.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 …

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 …

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 …

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 …

Jul 3, 2021 · Warning : Try using .loc [row_indexer,col_indexer] = value instead [duplicate] Asked 4 years, 4 months ago Modified 3 years, 8 months ago Viewed 27k times

Does anyone know if it is possible to use the DataFrame.loc method to select from a MultiIndex? I have the following DataFrame and would like to be able to access the values located in the …