Python3 | Getting Started with numpy

** Development environment: Jupyter Notebook **

First, read.

import numpy as np

It means "load a library called numpy as np". By declaring this, the method can be referred to as np.

Array

One-dimensional array

a = np.array([0, 1, 2])

print(a)
#array([0, 1, 2])

a[2]
#2

Create an array with np.array. In the case of a one-dimensional array, it can be created with array ([]). Any value can be called with a [].

A two-dimensional array

a = np.array([[0, 1, 2], [3, 4, 5]])

print(a)
#array([[0, 1, 2],
#       [3, 4, 5]])

a[0]
#array([0, 1, 2])
a[1]
#array([3, 4, 5])

a[0][0]
#0
a[1][2]
#5

In the case of a two-dimensional array, it can be created with array ([], []). Here, a [] calls the value for each row. With a [] [], you can specify a row and a column to retrieve a single value.

Array manipulation

shape, ndim, dtype, size

a.shape
#(2, 3)
a.ndim
#2
a.dtype
#'int64'
a.size
#6

You can retrieve the number of rows and columns with ** a.shape **. ** a.ndim ** represents the number of dimensions. In this case, 2 is taken out because it is a two-dimensional matrix. ** a.dtype ** represents the number of bits. This time, it is a 2x3 matrix, so if one value is 2 bits, 2 to the 6th power is calculated as int64. ** a.size ** is displayed as 6 from the 2x3 matrix.

arange, linspace

np.arange(6)
#array([0, 1, 2, 3, 4, 5])

np.arange(0, 20, 5)
#array([0, 5, 10, 15])

np.linspace(0, 20, 5)
#array([0., 5., 10., 15., 20.])

** np.arange ** can make arrays with arithmetic progression. In the above example, simply writing 6 will extract 0 to 6 elements. Alternatively, it is represented as an array starting from 0 and increasing by 5 from 0 to less than 20. ** np.linspace ** can be divided into equal parts to create an array. In the above example, the array is created by dividing it into 5 equal parts, starting from 0 and ending with 20.

zeros, ones

np.zeros((2, 3))
#array([[0., 0., 0.],
#       [0., 0., 0.]])

np.ones((2, 3))
#array([[1., 1., 1.],
#       [1., 1., 1.]])

** np.zeros ** can create an array with 0 as an element. Similarly, ** np.ones ** has 1 as an element.

append, vstack, hstack

a = np.arange(5)
b = np.arange(0, 10, 2)
c = np.arange(0, 100, 20)

a
#array([0, 1, 2, 3, 4])
b
#array([0, 2, 4, 6, 8])
c
#array([0, 20, 40, 60, 80])

np.append(a, b)
#array([0, 1, 2, 3, 4, 0, 2, 4, 6, 8])

np.vstack([a, b, c])
#array([[0,  1,  2,  3,  4],
#       [0,  2,  4,  6,  8],
#       [0, 20, 40, 60, 80]])

np.hstack([a, b, c])
#array([0,  1,  2,  3,  4,  0,  2,  4,  6,  8,  0, 20, 40, 60, 80])

** np.append ** can be followed by array values. ** np.vstack ** allows you to arrange the array row by row, that is, vertically. In addition, ** np.hstack ** can arrange arrays horizontally (horizontal).

random, sum, min, max, mean

a = np.random.rand((2, 3))

a
#array([[0.3026967 , 0.7533224 , 0.26057491],
#       [0.91502592, 0.54166851, 0.60867887]])

a.sum()
# 3.381967315194162

a.min()
# 0.26057491207931693

a.max()
# 0.9150259161906477

a.mean()
# 0.563661219199027

** np.random.rand ** can randomly describe numbers from 0 to less than 1. ** a.sum ** is the sum of all values, ** a.min ** is the minimum value of the array, ** a.max ** is the maximum value of the array, ** a.mean ** is the minimum value of the array Represents the average value.