![I have a DataFrame very similar to your ser at . But with many dates in Date column. Every date in Dates has its value.Need to find for every Symbol a date in Dates with the maximum amount of rows. df.groupby( ['Symbol', 'Date']['Value'].count() -works!It seems, all I need is to add a idxmax( )... But no, it gives only one max value, but I need for every group.How to find a date with maximum rows in every Symbol group? - How do I use the MultiIndex in pandas?](https://img.youtube.com/vi/tcRGa2soc-c/mqdefault.jpg)
I have a DataFrame very similar to your ser at . But with many dates in Date column. Every date in Dates has its value.Need to find for every Symbol a date in Dates with the maximum amount of rows. df.groupby( ['Symbol', 'Date']['Value'].count() -works!It seems, all I need is to add a idxmax( )... But no, it gives only one max value, but I need for every group.How to find a date with maximum rows in every Symbol group?
- How to combine 2 multiindex columns in one but in different rows?So first row will be AAPL(combined) - Close - Volume. Second row AAPL 2016-10-03 - Close Volume. Third row AAPL 2016-10-04 - Close - Volume and so on???
,,528.55,528.55,528.45,528.45
,,528.40,528.45,528.40,528.45
,,528.40,528.40,528.35,528.40
,,528.45,528.45,528.40,528.40I am trying to do a day HIGH/LOW studies on 1-min S&P futures data.I have 2 questions.1) How could I print the day HIGH / LOW for each day2) How could I print the day HIGH / LOW for the first hour of tradingI've tried the followings:df.groupby(['date']).high.max()and got thisdate1982-04-21 529.801982-04-22 530.601982-04-23 531.951982-04-26 532.80I am kind of stuck here. I want to have the output displayed all in 2 lines day by day like the following.Date, Time, HighDate, Time, LowCould you please help ? thanks a lot in advance. I appreciate your time.data typedate datetime64[ns]time datetime64[ns]open float64high float64low float64close float64dtype: [email protected]
dunno what version you are using but for me with pandas 1.1.3 out[21] does not work. I have to use loc[pd.IndexSlice[:,'key'], :] instead or xs('key', level = 1). Though i would prefer the easier way .loc[:, 'key'] but that does not work...
My feelings expressed in one sentence:
You saved my life with slice(None) (). Thank you so much.
In case anyone just came across this video, now there is a more intuitive way to do multiindex slicing with pd.IndexSlice!The following code would be equivalent to what is shown in the video:stocks.loc[pd.IndexSlice[:, ['2016-10-03', '2016-10-04']], :]
