2
$\begingroup$

My objective is to build an N-dimensional array in Python that has the mean of the intersection of a number of features.

For example, I have the number of customers in a store at any given time. I want to create a three dimensional array with axes [month, day_of_week, hour] where the values are the mean of the historical values.

One example data point, where the data covers several years but each axis here is just 12 months, 7 days, and 24 hours.

Array[January,Friday,1pm] = 3 customers on average

I've tried Pandas Groupby but I can't figure out how to translate that information from a Groupby object or Panel to a useful 3D array.

My closest attempt so far:

dataframe.groupby(["month","day_of_week","hour"])["count"].mean().unstack().to_panel()

This outputs a Panel object, but I can't find a way to translate this to just a 3D array.

Can anyone help me figure out a way to solve my problem and build this 3D array without for loops?

Improve this question
$\endgroup$
3
  • $\begingroup$ What is the exact format you want the data to be in? Is it like a dictionary - {(January,Friday,1pm): 3} for 3d plotting? $\endgroup$
    – Kiritee Gak
    Commented Apr 15, 2018 at 4:55
  • $\begingroup$ @KiriteeGak A numpy array, for 3D plotting and cluster analysis. The features wouldn't be strings, just the numerical indices. $\endgroup$
    – Jay Speidell
    Commented Apr 15, 2018 at 6:57
  • $\begingroup$ numpy is v weak for grouping. But using pandas to do the job instead is a mess. I'm also looking for a workable solution. $\endgroup$
    – WestCoastProjects
    Commented Mar 10, 2020 at 19:41

0

Reset to default

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Browse other questions tagged or ask your own question.