PySpark create new column with mapping from a dict

In PySpark, you can create a new column in a DataFrame by mapping values from a dictionary using the withColumn() function along with the udf (User Defined Function) feature. Here's how you can do it:

• Import the necessary modules:

from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType

• Create a Spark session:

spark = SparkSession.builder.appName("MappingExample").getOrCreate()

• Define the dictionary:

mapping_dict = {
    "key1": "value1",
    "key2": "value2",
    "key3": "value3"
}

• Create a DataFrame:

data = [("key1",), ("key2",), ("key3",)]
columns = ["key_column"]
df = spark.createDataFrame(data, columns)

• Define a UDF to perform the mapping:

def map_values(key):
    return mapping_dict.get(key, "default_value")

map_udf = udf(map_values, StringType())

• Use the withColumn() function to create a new column using the UDF:

df_with_mapped_column = df.withColumn("mapped_value", map_udf(df["key_column"]))
df_with_mapped_column.show()

Replace "default_value" with the value you want to assign if the key is not found in the dictionary.

• Stop the Spark session:

spark.stop()

Remember that when using UDFs, it's important to consider performance implications, as UDFs involve serialization and deserialization of data between Python and Spark's JVM. If possible, try to leverage built-in Spark functions for better performance.

Additionally, if your mapping dictionary is small and can be broadcasted, you might consider using pyspark.sql.functions.when() and pyspark.sql.functions.broadcast() for better performance. However, this approach is suitable only when the dictionary is relatively small.

Examples

1. "PySpark add new column with dictionary mapping example" Description: Demonstrates how to use a dictionary to create a new column in a PySpark DataFrame, mapping values based on keys.

   # Import necessary libraries
   from pyspark.sql.functions import lit, col
   
   # Sample DataFrame
   data = [("A", 1), ("B", 2), ("C", 3)]
   df = spark.createDataFrame(data, ["Key", "Value"])
   
   # Dictionary mapping
   mapping = {"A": "Apple", "B": "Banana", "C": "Cherry"}
   
   # Adding new column with mapping
   df = df.withColumn("Fruit", lit(mapping[col("Key")]))
   df.show()
   

2. "PySpark create new column based on dictionary values" Description: Shows how to create a new column in a PySpark DataFrame by mapping dictionary values to existing column values.

   # Import necessary libraries
   from pyspark.sql.functions import col, udf
   from pyspark.sql.types import StringType
   
   # Sample DataFrame
   data = [("A", 1), ("B", 2), ("C", 3)]
   df = spark.createDataFrame(data, ["Key", "Value"])
   
   # Dictionary mapping
   mapping = {"A": "Apple", "B": "Banana", "C": "Cherry"}
   
   # User-defined function to map values
   map_udf = udf(lambda key: mapping.get(key), StringType())
   
   # Adding new column with mapping
   df = df.withColumn("Fruit", map_udf(col("Key")))
   df.show()
   

3. "PySpark map dictionary to new column example" Description: Illustrates how to use a dictionary to map values from one column to create a new column in a PySpark DataFrame.

   # Import necessary libraries
   from pyspark.sql.functions import col
   
   # Sample DataFrame
   data = [("A", 1), ("B", 2), ("C", 3)]
   df = spark.createDataFrame(data, ["Key", "Value"])
   
   # Dictionary mapping
   mapping = {"A": "Apple", "B": "Banana", "C": "Cherry"}
   
   # Adding new column with mapping
   df = df.withColumn("Fruit", col("Key").map(mapping))
   df.show()
   

4. "PySpark create new column from dictionary values" Description: Demonstrates how to create a new column in a PySpark DataFrame by mapping dictionary values to existing column values.

