python numpy学习

以下代码来源于本博文作者观看大神视频并纯手敲。

目录

numpy的属性

创建array

numpy的运算1

随机数生成以及矩阵的运算2

numpy的索引

array合并

array分割

numpy的浅拷贝和深拷贝

numpy的属性

import numpy as np

array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(array)
print(array.ndim)  # 维度 2
print(array.shape)  # 形状 (3, 3)
print(array.size)  # 大小 9
print(array.dtype)  # 元素类型 int32

numpy创建array

import numpy as np

a = np.array([1, 2, 3], dtype=np.int32)
print(a.dtype)  # int32
b = np.array([1, 2, 3], dtype=np.float)
print(b.dtype)  # float64
c = np.array([1, 2, 3])
d = np.array([[1, 2, 3], [4, 5, 6]])
print(d)  # 二维矩阵
zero = np.zeros((2, 3))
print(zero)  # 生成两行三列全为零的矩阵
one = np.ones((3, 4))
print(one)  # 生成三行四列全为1的矩阵
empty = np.empty((3, 2))
print(empty)  # 生成三行两列全都接近于零的矩阵（但不等于0）
e = np.arange(10)
print(e)
f = np.arange(4, 12)
print(f)  # [ 4  5  6  7  8  9 10 11]
g = np.arange(1, 20, 3)
print(g)
h = np.arange(8).reshape(2, 4)
print(h)  # 重新定义矩阵形状

numpy矩阵的运算

import numpy as np

arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([[1, 1, 2], [2, 3, 3]])
print(arr1 + arr2)  # 按照位置相加
print(arr1 - arr2)
print(arr1 * arr2)
print(arr1 ** arr2)
print(arr1 / arr2)
print(arr1 % arr2)
print(arr1 // arr2)
print(arr1 + 2)  # 所有的元素都加2
arr3 = arr1 > 3
print(arr3)  # 判断哪些元素大于3
arr4 = np.ones((3, 5))
print(arr4)
print(arr1)
res = np.dot(arr1, arr4)  # 矩阵的乘法
print(res)
res1 = arr1.dot(arr4)  # 矩阵的乘法
print(res1)
print(arr1.T)  # 转置矩阵
print(np.transpose(arr1))  # 转置矩阵

随机数生成以及矩阵的运算2

import numpy as np

sample1 = np.random.random((3, 2))  # 生成3行2列的从0到1的随机数
print(sample1)
sample2 = np.random.normal(size=(3, 2))  # 生成3行2列符合标准正太分布的随机数
print(sample2)
sample3 = np.random.randint(0, 10, size=(3, 2))  # 生成3行2列的从0-10的随机整数
print(sample3)
print(np.sum(sample1))  # 求和
print(np.min(sample1))  # 求最小值
print(np.sum(sample1, axis=0))  # 对每一列进行求和
print(np.sum(sample1, axis=1))  # 对每一行进行求和
print(np.argmin(sample1))  # 求最小值的索引
print(np.argmax(sample1))  # 求最大值的索引
print(np.mean(sample1))  # 求平均值
print(sample1.mean())  # 求平均值
print(np.median(sample1))  # 求中位数
print(np.sqrt(sample1))  # 开方
sample4 = np.random.randint(0, 10, size=(1, 10))
print(sample4)
print(np.sort(sample4))  # 排序:按行升序
print(np.sort(sample1))
print(np.clip(sample4, 2, 7))  # 小于2的变成2，大于7的变成7

numpy的索引

import numpy as np

arr1 = np.arange(2, 14)
print(arr1)  # [ 2  3  4  5  6  7  8  9 10 11 12 13]
print(arr1[2])  # 4
print(arr1[1: 4])  # [3 4 5]
print(arr1[2: -1])  # [ 4  5  6  7  8  9 10 11 12]
print(arr1[: 5])  # [2 3 4 5 6]
print(arr1[-2:])  # [12 13]
arr2 = arr1.reshape(3, 4)
print(arr2)  #
print(arr2[1])  # [6 7 8 9]
print(arr2[1][1])  # 7
print(arr2[1, 2])  # 8
print(arr2[:, 2])  # [ 4  8 12] 所有行，第2列
for i in arr2:  # 迭代行
	print(i)
for i in arr2.T:  # 迭代列
	print(i)
for i in arr2.flat:  # 迭代一个个元素
	print(i)

array的合并

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr3 = np.vstack((arr1, arr2))  # 垂直合并
print(arr3)
print(arr3.shape)
arr4 = np.hstack((arr1, arr2))  # 水平合并
print(arr4)  # [1 2 3 4 5 6]
print(arr4.shape)
arrv = np.vstack((arr1, arr2, arr3))
print(arrv)
arrh = np.hstack((arr1, arr2, arr4))
print(arrh)
arr = np.concatenate((arr1, arr2, arr1))  # 合并
print(arr)
arr = np.concatenate((arr3, arrv), axis=0)  # 垂直合并。合并的array维度要相同，array形状要匹配，axis=0纵向合并
print(arr)
arr = np.concatenate((arr3, arr3), axis=1)  # 水平合并
print(arr)
print(arr1.T)  # 一维的array不能转置
print(arr1.shape)  # (3,)
arr1_1 = arr1[np.newaxis, :]
print(arr1_1)  # [[1 2 3]]
print(arr1_1.shape)  # (1, 3)
print(arr1_1.T)
arr1_2 = arr1[:, np.newaxis]
print(arr1_2)
print(arr1_2.shape)  # (3, 1)
arr1_3 = np.atleast_2d(arr1)
print(arr1_3)  # [[1 2 3]]
print(arr1_3.T)

array分割

import numpy as np

arr1 = np.arange(12).reshape((3, 4))
print(arr1)
arr2, arr3 = np.split(arr1, 2, axis=1)  # 水平方向分割，分成2份
print(arr2)
print(arr3)
arr4, arr5, arr6 = np.split(arr1, 3, axis=0)  # 垂直方向分割，分成2份
print(arr4)
print(arr5)
print(arr6)
arr7, arr8, arr9 = np.array_split(arr1, 3, axis=1)  # 水平方向分割成3份，不等分割
print(arr7)
print(arr8)
print(arr9)
arrv1, arrv2, arrv3 = np.vsplit(arr1, 3)  # 垂直分割
print(arrv1)
print(arrv2)
print(arrv3)
arrh1, arrh2 = np.hsplit(arr1, 2)  # 水平分割
print(arrh1)
print(arrh2)

numpy的浅拷贝和深拷贝

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = arr1  # 引用赋值，共享一块内存，浅拷贝
arr2[0] = 5
print(arr1)
print(arr2)
arr3 = arr1.copy()  # 深拷贝
arr3[0] = 10
print(arr1)
print(arr3)

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