_model.transform(test_df).select("mpg", "prediction")# Code snippet result:
+----+----------+
| mpg|prediction|
+----+----------+
| 9.0|13.4075...|
|11.0|15.2570...|
|12.0|13.2448...|
|12.0|13.0404...|
|12.0|13.1712...|
|13.0|16.2509...|
|13.0|15.3789...|
|13.0|14.6230...|
|13.0|14.3115...|
|13.0|13.2955...|
+----+----------+
only showing top 10 rows
from pyspark.ml import Pipeline
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml.regression import RandomForestRegressor
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
# Add manufacturer name we will use as a string column.
first_word_udf = udf(lambda x: x.split()[0], StringType())
df = df.withColumn("manufacturer", first_word_udf(df.carname))
manufacturer_encoded = StringIndexer(
inputCol="manufacturer", outputCol="manufacturer_encoded"
)
encoded_df = manufacturer_encoded.fit(df).transform(df)
# Set up our main ML pipeline.
columns_to_assemble = [
"manufacturer_encoded",
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
]
vector_assembler = VectorAssembler(
inputCols=columns_to_assemble,
outputCol="features",
handleInvalid="skip",
)
# Define the model.
rf = RandomForestRegressor(
numTrees=20,
featuresCol="features",
labelCol="mpg",
)
# Run the pipeline.
pipeline = Pipeline(stages=[vector_assembler, rf])
# Hyperparameter search.
target_metric = "rmse"
paramGrid = (
ParamGridBuilder().addGrid(rf.numTrees, list(range(20, 100, 10))).build()
)
crossval = CrossValidator(
estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=RegressionEvaluator(
labelCol="mpg", predictionCol="prediction", metricName=target_metric
),
numFolds=2,
parallelism=4,
)
# Run cross-validation, and choose the best set of parameters.
model = crossval.fit(encoded_df)
real_model = model.bestModel.stages[1]
print("Best model has {} trees.".format(real_model.getNumTrees))# Code snippet result:
Best model has 80 trees.
from pyspark.ml import Pipeline
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml.regression import RandomForestRegressor
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
# Add manufacturer name we will use as a string column.
first_word_udf = udf(lambda x: x.split()[0], StringType())
df = df.withColumn("manufacturer", first_word_udf(df.carname))
manufacturer_encoded = StringIndexer(
inputCol="manufacturer", outputCol="manufacturer_encoded"
)
encoded_df = manufacturer_encoded.fit(df).transform(df)
# Set up our main ML pipeline.
columns_to_assemble = [
"manufacturer_encoded",
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
]
vector_assembler = VectorAssembler(
inputCols=columns_to_assemble,
outputCol="features",
handleInvalid="skip",
)
# Define the model.
rf = RandomForestRegressor(
numTrees=20,
featuresCol="features",
labelCol="mpg",
)
# Run the pipeline.
pipeline = Pipeline(stages=[vector_assembler, rf])
# Hyperparameter search.
target_metric = "rmse"
paramGrid = (
ParamGridBuilder().addGrid(rf.numTrees, list(range(20, 100, 10))).build()
)
crossval = CrossValidator(
estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=RegressionEvaluator(
labelCol="mpg", predictionCol="prediction", metricName=target_metric
),
numFolds=2,
parallelism=4,
)
# Run cross-validation, get metrics for each parameter.
model = crossval.fit(encoded_df)
# Plot results using matplotlib.
import matplotlib
parameter_grid = [
{k.name: v for k, v in p.items()} for p in model.getEstimatorParamMaps()
]
pdf = pandas.DataFrame(
model.avgMetrics,
index=[x["numTrees"] for x in parameter_grid],
columns=[target_metric],
)
ax = pdf.plot(style="*-")
ax.figure.suptitle("Hyperparameter Search: RMSE by Number of Trees")
ax.figure.savefig("hyperparameters.png")# Code snippet result:
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
label_column = "cover_type"
vector_assembler = VectorAssembler(
inputCols=df.columns,
outputCol="features",
handleInvalid="skip",
)
# Define the model.
rf = RandomForestClassifier(
numTrees=50,
featuresCol="features",
labelCol=label_column,
)
# Run the pipeline.
pipeline = Pipeline(stages=[vector_assembler, rf])
# Hyperparameter search.
paramGrid = (
ParamGridBuilder().addGrid(rf.numTrees, list(range(50, 80, 10))).build()
)
crossval = CrossValidator(
estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=MulticlassClassificationEvaluator(
labelCol=label_column, predictionCol="prediction"
),
numFolds=2,
parallelism=4,
)
# Run cross-validation and choose the best set of parameters.
model = crossval.fit(df)
