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I have a dataframe (df below) and i want to convert it to an numpy array. the variable mjd takes 1065 unique values and i was trying to get an array for each object_id all the flux corresponding to the different values of Mjd. thanks in advance for the help ! 

df.head()

Object_id    Mjd     flux         passband   
  615        59750   -544.81         0
  615        59750   -574.80         1
  615        59750    54             2
  615        59750    -44.810303     3
  615        59750    644.810303     4

Output : object_id X flux(mjd) 

   array[(615,passband = 1, flux(mjd1), flux(mjd2)..flux(mjd1065)),
    (615,passband = 2, flux(mjd1), flux(mjd2)..flux(mjd1065))
    (615,passband = 3, NaN, flux(mjd2)..NaN)..

    (4512,passband = 4, flux(mjd1), flux(mjd2)..flux(mjd1065))]

if no flux for the corresponding Mjd, NaN is inserted.

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  • Here are two possible links stackoverflow.com/questions/13187778/… stackoverflow.com/questions/49180018/… Commented Mar 4, 2019 at 15:23
  • thanks, already seen it, but not what i am looking for. df.to_records , df.values convert to a numpy but not to the structure i need. Commented Mar 4, 2019 at 15:32
  • What type of structure do you need?Do you want all Object_id fields that match be grouped together in a numpy array. Commented Mar 4, 2019 at 15:39
  • Yes you summarized all i want in one sentence ! Commented Mar 4, 2019 at 15:41
  • I was about to write some code up for you but I found this and perhaps if you looked through this is may help.stackoverflow.com/questions/52621497/… ... If this doesn't help let me know Commented Mar 4, 2019 at 15:59
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