3. '-.' dash-dot line style 4. ':' dotted line style 三、数据点形状 1. '.' point marker 2. 'o' circle marker 3. 'v' triangle_down marker 4. '^' triangle_down marker 5. '>' triangle_left marker 6...
plt.title('Sample Plot Title', fontsize=18, color='navy')plt.legend(['Data Series A'], loc='upper left', fontsize=12)plt.annotate('Important Point', xy=(2, 5), xytext=(3, 6),arrowprops=dict(facecolor='black', shrink=0.05))5. 调整坐标轴 可以自定义坐标轴的范围、刻度、标签等...
plt.title("票房情况",fontsize=20, loc="center", pad=20) plt.plot([0.5,1, 3, 6], [1,3, 0.5, 6], marker='o', color='r') plt.annotate("最低点",xy=(3,0.5), xytext=(3, 3), color='r', arrowprops=dict(arrowstyle='|-|', color='r'),bbox=dict(boxstyle='round,pad=...
‘.’:点(pointmarker)‘,’:像素点(pixelmarker)‘o’:圆形(circlemarker)‘v’:朝下三角形(triangle_downmarker)‘^’:朝上三角形(triangle_upmarker)‘<‘:朝左三角形(triangle_leftmarker)‘>’:朝右三角形(triangle_rightmarker)‘1’:(tri_downmarker)‘2’:(tri_upmarker)‘3’:(tri_leftmarker)...
``'-.'`` dash-dot line style ``':'`` dotted line style ``'.'`` point marker ``','`` pixel marker ``'o'`` circle marker ``'v'`` triangle_down marker ``'^'`` triangle_up marker ``'<'`` triangle_left marker ``'>'`` triangle_right marker ...
line style 线样式 # lw: line width 线宽度 # label:标签 # mfc:marker face color 标记的背景颜色 # markersize:标记大小 # markeredgecolor:标记(点)的边缘颜色 # markeredgewidth:标记(点)边缘的宽度 # alpha:透明度(0~1) plt.plot(x, y1, c="r", marker="o", ls="--", lw=1, label="sinx...
""# c：color颜色# marker：标记# ls: line style 线样式# lw: line width 线宽度# label：标签# mfc：marker face color 标记的背景颜色# markersize：标记大小# markeredgecolor：标记（点）的边缘颜色# markeredgewidth：标记（点）边缘的宽度# alpha：透明度（0~1）plt.plot(x, y1, c="r", marker="...
figureimagemarkerplotsubplot 1. pyplot模块 1.1. color的值 blue 1.2. Marker的值 point marker 1.3. LineStyles的值 solid line style 例子: 'b' # blue markers with default shape 'ro' # red circles 'g-' # green solid line '--' # dashed line with default color 'k^:' # black triangle_...
matplotlib中有两种plot绘制折线的方式,分别是 matplotlib.axes.Axes.plot(……) matplotlib.pyplot.plot(……) 这两者的作用都是绘制折线,参数也相同,区别在于绘制的位置,Axes.plot用于在子画布上绘图,而pyplot.plot则是在总画布上绘图 比如我们有 fig, axs = plt.subplots(2, 2)#将一个画布分为2*2的子画布...
ax.plot(values) # 辅助点 point_xx = [point[0] for point in points] point_yy = [point[1] for point in points] point_cc = ["r" for i in range(len(points))] point_labels = ["A", "B", "C", "D"] ax.scatter(x=point_xx, y=point_yy, c=point_cc) ...