掌握Python的X篇_33_MATLAB的替代组合NumPy+SciPy+Matplotlib
numPy 通常与 SciPy( Scientific Python )和 Matplotlib (绘图库)一起使用，这种组合广泛用于替代 MatLab，是一个强大的科学计算环境，有助于我们通过 Python 学习数据科学或者机器学习。
文章目录
- 1. numpy
-
- 1.1 numpy简介
- 1.2 矩阵类型的nparray
- 2. Matplotlib
-
- 2.1 Matplotlib简介
- 2.2 Matplotlib使用实例
1. numpy
1.1 numpy简介
numpy /nampai/数值计算库，简单而言，可以被当做向量，线性代数计算。
pip install numpy
官方推荐导入方式：
以np的别名导入numpy，这可能是因为历史遗留问题，有些第三方库是以np的别名导入的numpy库。
import numpy as np
使用实例：
In [11]: import numpy as np
In [12]: np.pi
Out[12]: 3.141592653589793
1.2 矩阵类型的nparray
In [14]: x = np.linspace(-2*np.pi,2*np.pi,100) #在-2pi到2pi这个范围得到100个点，得到一个向量
In [15]: type(x)
Out[15]: numpy.ndarray
In [16]: x
Out[16]:
array([-6.28318531, -6.15625227, -6.02931923, -5.9023862 , -5.77545316,
-5.64852012, -5.52158709, -5.39465405, -5.26772102, -5.14078798,
-5.01385494, -4.88692191, -4.75998887, -4.63305583, -4.5061228 ,
-4.37918976, -4.25225672, -4.12532369, -3.99839065, -3.87145761,
-3.74452458, -3.61759154, -3.4906585 , -3.36372547, -3.23679243,
-3.10985939, -2.98292636, -2.85599332, -2.72906028, -2.60212725,
-2.47519421, -2.34826118, -2.22132814, -2.0943951 , -1.96746207,
-1.84052903, -1.71359599, -1.58666296, -1.45972992, -1.33279688,
-1.20586385, -1.07893081, -0.95199777, -0.82506474, -0.6981317 ,
-0.57119866, -0.44426563, -0.31733259, -0.19039955, -0.06346652,
0.06346652, 0.19039955, 0.31733259, 0.44426563, 0.57119866,
0.6981317 , 0.82506474, 0.95199777, 1.07893081, 1.20586385,
1.33279688, 1.45972992, 1.58666296, 1.71359599, 1.84052903,
1.96746207, 2.0943951 , 2.22132814, 2.34826118, 2.47519421,
2.60212725, 2.72906028, 2.85599332, 2.98292636, 3.10985939,
3.23679243, 3.36372547, 3.4906585 , 3.61759154, 3.74452458,
3.87145761, 3.99839065, 4.12532369, 4.25225672, 4.37918976,
4.5061228 , 4.63305583, 4.75998887, 4.88692191, 5.01385494,
5.14078798, 5.26772102, 5.39465405, 5.52158709, 5.64852012,
5.77545316, 5.9023862 , 6.02931923, 6.15625227, 6.28318531])
In [17]: y = np.cos(x) #每个点进行计算
In [18]: y
Out[18]:
array([ 1. , 0.99195481, 0.9679487 , 0.92836793, 0.87384938,
0.80527026, 0.72373404, 0.63055267, 0.52722547, 0.41541501,
0.29692038, 0.17364818, 0.04758192, -0.07924996, -0.20480667,
-0.32706796, -0.44406661, -0.55392006, -0.65486073, -0.74526445,
-0.82367658, -0.88883545, -0.93969262, -0.97542979, -0.99547192,
-0.99949654, -0.98743889, -0.95949297, -0.91610846, -0.85798341,
-0.78605309, -0.70147489, -0.60560969, -0.5 , -0.38634513,
-0.26647381, -0.14231484, -0.01586596, 0.1108382 , 0.23575894,
0.35688622, 0.47227107, 0.58005691, 0.67850941, 0.76604444,
0.84125353, 0.90292654, 0.95007112, 0.9819287 , 0.99798668,
0.99798668, 0.9819287 , 0.95007112, 0.90292654, 0.84125353,
0.76604444, 0.67850941, 0.58005691, 0.47227107, 0.35688622,
0.23575894, 0.1108382 , -0.01586596, -0.14231484, -0.26647381,
-0.38634513, -0.5 , -0.60560969, -0.70147489, -0.78605309,
-0.85798341, -0.91610846, -0.95949297, -0.98743889, -0.99949654,
-0.99547192, -0.97542979, -0.93969262, -0.88883545, -0.82367658,
-0.74526445, -0.65486073, -0.55392006, -0.44406661, -0.32706796,
-0.20480667, -0.07924996, 0.04758192, 0.17364818, 0.29692038,
0.41541501, 0.52722547, 0.63055267, 0.72373404, 0.80527026,
0.87384938, 0.92836793, 0.9679487 , 0.99195481, 1. ])
numPy 通常与 SciPy( Scientific Python )和 Matplotlib (绘图库)一起使用，这种组合广泛用于替代 MatLab，是一个强大的科学计算环境，有助于我们通过 Python 学习数据科学或者机器学习。
2. Matplotlib
2.1 Matplotlib简介
安装：
In [19]: pip install matplotlib
如果安装失败，可以尝试升级pip，命令如下：
python -m pip install -U pip
官网 : https://matplotlib.org/
官方推荐导入方式：
import matplotlib.pyplot as plt
2.2 Matplotlib使用实例
实例1：绘制cos图
In [21]: plt.plot(x,y)
In [21]: plt.plot(x,y)
Installed tk event loop hook.
Out[21]: [<matplotlib.lines.Line2D at 0x2b20f74b760>]
In [23]: plt.show()
运行结果：
实例2：以脚本形式，绘制复杂的图
import numpy as np
import matplotlib.pyplot as plt
if __name__ == "__main__":
x = np.linspace(-2*np.pi,2*np.pi,100)
y = np.cos(x) + np.cos(2*x) + np.cos(3*x)
plt.plot(x,y)
plt.show()
运行结果如下：
3. 学习视频地址：MATLAB的替代组合NumPy+SciPy+Matplotlib