Creating an Array of Values from a Dataframe in Python, Remove unnecessary items

Question

I have an array that currently looks like this:

DATA
Out[115]: 
array([ObjectId('5bf3e06e9a892068705d8415'), 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 2.0, 2.0, nan, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0,
       2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 0.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 2.0, 2.0, 0.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 0.0, 0.0, 0.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 0.0, 2.0, 2.0, 2.0, 2.0, 2.0,
       2.0, 0.0, 0.0, nan, nan, 0.9545454545454546, 352], dtype=object)

How do I remove the object ID at the beginning, all the nan values, and everything after 0.95?, So I am just left with the 2.0, 1.0 and 0.0 values?:

Below is what I want it to look like:

    DATA
    Out[115]:  
           [2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 0.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 0.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 0.0, 0.0, 0.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 0.0, 2.0, 2.0, 2.0, 2.0, 2.0,
           2.0, 0.0, 0.0]

Does anyone know how I can achieve this?

Answer 1

I suppose you want to do couple things there:

import pandas as pd

# in order to get rid of all the nan-s
res=DATA[~pd.isnull(DATA)]

# in order to further remove everyting as of 0.9545454545454546
res=res[:np.argwhere(res==0.9545454545454546)[0,0]]

# in order to skip first element
res=res[1:]

# alternatively - in order to skip particular element only (1d)
res=res[np.argwhere(res!=ObjectId('5bf3e06e9a892068705d8415')).ravel()]