np.linspace allows you to define how many values you get including the specified min and max value. It infers the stepsize:

>>> np.linspace(0,1,11)
array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])

np.arange allows you to define the stepsize and infers the number of steps(the number of values you get).

>>> np.arange(0,1,.1)
array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])

contributions from user2357112:

np.arange excludes the maximum value unless rounding error makes it do otherwise.

For example, the following results occur due to rounding error:

>>> numpy.arange(1, 1.3, 0.1)
array([1. , 1.1, 1.2, 1.3])

You can exclude the stop value (in our case 1.3) using endpoint=False:

>>> numpy.linspace(1, 1.3, 3, endpoint=False)
array([1. , 1.1, 1.2])

Update - 2022

As pointed out by @Joooeey and @Ehtesh Choudhury, pandas now has date_range, which makes creating numpy.linspace-like time series much simpler.

t = pd.date_range(
    start='2022-03-10',
    end='2022-03-15',
    periods=5
)

If it's important to have this time series as a numpy array, simply

>>> t.values

array(['2022-03-10T00:00:00.000000000', '2022-03-11T06:00:00.000000000',
       '2022-03-12T12:00:00.000000000', '2022-03-13T18:00:00.000000000',
       '2022-03-15T00:00:00.000000000'], dtype='datetime64[ns]')

Original answer

Have you considered using pandas? Using an approach from this possible duplicate question, you can make use of np.linspace in the following way

import pandas as pd

start = pd.Timestamp('2015-07-01')
end = pd.Timestamp('2015-08-01')
t = np.linspace(start.value, end.value, 100)
t = pd.to_datetime(t)

To obtain an np.array of the linear timeseries

In [3]: np.asarray(t)
Out[3]: 
array(['2015-06-30T17:00:00.000000000-0700',
       '2015-07-01T00:30:54.545454592-0700',
       '2015-07-01T08:01:49.090909184-0700',
               ...
       '2015-07-31T01:58:10.909090816-0700',
       '2015-07-31T09:29:05.454545408-0700',
       '2015-07-31T17:00:00.000000000-0700'], dtype='datetime64[ns]')

because linspace is defined on floats and returns in your case:

np.linspace(1,5,5, endpoint=False)
array([ 1. ,  1.8,  2.6,  3.4,  4.2])

then using int as dtype just rounds down giving you the result with two 1:

array([ 1 ,  1,  2,  3,  4])

Btw: Using np.arange might be better suited if you want to create arrays containing integer:

np.arange(1,5)
array([ 1,  2,  3,  4])