7 months ago
# Sure, let's consider that we have a CSV file with the following columns: Country, Health_Group, and Population. We will use PySpark to load the data, and then we will perform some basic analysis such as calculating the total population per country and per health group. # Please replace 'path_to_your_dataset.csv' with the actual path to your dataset. from pyspark.sql import SparkSession from pyspark.sql.functions import * # Create a SparkSession spark = SparkSession.builder.appName('health_group_analysis').getOrCreate() # Load the dataset df = spark.read.csv('path_to_your_dataset.csv', inferSchema=True, header=True) # Check the schema of the dataset df.printSchema() # Compute the total population per country total_population_per_country = df.groupBy('Country').agg(sum('Population').alias('Total_Population')).sort('Country') total_population_per_country.show() # Compute the total population per health group total_population_per_health_group = df.groupBy('Health_Group').agg(sum('Population').alias('Total_Population')).sort('Health_Group') total_population_per_health_group.show() # Stop the SparkSession spark.stop() # This is a simple analysis. Depending on the specific requirements of your task, you might need more complex queries or data preprocessing. # Running this code requires a Spark environment. If you're running this locally, you'll need to have Apache Spark installed and configured properly. If you're using a cloud-based service like Databricks or AWS EMR, these environments are already set up to run PySpark code.