importmatplotlib.pyplotaspltimportnumpyasnp# 数据x=np.arange(1,11)y1=x**2y2=x**3plt.plot(x,y1,label='y=x^2')plt.plot(x,y2,label='y=x^3')plt.xlabel('x-axis')plt.ylabel('y-axis')plt.title('Multi-Line Plot')plt.leg
ax.xaxis.set_ticks_position('top')# x刻度颜色设置为灰色,大小为12ax.tick_params(axis='x',colors='#777777',labelsize=12)# 去掉y刻度 ax.set_yticks([])# 设置 xy轴内边距 ax.margins(0,0.01)# 显示网格(x轴向虚线) ax.grid(which='major',axis='x',linestyle='--')# 网格线至于底部 a...
ax.plot(x, np.cos(x), lw=3, label='Cosine')#设置网格ax.grid(True)#设置图例ax.legend(frameon=False)#设置坐标轴等距ax.axis('equal')#设置x坐标轴上下限ax.set_xlim(0,3* np.pi) #自定义坐标标签#使用美元符号$将LaTex字符串括起来,可以显示数学符号和公式:$\pi$defformat_func(value, tick_...
代码运行次数:0 defplot_and_save(percentages,output_directory,filename,title,legend,x_label,y_label,font_size=16):"""Plot data and save figure to file."""matplotlib.rcParams['font.size']=font_size ax1=percentages.plot.bar(color=['#484D7A','#F6A124'])ax1.grid(which='major',axis='y...
data = df.melt(id_vars='地区',value_vars=range(1993,2020),var_name='年份',value_name='生产总值(亿)') data.head() 1. 2. 3. 4.matplotlib动态可视化 matplotlib动态图用到的是animation.FuncAnimation方法,其实动态就是N张图一张一张按照一定频率刷新,我们也有其他方法实现,这里不展开。
result = pd.concat([many_country_list, many_suc_list], axis=1) result.columns = ['all', 'suc'] result 1 2 3 4 5 6 7 8 9 10 11 12 13 14 plt.figure(figsize=(16, 9)) plt.bar([x for x in range(10)], result['all']) plt.xticks([x for x in range(10)], result.ind...
ax.xaxis.set_major_locator(MultipleLocator(1)) # 自动绘制刻度的话显示的可能比较分散,不好看 # ax.xaxis.set_major_locator(mdates.AutoDateLocator()) # 在折线图上显示数值 for i in range(len(x)): # 每个点都展示数据 plt.text( x[i], y[i], str(y[i]), ha='left', va='bottom' )...
axins.plot(x,y)# sub region of the original image x1, x2, y1, y2 = 2.5, 7.5, 2.5, 7.5 axins.set_xlim(x1, x2)axins.set_ylim(y1, y2)# fix the number of ticks on the inset axes axins.yaxis.get_major_locator().set_params(nbins=10)axins.xaxis.get_major_locator()....
plt.rcParams['font.sans-serif']=['SimHei']plt.rcParams['axes.unicode_minus']=False# 最后,以5秒为一个间隔,看看弹幕的分布情况(有时候精细未必是好事,扩大间隔有助于分析问题)foriinrange(0,110,5):count=df.loc[(i<=df['时间'])&(df['时间']...
And if the user wants to plot an even smaller slope, the ratio (y-axis range)/(x-axis range) will be be even smaller, and the corresponding tolerance will be smaller than 1E-16, although unnecessarily, because of machine error. The benefit of this fix is that both ways of specifying...