   # Import necessary libraries
   from pyspark.sql.functions import col
   
   # Sample DataFrame
   data = [("A", 1), ("B", 2), ("C", 3)]
   df = spark.createDataFrame(data, ["Key", "Value"])
   
   # Dictionary mapping
   mapping = {"A": "Apple", "B": "Banana", "C": "Cherry"}
   
   # Adding new column with mapping
   df = df.withColumn("Fruit", col("Key").cast("string").rlike("|".join(mapping.keys())))
   df.show()
   

5. "PySpark add new column based on dictionary mapping" Description: Shows how to add a new column in a PySpark DataFrame by mapping values from a dictionary.

   # Import necessary libraries
   from pyspark.sql.functions import col
   
   # Sample DataFrame
   data = [("A", 1), ("B", 2), ("C", 3)]
   df = spark.createDataFrame(data, ["Key", "Value"])
   
   # Dictionary mapping
   mapping = {"A": "Apple", "B": "Banana", "C": "Cherry"}
   
   # Adding new column with mapping
   df = df.withColumn("Fruit", col("Key").map(mapping))
   df.show()
   

6. "PySpark create new column from dictionary" Description: Demonstrates how to create a new column in a PySpark DataFrame using values from a dictionary.

   # Import necessary libraries
   from pyspark.sql.functions import col
   
   # Sample DataFrame
   data = [("A", 1), ("B", 2), ("C", 3)]
   df = spark.createDataFrame(data, ["Key", "Value"])
   
   # Dictionary mapping
   mapping = {"A": "Apple", "B": "Banana", "C": "Cherry"}
   
   # Adding new column with mapping
   df = df.withColumn("Fruit", col("Key").map(mapping))
   df.show()
   

7. "PySpark create new column with dictionary mapping" Description: Shows how to add a new column to a PySpark DataFrame by mapping values from a dictionary.

   # Import necessary libraries
   from pyspark.sql.functions import col
   
   # Sample DataFrame
   data = [("A", 1), ("B", 2), ("C", 3)]
   df = spark.createDataFrame(data, ["Key", "Value"])
   
   # Dictionary mapping
   mapping = {"A": "Apple", "B": "Banana", "C": "Cherry"}
   
   # Adding new column with mapping
   df = df.withColumn("Fruit", col("Key").map(mapping))
   df.show()
   

8. "PySpark create new column from dictionary column" Description: Illustrates how to create a new column in a PySpark DataFrame by mapping values from a dictionary column.

   # Import necessary libraries
   from pyspark.sql.functions import col
   
   # Sample DataFrame
   data = [("A", 1), ("B", 2), ("C", 3)]
   df = spark.createDataFrame(data, ["Key", "Value"])
   
   # Dictionary column
   mapping_df = spark.createDataFrame([(key, mapping[key]) for key in mapping], ["Key", "Fruit"])
   
   # Adding new column with mapping
   df = df.join(mapping_df, "Key", "left").drop("Key").withColumnRenamed("Fruit", "New_Column")
   df.show()
   

9. "PySpark add new column based on dictionary" Description: Demonstrates how to add a new column in a PySpark DataFrame using values from a dictionary.

   # Import necessary libraries
   from pyspark.sql.functions import col
   
   # Sample DataFrame
   data = [("A", 1), ("B", 2), ("C", 3)]
   df = spark.createDataFrame(data, ["Key", "Value"])
   
   # Dictionary mapping
   mapping = {"A": "Apple", "B": "Banana", "C": "Cherry"}
   
   # Adding new column with mapping
   df = df.withColumn("Fruit", col("Key").map(mapping))
   df.show()
   

10. "PySpark create new column from dictionary values" Description: Shows how to create a new column in a PySpark DataFrame by mapping values from a dictionary.

    # Import necessary libraries
    from pyspark.sql.functions import col
    
    # Sample DataFrame
    data = [("A", 1), ("B", 2), ("C", 3)]
    df = spark.createDataFrame(data, ["Key", "Value"])
    
    # Dictionary mapping
    mapping = {"A": "Apple", "B": "Banana", "C": "Cherry"}
    
    # Adding new column with mapping
    df = df.withColumn("Fruit", col("Key").map(mapping))
    df.show()
    

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