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Consider the following df:

d = {'.KS200': {datetime.date(2016, 10, 3): nan, datetime.date(2016, 10, 4): 259.18, datetime.date(2016, 10, 5): 258.99, datetime.date(2016, 10, 6): 261.13, datetime.date(2016, 10, 7): 260.06}, '0001.HK': {datetime.date(2016, 10, 3): 99.45, datetime.date(2016, 10, 4): 99.45, datetime.date(2016, 10, 5): 99.25, datetime.date(2016, 10, 6): 98.7, datetime.date(2016, 10, 7): 98.0}}

df = pd.DataFrame.from_dict(d)
print(df)

            .KS200  0001.HK
2016-10-03    7.00    99.45
2016-10-04  259.18    99.45
2016-10-05  258.99    99.25
2016-10-06  261.13    98.70
2016-10-07  260.06    98.00

Now if I try to index:

df.loc['2016-10-03']

I get a raise KeyError(key) from err

Oddly enough when I print:

print(df.index) I get dtype=object instead of datetime so is it a datetime or is it an object my index ???

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  • 2
    It's not a string: it's a date. Use a datetime object to index it. You explicitly set the values to datetime dates in your first code line even. Commented Nov 16, 2021 at 17:38
  • I am printing a portion of a large dataset using to_dict() , and it prints datetime but I never specify before in my code, whenever a print df.index I get object as dtype . That's the confusion Commented Nov 16, 2021 at 17:44
  • @9769953 if it's a datetime why print(df.index) is equal to object ?????? Commented Nov 16, 2021 at 17:50
  • Because the index can contain any type of object - it just so happens that all objects are of the same type in the dataframe you created. Commented Nov 16, 2021 at 18:13
  • The reason for the dtype being object is probably the underlying use of NumPy. It should indeed be datetime.date (or perhaps a Pandas datetime object). NumPy, however, doesn't know datetimes as standard types. If you look at df.index.values, you'll see it's a NumPy array, and df.index.values.dtype is also object. Possibly a shortcoming of Pandas (might be noted in an issue somewhere even, if you search around). Commented Nov 16, 2021 at 19:58

1 Answer 1

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Your DataFrame doesn't have a string index - it has a datetime.date index.

try

df.loc[datetime.date(2016, 10, 3)]

Better yet, create a datetime index:

df.index = pd.to_datetime(df.index)

Then, your original indexing will work.

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7 Comments

why print(df.index) is equal to object then Index([2016-10-03, 2016-10-04, 2016-10-05, 2016-10-06, 2016-10-07], dtype='object') . Shouldn't be datetime
Because you didn't specify a type for the index. Updated my answer for a better solution.
First you say that my data frame has a datetime index, then you tell me to assign a datetime to my index. Isn't the first statement contradicting the second ???
No. I did not say your dataframe has a datetime index - I said it has a datetime.date index. To convert it to a pandas.DateTimeIndex, use pd.to_datetime as specified in the answer. Did the solution work?
A more precise way to put it is that your dataframe has an index with type object that happens to contain datetime.date objects.
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