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Converting Types

If you need to go from a number back to a string, use the .astype() method:

df["customer_id"] = df["customer_id"].astype(str)

This is especially useful when you have a column with mixed types: the astype() call will convert any individual number rows into strings.

Also, remember that sometimes conversion isn't straightforward and needs to start with some basic cleanup. For example, say we need to remove a % sign and then convert from a string to a number... we can use str.replace() before using pd.to_numeric():

df["discount"] = df["discount"].str.replace("%", "", regex=False)
df["discount"] = pd.to_numeric(df["discount"], errors="coerce")

Assignment

SnackStack's accounting team has discount data in mixed percentage/decimal formats like "20.0%" and "0.15". The team needs to sort products by the biggest discounts but can't because of all the mixed types.

Complete the normalize_discounts function.

Create a copy of the DataFrame to avoid modifying the original.
Ensure all rows in the discount column are strings, converting any that aren't.
Create and save a boolean mask identifying rows whose discount contains a "%".
Remove all "%" characters.
Convert the cleaned values back to floats and save them in a new column called cleaned_discount.
Use the boolean mask with .loc[] to divide only the percent-style values in cleaned_discount by 100. Leave existing decimals like 0.15 unchanged.
Sort the DataFrame by cleaned_discount in descending order and return the result.