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: df.loc[index,
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 loops, b...
If you get confused by .loc and .iloc, keep in mind that .iloc is based on the index (starting with i) position, while .loc is based on the label (starting with l).
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 lookups. You can
0 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 columns using .loc? Here, you can't generate multiple columns using df.loc[:,['name1','name2']] but you can do by just using double bracket df[['name1','name2']].
What is the difference between using loc and using just square brackets ...
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 number.
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 '