做瑞士网站,深圳外贸网站怎么建,怎么查个人征信记录,wordpress 下载选择numPy 通常与 SciPy( Scientific Python )和 Matplotlib (绘图库)一起使用#xff0c;这种组合广泛用于替代 MatLab#xff0c;是一个强大的科学计算环境#xff0c;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. numpy1.1 numpy简介1.2 矩阵类型的nparra…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. numpy1.1 numpy简介1.2 矩阵类型的nparray 2. Matplotlib2.1 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 npIn [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 plt2.2 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 pltif __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NumPySciPyMatplotlib
