points, = ax.plot(x, y, marker='o', linestyle='-', color='blue')defon_hover(event):ifevent.inaxes == ax:fori, (px, py)inenumerate(zip(x, y)):ifabs(event.xdata - px) <0.2andabs(event.ydata - py) <2: plt.annotate(f"({px},{py})", (px, py), textcoords="offset po...
使用googlecolab(类似于Jupyter笔记本),我试图读取格式为city,lat,long的cities.csv文件 我能画出这幅图,但我似乎不知道如何把[城市]写在每个绘图点的上方。 import matplotlib import matplotlib.pyplot as plt import pandas as pd import numpy as np col_names=['City','Lat','Long'] da = pd.read_csv(...
fig=plt.figure(figsize=(15,6))ax=fig.add_subplot(111,xlim=(2002.5,2021.5),ylim=(0,6.5),yticks=([]))ax.tick_params("x",labelsize="x-small",which="major")ax.set_xticks(np.arange(2003,2022,2))# step2 plt.plot([2002.5,2021.5],[0,0],color="black",linewidth=1.0,clip_on=False)...
ax.plot([2], [1], 'o') ax.annotate('annotate', xy=(2, 1), xytext=(3, 4), arrowprops=dict(facecolor='black', shrink=0.05)) # 设置显示范围 ax.axis([0, 10, 0, 10]) plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20...
clim(3, 7) # Here we create a legend: # we'll plot empty lists with the desired size and label for area in [100, 300, 500]: plt.scatter([], [], c='k', alpha=0.3, s=area,#通过s控制图例的大小 label=str(area) + ' km^2') plt.legend(scatterpoints=1, frameon=False, ...
# Plot the data, set the size (s), color and transparency (alpha) # of the points ax.scatter(x_data, y_data, s = 10, color = color, alpha = 0.75) if yscale_log == True: ax.set_yscale('log') # Label the axes and provide a title ...
# step1 fig = plt.figure(figsize=(15, 6)) ax = fig.add_subplot(111, xlim=(2002.5, 2021.5), ylim=(0, 6.5), yticks=([])) ax.tick_params("x", labelsize="x-small", which="major") ax.set_xticks(np.arange(2003, 2022, 2)) # step2 plt.plot([2002.5, 2021.5], [0, 0], ...
x = np.linspace(0, 2, 100)fig, ax = plt.subplots() # Create a figure and an axes.l1 = ax.plot(x, x, label="linear")l2 = ax.plot(x, x ** 2, label="quadratic")l3 = ax.plot(x, x ** 3, label="cubic")ax.set_title("Simple Plot")plt.show()这很简单，只需在axes...
ax.plot(x, np.sin(x)); 同样的,我们可以使用 pylab 接口(MATLAB 风格的接口)帮我们在后台自动创建这两个对象: plt.plot(x, np.sin(x)); 如果我们需要在同一幅图形中绘制多根线条,只需要多次调用plot函数即可: plt.plot(x, np.sin(x)) plt.plot...
可以在Ipython中输入类似"plt.plot??"的命令查看pyplot模块的函数是如何对各种绘图对象进行包装的。 配置属性 matplotlib所绘制的图表的每个组成部分都和一个对象对应,我们可以通过调用这些对象的属性设置方法set_*()或者pyplot模块的属性设置函数setp()设置它们的属性值。