coef1 = pd.DataFrame() for alpha in np.linspace(0.0001,0.2,20): lasso_clf = Lasso(alpha=alpha) lasso_clf.fit(X_train[features_without_ones],y_train) df = pd.DataFrame([lasso_clf.coef_],columns=X_train[features_without_ones].columns) df['alpha'] = alpha coef1 = pd.concat([coef1,df],ignore_index=True) coef1.head() plt.figure(figure=(9,6),dpi=600) for feature in X_train.columns[:-1]: plt.plot('alpha',feature,data=coef1) plt.legend(loc='upper right') plt.xlabel(r'$\alpha$',fontsize=15) plt.ylabel("coefficient",fontsize=15) plt.show() RuntimeError: Can not put single artist in more than one figure Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

这个错误是因为您尝试将单个艺术家（Artist）放置到多个Figure中。您需要在循环外面创建一个Figure，然后在循环中使用subplot来绘制每个特征的系数。以下是修改后的代码：

coef1 = pd.DataFrame()
fig, ax = plt.subplots(figsize=(9,6), dpi=600)
for feature in X_train.columns[:-1]:
    for alpha in np.linspace(0.0001,0.2,20):
        lasso_clf = Lasso(alpha=alpha)
        lasso_clf.fit(X_train[features_without_ones],y_train)
        df = pd.DataFrame([lasso_clf.coef_],columns=X_train[features_without_ones].columns)
        df['alpha'] = alpha
        coef1 = pd.concat([coef1,df],ignore_index=True)
    ax.plot('alpha',feature,data=coef1)
ax.legend(loc='upper right')
ax.set_xlabel(r'$\alpha$',fontsize=15)
ax.set_ylabel("coefficient",fontsize=15)
plt.show()

这个修改后的代码将循环嵌套，首先对每个特征进行循环，然后在每个特征的循环中对alpha进行循环。在每个特征的循环中，我们使用subplot来绘制每个特征的系数。