DateTimes64

Julia Time Types binary-compatible with Numpy's datetime64.

Quick Start

Inter-operating with Python date and datetime types can be a pain. Here we implement a Julia TimeType which has the same underlying memory representation as numpy's datetime64 dtype. This means that array buffers or binary data on disk can directly be wrapped and will be represented in Julia as a valid Time type with easy conversions to types from Dates.jl.

using PythonCall
np = pyimport("numpy")
datearray = np.array(["2007-07-13", "2006-01-13", "2010-08-13"], dtype="datetime64")
jlbytes = pyconvert(Array,parray.tobytes())

UInt8[0x8b, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xf2, 0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]

We can reinterpret this byte vector as a DateTime64 vector:

t64 = reinterpret(DateTime64{Dates.Day},jlbytes)

3-element reinterpret(DateTime64{Day}, ::Vector{UInt8}):
 DateTime64[Day]: 2007-07-13T00:00:00
 DateTime64[Day]: 2006-01-13T00:00:00
 DateTime64[Day]: 2010-08-13T00:00:00

and convert the result to Date or DateTime

Date.(dt64)

3-element Vector{Date}:
 2007-07-13
 2006-01-13
 2010-08-13