.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.

df.loc[['B', 'A'], 'X'] B 3 A 1 Name: X, dtype: int64 Notice the dimensionality of the return object when passing arrays. i is an array as it was above, loc returns an object in which an index with those …

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 '

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 …

What is the SettingWithCopyWarning? To know how to deal with this warning, it is important to understand what it means and why it is raised in the first place. When filtering DataFrames, it is …

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 index …

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 scalar …

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 …

207 loc: only work on index iloc: work on position at: get scalar values. It's a very fast loc iat: Get scalar values. It's a very fast iloc Also, at and iat are meant to access a scalar, that is, a single …