Manual feature extraction I
You want to compare prices for specific products between stores. The features in the pre-loaded dataset sales_df are: storeID, product, quantity and revenue. The quantity and revenue features tell you how many items of a particular product were sold in a store and what the total revenue was. For the purpose of your analysis it's more interesting to know the average price per product.
Deze oefening maakt deel uit van de cursus
Dimensionality Reduction in Python
Oefeninstructies
- Calculate the product price from the quantity sold and total revenue.
- Drop the quantity and revenue features from the dataset.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Calculate the price from the quantity sold and revenue
sales_df['price'] = ____
# Drop the quantity and revenue features
reduced_df = sales_df.drop(____, axis=1)
print(reduced_df.head())