NEP 4 — A (third) proposal for implementing some date/time types in NumPy#

Author:

Francesc Alted i Abad

Contact:

faltet@pytables.com

Author:

Ivan Vilata i Balaguer

Contact:

ivan@selidor.net

Date:

2008-07-30

Status:

Deferred

Executive summary#

A date/time mark is something very handy to have in many fields where one has to deal with data sets. While Python has several modules that define a date/time type (like the integrated datetime [1] or mx.DateTime [2]), NumPy has a lack of them.

In this document, we are proposing the addition of a series of date/time types to fill this gap. The requirements for the proposed types are two-folded: 1) they have to be fast to operate with and 2) they have to be as compatible as possible with the existing datetime module that comes with Python.

Types proposed#

To start with, it is virtually impossible to come up with a single date/time type that fills the needs of every case of use. So, after pondering about different possibilities, we have stuck with two different types, namely datetime64 and timedelta64 (these names are preliminary and can be changed), that can have different time units so as to cover different needs.

Important

the time unit is conceived here as metadata that complements a date/time dtype, without changing the base type. It provides information about the meaning of the stored numbers, not about their structure.

Now follows a detailed description of the proposed types.

datetime64#

It represents a time that is absolute (i.e. not relative). It is implemented internally as an int64 type. The internal epoch is the POSIX epoch (see [3]). Like POSIX, the representation of a date doesn’t take leap seconds into account.

In time unit conversions and time representations (but not in other time computations), the value -2**63 (0x8000000000000000) is interpreted as an invalid or unknown date, Not a Time or NaT. See the section on time unit conversions for more information.

Time units#

It accepts different time units, each of them implying a different time span. The table below describes the time units supported with their corresponding time spans.

Time unit

Time span (years)

Code

Meaning

Y

year

[9.2e18 BC, 9.2e18 AD]

M

month

[7.6e17 BC, 7.6e17 AD]

W

week

[1.7e17 BC, 1.7e17 AD]

B

business day

[3.5e16 BC, 3.5e16 AD]

D

day

[2.5e16 BC, 2.5e16 AD]

h

hour

[1.0e15 BC, 1.0e15 AD]

m

minute

[1.7e13 BC, 1.7e13 AD]

s

second

[ 2.9e9 BC, 2.9e9 AD]

ms

millisecond

[ 2.9e6 BC, 2.9e6 AD]

us

microsecond

[290301 BC, 294241 AD]

c#

ticks (100ns)

[ 2757 BC, 31197 AD]

ns

nanosecond

[ 1678 AD, 2262 AD]

The value of an absolute date is thus an integer number of units of the chosen time unit passed since the internal epoch. When working with business days, Saturdays and Sundays are simply ignored from the count (i.e. day 3 in business days is not Saturday 1970-01-03, but Monday 1970-01-05).

Building a datetime64 dtype#

The proposed ways to specify the time unit in the dtype constructor are:

Using the long string notation:

dtype('datetime64[us]')

Using the short string notation:

dtype('M8[us]')

The default is microseconds if no time unit is specified. Thus, ‘M8’ is equivalent to ‘M8[us]’

Setting and getting values#

The objects with this dtype can be set in a series of ways:

t = numpy.ones(3, dtype='M8[s]')
t[0] = 1199164176    # assign to July 30th, 2008 at 17:31:00
t[1] = datetime.datetime(2008, 7, 30, 17, 31, 01) # with datetime module
t[2] = '2008-07-30T17:31:02'    # with ISO 8601

And can be get in different ways too:

str(t[0])  -->  2008-07-30T17:31:00
repr(t[1]) -->  datetime64(1199164177, 's')
str(t[0].item()) --> 2008-07-30 17:31:00  # datetime module object
repr(t[0].item()) --> datetime.datetime(2008, 7, 30, 17, 31)  # idem
str(t)  -->  [2008-07-30T17:31:00  2008-07-30T17:31:01  2008-07-30T17:31:02]
repr(t)  -->  array([1199164176, 1199164177, 1199164178],
                    dtype='datetime64[s]')

Comparisons#

The comparisons will be supported too:

numpy.array(['1980'], 'M8[Y]') == numpy.array(['1979'], 'M8[Y]')
--> [False]

or by applying broadcasting:

numpy.array(['1979', '1980'], 'M8[Y]') == numpy.datetime64('1980', 'Y')
--> [False, True]

The next should work too:

numpy.array(['1979', '1980'], 'M8[Y]') == '1980-01-01'
--> [False, True]

because the right hand expression can be broadcasted into an array of 2 elements of dtype ‘M8[Y]’.

