Computing the kurtosis value of a numeric column in a DataFrame in PySpark-kurtosis

The kurtosis function in PySpark aids in computing the kurtosis value of a numeric column in a DataFrame. Kurtosis gauges the “tailedness” of a data distribution, where higher values indicate heavier tails and a sharper peak, and lower values indicate lighter tails and a flatter peak relative to a normal distribution.

Example

from pyspark.sql import SparkSession
from pyspark.sql.functions import kurtosis

# Initialize SparkSession
spark = SparkSession.builder \
    .appName("KurtosisFunctionDemo") \
    .getOrCreate()

# Sample data
data = [(85,),
        (90,),
        (78,),
        (92,),
        (89,),
        (76,),
        (95,),
        (87,)]

# Define DataFrame
df = spark.createDataFrame(data, ["score"])

# Compute kurtosis of the scores
kurt_value = df.select(kurtosis(df["score"])).collect()[0][0]
print(f"Kurtosis of scores: {kurt_value:.2f}")

Output

Kurtosis of scores: -0.97

Benefits of using the kurtosis function:

1. Insightful Analysis: Offers deeper insights into data distribution, especially the extremities.
2. Performance: Swiftly computes kurtosis values across vast datasets, leveraging PySpark’s distributed processing capabilities.
3. Decision-making: Aids businesses in making informed decisions by understanding data behavior, especially in risk-prone sectors.
4. Comprehensive Data Studies: Acts as an essential statistical tool in conjunction with other measures like mean, variance, and skewness, providing a holistic view of data.

Where can we use kurtosis function:

1. Financial Analysis: To analyze financial data where extremes (both gains and losses) hold significance.
2. Quality Control: In industries, detecting outliers or abnormal behaviors in manufacturing processes.
3. Meteorological Studies: Observing unusual weather patterns by analyzing the “tailedness” of meteorological datasets.
4. Risk Management: Assessing the likelihood of rare and extreme events in various fields, from insurance to finance.

Spark important urls to refer

1. Spark Examples
2. PySpark Blogs
3. Bigdata Blogs
4. Spark Interview Questions
5. Official Page