Python numpy: cannot convert datetime64[ns] to datetime64[D] (to use with Numba)

Series.astype converts all date-like objects to datetime64[ns]. To convert to datetime64[D], use values to obtain a NumPy array before calling astype:

dates_input = df["month_15"].values.astype('datetime64[D]')

Note that NDFrames (such as Series and DataFrames) can only hold datetime-like objects as objects of dtype datetime64[ns]. The automatic conversion of all datetime-likes to a common dtype simplifies subsequent date computations. But it makes it impossible to store, say, datetime64[s] objects in a DataFrame column. Pandas core developer, Jeff Reback explains,

"We don't allow direct conversions because its simply too complicated to keep anything other than datetime64[ns] internally (nor necessary at all)."

Also note that even though df['month_15'].astype('datetime64[D]') has dtype datetime64[ns]:

In [29]: df['month_15'].astype('datetime64[D]').dtypeOut[29]: dtype('<M8[ns]')

when you iterate through the items in the Series, you get pandas Timestamps, not datetime64[ns]s.

In [28]: df['month_15'].astype('datetime64[D]').tolist()Out[28]: [Timestamp('2010-01-15 00:00:00'), Timestamp('2011-01-15 00:00:00')]

Therefore, it is not clear that Numba actually has a problem with datetime64[ns], it might just have a problem with Timestamps. Sorry, I can't check this -- I don't have Numba installed.

However, it might be useful for you to try

testf(df['month_15'].astype('datetime64[D]').values)

since df['month_15'].astype('datetime64[D]').values is truly a NumPy array of dtype datetime64[ns]:

In [31]: df['month_15'].astype('datetime64[D]').values.dtypeOut[31]: dtype('<M8[ns]')

If that works, then you don't have to convert everything to datetime64[D], you just have to pass NumPy arrays -- not Pandas Series -- to testf.

Ran into the same error when calculating number of business days between two dates:

from pandas.tseries.offsets import MonthBeginimport numpy as np # Calculate the beginning of the month from a given datedf['Month_Begin'] = pd.to_datetime(df['MyDateColumn'])+ MonthBegin(-1)# Calculate # of Business Days# Convert dates to string to prevent type error [D]df['TS_Period_End_Date'] = df['TS_Period_End_Date'].dt.strftime('%Y-%m-%d')df['Month_Begin'] = df['Month_Begin'].dt.strftime('%Y-%m-%d')df['Biz_Days'] = np.busday_count(df['Month_Begin'], df['MyDateColumn']) #<-- Error if not converted into strings.

My workaround was to convert the dates using ".dt.strftime(''%Y-%m-%d')". It worked in my particular case.