Compatibility issues#

This will be fully compatible with the datetime class of the datetime module of Python only when using a time unit of microseconds. For other time units, the conversion process will lose precision or will overflow as needed. The conversion from/to a datetime object doesn’t take leap seconds into account.

timedelta64#

It represents a time that is relative (i.e. not absolute). It is implemented internally as an int64 type.

In time unit conversions and time representations (but not in other time computations), the value -2**63 (0x8000000000000000) is interpreted as an invalid or unknown time, Not a Time or NaT. See the section on time unit conversions for more information.

Time units#

It accepts different time units, each of them implying a different time span. The table below describes the time units supported with their corresponding time spans.

Time unit

Time span

Code

Meaning

Y

year

+- 9.2e18 years

M

month

+- 7.6e17 years

W

week

+- 1.7e17 years

B

business day

+- 3.5e16 years

D

day

+- 2.5e16 years

h

hour

+- 1.0e15 years

m

minute

+- 1.7e13 years

s

second

+- 2.9e12 years

ms

millisecond

+- 2.9e9 years

us

microsecond

+- 2.9e6 years

c#

ticks (100ns)

+- 2.9e4 years

ns

nanosecond

+- 292 years

ps

picosecond

+- 106 days

fs

femtosecond

+- 2.6 hours

as

attosecond

+- 9.2 seconds

The value of a time delta is thus an integer number of units of the chosen time unit.

Building a timedelta64 dtype#

The proposed ways to specify the time unit in the dtype constructor are:

Using the long string notation:

dtype('timedelta64[us]')

Using the short string notation:

dtype('m8[us]')

The default is micro-seconds if no default is specified: ‘m8’ is equivalent to ‘m8[us]’

Setting and getting values#

The objects with this dtype can be set in a series of ways:

t = numpy.ones(3, dtype='m8[ms]')
t[0] = 12    # assign to 12 ms
t[1] = datetime.timedelta(0, 0, 13000)   # 13 ms
t[2] = '0:00:00.014'    # 14 ms

And can be get in different ways too:

str(t[0])  -->  0:00:00.012
repr(t[1]) -->  timedelta64(13, 'ms')
str(t[0].item()) --> 0:00:00.012000   # datetime module object
repr(t[0].item()) --> datetime.timedelta(0, 0, 12000)  # idem
str(t)     -->  [0:00:00.012  0:00:00.014  0:00:00.014]
repr(t)    -->  array([12, 13, 14], dtype="timedelta64[ms]")

Comparisons#

The comparisons will be supported too:

numpy.array([12, 13, 14], 'm8[ms]') == numpy.array([12, 13, 13], 'm8[ms]')
--> [True, True, False]

or by applying broadcasting:

numpy.array([12, 13, 14], 'm8[ms]') == numpy.timedelta64(13, 'ms')
--> [False, True, False]

The next should work too:

numpy.array([12, 13, 14], 'm8[ms]') == '0:00:00.012'
--> [True, False, False]

because the right hand expression can be broadcasted into an array of 3 elements of dtype ‘m8[ms]’.

Compatibility issues#

This will be fully compatible with the timedelta class of the datetime module of Python only when using a time unit of microseconds. For other units, the conversion process will lose precision or will overflow as needed.

Examples of use#

Here it is an example of use for the datetime64:

In [5]: numpy.datetime64(42, 'us')
Out[5]: datetime64(42, 'us')

In [6]: print numpy.datetime64(42, 'us')
1970-01-01T00:00:00.000042  # representation in ISO 8601 format

In [7]: print numpy.datetime64(367.7, 'D')  # decimal part is lost
1971-01-02  # still ISO 8601 format

In [8]: numpy.datetime('2008-07-18T12:23:18', 'm')  # from ISO 8601
Out[8]: datetime64(20273063, 'm')

In [9]: print numpy.datetime('2008-07-18T12:23:18', 'm')
Out[9]: 2008-07-18T12:23

In [10]: t = numpy.zeros(5, dtype="datetime64[ms]")

In [11]: t[0] = datetime.datetime.now()  # setter in action

In [12]: print t
[2008-07-16T13:39:25.315  1970-01-01T00:00:00.000
 1970-01-01T00:00:00.000  1970-01-01T00:00:00.000
 1970-01-01T00:00:00.000]

