6))plt.scatter(x,y,alpha=0.5)plt.axhline(y=0.5,color='r',linestyle='--',label='Threshold')plt.title('Scatter Plot with Horizontal Reference Line - how2matplotlib.com')plt.xlabel('X-axis')plt.ylabel('Y-axis')plt.legend()plt.show()...
rotation参数可以接受度数值或’vertical’、’horizontal’等预设值。 3. 结合axhline和text 现在我们已经了解了axhline和text的基本用法,让我们看看如何结合这两个函数来创建更有信息量的图表。 3.1 标记平均值 一个常见的用例是使用水平线标记数据的平均值,并添加文本说明: importmatplotlib.pyplotaspltimportnumpyas...
ax=plt.subplots()# 绘制水平柱状图bars=ax.barh(categories,values,color='skyblue')ax.bar_label(bars,labels=[f'{val*100:.2f}%'forvalinvalues])# 设置标题和标签ax.set_title('Horizontal Bar Chart with Percent Labels
drawedges: 布尔值,是否在颜色边界处绘制线条。 label: 字符串,颜色条长轴上的标签。 boundaries: None 或序列,颜色条的边界。 values: None 或序列,颜色条的值,必须比 boundaries 长度少一。 cmap展示 这个程序展示了所有的cmap import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import...
# Draw Horizontal Tick lines for y in range(5, 10, 1): plt.hlines(y, xmin=s, xmax=e, colors='black', alpha=0.5, linestyles="--", lw=0.5) plt.show() 43 堆积面积图 (Stacked Area Chart) 堆积面积图可以直观地显示多个时间序列的贡献程度,因此很容易相互比较。
→ ax.axvline(x=0.5) … draw outside frame? → ax.plot(…, clip_on=False) … use transparency? → ax.plot(…, alpha=0.25) … convert an RGB image into a gray image? → gray = 0.2989*R+0.5870*G+0.1140*B … set figure background color? → fig.patch.set_facecolor(“grey”) ...
# 黄色填充满足条件的区域ax.fill_between(x,y1,y2,where=(y2>y1),facecolor='yellow',alpha=0.5)# set the transparency of the areaxmin,xmax,ymin,ymax=ax.axis()ax.hlines(0,xmin,xmax,ls='-.')# draw a horizontal line# 使用annotate指令添加箭头添加标记ax.annotate('y2>y1',xy=(1.2,10...
# Draw plot import matplotlib.patches as patches fig, ax = plt.subplots(figsize=(16,10), facecolor='white', dpi= 80) ax.vlines(x=df.index, ymin=0, ymax=df.cty, color='firebrick', alpha=0.7, linewidth=20) # Annotate Text
# Draw Plot fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(16,6), dpi= 80) plot_acf(df.traffic.tolist(), ax=ax1, lags=50) plot_pacf(df.traffic.tolist(), ax=ax2, lags=20) # Decorate # lighten the borders ax1.spines["top"].set_alpha(.3); ax2.spines["top"].set_alph...
line, = ax.plot(x,np.sin(x)) def animate(i): line.set_ydata(np.sin(x + i/10.0))#updata the data return line, def init(): line.set_ydata(np.sin(x)) return line, # call the animator. blit=True means only re-draw the parts that have changed. ...