[numpy] Basic operations of Numpy array

1. What is Numpy?

Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays.The Numpy is an abbreviation for Numpy Python and is a Python package used in algebric calculations.

 

import numpy as np

 

2. Array

2.1 Creating an Array object

Numpy's core data structure is ndarray, which has a similar structure to Python's list. The array may be generated through an np.array() function.

 

import numpy as np 
x = np.array([10, 20, 30]) 
print(len(x))

 

2.2 Indexing Array object

Like Python's list, Array can index and assign.

 

import numpy as np 
x = np.array([10, 20, 30]) 
x0 = x[0] 
x2 = x[2]
x[1] = 42

 

2.3 Slicing Array

We can extract a sequence of elements stored in array using slicing index. By designating start ande end index separated by : operator, we can get a sequence of elements from array.

 

x = np.array([1, 3, 4, 2, 7, 0, 9]) 
first_third = x[:3]
middle = x[1:-1]

 

If additional step is specified when slicing the array, the index skipped by step is sliced.

 

even = x[0:len(x):2]
odd = x[1:len(x):2] 
mul_3 = x[0:len(x):3]

 

2.4 Result of slicing

The result of array slicing works as a view of an existing array, not a new array. That is, when the sliced array is changed, the value of existing array is also changed. To create a new array, we must copy it using the copy() method.

 

x = np.array([1, 0, 0, 0, 1])

y = x[1:-1]
z = y.copy()

z[0] = 9
print(x, y, z) 

y[1] = 7
print(x, y, z)

 

2.5 Reverse slicing 

Like Python's reverse index, the array starts with -1, -2, ... is available. Likewise, in step slicing, negative number can be used to slice elements of the reduced index. 

 

x = np.array([9, 1, 5, 6, 2, 0, 4, 3, 8, 7])
second_to_last = x[-2]
reversed_x = x[::-1]
first_5_reversed = x[4::-1]
last_5_reversed = x[-1:4:-1]

 

3. N-dimensional array

The ndarray may be an n-dimensional array and may use a multidimensional array. A previous used array is a 1-dimensional array, and a 2-dimensional array is configured in the form of a list of lists. When indexing 2darray, we can use array2d[row, col]. In the case of 2-dimensional array, the same indexing method as 1-dimensional array is applicable.

array2d[start:end, start:end, step]. We can also store the index values of the desired rows and columns in the list and specify them directly.

 

2d_array = np.array([[1, 4, 3], 
                     [4, 2, 6], 
                     [3, 7, 9]) 
2d_array[1, 1] = 42
print(2d_array) 

array2d = np.array([
    [1, 2, 3, 4, 5],
    [6, 7, 8, 9, 10],
    [11, 12, 13, 14, 15]
])

x = array2d[:, 3:]
y = array2d[::2,:]
z = array2d[::2,::2]

 

 

Source from :

• https://www.geeksforgeeks.org/numpy-in-python-set-1-introduction/