fix picture not shown problem

docs/6.md

@@ -171,7 +171,7 @@ sns.residplot(x="x", y="y", data=anscombe.query("dataset == 'II'"),
 sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips);
 ```
 
-![http://seaborn.pydata.org/_images/regression_37_0.png](/Users/huangcancan/Dropbox%20(Brown)/3_Private_Files/img/c1be6d813a335d32887cfd051ef9167f.jpg)
+![http://seaborn.pydata.org/_images/regression_37_0.png](img/c1be6d813a335d32887cfd051ef9167f.jpg)
 
 除了颜色之外，还可以使用不同的散点图标记来使绘图更好地再现为黑白。你还可以完全控制使用的颜色：
 
@@ -180,7 +180,7 @@ sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips,
            markers=["o", "x"], palette="Set1");
 ```
 
-![http://seaborn.pydata.org/_images/regression_39_0.png](/Users/huangcancan/Dropbox%20(Brown)/3_Private_Files/img/b065f9278c97b242c9a517aa98c090fa.jpg)
+![http://seaborn.pydata.org/_images/regression_39_0.png](img/b065f9278c97b242c9a517aa98c090fa.jpg)
 
 要添加另一个变量，你可以绘制多个"facet"，其中每个级别的变量出现在网络的行或列中：
 
@@ -188,14 +188,14 @@ sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips,
 sns.lmplot(x="total_bill", y="tip", hue="smoker", col="time", data=tips);
 ```
 
-![http://seaborn.pydata.org/_images/regression_41_0.png](/Users/huangcancan/Dropbox%20(Brown)/3_Private_Files/img/02113b4a6b876c8e3b32dd2bb7eae74c.jpg)
+![http://seaborn.pydata.org/_images/regression_41_0.png](img/02113b4a6b876c8e3b32dd2bb7eae74c.jpg)
 
 ```python
 sns.lmplot(x="total_bill", y="tip", hue="smoker",
            col="time", row="sex", data=tips);
 ```
 
-![http://seaborn.pydata.org/_images/regression_42_0.png](/Users/huangcancan/Dropbox%20(Brown)/3_Private_Files/img/eb0843cd39205e7144eb4acdf1b8356d.jpg)
+![http://seaborn.pydata.org/_images/regression_42_0.png](img/eb0843cd39205e7144eb4acdf1b8356d.jpg)
 
 ## 控制绘图的大小和形状
 
@@ -206,7 +206,7 @@ f, ax = plt.subplots(figsize=(5, 6))
 sns.regplot(x="total_bill", y="tip", data=tips, ax=ax);
 ```
 
-![http://seaborn.pydata.org/_images/regression_44_0.png](/Users/huangcancan/Dropbox%20(Brown)/3_Private_Files/img/a94aa08c017f4688743921ccc9d8a4d0.jpg)
+![http://seaborn.pydata.org/_images/regression_44_0.png](img/a94aa08c017f4688743921ccc9d8a4d0.jpg)
 
 相比之下，[`lmplot()`](../generated/seaborn.lmplot.html#seaborn.lmplot "seaborn.lmplot")图的大小和形状是通过[`lmplot()`](http://typora-app/generated/seaborn.lmplot.html#seaborn.lmplot)接口，使用`size`和`aspect`参数控制，这些参数适用于绘图中的每个`facet`，而不是整个图形本身：
 
@@ -215,14 +215,14 @@ sns.lmplot(x="total_bill", y="tip", col="day", data=tips,
            col_wrap=2, height=3);
 ```
 
-![http://seaborn.pydata.org/_images/regression_46_0.png](/Users/huangcancan/Dropbox%20(Brown)/3_Private_Files/img/fce6798715088ab4d9f615ae89a67b2c.jpg)
+![http://seaborn.pydata.org/_images/regression_46_0.png](img/fce6798715088ab4d9f615ae89a67b2c.jpg)
 
 ```python
 sns.lmplot(x="total_bill", y="tip", col="day", data=tips,
            aspect=.5);
 ```
 
-![http://seaborn.pydata.org/_images/regression_47_0.png](/Users/huangcancan/Dropbox%20(Brown)/3_Private_Files/img/ea6e78ca63d3d86dece589f475f2338d.jpg)
+![http://seaborn.pydata.org/_images/regression_47_0.png](img/ea6e78ca63d3d86dece589f475f2338d.jpg)
 
 ## 在其他情境中绘制回归
 
@@ -232,7 +232,7 @@ sns.lmplot(x="total_bill", y="tip", col="day", data=tips,
 sns.jointplot(x="total_bill", y="tip", data=tips, kind="reg");
 ```
 
-![http://seaborn.pydata.org/_images/regression_49_0.png](/Users/huangcancan/Dropbox%20(Brown)/3_Private_Files/img/0a3f41c0a016c66f0f3379c128f550b9.jpg)
+![http://seaborn.pydata.org/_images/regression_49_0.png](img/0a3f41c0a016c66f0f3379c128f550b9.jpg)
 
 使用[`pairplot()`](../generated/seaborn.pairplot.html#seaborn.pairplot "seaborn.pairplot")函数与`kind="reg"`将 [`regplot()`](../generated/seaborn.regplot.html#seaborn.regplot "seaborn.regplot")和[`PairGrid`](../generated/seaborn.PairGrid.html#seaborn.PairGrid "seaborn.PairGrid") 结合起来，来显示数据集中变量的线性关系。请注意这与[`lmplot()`](../generated/seaborn.lmplot.html#seaborn.lmplot "seaborn.lmplot")的不同之处。在下图中，两个轴在第三变量上的两个级别上没有显示相同的关系；相反，[`PairGrid()`](../generated/seaborn.PairGrid.html#seaborn.PairGrid "seaborn.PairGrid")用于显示数据集中变量的不同配对之间的多个关系。
 
@@ -241,7 +241,7 @@ sns.pairplot(tips, x_vars=["total_bill", "size"], y_vars=["tip"],
              height=5, aspect=.8, kind="reg");
 ```
 
-![http://seaborn.pydata.org/_images/regression_51_0.png](/Users/huangcancan/Dropbox%20(Brown)/3_Private_Files/img/65fcd97ee44e136d797a4d343a58cc4f.jpg)
+![http://seaborn.pydata.org/_images/regression_51_0.png](img/65fcd97ee44e136d797a4d343a58cc4f.jpg)
 
 像[`lmplot()`](../generated/seaborn.lmplot.html#seaborn.lmplot "seaborn.lmplot")，但不像[`jointplot()`](../generated/seaborn.jointplot.html#seaborn.jointplot "seaborn.jointplot")，额外的分类变量调节是通过`hue`参数内置在函数[`pairplot()`](../generated/seaborn.pairplot.html#seaborn.pairplot "seaborn.pairplot")中：
 
@@ -250,4 +250,4 @@ sns.pairplot(tips, x_vars=["total_bill", "size"], y_vars=["tip"],
              hue="smoker", height=5, aspect=.8, kind="reg");
 ```
 
-![http://seaborn.pydata.org/_images/regression_53_0.png]()
\ No newline at end of file
+![http://seaborn.pydata.org/_images/regression_53_0.png](img/b166f746ed213b5ac4e495320f99b0fd.jpg)