Skip to content

As a Data Engineer at an electronics e-commerce company, Voltmart, you have been requested by a peer Machine Learning team to clean the data containing the information about orders made last year. They are planning to further use this cleaned data to build a demand forecasting model. To achieve this, they have shared their requirements regarding the desired output table format.

An analyst shared a parquet file called "orders_data.parquet" for you to clean and preprocess.

You can see the dataset schema below along with the cleaning requirements:

orders_data.parquet

columndata typedescriptioncleaning requirements
order_datetimestampDate and time when the order was madeModify: Remove orders placed between 12am and 5am (inclusive); convert from timestamp to date
time_of_daystringPeriod of the day when the order was madeNew column containing (lower bound inclusive, upper bound exclusive): "morning" for orders placed 5-12am, "afternoon" for orders placed 12-6pm, and "evening" for 6-12pm
order_idlongOrder IDN/A
productstringName of a product orderedRemove rows containing "TV" as the company has stopped selling this product; ensure all values are lowercase
product_eandoubleProduct IDN/A
categorystringBroader category of a productEnsure all values are lowercase
purchase_addressstringAddress line where the order was made ("House Street, City, State Zipcode")N/A
purchase_statestringUS State of the purchase addressNew column containing: the State that the purchase was ordered from
quantity_orderedlongNumber of product units orderedN/A
price_eachdoublePrice of a product unitN/A
cost_pricedoubleCost of production per product unitN/A
turnoverdoubleTotal amount paid for a product (quantity x price)N/A
margindoubleProfit made by selling a product (turnover - cost)N/A

from pyspark.sql import (
    SparkSession,
    types,
    functions as F,
)

spark = (
    SparkSession
    .builder
    .appName('cleaning_orders_dataset_with_pyspark')
    .getOrCreate()
)
orders_data = spark.read.parquet('orders_data.parquet')
orders_data.toPandas().head()
orders_data = orders_data.withColumn(
    "time_of_day",
      F.when((F.hour('order_date') >= 0) & (F.hour('order_date') <= 5), 'night')
         .when((F.hour('order_date') >= 6) & (F.hour('order_date') <= 11), 'morning')
         .when((F.hour('order_date') >= 12) & (F.hour('order_date') <= 17), 'afternoon')
         .when((F.hour('order_date') >= 18) & (F.hour('order_date') <= 23), 'evening')
        # You can keep the otherwise statement as None to validate whether the conditions are exhaustive
         .otherwise(None)
    )
orders_data = orders_data.filter(~(F.col('time_of_day') == 'night')).withColumn('order_date',
        F.col('order_date').cast(types.DateType()))
orders_data = orders_data.filter(~(F.col('product').contains('TV')))
orders_data = orders_data.withColumn('product', F.lower(F.col('product')))
orders_data = orders_data.withColumn('category', F.lower(F.col('category')))
orders_data = (
    orders_data
    # First you split the purchase address by space (" ")
    .withColumn(
        'address_split',
        F.split('purchase_address', ' ')
    )
    # If you look at the address lines, you can see that the state abbreviation is always at the 2nd last position
    .withColumn(
        'purchase_state',
        F.col('address_split').getItem(F.size('address_split') - 2)
    )
    # Dropping address_split columns as it is a temporary technical column
    .drop('address_split')
)
orders_data.toPandas().head(25)
orders_data.write.mode("overwrite").parquet("orders_data_clean.parquet")