Getting the location of indices missing from secondary DataFrame

Please examine the commented text in the code below in order to understand the problem.

import pandas as pd
import numpy as np

primary = pd.DataFrame(
    data = ['little','mary','had','a','swan'],
    index =pd.DatetimeIndex(['2015-09-25 12:00:00', 
                           '2015-09-25 13:00:00',
                           '2015-09-25 14:00:00',
                           '2015-09-25 15:00:00',
                           '2015-09-25 16:00:00']),
    columns=['some_nonsense'])

secondary = pd.DataFrame(
    data = ['mommy',np.nan],
    index =pd.DatetimeIndex(['2015-09-25 14:00:00',
                           '2015-09-25 15:00:00']),
    columns=['copy_me'])

# 1. secondary dataframe values have already been computed
# 2. we want to assign them to the primary dataframe for available dates
# 3. once done, we want to return dataframe index locations for missing values
# 4. nan is one of the valid values the secondary dataframe can take

primary['copy_me'] = secondary['copy_me']

print (secondary)
print (primary)

# The values have been copied successfully
# But how to get the locations of missing indices?
# The expected result is as follows:
# If I know these values I could pass them to my computing function

missing_indices = np.array([0,1,4])
print('needed result: ', missing_indices)

>Solution :

If I understand correctly, this might help:

(~primary.index.isin(secondary.index)).nonzero()[0]

Breakdown:

1. Find which primary indixes are present in secondary (primary.index.isin(secondary.index)).
2. Negate that condition (~).
3. Find positions where value is non-zero, meaning True, using numpy.nonzero. (.nonzero()[0], [0] because it returns a tuple)