How to check if one datetime object is in a list of datetime objects?

Question

I am perplexed. I have a list of datetime.datetime objects and a matching datetime.datetime object to one of those in the list. I want to check the index of the selected object. I will not post the code that led to this, but the output should be illuminating enough Observe: This is the successful script:

import pandas as pd
import numpy as np
from datetime import datetime

def nearest(items, pivot):
    return min(items, key=lambda x: abs(x-pivot))

currTime = '20120329-224716'
DATE_TIME_FORMAT = '%Y%m%d-%H%M%S'

df = pd.DataFrame({'timeStamp' : ['20120329-224716', '20120329-223340']})
df.to_excel(path + "\\track.xlsx")

df1 = pd.read_excel(path + "\\track.xlsx")

time = df1["timeStamp"]

for idx, item in enumerate(time):
    time[idx] = datetime.strptime(item, DATE_TIME_FORMAT) 
t = nearest(time, datetime.strptime(currTime, DATE_TIME_FORMAT))

idx = np.where(time==t)[0][0]

print(idx)
# returns 0

print(type(t))
# <class 'datetime.datetime'>
print(type(time[0]))
# <class 'datetime.datetime'>

The following does not.

import pandas as pd
import datetime
import numpy as np

df = pd.DataFrame({'timeStamp' : ['20120329-224716', '20120329-223340']})


DATE_TIME_FORMAT = '%Y%m%d-%H%M%S'

times = df["timeStamp"].tolist()

for idx, item in enumerate(times):
    times[idx] = datetime.datetime.strptime(item, DATE_TIME_FORMAT)
t = datetime.datetime.strptime('20120329-224716', DATE_TIME_FORMAT)

print(t)
# 2012-03-29 22:47:16
print(type(t))
# <class 'datetime.datetime'>
print(times[0:1])
# [datetime.datetime(2012, 3, 29, 22, 47, 16), datetime.datetime(2012, 3, 29, 22, 33, 40)]
print(times[0])
# 2012-03-29 22:47:16
print(type(times[0]))
# <class 'datetime.datetime'>

# As you can see, the two objects are datetime.datetime and refer to the same date
print(np.where(times==t))
# (array([], dtype=int64),)
# It is empty, so the next line obviously does not work
idx = np.where(times==t)[0][0]
# IndexError: index 0 is out of bounds for axis 0 with size 0

t = datetime.datetime.strptime('20120329-224717', DATE_TIME_FORMAT)
idx = np.where(times==t)[0][0]

For a strange reason, this was downvoted even though I edited the document. Anyway, I solved the problem by keeping everything as strings. Still confused because the above code works with a different dataset.

Anyway, solution:

import pandas as pd
import datetime
import numpy as np

df = pd.DataFrame({'timeStamp' : ['20120329-224716', '20120329-223340']})


DATE_TIME_FORMAT = '%Y%m%d-%H%M%S'

times = df["timeStamp"].tolist()
print(times)
times_object = []

t = datetime.datetime.strptime('20120329-224716', DATE_TIME_FORMAT)
t = '20120329-224716'
idx = times.index(t)
print(idx)
# 0

Answer 1

In [5]: t
Out[5]: datetime.datetime(2012, 3, 29, 22, 47, 16)
In [6]: times
Out[6]: 
[datetime.datetime(2012, 3, 29, 22, 47, 16),
 datetime.datetime(2012, 3, 29, 22, 33, 40)]

Equality test on a list is a simple True/False, not an element by element test:

In [7]: times==t
Out[7]: False

A list comprehension works:

In [8]: [x==t for x in times]
Out[8]: [True, False]

We can now use nonzero to find the True values.

In [9]: np.nonzero(_)
Out[9]: (array([0]),)

The validity of a nonzero/where depends entirely on the conditional. times==t returns False, so obviously where can only return an empty find.

There are other ways of testing a list for items:

In [13]: t in times
Out[13]: True
In [14]: times.index(t)
Out[14]: 0

[14] would raise an error if [13] was False.