evaluate mixed type with eval() - python-2.7

Question

I have two date/time variables that contains list of the date/time values and another variable containing the list of operator to operate over the date/time variables. The format can be expressed as follows:
column1 = np.array([date1, date2,.......,dateN])
column2 = np.array([date1, date2,.......,dateN])
Both of the above variables of type Date/Time. Then I have the following variable operator that has the same length of column1 and column2:
operator = np.array(['>=','<=','==','=!',......])
I am getting "Invalid Token" with the following operation:
np.array([eval('{}{}{}'.format(v1,op,v2)) for v1,op,v2 in zip(column1,operator,column2)])
Any hint to get around this issue ?
-------------------EDIT----------------------
With some sample data and without eval I get the following output:
np.array(['{} {} {}'.format(v1,op,v2) for v1,op,v2 in zip(datelist1,operator,datelist2)])
array(['2017-03-30 10:30:22.928000 <= 2012-05-23 00:00:00',
'2011-01-07 00:00:00 == 2017-03-30 10:31:14.477000'],
dtype='|S49')
Once I bring in eval(), I get the following error:
eval('2011-01-07 00:00:00 == 2017-03-30 10:31:14.477000')
File "<string>", line 1
2011-01-07 00:00:00 == 2017-03-30 10:31:14.477000
^
SyntaxError: invalid syntax
----------------------EDIT & CORRECTIONS ----------------------------
Date/Time variables that I mentioned before are basically of type numpy datetime64 type and I am now getting the following issue while trying two date comparions with eval:
np.array([(repr(d1)+op+repr(d2)) for d1,op,d2 in zip(${Column Name1},${Operator},${Column Name2})])
The above snippet is tried over a table with three columns where ${Column Name1} and ${Column Name2} is of numpy.datetime64 type and ${Operator} is of string type. The result is as follows for one of the rows:
numpy.datetime64('2014-08-13T02:00:00.000000+0200')>=numpy.datetime64('2014-08-13T02:00:00.000000+0200')
Now I want to evaluate the above expression with function eval as follows:
np.array([eval(repr(d1)+op+repr(d2)) for d1,op,d2 in zip(${Column Name1},${Operator},${Column Name2})])
Eventually I get the following error:
NameError:name 'numpy' is not defined
I can assume the problem. The Open Source Tool that I am using is importing numpy as np whereas repr() returning numpy that it does not recognize. If this is the problem , how to fix this issue ?

Answer 1

datetime objects can be compared:
In [506]: datetime.datetime.today()
Out[506]: datetime.datetime(2017, 3, 30, 10, 43, 18, 363747)
In [507]: t1=datetime.datetime.today()
In [508]: t2=datetime.datetime.today()
In [509]: t1 < t2
Out[509]: True
In [510]: t1 == t2
Out[510]: False
Numpy's own version of datetime objects can also be compared
In [516]: nt1 = np.datetime64('2017-03-30 10:30:22.928000')
In [517]: nt2 = np.datetime64('2017-03-30 10:31:14.477000')
In [518]: nt1 < nt2
Out[518]: True
In [519]: nt3 = np.datetime64('2012-05-23 00:00:00')
In [520]: [nt1 <= nt2, nt2==nt3]
Out[520]: [True, False]
Using the repr string version of a datetime object works:
In [524]: repr(t1)+'<'+repr(t2)
Out[524]: 'datetime.datetime(2017, 3, 30, 10, 47, 29, 69324)<datetime.datetime(2017, 3, 30, 10, 47, 33, 669494)'
In [525]: eval(repr(t1)+'<'+repr(t2))
Out[525]: True
Not that I recommend that sort of construction. I like the dictionary mapping to an operator better.

Answer 2

You might want to use python operator for this:
# import operators used
import operator
from operator import ge, eq, le, ne
# build a look up table from string to operators
ops = {">=": ge, "==": eq, "<=": le, "!=": ne}
import numpy as np
# used some numbers to simplify the case, should work on datetime as well
a = np.array([1, 3, 5, 3])
b = np.array([2, 3, 2, 1])
operator = np.array(['>=','<=','==','!='])
# evaluate the operation
[ops[op](x, y) for op, x, y in zip(operator, a, b)]
# [False, True, False, True]