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Finding the largest value among the list of columns provided using PySpark : greatest

   October 26, 2023
PySpark @ Freshers.in

This article presents a thorough exploration of the greatest function, supported by real-world examples. The greatest function in PySpark identifies the largest value among the list of columns provided. It returns the greatest value for each row.

Here’s a simple demonstration to find the greatest value among given columns:

from pyspark.sql import SparkSession
from pyspark.sql.functions import greatest
spark = SparkSession.builder \
    .appName("PySpark greatest Function") \
    .getOrCreate()
data = [(10, 20, 5), (15, 5, 30), (25, 25, 10)]
df = spark.createDataFrame(data, ["col1", "col2", "col3"])
df.withColumn("greatest_value", greatest(df["col1"], df["col2"], df["col3"])).show()

Output:

+----+----+----+--------------+
|col1|col2|col3|greatest_value|
+----+----+----+--------------+
|  10|  20|   5|            20|
|  15|   5|  30|            30|
|  25|  25|  10|            25|
+----+----+----+--------------+

Use case: Product sales analysis

Imagine an e-commerce platform that sells three products, and you wish to determine which product had the highest sales for each month:

sales_data = [
    ("January", 500, 700, 600),
    ("February", 650, 620, 750),
    ("March", 780, 770, 760)
]
df_sales = spark.createDataFrame(sales_data, ["Month", "Product_A", "Product_B", "Product_C"])
# Finding the product with maximum sales for each month
df_sales.withColumn("Highest_Sales", greatest(df_sales["Product_A"], df_sales["Product_B"], df_sales["Product_C"])).show()
Output
+---------+---------+---------+---------+------------+
|   Month |Product_A|Product_B|Product_C|Highest_Sales|
+---------+---------+---------+---------+------------+
| January |     500 |     700 |     600 |        700 |
|February |     650 |     620 |     750 |        750 |
|   March |     780 |     770 |     760 |        780 |
+---------+---------+---------+---------+------------+
This is used in 

Data Comparisons: When working with datasets that require row-wise comparisons across multiple columns, greatest becomes invaluable.

Data Cleaning: Sometimes, datasets contain multiple entries (like versions) for a single item. The greatest function can help determine the latest version or the most updated value.

Analytics: For scenarios involving analytics where you need to find peaks, maxima, or other highest values from multiple metrics, the greatest function is beneficial.

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