# Identify the best hyperparameters.
real_model = model.bestModel.stages[1]
print("Best model has {} trees.".format(real_model.getNumTrees))from pyspark.ml.feature import VectorAssembler
from pyspark.ml.stat import Correlation
# Remove non-numeric columns.
df = df.drop("carname")
# Assemble all columns except mpg into a vector.
feature_columns = list(df.columns)
feature_columns.remove("mpg")
vector_col = "features"
vector_assembler = VectorAssembler(
inputCols=feature_columns,
outputCol=vector_col,
handleInvalid="skip",
)
df_vector = vector_assembler.transform(df).select(vector_col)
# Compute the correlation matrix.
matrix = Correlation.corr(df_vector, vector_col)
corr_array = matrix.collect()[0]["pearson({})".format(vector_col)].toArray()
# This part is just for pretty-printing.
pdf = pandas.DataFrame(
corr_array, index=feature_columns, columns=feature_columns
)# Code snippet result:
cylinders displacement horsepower ... acceleration modelyear origin
cylinders 1.000000 0.950823 0.842983 ... -0.504683 -0.345647 -0.568932
displacement 0.950823 1.000000 0.897257 ... -0.543800 -0.369855 -0.614535
horsepower 0.842983 0.897257 1.000000 ... -0.689196 -0.416361 -0.455171
weight 0.897527 0.932994 0.864538 ... -0.416839 -0.309120 -0.585005
acceleration -0.504683 -0.543800 -0.689196 ... 1.000000 0.290316 0.212746
modelyear -0.345647 -0.369855 -0.416361 ... 0.290316 1.000000 0.181528
origin -0.568932 -0.614535 -0.455171 ... 0.212746 0.181528 1.000000
[7 rows x 7 columns]
A few performance tips and tricks.
print(spark.sparkContext.version)# Code snippet result:
3.1.2
from pyspark import StorageLevel
from pyspark.sql.functions import lit
# Make some copies of the DataFrame.
df1 = df.where(lit(1) > lit(0))
df2 = df.where(lit(2) > lit(0))
df3 = df.where(lit(3) > lit(0))
print("Show the default storage level (NONE).")
print(df.storageLevel)
print("\nChange storage level to Memory/Disk via the cache shortcut.")
df1.cache()
print(df1.storageLevel)
print(
"\nChange storage level to the equivalent of cache using an explicit StorageLevel."
)
df2.persist(storageLevel=StorageLevel(True, True, False, True, 1))
print(df2.storageLevel)
print("\nSet storage level to NONE using an explicit StorageLevel.")
df3.persist(storageLevel=StorageLevel(False, False, False, False, 1))
print(df3.storageLevel)# Code snippet result:
Show the default storage level (NONE).
Serialized 1x Replicated
Change storage level to Memory/Disk via the cache shortcut.
Disk Memory Deserialized 1x Replicated
Change storage level to the equivalent of cache using an explicit StorageLevel.
Disk Memory Deserialized 1x Replicated
Set storage level to NONE using an explicit StorageLevel.
Serialized 1x Replicated
# rows is an iterable, e.g. itertools.chain
def number_in_partition(rows):
try:
first_row = next(rows)
partition_size = sum(1 for x in rows) + 1
partition_value = first_row.modelyear
print(f"Partition {partition_value} has {partition_size} records")
except StopIteration:
print("Empty partition")
df = df.repartition(20, "modelyear")
df.foreachPartition(number_in_partition)# Code snippet result:
Partition 82 has 31 records
Partition 76 has 34 records
Partition 77 has 28 records
Partition 80 has 29 records
Partition 81 has 29 records
Partition 70 has 29 records
Partition 72 has 55 records
Partition 78 has 36 records
Empty partition
Empty partition
Empty partition
Partition 75 has 30 records
Empty partition
Partition 71 has 68 records
Partition 79 has 29 records
Empty partition
Empty partition
Empty partition
Empty partition
Empty partition
from pyspark.sql.functions import col
# rows is an iterable, e.g. itertools.chain
def count_in_partition(rows):
my_years = set()
number_in_partition = 0
for row in rows:
my_years.add(row.modelyear)
number_in_partition += 1
seen_years = sorted(list(my_years))
if len(seen_years) > 0:
seen_values = ",".join(seen_years)
print(
f"This partition has {number_in_partition} records with years {seen_values}"
)
else:
print("Empty partition")
number_of_partitions = 5