In [13]: repr(t)
Out[13]: array([267859210457, 0, 0, 0, 0], dtype="datetime64[ms]")

In [14]: t[0].item()     # getter in action
Out[14]: datetime.datetime(2008, 7, 16, 13, 39, 25, 315000)

In [15]: print t.dtype
dtype('datetime64[ms]')

And here it goes an example of use for the timedelta64:

In [5]: numpy.timedelta64(10, 'us')
Out[5]: timedelta64(10, 'us')

In [6]: print numpy.timedelta64(10, 'us')
0:00:00.000010

In [7]: print numpy.timedelta64(3600.2, 'm')  # decimal part is lost
2 days, 12:00

In [8]: t1 = numpy.zeros(5, dtype="datetime64[ms]")

In [9]: t2 = numpy.ones(5, dtype="datetime64[ms]")

In [10]: t = t2 - t1

In [11]: t[0] = datetime.timedelta(0, 24)  # setter in action

In [12]: print t
[0:00:24.000  0:00:01.000  0:00:01.000  0:00:01.000  0:00:01.000]

In [13]: print repr(t)
Out[13]: array([24000, 1, 1, 1, 1], dtype="timedelta64[ms]")

In [14]: t[0].item()     # getter in action
Out[14]: datetime.timedelta(0, 24)

In [15]: print t.dtype
dtype('timedelta64[s]')

Operating with date/time arrays#

datetime64 vs datetime64#

The only arithmetic operation allowed between absolute dates is the subtraction:

In [10]: numpy.ones(3, "M8[s]") - numpy.zeros(3, "M8[s]")
Out[10]: array([1, 1, 1], dtype=timedelta64[s])

But not other operations:

In [11]: numpy.ones(3, "M8[s]") + numpy.zeros(3, "M8[s]")
TypeError: unsupported operand type(s) for +: 'numpy.ndarray' and 'numpy.ndarray'

Comparisons between absolute dates are allowed.

Casting rules#

When operating (basically, only the subtraction will be allowed) two absolute times with different unit times, the outcome would be to raise an exception. This is because the ranges and time-spans of the different time units can be very different, and it is not clear at all what time unit will be preferred for the user. For example, this should be allowed:

>>> numpy.ones(3, dtype="M8[Y]") - numpy.zeros(3, dtype="M8[Y]")
array([1, 1, 1], dtype="timedelta64[Y]")

But the next should not:

>>> numpy.ones(3, dtype="M8[Y]") - numpy.zeros(3, dtype="M8[ns]")
raise numpy.IncompatibleUnitError  # what unit to choose?

datetime64 vs timedelta64#

It will be possible to add and subtract relative times from absolute dates:

In [10]: numpy.zeros(5, "M8[Y]") + numpy.ones(5, "m8[Y]")
Out[10]: array([1971, 1971, 1971, 1971, 1971], dtype=datetime64[Y])

In [11]: numpy.ones(5, "M8[Y]") - 2 * numpy.ones(5, "m8[Y]")
Out[11]: array([1969, 1969, 1969, 1969, 1969], dtype=datetime64[Y])

But not other operations:

In [12]: numpy.ones(5, "M8[Y]") * numpy.ones(5, "m8[Y]")
TypeError: unsupported operand type(s) for *: 'numpy.ndarray' and 'numpy.ndarray'

Casting rules#

In this case the absolute time should have priority for determining the time unit of the outcome. That would represent what the people wants to do most of the times. For example, this would allow to do:

>>> series = numpy.array(['1970-01-01', '1970-02-01', '1970-09-01'],
dtype='datetime64[D]')
>>> series2 = series + numpy.timedelta(1, 'Y')  # Add 2 relative years
>>> series2
array(['1972-01-01', '1972-02-01', '1972-09-01'],
dtype='datetime64[D]')  # the 'D'ay time unit has been chosen

timedelta64 vs timedelta64#

Finally, it will be possible to operate with relative times as if they were regular int64 dtypes as long as the result can be converted back into a timedelta64:

In [10]: numpy.ones(3, 'm8[us]')
Out[10]: array([1, 1, 1], dtype="timedelta64[us]")

In [11]: (numpy.ones(3, 'm8[M]') + 2) ** 3
Out[11]: array([27, 27, 27], dtype="timedelta64[M]")

But:

In [12]: numpy.ones(5, 'm8') + 1j
TypeError: the result cannot be converted into a ``timedelta64``

Casting rules#

When combining two timedelta64 dtypes with different time units the outcome will be the shorter of both (“keep the precision” rule). For example:

In [10]: numpy.ones(3, 'm8[s]') + numpy.ones(3, 'm8[m]')
Out[10]: array([61, 61, 61],  dtype="timedelta64[s]")

However, due to the impossibility to know the exact duration of a relative year or a relative month, when these time units appear in one of the operands, the operation will not be allowed:

In [11]: numpy.ones(3, 'm8[Y]') + numpy.ones(3, 'm8[D]')
raise numpy.IncompatibleUnitError  # how to convert relative years to days?

