Filtering pandas DataFrames
Once data has been extracted from a source system, it's time to transform it! Often, source data may have more information than what is needed for downstream use cases. If this is the case, dimensionality should be reduced during the "transform" phase of the data pipeline.
pandas has been imported as pd, and the extract() function is available to load a DataFrame from the path that is passed.
Questo esercizio fa parte del corso
ETL and ELT in Python
Istruzioni dell'esercizio
- Use the
extract()function to load the DataFrame stored in the"sales_data.parquet"path. - Update the
transform()function to return all rows and columns with"Quantity Ordered"greater than 1. - Further filter the
clean_dataDataFrame to only include columns"Order Date","Quantity Ordered"and"Purchase Address". - Return the filtered DataFrame.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Extract data from the sales_data.parquet path
raw_sales_data = ____("sales_data.parquet")
def transform(raw_data):
# Only keep rows with `Quantity Ordered` greater than 1
clean_data = raw_data.____[____, :]
# Only keep columns "Order Date", "Quantity Ordered", and "Purchase Address"
clean_data = ____
# Return the filtered DataFrame
return ____
transform(raw_sales_data)