df = df.repartitionByRange(number_of_partitions, col("modelyear"))
df.foreachPartition(count_in_partition)# Code snippet result:
This partition has 60 records with years 81,82
This partition has 62 records with years 76,77
This partition has 85 records with years 70,71,72
This partition has 97 records with years 73,74,75
This partition has 94 records with years 78,79,80
from pyspark.sql.functions import col
df = df.repartition(col("modelyear"))
number_of_partitions = 5
df = df.repartitionByRange(number_of_partitions, col("mpg"))# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
|15.0| 8| 383.0| 170.0| 3563.| 10.0| 70| 1|dodge c...|
|14.0| 8| 340.0| 160.0| 3609.| 8.0| 70| 1|plymout...|
|15.0| 8| 400.0| 150.0| 3761.| 9.5| 70| 1|chevrol...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
import math
target_partitions = math.ceil(df.rdd.getNumPartitions() / 2)
df = df.coalesce(target_partitions)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|18.0| 8| 307.0| 130.0| 3504.| 12.0| 70| 1|chevrol...|
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|18.0| 8| 318.0| 150.0| 3436.| 11.0| 70| 1|plymout...|
|16.0| 8| 304.0| 150.0| 3433.| 12.0| 70| 1|amc reb...|
|17.0| 8| 302.0| 140.0| 3449.| 10.5| 70| 1|ford to...|
|15.0| 8| 429.0| 198.0| 4341.| 10.0| 70| 1|ford ga...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 440.0| 215.0| 4312.| 8.5| 70| 1|plymout...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|15.0| 8| 390.0| 190.0| 3850.| 8.5| 70| 1|amc amb...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
# Default shuffle partitions is usually 200.
grouped1 = df.groupBy("cylinders").count()
print("{} partition(s)".format(grouped1.rdd.getNumPartitions()))
# Set the shuffle partitions to 20.
# This can reduce the number of files generated when saving DataFrames.
spark.conf.set("spark.sql.shuffle.partitions", 20)
grouped2 = df.groupBy("cylinders").count()
print("{} partition(s)".format(grouped2.rdd.getNumPartitions()))# Code snippet result:
200 partition(s)
20 partition(s)
df = (
spark.read.format("csv")
.option("header", True)
.load("data/auto-mpg.csv")
.sample(0.1)
)# Code snippet result:
+----+---------+------------+----------+------+------------+---------+------+----------+
| mpg|cylinders|displacement|horsepower|weight|acceleration|modelyear|origin| carname|
+----+---------+------------+----------+------+------------+---------+------+----------+
|15.0| 8| 350.0| 165.0| 3693.| 11.5| 70| 1|buick s...|
|14.0| 8| 454.0| 220.0| 4354.| 9.0| 70| 1|chevrol...|
|14.0| 8| 455.0| 225.0| 4425.| 10.0| 70| 1|pontiac...|
|14.0| 8| 455.0| 225.0| 3086.| 10.0| 70| 1|buick e...|
|24.0| 4| 113.0| 95.00| 2372.| 15.0| 70| 3|toyota ...|
|21.0| 6| 200.0| 85.00| 2587.| 16.0| 70| 1|ford ma...|
|19.0| 6| 250.0| 88.00| 3302.| 15.5| 71| 1|ford to...|
|23.0| 4| 122.0| 86.00| 2220.| 14.0| 71| 1|mercury...|
|30.0| 4| 79.00| 70.00| 2074.| 19.5| 71| 2|peugeot...|
|25.0| 4| 97.50| 80.00| 2126.| 17.0| 72| 1|dodge c...|
+----+---------+------------+----------+------+------------+---------+------+----------+
only showing top 10 rows
print(spark.sparkContext.getConf().getAll())# Code snippet result:
[('spark.driver.memory', '2G'), ('spark.files', 'file:///Users/cshankli/.ivy2/jars/io.delta_delta-core_2.12-1.0.0.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr4-4.7.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr4-runtime-4.7.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr-runtime-3.5.2.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_ST4-4.0.8.jar,file:///Users/cshankli/.ivy2/jars/org.abego.treelayout_org.abego.treelayout.core-1.0.3.jar,file:///Users/cshankli/.ivy2/jars/org.glassfish_javax.json-1.0.4.jar,file:///Users/cshankli/.ivy2/jars/com.ibm.icu_icu4j-58.2.jar'), ('spark.jars', 'file:///Users/cshankli/.ivy2/jars/io.delta_delta-core_2.12-1.0.0.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr4-4.7.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr4-runtime-4.7.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr-runtime-3.5.2.