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I have a data Frame, and I am doing the following:

def calculate_planungsphase(audit, phase1, phase2):

datum_first_milestone = data_audit[(data_audit.Audit == audit) & (data_audit.Meilenstein == phase1)]
datum_second_milestone = data_audit[(data_audit.Audit == audit) & (data_audit.Meilenstein == phase2)]

print(datum_first_milestone['GeplantesErledigungsdatum'])
print(datum_second_milestone['GeplantesErledigungsdatum'])

print(datum_first_milestone['GeplantesErledigungsdatum'] - datum_second_milestone['GeplantesErledigungsdatum'])

The result of print(datum_first_milestone['GeplantesErledigungsdatum']) =

2018-01-01 Name: GeplantesErledigungsdatum, dtype: datetime64[ns]

The result of print(datum_second_milestone['GeplantesErledigungsdatum']) =

2018-01-02 Name: GeplantesErledigungsdatum, dtype: datetime64[ns]

The result of the difference calculation is:

0 NaT 1 NaT Name: GeplantesErledigungsdatum, dtype: timedelta64[ns

Why is the result of the calculation NaT? And why do i have two results, when I am doing only one calculation? (Index 0 and Index 1 = NaT)

Thank you for your help!

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1 Answer 1

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There is problem different index values, so in subtraction Series are not aligned.

Possible solution, if same size of both filtered Series, is create same index values:

datum_first_milestone.index = datum_second_milestone.index

Also solution should be simplify if need filter only column by loc + column name:

datum_first_milestone = data_audit.loc[(data_audit.Audit == audit) & (data_audit.Meilenstein == phase1), 'GeplantesErledigungsdatum']
datum_second_milestone = data_audit.loc[(data_audit.Audit == audit) & (data_audit.Meilenstein == phase2), 'GeplantesErledigungsdatum']

print(datum_first_milestone)
print(datum_second_milestone)

and if always is returned one value Series.item return scalars:

print (datum_first_milestone.item() - datum_second_milestone.item())

More general if there is one or more values is possible select first value for scalars:

print (datum_first_milestone.iat[0] - datum_second_milestone.iat[0])
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