NumPy 是 Python 在科学计算领域取得成功的关键之一,如果你想通过 Python 学习数据科学或者机器学习,就必须学习 NumPy。我认为 NumPy 的功能很强大,而且入门也不难。
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 18/5/14 下午2:23
# @Author : Wugang Li
# @File : np.py
# @Software: PyCharm
# @license : Copyright(C), olei.me
# @Contact : i@olei.me
import numpy as np
#创建数组
a = np.array([0,1,2,3,4]) # [0 1 2 3 4]
b = np.array([0,1,2,3,4]) # [0 1 2 3 4]
c = np.arange(5) # [0 1 2 3 4]
d = np.linspace(0, 2 * np.pi, 5) # [ 0. 1.57079633 3.14159265 4.71238898 6.28318531]
print(a[3]) # 3
其中的linspace的意思是:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 18/5/15 下午2:52
# @Author : Wugang Li
# @File : juzhen.py
# @Software: PyCharm
# @license : Copyright(C), olei.me
# @Contact : i@olei.me
import numpy as np
a = np.array(
[
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
[26, 27, 28, 29, 30],
[31, 32, 33, 34, 35]
]
)
print(a[2, 4]) # 25
b = a[0, 1:4]
print(b) # [12,13,14]
c = a[1:4, 0]
print(c) # [16,21,26]
d = a[::2, ::2]
print(d)
# [[11 13 15]
# [21 23 25]
# [31 33 35]]
e = a[:,1]
print(e)
# [12 17 22 27 32]
上面展示的了矩阵的创建,以及矩阵的切片操作~
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 18/5/15 下午3:11
# @Author : Wugang Li
# @File : juzhen_method.py
# @Software: PyCharm
# @license : Copyright(C), olei.me
# @Contact : i@olei.me
import numpy as np
a = np.array(
[
[11, 12, 13, 14, 15, 0],
[16, 17, 18, 19, 20, 1],
[21, 22, 23, 24, 25, 2],
[26, 27, 28, 29, 30, 3],
[31, 32, 33, 34, 35, 4]
]
)
print(type(a)) # <class 'numpy.ndarray'>
print(a.dtype) # int64,总大小的字节
print(a.size) # 30
print(a.shape) # (5,6),表示五行五列
print(a.itemsize) # 每一个条目所占的字节,8bit为1字节,一个int64大小为64bite,64 / 8 = 8
print(a.ndim) # 2,表示二维数组
print(a.nbytes) # 240,8x30
nbytes表示这个数组中所有元素占用的字节数。你应该注意,这个数值并没有把额外的空间计算进去,因此实际上这个数组占用的空间会比这个值大点
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 18/5/15 下午3:21
# @Author : Wugang Li
# @File : juzhen_ys.py
# @Software: PyCharm
# @license : Copyright(C), olei.me
# @Contact : i@olei.me
import numpy as np
a = np.arange(25)
print(a)
# [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
a = a.reshape((5, 5))
# [[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]
# [20 21 22 23 24]]
print(a)
b = np.array(
[10, 62, 1, 14, 2, 56, 79, 2, 1, 45, 4, 92, 5, 55, 63, 43, 35, 6, 53, 24, 56, 3, 56, 44, 78]
)
b = b.reshape((5, 5))
print(b)
# [[10 62 1 14 2]
# [56 79 2 1 45]
# [ 4 92 5 55 63]
# [43 35 6 53 24]
# [56 3 56 44 78]]
print(a + b)
print(b - a)
print(a * b)
print(a / b)
print(a ** 2)
print(a < b)
print(b < a)
print(a.dot(b)) # 点积,a的第一行与b的第一列对应的元素相乘的和为新产生的第一个元素
# [[ 417 380 254 446 555]
# [1262 1735 604 1281 1615]
# [2107 3090 954 2116 2675]
# [2952 4445 1304 2951 3735]
# [3797 5800 1654 3786 4795]]
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 18/5/15 下午4:56
# @Author : Wugang Li
# @File : spectal_juzhen.py
# @Software: PyCharm
# @license : Copyright(C), olei.me
# @Contact : i@olei.me
import numpy as np
a = np.arange(10)
print(a)
print(a.sum()) # 45
print(a.max()) # 0
print(a.min()) # 9
print(a.cumsum()) # [ 0 1 3 6 10 15 21 28 36 45]
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 18/5/15 下午5:13
# @Author : Wugang Li
# @File : high_juzhen.py
# @Software: PyCharm
# @license : Copyright(C), olei.me
# @Contact : i@olei.me
import numpy as np
a = np.arange(0, 100, 10)
print(a) # [ 0 10 20 30 40 50 60 70 80 90]
indics = [0, 1, 5, -1]
b = a[indics]
print(b) # [0,10 50 90]
布尔屏蔽是一个奇妙的特性,它允许我们根据指定条件获取数组中的元素。
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 18/5/15 下午5:56
# @Author : Wugang Li
# @File : boolen_masking.py
# @Software: PyCharm
# @license : Copyright(C), olei.me
# @Contact : i@olei.me
import matplotlib.pyplot as plt
import numpy as np
a = np.linspace(0, 2 * np.pi, 50)
print(a)
b = np.sin(a)
print(b)
plt.plot(a,b)
mask = b >= 0
plt.plot(a[mask], b[mask], 'bo')
mask = (b >= 0) & (a <= np.pi / 2)
plt.plot(a[mask], b[mask], 'go')
plt.show()
出现下面的图:
我们用条件式选择了图中不同的点。蓝色的点(也包含图中的绿点,只是绿点覆盖了蓝点),显示的是值大于零的点。绿点显示的是值大于 0 小于 Pi / 2 的点。
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 18/5/15 下午6:15
# @Author : Wugang Li
# @File : quexing_index_juzhen.py
# @Software: PyCharm
# @license : Copyright(C), olei.me
# @Contact : i@olei.me
import numpy as np
a = np.arange(0, 100, 10)
print(a) # [ 0 10 20 30 40 50 60 70 80 90]
b = a[:5]
c = a[a >= 50]
print(b) # [ 0 10 20 30 40]
print(c) # [50 60 70 80 90]
d = np.where(a < 50)
print(d) # (array([0, 1, 2, 3, 4]),)
e = np.where(a > 50)[0]
print(e) # [6 7 8 9]