Python numpy: cannot convert datetime64[ns] to datetime64[D] (to use with Numba)
Question:
I want to pass a datetime array to a Numba function (which cannot be vectorised and would otherwise be very slow). I understand Numba supports numpy.datetime64. However, it seems it supports datetime64[D] (day precision) but not datetime64[ns] (nanosecond precision) (I learnt this the hard way: is it documented?).
I tried to convert from datetime64[ns] to datetime64[D], but can’t seem to find a way! Any ideas?
I have summarised my problem with the minimal code below. If you run testdf(mydates), which is datetime64[D], it works fine. If you run testdf(dates_input), which is datetime64[ns], it doesn’t. Note that this example simply passes the dates to the Numba function, which doesn’t (yet) do anything with them. I try to convert dates_input to datetime64[D], but the conversion doesn’t work. In my original code I read from a SQL table into a pandas dataframe, and need a column which changes the day of each date to the 15th.
import numba
import numpy as np
import pandas as pd
import datetime
mydates =np.array(['2010-01-01','2011-01-02']).astype('datetime64[D]')
df=pd.DataFrame()
df["rawdate"]=mydates
df["month_15"] = df["rawdate"].apply(lambda r: datetime.date( r.year, r.month,15 ) )
dates_input = df["month_15"].astype('datetime64[D]')
print dates_input.dtype # Why datetime64[ns] and not datetime64[D] ??
@numba.jit(nopython=True)
def testf(dates):
return 1
print testf(mydates)
The error I get if I run testdf(dates_input) is:
numba.typeinfer.TypingError: Failed at nopython (nopython frontend)
Var 'dates' unified to object: dates := {pyobject}
Answers:
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]').dtype
Out[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.dtype
Out[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 MonthBegin
import numpy as np
# Calculate the beginning of the month from a given date
df['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.
I want to pass a datetime array to a Numba function (which cannot be vectorised and would otherwise be very slow). I understand Numba supports numpy.datetime64. However, it seems it supports datetime64[D] (day precision) but not datetime64[ns] (nanosecond precision) (I learnt this the hard way: is it documented?).
I tried to convert from datetime64[ns] to datetime64[D], but can’t seem to find a way! Any ideas?
I have summarised my problem with the minimal code below. If you run testdf(mydates), which is datetime64[D], it works fine. If you run testdf(dates_input), which is datetime64[ns], it doesn’t. Note that this example simply passes the dates to the Numba function, which doesn’t (yet) do anything with them. I try to convert dates_input to datetime64[D], but the conversion doesn’t work. In my original code I read from a SQL table into a pandas dataframe, and need a column which changes the day of each date to the 15th.
import numba
import numpy as np
import pandas as pd
import datetime
mydates =np.array(['2010-01-01','2011-01-02']).astype('datetime64[D]')
df=pd.DataFrame()
df["rawdate"]=mydates
df["month_15"] = df["rawdate"].apply(lambda r: datetime.date( r.year, r.month,15 ) )
dates_input = df["month_15"].astype('datetime64[D]')
print dates_input.dtype # Why datetime64[ns] and not datetime64[D] ??
@numba.jit(nopython=True)
def testf(dates):
return 1
print testf(mydates)
The error I get if I run testdf(dates_input) is:
numba.typeinfer.TypingError: Failed at nopython (nopython frontend)
Var 'dates' unified to object: dates := {pyobject}
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]').dtype
Out[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.dtype
Out[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 MonthBegin
import numpy as np
# Calculate the beginning of the month from a given date
df['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.