Basic knowledge of python (thirteen): basic usage of numpy library

1. Introduction to numpy

The numpy library is numpy is an array object based scientific computing library in python.

2. The numpy library generates matrices

2.1 numpy converts the list into a matrix

import numpy as np

array = np.array([[1, 2, 3],
                 [4, 5, 7]])  # 将列表转换成矩阵
print(array)  # 输出矩阵
print('number of dim', array.ndim)  # 输出矩阵的维度
print('shape', array.shape)  # 输出矩阵的形状
print('size', array.size)  # 输出矩阵的大小

In this code, the numpy library is first imported, and then the array() method is used to convert the list into a matrix.

2.2 numpy create matrix

• Use the array() method to generate one-dimensional and two-dimensional matrices

import numpy as np
# 一维矩阵
a = np.array([2, 3, 4], dtype=np.int64)
print(a, a.dtype)

# 二维矩阵
a = np.array([[1, 2, 3],
              [4, 5, 6]])
print(a)

• Use the zeros() method to generate a matrix of all 0s

# 0矩阵
a = np.zeros((3, 4))
print(a)

• Use the ones() method to generate a matrix of all 1s

# 1矩阵
a = np.ones((3, 4))
print(a)

• Use the arrange() method to generate a uniform matrix,

# 均匀矩阵
a = np.arange(10, 20, 2)  # 10-20,步长为2
print(a)
a = np.arange(12).reshape((3, 4))  # 将形状改变成3*4
print(a)

• Use the linspace() method to generate a one-dimensional row vector

# 一维行向量
a = np.linspace(1, 10, 6)
print(a)
a = np.linspace(1, 10, 6).reshape((2, 3))
print(a)

• full code

import numpy as np
# 一维矩阵
a = np.array([2, 3, 4], dtype=np.int64)
print(a, a.dtype)

# 二维矩阵
a = np.array([[1, 2, 3],
              [4, 5, 6]])
print(a)

# 0矩阵
a = np.zeros((3, 4))
print(a)

# 1矩阵
a = np.ones((3, 4))
print(a)

# 均匀矩阵
a = np.arange(10, 20, 2)  # 10-20,步长为2
print(a)
a = np.arange(12).reshape((3, 4))  # 将形状改变成3*4
print(a)

# 一维行向量
a = np.linspace(1, 10, 6)
print(a)
a = np.linspace(1, 10, 6).reshape((2, 3))
print(a)

3. Basic operations of numpy

• Addition

import numpy as np

a = np.array([10, 20, 30, 40])
b = np.arange(4)
print(b)
# 加法
c = a + b
print(c)

• Subtraction

# 减法
c = a - b
print(c)

• Power operation

# 乘方
b = b ** 2
print(b)

• Sine operation

# 正弦
c = 10 * np.sin(a)
print(c)

• Matrix multiplication, using * is matrix point multiplication, using dot() method is matrix multiplication

a = np.array([[10, 20],
              [30, 40]])
b = np.arange(4).reshape((2, 2))
print(a)
print(b)
c = a * b  # 矩阵对应相乘,点乘
c_dot = np.dot(a, b)  # 矩阵相乘
c_dot_2 = a.dot(b)
print(c)
print(c_dot)
print(c_dot_2)

• Sum operation, maximum and minimum

# 随机矩阵
a = np.random.random((2, 4))
print(a)

print(np.sum(a))
print(np.max(a))
print(np.min(a))
print(np.sum(a, axis=1))  # 对每一行求和
print(np.sum(a, axis=0))  # 对每一列求和

• full code

import numpy as np

a = np.array([10, 20, 30, 40])
b = np.arange(4)
print(b)
# 加法
c = a + b
print(c)
# 减法
c = a - b
print(c)
# 乘方
b = b ** 2
print(b)
# 正弦
c = 10 * np.sin(a)
print(c)
# 矩阵相乘
a = np.array([[10, 20],
              [30, 40]])
b = np.arange(4).reshape((2, 2))
print(a)
print(b)
c = a * b  # 矩阵对应相乘,点乘
c_dot = np.dot(a, b)  # 矩阵相乘
c_dot_2 = a.dot(b)
print(c)
print(c_dot)
print(c_dot_2)


