apache spark sql - to change a column of month numbers in a dataframe to month name (pyspark)

Apache spark sql - to change a column of month numbers in a dataframe to month name (pyspark)

In Apache Spark SQL with PySpark, you can convert a column of month numbers into month names using the month() function and withColumn() transformation. Here's how you can achieve this:

1. Setup

Assume you have a DataFrame df with a column month_number containing integers representing months (1 for January, 2 for February, etc.).

2. Define Mapping from Month Number to Month Name

First, define a mapping dictionary or function to convert month numbers to month names.

# Mapping dictionary from month number to month name month_map = { 1: "January", 2: "February", 3: "March", 4: "April", 5: "May", 6: "June", 7: "July", 8: "August", 9: "September", 10: "October", 11: "November", 12: "December" } # Function to get month name from month number def get_month_name(month_number): return month_map.get(month_number, "Invalid Month")

3. Convert Month Number to Month Name in DataFrame

Use the withColumn() function along with udf (User Defined Function) to apply the conversion logic to create a new column with month names.

Using udf (User Defined Function):

from pyspark.sql.functions import col, udf from pyspark.sql.types import StringType # Register the UDF month_name_udf = udf(lambda month_number: month_map.get(month_number, "Invalid Month"), StringType()) # Convert month number to month name df = df.withColumn("month_name", month_name_udf(col("month_number")))

Using withColumn() with when() and otherwise():

Alternatively, you can use Spark SQL's when() and otherwise() functions to achieve the same result without udf:

from pyspark.sql.functions import col, when # Convert month number to month name using when() and otherwise() df = df.withColumn("month_name", when(col("month_number") == 1, "January") .when(col("month_number") == 2, "February") .when(col("month_number") == 3, "March") .when(col("month_number") == 4, "April") .when(col("month_number") == 5, "May") .when(col("month_number") == 6, "June") .when(col("month_number") == 7, "July") .when(col("month_number") == 8, "August") .when(col("month_number") == 9, "September") .when(col("month_number") == 10, "October") .when(col("month_number") == 11, "November") .when(col("month_number") == 12, "December") .otherwise("Invalid Month"))

4. Display the Transformed DataFrame

df.show()

Summary:

  • Mapping: Define a mapping from month numbers to month names using a dictionary or a function.
  • UDF: Use udf for more complex mappings or when() and otherwise() for simpler mappings directly within Spark SQL functions.
  • Apply: Use withColumn() to apply the transformation and create a new column in the DataFrame.

By following these steps, you can effectively convert a column of month numbers into month names in a PySpark DataFrame using Apache Spark SQL functions. Adjust the mapping and transformation logic as per your specific requirements and dataset characteristics.

Examples

  1. PySpark convert month number to month name

    • Description: How to transform a column containing month numbers into month names in a PySpark DataFrame.
    • Code:
      from pyspark.sql import functions as F # Sample DataFrame with a column 'month' df = spark.createDataFrame([(1,), (2,), (3,), (4,), (5,), (6,), (7,), (8,), (9,), (10,), (11,), (12,)], ['month']) # Mapping month numbers to month names month_names = {1: 'January', 2: 'February', 3: 'March', 4: 'April', 5: 'May', 6: 'June', 7: 'July', 8: 'August', 9: 'September', 10: 'October', 11: 'November', 12: 'December'} # Adding a new column with month names df = df.withColumn('month_name', F.udf(lambda x: month_names[x], StringType())(df['month'])) df.show()

