values)ax.set_xlabel('Categories - how2matplotlib.com')ax.set_ylabel('Values - how2matplotlib.com')x_label=ax.xaxis.get_label_text()y_label=ax.yaxis.get_label_text()print(f"X轴标签:{x_label}")print(f"Y轴标签:{y_label}
x=np.arange(5)y=[2,4,1,5,3]fig,ax=plt.subplots(figsize=(10,6))ax.bar(x,y)ax.set_xticks(x)ax.set_xticklabels(['Category A','Category B','Category C','Category D','Category E'])ax.tick_params(axis='x',rotation=45)ax.set_title('Rotating Labels with tick_params() - how...
x=np.linspace(-5,5,100)y1=0.5*x y2=x*x plt.figure()plt.xlabel('X axis...')plt.ylabel('Y axis...')#以上为常规操作,就是设置基础数据 ax=plt.gca()#getcurrent axis 获得坐标轴对象以下以ax为基础进行操作 ax.spines['right'].set_color('none')#隐藏掉右边框线 ax.spines['top'].set...
fig, ax = plt.subplots()top_10.plot(kind='barh', y="Sales", x="Name", ax=ax)ax.set_xlim([-10000,140000])ax.set(title='2014 Revenue', xlabel='Total Revenue', ylabel='Customer')formatter =FuncFormatter(currency)ax.xaxis.set_major_formatter(formatter)ax.legend().set_visible(False)...
linestyle='-.')# 隐藏坐标轴的线条forspineinax.spines.values():spine.set_visible(False)# 隐藏上边与右边的刻度 ax.xaxis.tick_bottom() ax.yaxis.tick_left()# 弱化刻度与标签ax.tick_params(colors='gray',direction='out')fortickinax.get_xticklabels():tick.set_color('gray')fortickinax.get_...
values) ax.tick_params(axis='x', labelrotation=45) plt.grid(True) plt.show()旋转 xtickla...
values, c = 'k', linewidth = 1.5, alpha = .7) ax1.set_ylabel('pm2.5 浓度', font1) #设置y轴标签,字体样式为新建样式 #设置x轴刻度字体样式 plt.setp(ax1.get_xticklabels(), fontproperties = 'Times New Roman', size = 13) plt.setp(ax1.get_yticklabels(), fontproperties = 'Times...
, fontproperties = font_S) ax.set_xlabel('xaxis label', fontproperties=font_M) ax.plot(x,...
ax.spines['bottom'].set_visible(False)# 格式化x xticks = np.arange(0,1.1,0.1) xlabels = ['{}%'.format(i) for i in np.arange(0,101,10)] plt.xticks(xticks, xlabels)# 调整界限并绘制网格线 plt.ylim(-0.5, ax.get_yticks()[-1] + 0.5) ax.xaxis.grid(colo...
点的个数是30个,x坐标范围【0,10】。y坐标范围【-1.2,1.2】。 有xy坐标的标签文本,分别为x-axis,y-axis 有网格线,颜色与两条曲线不同,线型为: x = np.linspace(0,10,30) y = np.sin(x) z = np.cos(x) plt.xlim(0,10) plt.ylim(-1.2,1.2) plt.xlabel("x-axis") plt.ylabel("y-axis...