In order to being able to perform the above operation a new NumPy function, called change_timeunit is proposed. Its signature will be:

change_timeunit(time_object, new_unit, reference)

where ‘time_object’ is the time object whose unit is to be changed, ‘new_unit’ is the desired new time unit, and ‘reference’ is an absolute date (NumPy datetime64 scalar) that will be used to allow the conversion of relative times in case of using time units with an uncertain number of smaller time units (relative years or months cannot be expressed in days).

With this, the above operation can be done as follows:

In [10]: t_years = numpy.ones(3, 'm8[Y]')

In [11]: t_days = numpy.change_timeunit(t_years, 'D', '2001-01-01')

In [12]: t_days + numpy.ones(3, 'm8[D]')
Out[12]: array([366, 366, 366],  dtype="timedelta64[D]")

dtype vs time units conversions#

For changing the date/time dtype of an existing array, we propose to use the .astype() method. This will be mainly useful for changing time units.

For example, for absolute dates:

In[10]: t1 = numpy.zeros(5, dtype="datetime64[s]")

In[11]: print t1
[1970-01-01T00:00:00  1970-01-01T00:00:00  1970-01-01T00:00:00
 1970-01-01T00:00:00  1970-01-01T00:00:00]

In[12]: print t1.astype('datetime64[D]')
[1970-01-01  1970-01-01  1970-01-01  1970-01-01  1970-01-01]

For relative times:

In[10]: t1 = numpy.ones(5, dtype="timedelta64[s]")

In[11]: print t1
[1 1 1 1 1]

In[12]: print t1.astype('timedelta64[ms]')
[1000 1000 1000 1000 1000]

Changing directly from/to relative to/from absolute dtypes will not be supported:

In[13]: numpy.zeros(5, dtype="datetime64[s]").astype('timedelta64')
TypeError: data type cannot be converted to the desired type

Business days have the peculiarity that they do not cover a continuous line of time (they have gaps at weekends). Thus, when converting from any ordinary time to business days, it can happen that the original time is not representable. In that case, the result of the conversion is Not a Time (NaT):

In[10]: t1 = numpy.arange(5, dtype="datetime64[D]")

In[11]: print t1
[1970-01-01  1970-01-02  1970-01-03  1970-01-04  1970-01-05]

In[12]: t2 = t1.astype("datetime64[B]")

In[13]: print t2  # 1970 begins in a Thursday
[1970-01-01  1970-01-02  NaT  NaT  1970-01-05]

When converting back to ordinary days, NaT values are left untouched (this happens in all time unit conversions):

In[14]: t3 = t2.astype("datetime64[D]")

In[13]: print t3
[1970-01-01  1970-01-02  NaT  NaT  1970-01-05]

Final considerations#

Why the origin metadata disappeared#

During the discussion of the date/time dtypes in the NumPy list, the idea of having an origin metadata that complemented the definition of the absolute datetime64 was initially found to be useful.

However, after thinking more about this, we found that the combination of an absolute datetime64 with a relative timedelta64 does offer the same functionality while removing the need for the additional origin metadata. This is why we have removed it from this proposal.

Operations with mixed time units#

Whenever an operation between two time values of the same dtype with the same unit is accepted, the same operation with time values of different units should be possible (e.g. adding a time delta in seconds and one in microseconds), resulting in an adequate time unit. The exact semantics of this kind of operations is defined int the “Casting rules” subsections of the “Operating with date/time arrays” section.

Due to the peculiarities of business days, it is most probable that operations mixing business days with other time units will not be allowed.

Why there is not a quarter time unit?#

This proposal tries to focus on the most common used set of time units to operate with, and the quarter can be considered more of a derived unit. Besides, the use of a quarter normally requires that it can start at whatever month of the year, and as we are not including support for a time origin metadata, this is not a viable venue here. Finally, if we were to add the quarter then people should expect to find a biweekly, semester or biyearly just to put some examples of other derived units, and we find this a bit too overwhelming for this proposal purposes.