Loading sales data to a CSV file

Loading data is an essential component of any data pipeline. It ensures that any data consumers and processes have reliable access to data that you've extracted and transformed earlier in a pipeline. In this exercise, you'll practice loading transformed sales data to a CSV file using pandas, which has been imported as pd. In addition to this, the raw data has been extracted and is available in the DataFrame raw_sales_data.

Deze oefening maakt deel uit van de cursus

ETL and ELT in Python

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Oefeninstructies

Filter the raw_sales_data DataFrame to only keep all items with a price less than 25 dollars.
Update the load() function to write the transformed sales data to a file named "transformed_sales_data.csv", making sure not include the index column.
Call the load() function on the cleaned Data Frame.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

def transform(raw_data):
	# Find the items prices less than 25 dollars
	return raw_data.loc[raw_data["Price Each"] ____ ____, ["Order ID", "Product", "Price Each", "Order Date"]]

def load(clean_data):
	# Write the data to a CSV file without the index column
	____.____("transformed_sales_data.csv", index=____)


clean_sales_data = transform(raw_sales_data)

# Call the load function on the cleaned DataFrame
____(____)

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