numpy Part 10 – Data Type Objects in numpy

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Today we’ll be talking about data type objects in numpy.

Up to now we’ve been using dtype to determine the type of data in an array. Here’s an example:

import numpy as np

nums = [[1.1, 2.2, 3.3],
        [4.4, 5.5, 6.6],
        [7.7, 8.8, 9.9]]

n = np.array(nums, dtype = int)

print(n)

Here’s the output:

[[1 2 3]
 [4 5 6]
 [7 8 9]]

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You can use dtype to define a data type and assign it to a variable. Let’s modify our code:

import numpy as np

nums = [[1.1, 2.2, 3.3],
        [4.4, 5.5, 6.6],
        [7.7, 8.8, 9.9]]

# Let's define a data type, like for instance int16, and assign it to the 
# variable a.
a = np.dtype(np.int16)

# Now a is a data type and can be used like so:
n = np.array(nums, dtype = a)

print(n)

If you run this program, you’ll get the same output as before.

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In the example above we used the int16 type, which means 16-bit integer. int16 corresponds to ‘i2’, int32 corresponds to ‘i4’, and so on. ‘i2’ means a 2-byte integer, ‘i4’ means a 4-byte integer. So, we could use this notation instead:

a = np.dtype('i2')

i1, i2, i4, etc. stand for 1-byte integers, 2-byte integers, 4-byte integers and so on. Generally, the letter i is used for integers. We use other letters for other data types, for example f for floats, b for bools, S for strings, and so on.