apache spark sql - Pyspark dataframe LIKE operator

Apache spark sql - Pyspark dataframe LIKE operator

In PySpark SQL, you can use the like() function to filter DataFrame columns using the SQL LIKE operator, which is commonly used to match patterns in strings. The like() function is available as part of the pyspark.sql.functions module.

Here's an example of how to use the like() function to filter DataFrame columns:

from pyspark.sql import SparkSession from pyspark.sql.functions import col # Create a SparkSession spark = SparkSession.builder \ .appName("PySpark DataFrame LIKE Operator") \ .getOrCreate() # Sample DataFrame data = [("John",), ("Alice",), ("Bob",)] df = spark.createDataFrame(data, ["name"]) # Filter DataFrame using LIKE operator filtered_df = df.filter(col("name").like("%o%")) # Show the resulting DataFrame filtered_df.show() # Output: # +----+ # |name| # +----+ # |John| # |Bob | # +----+

In this example:

  • We create a sample DataFrame df with a column named "name".
  • We use the like() function with the filter() method to filter rows where the "name" column contains the letter 'o'.
  • The like() function takes a pattern as an argument. The '%' symbol is a wildcard character that matches any sequence of characters.
  • The resulting DataFrame filtered_df contains only the rows where the "name" column contains the letter 'o'.

You can adjust the pattern in the like() function to match different patterns in the DataFrame column.

Examples

  1. PySpark SQL: Using LIKE Operator in DataFrame Filtering

    • Use the LIKE operator in SQL queries with wildcard characters.
    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("LikeOperatorExample").getOrCreate() # Create a sample DataFrame df = spark.createDataFrame([ (1, "Alice"), (2, "Bob"), (3, "Charlie"), (4, "David") ], ["id", "name"]) # Register the DataFrame as a temporary SQL table df.createOrReplaceTempView("people") # Use LIKE operator to find names starting with 'A' result = spark.sql("SELECT * FROM people WHERE name LIKE 'A%'") result.show()

  2. PySpark SQL: LIKE Operator with Wildcards

    • Demonstrate using different wildcard characters with the LIKE operator.
    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("LikeOperatorWildcards").getOrCreate() # Create a sample DataFrame df = spark.createDataFrame([ (1, "Ann"), (2, "Ben"), (3, "Anna"), (4, "Mike"), (5, "Jenny") ], ["id", "name"]) # Find names that end with 'n' result = df.filter(df.name.like("%n")) result.show()

  3. PySpark SQL: LIKE Operator with Case Sensitivity

    • Check case sensitivity when using the LIKE operator in PySpark.
    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("LikeOperatorCaseSensitivity").getOrCreate() # Create a sample DataFrame df = spark.createDataFrame([ (1, "alice"), (2, "Alice"), (3, "bob"), (4, "Bob") ], ["id", "name"]) # Check if LIKE operator is case-sensitive result = df.filter(df.name.like("A%")) # This should only find 'Alice' result.show()

  4. PySpark SQL: LIKE Operator with Escape Character

    • Use the escape character to search for a literal underscore or percent sign.
    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("LikeOperatorEscape").getOrCreate() # Create a sample DataFrame with special characters df = spark.createDataFrame([ (1, "John_Doe"), (2, "Jane_Doe"), (3, "Alice_Smith"), (4, "Bob.Jones") ], ["id", "name"]) # Find names containing a literal underscore result = df.filter(df.name.like("%\\_%")) result.show()

  5. PySpark SQL: LIKE Operator with Complex Patterns

    • Combine multiple wildcards to create complex patterns with the LIKE operator.
    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("LikeOperatorComplexPatterns").getOrCreate() # Create a sample DataFrame df = spark.createDataFrame([ (1, "Michael"), (2, "Michele"), (3, "Michelle"), (4, "Mick") ], ["id", "name"]) # Find names starting with 'Mich' and ending with 'le' result = df.filter(df.name.like("Mich%le")) result.show()

  6. PySpark SQL: LIKE Operator with Non-ASCII Characters

    • Use the LIKE operator with non-ASCII characters in PySpark.
    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("LikeOperatorNonASCII").getOrCreate() # Create a sample DataFrame with non-ASCII characters df = spark.createDataFrame([ (1, "René"), (2, "Søren"), (3, "Björk"), (4, "François") ], ["id", "name"]) # Find names containing the letter 'é' result = df.filter(df.name.like("%é%")) result.show()

  7. PySpark SQL: LIKE Operator with OR Condition

    • Combine the LIKE operator with OR conditions to filter DataFrame.
    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("LikeOperatorOrCondition").getOrCreate() # Create a sample DataFrame df = spark.createDataFrame([ (1, "Samuel"), (2, "Samson"), (3, "Samuelson"), (4, "Sam") ], ["id", "name"]) # Use LIKE with OR condition to find names starting with 'Sam' result = df.filter((df.name.like("Samuel%")) | (df.name.like("Samson%"))) result.show()

  8. PySpark SQL: LIKE Operator in UDFs

    • Use the LIKE operator within a User-Defined Function (UDF) in PySpark.
    from pyspark.sql import SparkSession from pyspark.sql.functions import udf from pyspark.sql.types import BooleanType spark = SparkSession.builder.appName("LikeOperatorInUDF").getOrCreate() # Create a sample DataFrame df = spark.createDataFrame([ (1, "Danielle"), (2, "Daniel"), (3, "Dan") ], ["id", "name"]) # Create a UDF to check if the name contains 'Daniel' def is_daniel(name): return "Daniel" in name is_daniel_udf = udf(is_daniel, BooleanType()) # Apply the UDF to filter the DataFrame result = df.filter(is_daniel_udf("name")) result.show()

  9. PySpark SQL: LIKE Operator in Complex SQL Queries

    • Use the LIKE operator in complex SQL queries with JOIN and WHERE clauses.
    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("LikeOperatorInComplexSQL").getOrCreate() # Create two sample DataFrames df1 = spark.createDataFrame([ (1, "Product_A"), (2, "Product_B"), (3, "Product_C") ], ["id", "product_name"]) df2 = spark.createDataFrame([ (1, "Order_101"), (2, "Order_102"), (3, "Order_103") ], ["order_id", "order_number"]) # Register DataFrames as SQL tables df1.createOrReplaceTempView("products") df2.createOrReplaceTempView("orders") # Use LIKE operator in a complex SQL query with JOIN and WHERE result = spark.sql(""" SELECT p.product_name, o.order_number FROM products p JOIN orders o ON p.id = o.order_id WHERE o.order_number LIKE 'Order_1%' """) result.show()

  10. PySpark SQL: LIKE Operator with DataFrame API

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