use .loc vs not using .loc?

Question

Let's take a look at the following codes:

# First Code

temp = df_purchase_predictors[df_purchase_predictors['Brand'] == 1]
temp.loc[:, 'Revenue Brand 1'] = temp['Price_1'] * temp['Quantity']
temp

versus:

# Second Code

temp1 = df_purchase_predictors[df_purchase_predictors['Brand'] == 1] temp1['Revenue Brand 1'] = temp1['Price_1'] * temp1['Quantity'] temp1

The only difference is using loc or not.

and it returned exactly the same table.

is there any difference?

Answer 1

ChatGPT says:

"

The key difference between the two lines of code lies in how the target DataFrame is referenced:

temp.loc[:, 'Revenue Brand 1']:
- This explicitly selects all rows (:) and only the 'Revenue Brand 1' column. It is useful when you want to modify a specific column while keeping the structure of the DataFrame intact, avoiding the creation of new columns in some cases.
temp1['Revenue Brand 1']:
- This directly references (or creates) the 'Revenue Brand 1' column in temp1. If it doesn't exist, it will be created.

Both approaches work for creating a new column but loc can be more flexible in complex scenarios (like modifying slices).
"

Given this is a new column, 2. seems better than 1. to me.

use .loc vs not using .loc?

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