In NumPy, the key is its array object class, which is quite similar to Python lists
In NumPy, the key is its array object class, which is quite similar to Python lists. However, arrays have a special rule: every element inside an array must be of the same type, like a number (float or integer). This unique rule allows arrays to work really fast with a lot of numbers, making them much quicker than lists for dealing with big sets of data.
The first step is always to import the NumPy library.
1 >>> import numpy as npWhen using NumPy in Python, a common practice is to import it using the shorthand alias np. This convention is widely adopted and helps save a bit of typing when referring to NumPy functions and objects.
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How to Create a NumPy array?
2 >>> a = np.array([1,2,3,4,5]) #passing a list in array function to create 1d array
3 >>> a
4 array([1, 2, 3, 4, 5])
5 >>> type(a)
6 numpy.ndarrayIn line 2, we use an array() function to create a NumPy array. We pass a list of numbers in the array function to create a 1D array. In line 3, we print the array and in line 5, we check the type of the array and we see that the array is numpy.ndarray. This is a 1-D array.
You can also use a tuple to create an array.
7 >>> a = np.array((1,2,3,4,5))You can also create the arrays of different dimensions as follows:
8 >>> a = np.array(1) # 0 dim
9 >>> b = np.array([1, 2, 3, 4, 5]) # 1d Array
10 >>> c = np.array([[1, 2, 3], [4, 5, 6]]) #2d array
11 >>> d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]) #3d array
12 >>> e = np.array([1, 2, 3, 4, 5], nddim=5)13 >>> print(a)
1
14 >>> print(b)
[1 2 3 4 5]
15 >>> print(c)
[[1 2 3]
[4 5 6]]
16 >>> print(d)
[[[ 1 2 3]
[ 4 5 6]]
[[ 7 8 9]
[10 11 12]]]
17 >>> print(e)
[[[[[1 2 3 4 5]]]]]
You can also make an array of complex numbers.
18 >>> a = np.array([1, 2, 3], dtype=complex)
19 >>> a
array([ 1.+0.j, 2.+0.j…
