prop={'size': 14},framealpha=0.3) ax[0,0].tick_params(labelcolor='k', labelsize='20', width=3) ax[0,0].set_ylim...prop={'size': 14},framealpha=0.3) ax[0,1].tick_params(labelcolor='k', labelsize='20', width=3) ax[0,1].set_ylim...prop={'size': 14},framealpha=0.3) ax[1,0].tick_params(labelcolor='k', labelsize='20', width=3) ax[1,0].set_ylim...prop={'size': 14},framealpha=0.3) ax[1,1].tick_params(labelcolor='k', labelsize='20', width=3) ax[1,1].set_ylim
可以通过set_xlim/set_ylim实现。 3、添加文本，使用text方法。 实例 绘制双Y轴。
(x='param_min_samples_split', y='mean_test_score', data=rs_df, ax=axs[0,1], color='coral') axs[0,1].set_ylim...param_min_samples_leaf', y='mean_test_score', data=rs_df, ax=axs[0,2], color='lightgreen') axs[0,2].set_ylim...sns.barplot(x='param_max_features', y='mean_test_score', data=rs_df, ax=axs[1,0], color='wheat') axs[1,0].set_ylim...sns.barplot(x='param_max_depth', y='mean_test_score', data=rs_df, ax=axs[1,1], color='lightpink') axs[1,1].set_ylim...sns.barplot(x='param_bootstrap',y='mean_test_score', data=rs_df, ax=axs[1,2], color='skyblue') axs[1,2].set_ylim
lambda x: x / distance, args) f, ax = plt.subplots(nrows=3, figsize=(5, 10)) plt.tight_layout() ax[0].set_ylim...text(x + .05, y + .05, r"$y$") ax[0].set_title("No steps") #step 1 ax[1].set_title("Step 1") ax[1].set_ylim...', facecolor='black') ax[1].text(x + .05, y + .05, r"$y$") #step 2 ax[2].set_title("Step 2") ax[2].set_ylim
axs = plt.subplots(nrows=1, ncols=3, figsize=(10, 2), dpi=100) axs[0].set_title("Position") axs[0].set_ylim...(0, 700) axs[1].set_title("Velocity") axs[1].set_ylim(-200, 200) axs[2].set_title("Acceleration") axs...[2].set_ylim(-30, 10) for ax in axs: ax.set_xlim(0, 20) ax.grid(True) 我们只对图像中的y位置（数组索引1）感兴趣，
',data = titanic,hue = 'Sex' , fit_reg=False) lm.set(title = 'Fare x Age') axes = lm.axes axes[0,0].set_ylim
0, 0, 1, head_width=0.1, head_length=0.1, fc='red', ec='red') ax[0].set_xlim(-1, 1.5) ax[0].set_ylim...0.7, 0.7, head_width=0.1, head_length=0.1, fc='red', ec='red') ax[1].set_xlim(-1, 1.5) ax[1].set_ylim
patches.Polygon(xy=list(zip(x,y)),color="#DF7373",alpha=.5)) axs[0,0].set_xlim(left=-.6,right=1.6) axs[0,0].set_ylim...patches.Polygon(xy=list(zip(x,y)),color="#DEA060",alpha=.5)) axs[0,1].set_xlim(left=-.6,right=1.6) #axs[1].set_ylim...patches.Polygon(xy=list(zip(x,y)),color="#A8A3C7",alpha=.5)) axs[1,0].set_xlim(left=-.6,right=1.6) #axs[1].set_ylim...patches.Polygon(xy=list(zip(x,y)),color="#527B91",alpha=.5)) axs[1,1].set_xlim(left=-.6,right=1.6) #axs[1].set_ylim
linefmt='b-', markerfmt='bo', basefmt='w-') ax[i].set_xlim(-50, 4350) ax[i].set_ylim...:], linefmt='r-', markerfmt='ro', basefmt='w-') ax[i].set_xlim(-1, 21) ax[i].set_ylim...linefmt='b-', markerfmt='bo', basefmt='w-') ax[i].set_xlim(-50, 4350) ax[i].set_ylim...:], linefmt='r-', markerfmt='ro', basefmt='w-') ax[i].set_xlim(-1, 21) ax[i].set_ylim
np.arange(1, 1.5, 0.01)*np.pi, np.cos(np.arange(1, 1.5, 0.01)*np.pi), 1, alpha=0.5, color='red') axe[0, 0].set_ylim...2.5, 3, 0.01)*np.pi), 1, alpha=0.5, color='red') axe[0, 1].set_title('convex+decreasing') axe[0, 1].set_ylim...1.5, 2, 0.01)*np.pi), 1, alpha=0.5, color='red') axe[1, 0].set_title('concave+increasing') axe[1, 0].set_ylim..., 2.5, 0.01)*np.pi), 1, alpha=0.5, color='red') axe[1, 1].set_title('concave+decreasing') axe[1, 1].set_ylim
然后就是通过set_xlim和set_ylim来设置坐标轴xy的范围。set_aspect设置坐标系的长宽比为1，保持长宽比相等。
markerfmt='bo', basefmt='w-') ax[i].set_xlim(-2,20) ax[i].set_ylim...markerfmt='ro', basefmt='w-') ax[i].set_xlim(-1, 2) #x坐标下标 ax[i].set_ylim
= axs[0].add_collection(lc) fig.colorbar(line, ax=axs[0]) axs[0].set_xlim(x.min(), x.max()) axs[0].set_ylim
[0, 0].plot(X_, true_y, '--', label='True linear relation') axes[0, 0].set_xlim(0, 11) axes[0, 0].set_ylim...[0, 0].plot(X_, true_y, '--', label='True linear relation') axes[0, 0].set_xlim(0, 11) axes[0, 0].set_ylim
再配合set_xlim()、set_ylim()，以及极坐标系子图专有的set_thetagrids()、set_rgrids和set_theta_offset()来完成限定圆形显示的角度范围、半径范围
plot_learning_curves(history): pd.DataFrame(history.history).plot(figsize=(8, 5)) plt.grid(True) plt.gca().set_ylim
图的范围 我们想做的第一件事也许是设置坐标轴的范围，可以使用 set_ylim 或是set_xlim 方法或者 axis('tight') 自动将坐标轴调整的紧凑 The first thing we...We can do this using the set_ylim and set_xlim methods in the axis object, oraxis('tight') for automatrically...x**3) axes[1].axis('tight') axes[1].set_title("tight axes") axes[2].plot(x, x**2, x, x**3) axes[2].set_ylim
matplotlib import pyplot as plt pd.DataFrame(history.history).plot(figsize=(8, 5)) plt.grid(True) plt.gca().set_ylim
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