importmatplotlib.pyplotaspltimportnumpyasnp x=np.linspace(0,10,100)y=np.log(x+1)fig,ax=plt.subplots()ax.plot(x,y)ax.xaxis.set(label='X-axis (how2matplotlib.com)',labelsize=14,labelcolor='red')ax.yaxis.set(label='Y-axis (how2matplotlib.com)',labelsize=14,labelcolor='blue')...
2,100)y=x**2ax.plot(x,y)# 设置刻度位置和LaTeX格式的标签ticks=[0,0.5,1,1.5,2]labels=['0','\\frac{1}{2}','1','\\frac{3}{2}','2']ax.xaxis.set_ticks(ticks)ax.xaxis.set_ticklabels(labels)plt.title('LaTeX formatted labels - how2matplotlib.com')plt.show()...
默认情况下,matplotlib会自动帮我们调节刻度的数量,但有时候也需要我们自定义刻度数量: fig, ax = plt.subplots(4, 4, sharex=True, sharey=True) for axi in ax.flat: axi.xaxis.set_major_locator(plt.MaxNLocator(4)) axi.yaxis.set_major_locator(plt.MaxNLocator(4)) plt.show() 1. 2. 3. ...
通过调用axis(),可以实现将图形变得紧凑、调整坐标轴的刻度范围和隐藏坐标轴的显示。 import matplotlib.pyplot as plt import numpy as np #set #1 plot plt.axes([0.05,0.7,0.3,0.3],frameon=True,facecolor="y",aspect="equal") plt.plot(np.arange(3),[0,1,0],color="blue",linewidth = 2,linestyle...
importmatplotlib.pyplotaspltfromcollectionsimportCounter# 统计set中每个元素的频率counter=Counter(data)# 绘制饼状图plt.pie(counter.values(),labels=counter.keys(),autopct='%1.1f%%')plt.axis('equal')plt.show() 1. 2. 3. 4. 5. 6. 7. ...
import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1) x = np.concatenate([x] * 3, axis=1) # generate 3 curves y = np.copy(x) y[:, 0] = np.cos(y[:, 0]) y[:, 1] = np.sin(y[:, 1] ) ...
Plot.set_aspect('equal') In this case X,Y,Z describe points on the surface of a sphere (could be something else); notice the use of get_xlim3d to establish the axis dimensions. As far as I can tell from the discussion, folks were trying to use set_aspect(“equal”) to do this....
importmatplotlib.pyplotaspltdefexample_plot(xmax):fig, (ax1,ax2)=plt.subplots(ncols=2,layout='constrained')ax1.set_xlim([0,xmax])ax1.set_xticks([0,100])ax1.xaxis.set_in_layout(False)fig.savefig(f'example_x{xmax}.png')plt.close(fig)forxmaxin100,120:example_plot(xmax) ...
import pandas as pd import numpy as np import matplotlib.pyplot as plt categories = ['Cat1', 'Cat2', 'Cat3', 'Cat4', 'Cat5'] data_sets = {'Set1': [0.151, 0.015, 0.110, 0.204, 0.110], 'Set2': [0.146, 0.025, 0.088, 0.151, 0.088], 'Set3': [0.161, 0.027, 0.122, 0.217...
在Python的matplotlib库中,你可以使用set_axis_比例尺()函数来改变图形的大小,但这不是严格意义上的"setzoom"函数,因为matplotlib不直接提供这个函数。下面是一个示例: python import matplotlib.pyplot as plt fig, ax = plt.subplots() ax.plot([1, 2, 3, 4]) ax.set_ylim(0, 10) #设置y轴范围 ax....