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_ST4-4.0.8.jar,file:///Users/cshankli/.ivy2/jars/org.abego.treelayout_org.abego.treelayout.core-1.0.3.jar,file:///Users/cshankli/.ivy2/jars/org.glassfish_javax.json-1.0.4.jar,file:///Users/cshankli/.ivy2/jars/com.ibm.icu_icu4j-58.2.jar'), ('spark.driver.port', '60367'), ('spark.executor.memory', '2G'), ('spark.repl.local.jars', 'file:///Users/cshankli/.ivy2/jars/io.delta_delta-core_2.12-1.0.0.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr4-4.7.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr4-runtime-4.7.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr-runtime-3.5.2.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_ST4-4.0.8.jar,file:///Users/cshankli/.ivy2/jars/org.abego.treelayout_org.abego.treelayout.core-1.0.3.jar,file:///Users/cshankli/.ivy2/jars/org.glassfish_javax.json-1.0.4.jar,file:///Users/cshankli/.ivy2/jars/com.ibm.icu_icu4j-58.2.jar'), ('spark.executor.id', 'driver'), ('spark.app.startTime', '1630704028858'), ('spark.submit.pyFiles', '/Users/cshankli/.ivy2/jars/io.delta_delta-core_2.12-1.0.0.jar,/Users/cshankli/.ivy2/jars/org.antlr_antlr4-4.7.jar,/Users/cshankli/.ivy2/jars/org.antlr_antlr4-runtime-4.7.jar,/Users/cshankli/.ivy2/jars/org.antlr_antlr-runtime-3.5.2.jar,/Users/cshankli/.ivy2/jars/org.antlr_ST4-4.0.8.jar,/Users/cshankli/.ivy2/jars/org.abego.treelayout_org.abego.treelayout.core-1.0.3.jar,/Users/cshankli/.ivy2/jars/org.glassfish_javax.json-1.0.4.jar,/Users/cshankli/.ivy2/jars/com.ibm.icu_icu4j-58.2.jar'), ('spark.sql.extensions', 'io.delta.sql.DeltaSparkSessionExtension'), ('spark.rdd.compress', 'True'), ('spark.app.initial.file.urls', 'file:///Users/cshankli/.ivy2/jars/org.glassfish_javax.json-1.0.4.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr-runtime-3.5.2.jar,file:///Users/cshankli/.ivy2/jars/org.abego.treelayout_org.abego.treelayout.core-1.0.3.jar,file:///Users/cshankli/.ivy2/jars/com.ibm.icu_icu4j-58.2.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr4-4.7.jar,file:///Users/cshankli/.ivy2/jars/io.delta_delta-core_2.12-1.0.0.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_antlr4-runtime-4.7.jar,file:///Users/cshankli/.ivy2/jars/org.antlr_ST4-4.0.8.jar'), ('spark.serializer.objectStreamReset', '100'), ('spark.master', 'local[*]'), ('spark.submit.deployMode', 'client'), ('spark.app.id', 'local-1630704029931'), ('spark.app.initial.jar.urls', 'spark://192.168.1.197:60367/jars/io.delta_delta-core_2.12-1.0.0.jar,spark://192.168.1.197:60367/jars/org.antlr_antlr-runtime-3.5.2.jar,spark://192.168.1.197:60367/jars/com.ibm.icu_icu4j-58.2.jar,spark://192.168.1.197:60367/jars/org.abego.treelayout_org.abego.treelayout.core-1.0.3.jar,spark://192.168.1.197:60367/jars/org.antlr_antlr4-4.7.jar,spark://192.168.1.197:60367/jars/org.glassfish_javax.json-1.0.4.jar,spark://192.168.1.197:60367/jars/org.antlr_antlr4-runtime-4.7.jar,spark://192.168.1.197:60367/jars/org.antlr_ST4-4.0.8.jar'), ('spark.driver.host', '192.168.1.197'), ('spark.app.name', 'cheatsheet'), ('spark.ui.showConsoleProgress', 'true'), ('spark.sql.warehouse.dir', 'file:///Users/cshankli/git/pyspark-cheatsheet/spark_warehouse'), ('spark.jars.packages', 'io.delta:delta-core_2.12:1.0.0'), ('spark.sql.catalog.spark_catalog', 'org.apache.spark.sql.delta.catalog.DeltaCatalog')]
key = "spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version"
value = 2
# Wrong! Settings cannot be changed this way.
# spark.sparkContext.getConf().set(key, value)
# Correct.
spark.conf.set(key, value)
# Alternatively: Set at build time.
# Some settings can only be made at build time.
spark_builder = SparkSession.builder.appName("My App")
spark_builder.config(key, value)
spark = spark_builder.getOrCreate()# Memory configuration depends entirely on your runtime.
# In OCI Data Flow you control memory by selecting a larger or smaller VM.
# No other configuration is needed.
#
# For other environments see the Spark "Cluster Mode Overview" to get started.
# https://spark.apache.org/docs/latest/cluster-overview.html
# And good luck!