# Petal Power Inventory Doubt

Hello, I want to know what is the difference between both codes, one is the one I made and the other one is the one shown is the walkthrough video.

inventory[“total_value”] = inventory.price * inventory.quantity

inventory[“total_value”] = inventory.apply(lambda row: row.price * row.quantity, axis=1)

Hi there.

They both do the same thing.

`inventory['total_value'] = inventory.price * inventory.quantity` creates a new column, setting each value in the column to the value of `inventory.price * inventory.quantity` for each row.

`inventory['total_value'] = inventory.apply(lambda row: row.price * row.quantity, axis=1)` is telling Pandas to create the new column `total_value`, and then to apply the `lambda` function along the column. (`axis=1` means apply the function to the column, so it calculates `price * quantity` for each row in the column.)

Not sure if my explanation will help, but essentially they both will have the same result - they’re just different ways of getting there.

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I had this same question for the inventory[‘in_stock’] column. (I did the same total_value as you, OP, and came here to see if there was a difference in my code.)
Is there a tangible difference, rather than style, between my first code and the instructor’s code? Is lambda more efficient?
My code:
`inventory['in_stock'] = inventory.quantity.apply(lambda x: True if x>0 else False)`
Video Tutorial Code:
`inventory['in_stock'] = inventory.apply(lambda row: True if row.quantity >0 else False, axis = 1)`
They yielded the same result.

I too ran into this same question for the inventory[‘full_description’] column.

My code:

``````inventory['full_description'] = inventory.product_type + ' - ' + inventory.product_description
``````

Tutorial Code:

``````combine_lambda = lambda row: \
'{} - {}'.format(row.product_type,
row.product_description)

inventory['full_description'] = inventory.apply(combine_lambda, axis=1)
``````

If they do in fact yield the same result why would we ever want to use the latter?

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