complex_numbers=[1+2j,2-1j,-3+4j,-2-2j]plt.figure(figsize=(10,8))forzincomplex_numbers:plt.quiver(0,0,z.real,z.imag,angles='xy',scale_units='xy',scale=1,label=f'{z}')plt.xlim(-5,5)plt.ylim(-5,5)plt.axhline(y=0,color='k',linestyle='--')plt.axvline(x=0,color=...
importnumpyasnp # create data of complex numbers using numpy data=np.arange(8)+1j*np.arange(-4,4) # extract real part using numpy x=data.real # extract imaginary part using numpy y=data.imag # plot the complex numbers plt.plot(x,y,'-.r*') plt.ylabel('Imaginary') plt.xlabel('Re...
def double_ball_number(red,blue): """Combine the red_numbers and the blue_numbers.""" numbers = int(input("Please eter your number that hou many you want: ")) for i in range(numbers): return "\nRed: {} Blue: {}\n".format(",".join(red),blue) #"".join():change list to ...
plt.plot(x_value, y_value) importmatplotlib.pyplotasplt x_value = [1,2,3,4,5] squares = [1,4,9,16,25] plt.plot(x_value, squares) plt.show() 执行后的结果: 绘制散点图 plt.scatter(x, y) importmatplotlib.pyplotasplt x_value = [1,2,3,4,5] y_value = [1,4,9,16,25] ...
我使用了 plot 函数并从 (0,0) 指定了实线。这些可以通过将ro-替换为ro来删除。 def argand(a):importmatplotlib.pyplotaspltimportnumpyasnpforxinrange(len(a)): plt.plot([0,a[x].real],[0,a[x].imag],'ro-',label='python')limit=np.max(np.ceil(np.absolute(a))) #setlimitsforaxis ...
importmatplotlib.pyplotaspltplt.plot([1,2,3,4])plt.ylabel('Some Numbers')plt.show() 如果一切顺利,您应该会看到一个简单的图表窗口,显示一个上升的线条。 基本图形的绘制 Matplotlib的核心是其绘图功能。让我们从绘制一些基本的图形开始。下面是一些常用图形的示例: ...
最基本的三维图是由(x, y, z)三维坐标点构成的线图与散点图,可以用ax.plot3D和ax.scatter3D函数来创建,默认情况下,散点会自动改变透明度,以在平面上呈现出立体感三维的线图和散点图#绘制三角螺旋线from mpl_toolkitsimport mplot3d%matplotlib inlineimport matplotlib.pyplot as pltimport python画三维散点图 ...
pip3 install fastplot Dependencies are:matplotlib numpy pandas statsmodels. FastPlot requires updated versions of such libraries, so, in case of error try first to upgrade them. Forseriffonts you needTimes New Roman, that, on Ubuntu, can be installed with: ...
Matplotlib is the most popular plotting library in python. Using matplotlib, you can create pretty much any type of plot. However, as your plots get more complex, the learning curve can get steeper. The goal of this tutorial is to make you understand ‘how plotting with matplotlib works’ ...
plot() accepts real and complex numbers as well as numpy.ndarray's lines can be automatically interpolated to improve the optical appearance data ranges can be interpolated to an equidistant spacing start/end markers of lines can be modified and rotate tangential ...