.loc and .iloc are used for indexing, i.e., to pull out portions of data. In essence, the difference is that .loc allows label-based indexing, while .iloc allows position-based indexing.

Why do we use loc for pandas dataframes? it seems the following code with or without using loc both compiles and runs at a similar speed: %timeit df_user1 = df.loc[df.user_id=='5561'] 100 …

Feb 27, 2019 · Also, while where is only for conditional filtering, loc is the standard way of selecting in Pandas, along with iloc. loc uses row and column names, while iloc uses their …

In general, you should use loc for label-based assignment, and iloc for integer/positional based assignment, as the spec guarantees that they always operate on the original. Additionally, for …

Jan 17, 2017 · i want to have 2 conditions in the loc function but the && or and operators dont seem to work.: df: business_id ratings review_text xyz 2 'very bad' xyz 1 '

I saw this code in someone's iPython notebook, and I'm very confused as to how this code works. As far as I understood, pd.loc[] is used as a location based indexer where the format is: …

Oct 13, 2019 · But using .loc should be sufficient as it guarantees the original dataframe is modified. If I add new columns to the slice, I would simply expect the original df to have …

I've been exploring how to optimize my code and ran across pandas .at method. Per the documentation Fast label-based scalar accessor Similarly to loc, at provides label based …

There seems to be a difference between df.loc [] and df [] when you create dataframe with multiple columns. You can refer to this question: Is there a nice way to generate multiple …

Jan 1, 2023 · As far as I understood it is preferable to update the column values using .loc method rather than slicing [], because of the potential SettingWithCopy issue. When I use slicing I …