  2. PySpark SQL convert month number to month name

    • Description: Using Spark SQL to convert a column of month numbers into month names in a PySpark DataFrame.
    • Code:
      from pyspark.sql import SparkSession spark = SparkSession.builder \ .appName("MonthNameConversion") \ .getOrCreate() # Sample DataFrame with a column 'month' df = spark.createDataFrame([(1,), (2,), (3,), (4,), (5,), (6,), (7,), (8,), (9,), (10,), (11,), (12,)], ['month']) df.createOrReplaceTempView("months") # Using Spark SQL to convert month numbers to names df_with_names = spark.sql(""" SELECT month, CASE WHEN month = 1 THEN 'January' WHEN month = 2 THEN 'February' WHEN month = 3 THEN 'March' WHEN month = 4 THEN 'April' WHEN month = 5 THEN 'May' WHEN month = 6 THEN 'June' WHEN month = 7 THEN 'July' WHEN month = 8 THEN 'August' WHEN month = 9 THEN 'September' WHEN month = 10 THEN 'October' WHEN month = 11 THEN 'November' WHEN month = 12 THEN 'December' END AS month_name FROM months """) df_with_names.show()

  3. PySpark DataFrame add month name column

    • Description: Adding a new column with month names based on month numbers in a PySpark DataFrame.
    • Code:
      from pyspark.sql import functions as F # Sample DataFrame with a column 'month' df = spark.createDataFrame([(1,), (2,), (3,), (4,), (5,), (6,), (7,), (8,), (9,), (10,), (11,), (12,)], ['month']) # Mapping month numbers to month names month_names = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] # Adding a new column with month names df = df.withColumn('month_name', F.udf(lambda x: month_names[x-1], StringType())(df['month'])) df.show()

  4. PySpark convert month number to month abbreviation

    • Description: Converting month numbers into month abbreviations (e.g., Jan, Feb) in a PySpark DataFrame.
    • Code:
      from pyspark.sql import functions as F # Sample DataFrame with a column 'month' df = spark.createDataFrame([(1,), (2,), (3,), (4,), (5,), (6,), (7,), (8,), (9,), (10,), (11,), (12,)], ['month']) # Mapping month numbers to month abbreviations month_abbr = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'] # Adding a new column with month abbreviations df = df.withColumn('month_abbr', F.udf(lambda x: month_abbr[x-1], StringType())(df['month'])) df.show()

  5. PySpark SQL convert month number to month name using CASE

    • Description: Using a CASE statement in Spark SQL to convert month numbers into month names in a PySpark DataFrame.
    • Code:
      from pyspark.sql import SparkSession spark = SparkSession.builder \ .appName("MonthNameConversion") \ .getOrCreate() # Sample DataFrame with a column 'month' df = spark.createDataFrame([(1,), (2,), (3,), (4,), (5,), (6,), (7,), (8,), (9,), (10,), (11,), (12,)], ['month']) df.createOrReplaceTempView("months") # Using Spark SQL with CASE to convert month numbers to names df_with_names = spark.sql(""" SELECT month, CASE month WHEN 1 THEN 'January' WHEN 2 THEN 'February' WHEN 3 THEN 'March' WHEN 4 THEN 'April' WHEN 5 THEN 'May' WHEN 6 THEN 'June' WHEN 7 THEN 'July' WHEN 8 THEN 'August' WHEN 9 THEN 'September' WHEN 10 THEN 'October' WHEN 11 THEN 'November' WHEN 12 THEN 'December' END AS month_name FROM months """) df_with_names.show()

  6. PySpark DataFrame change month number to month name with map function

    • Description: Using the map function in PySpark to convert month numbers into month names in a DataFrame.
    • Code:
      from pyspark.sql import functions as F # Sample DataFrame with a column 'month' df = spark.createDataFrame([(1,), (2,), (3,), (4,), (5,), (6,), (7,), (8,), (9,), (10,), (11,), (12,)], ['month']) # Mapping month numbers to month names month_names = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] # Adding a new column with month names using map function df = df.withColumn('month_name', F.expr("CASE month " + " ".join([f"WHEN {i+1} THEN '{name}'" for i, name in enumerate(month_names)]) + " END")) df.show()

More Tags

sourcetree ear proto mindate ibm-cloud database-design onclicklistener read.table angular2-pipe oracle-manageddataaccess

More Programming Questions

More Electronics Circuits Calculators

More Organic chemistry Calculators

More Trees & Forestry Calculators

More Statistics Calculators