I have created a TimeSeries in pandas:
In [346]: from datetime import datetime
In [347]: dates = [datetime(2011, 1, 2), datetime(2011, 1, 5), datetime(2011,
datetime64[ns] is a general dtype, while
On a machine whose byte order is little endian, there is no difference between
np.dtype('datetime64[ns]') and np.dtype('
In [6]: np.dtype('datetime64[ns]') == np.dtype('
However, on a big endian machine, np.dtype('datetime64[ns]') would equal np.dtype('>M8[ns]').
So datetime64[ns] maps to either >M8[ns] depending on the endian-ness of the machine.
There are many other similar examples of general dtypes mapping to specific dtypes:
int64 maps to >i8, and int maps to either int32 or int64
depending on the bit architecture of the OS and how NumPy was compiled.
Apparently, the repr of the datetime64 dtype has change since the time the book was written to show the endian-ness of the dtype.