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Onyejiaku Theophilus Chidalu

** NumPy**, or simply

`numpy`

, is short for An ** array** in Python is simply a collection of multiple items of the same type. An

`array`

is also known as a `list`

. The difference here is that a `list`

takes longer to process in Python when compared to an `array`

in `numpy`

.The `numpy`

library also has functions that help manipulate numerical data, unlike Python, which is limited to the following data types by default:

`integer`

: Represents integers (positive or negative whole numbers). For example, -5,-4, 2, 6, etc.`float`

: Represents floating-point numbers. For example, 0.1, 110.24, etc.`string`

: Represents text data given in quotation marks. For example, 'PYTHON`, “PYTHON”.`boolean`

- Represents statements, e.g.,`True`

or`False`

.`complex`

- Represents complex numbers. For example, 2.0 + 3.5j, 1.2 + 3.5j, etc.

`Numpy`

has all the data types listed above, and has even more data types than the traditional Python. These data types are always denoted with a character. Below is a list of some of the data types in `Numpy`

that Python does not have:

`unsigned integers(u)`

: Represents a 32-bit non-negative integer (0 or positive numbers in the range of $2^{32-1}$.`timedelta(m)`

: Calculates the duration between two dates and times.`datetime(M)`

: Represents a single moment in time.`object(O)`

: Represents how to interpret bytes in the fixed-sized block of memory that corresponds to an array item.`Unicode(U)`

- Represents Unicode strings.

`numpy`

We use the ** dtype** property in

`numpy`

to return the data type of an array.Let’s use the `dtype`

property to check for the data types of some arrays.

import numpy as np array1 = np.array([1, 2, 3, 4, 5]) print('The array type for array1 is: ', array1.dtype) array2 = np.array(['engineer', 'doctor', 'lawyer', 'Pilot']) print('The array type for array2 is: ', array2.dtype)

From the output of the program, `int64`

means that the array is an `integer`

data type, while `<U8`

is a Unicode string, with the longest string being `8`

.

We can also create an array with a defined datatype using their respective codes.

import numpy as np # using the string datatype S array1 = np.array([1, 2, 3, 4, 5], dtype = 'S') print(array1) print(array1.dtype)

- We import the
`numpy`

library. - We use the
`numpy.array()`

method to create an array and instruct it to create a string datatype with the`S`

Unicode. We assign this to a variable we call`array1`

- We print the
`array1`

variable. - We use the
`dtype`

property to check for the data type.

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