# 随机矩阵
a = np.random.random((2, 4))
print(a)

print(np.sum(a))
print(np.max(a))
print(np.min(a))
print(np.sum(a, axis=1))  # 对每一行求和
print(np.sum(a, axis=0))  # 对每一列求和

4. Basic operation of numpy 2

import numpy as np
A = np.arange(2, 14).reshape((3, 4))

print(A)
print(np.nanargmin(A))  # 对最小值的索引
print(np.nanargmax(A))  # 对最大值的索引
print(np.mean(A))  # 平均值
print(np.median(A))  # 中位数
print(np.cumsum(A))  # 累加和
print(np.diff(A))  # 累差
print(np.nonzero(A))  # 输出非零的数的位置
print(np.sort(A))  # 逐行进行排序
print(np.transpose(A))  # 矩阵转置
print(A.T)  # 矩阵转置
print((A.T).dot(A))  # 实对称矩阵
print(np.clip(A, 5, 9))  # A中小于5的数等于5，大于9的数等于9，其余不变
print(np.nanmean(A, axis=0))  # 对列进行计算平均数
print(np.nanmean(A, axis=1))  # 对行进行计算平均数

结果：
[[ 2  3  4  5]
 [ 6  7  8  9]
 [10 11 12 13]]
0
11
7.5
7.5
[ 2  5  9 14 20 27 35 44 54 65 77 90]
[[1 1 1]
 [1 1 1]
 [1 1 1]]
(array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], dtype=int64), array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], dtype=int64))
[[ 2  3  4  5]
 [ 6  7  8  9]
 [10 11 12 13]]
[[ 2  6 10]
 [ 3  7 11]
 [ 4  8 12]
 [ 5  9 13]]
[[ 2  6 10]
 [ 3  7 11]
 [ 4  8 12]
 [ 5  9 13]]
[[140 158 176 194]
 [158 179 200 221]
 [176 200 224 248]
 [194 221 248 275]]
[[5 5 5 5]
 [6 7 8 9]
 [9 9 9 9]]
[6. 7. 8. 9.]
[ 3.5  7.5 11.5]

5. Index

import numpy as np
# 一维数组
A = np.arange(3, 15)
print(A)
print(A[3])  # 对A中的值进行索引，位置是3
# 二维矩阵
A = np.arange(3, 15).reshape((3, 4))
print(A)
print(A[1])  # 对A中的值进行索引，位置是1，为第一行的数
print(A[1][1])  # 对A中第一行第一列的数进行索引
print(A[1, 1])  # 对A中第一行第一列的数进行索引
print(A[1, :])  # A中第一行的所有的数
print(A[:, 1])  # A中第一列的所有的数
print(A[1, 1:3])  # A中第一行的1-3的数，取左不取右
# 输出矩阵中每一行的数
for row in A:
    print(row)

# 输出矩阵中每一列的数
for col in A.T:
    print(col)

# 输出A中每一个数
print(A.flatten())  # 将矩阵转换成一维数组
for item in A.flat:
    print(item)

结果：
[ 3  4  5  6  7  8  9 10 11 12 13 14]
6
[[ 3  4  5  6]
 [ 7  8  9 10]
 [11 12 13 14]]
[ 7  8  9 10]
8
8
[ 7  8  9 10]
[ 4  8 12]
[8 9]
[3 4 5 6]
[ 7  8  9 10]
[11 12 13 14]
[ 3  7 11]
[ 4  8 12]
[ 5  9 13]
[ 6 10 14]
[ 3  4  5  6  7  8  9 10 11 12 13 14]
3
4
5
6
7
8
9
10
11
12
13
14