diff --git a/doc/pub/week43/html/._week43-bs069.html b/doc/pub/week43/html/._week43-bs069.html
index 752708b85..76f663a70 100644
--- a/doc/pub/week43/html/._week43-bs069.html
+++ b/doc/pub/week43/html/._week43-bs069.html
@@ -326,7 +326,7 @@ principal component is equal to
 <p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>]<span style="color: #666666">.</span>
+<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>]
 </pre></div>
 <p>
 Another very useful piece of information is the explained variance ratio of each principal component,
diff --git a/doc/pub/week43/html/week43-reveal.html b/doc/pub/week43/html/week43-reveal.html
index b6801081e..e2f0bd997 100644
--- a/doc/pub/week43/html/week43-reveal.html
+++ b/doc/pub/week43/html/week43-reveal.html
@@ -3886,7 +3886,7 @@ principal component is equal to
 <p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>pca.components_.T[:, <span style="color: #B452CD">0</span>].
+<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>pca.components_.T[:, <span style="color: #B452CD">0</span>]
 </pre></div>
 <p>
 Another very useful piece of information is the explained variance ratio of each principal component,
diff --git a/doc/pub/week43/html/week43-solarized.html b/doc/pub/week43/html/week43-solarized.html
index 9924a7678..09d1bb33a 100644
--- a/doc/pub/week43/html/week43-solarized.html
+++ b/doc/pub/week43/html/week43-solarized.html
@@ -3744,7 +3744,7 @@ principal component is equal to
 <p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>pca.components_.T[:, <span style="color: #B452CD">0</span>].
+<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>pca.components_.T[:, <span style="color: #B452CD">0</span>]
 </pre></div>
 <p>
 Another very useful piece of information is the explained variance ratio of each principal component,
diff --git a/doc/pub/week43/html/week43.html b/doc/pub/week43/html/week43.html
index 73d3a0ec0..b9f2a17bf 100644
--- a/doc/pub/week43/html/week43.html
+++ b/doc/pub/week43/html/week43.html
@@ -3749,7 +3749,7 @@ principal component is equal to
 <p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>]<span style="color: #666666">.</span>
+<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>]
 </pre></div>
 <p>
 Another very useful piece of information is the explained variance ratio of each principal component,
diff --git a/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz b/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz
index 4dd615e7b..77e59ea79 100644
Binary files a/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz and b/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz differ
diff --git a/doc/pub/week43/ipynb/week43.ipynb b/doc/pub/week43/ipynb/week43.ipynb
index eb7eceac0..8d0777c16 100644
--- a/doc/pub/week43/ipynb/week43.ipynb
+++ b/doc/pub/week43/ipynb/week43.ipynb
@@ -4388,7 +4388,7 @@
    },
    "outputs": [],
    "source": [
-    "pca.components_.T[:, 0]."
+    "pca.components_.T[:, 0]"
    ]
   },
   {
diff --git a/doc/src/week43/week43.do.txt b/doc/src/week43/week43.do.txt
index 90d46b9d7..85c0cb449 100644
--- a/doc/src/week43/week43.do.txt
+++ b/doc/src/week43/week43.do.txt
@@ -3204,7 +3204,7 @@ After fitting the PCA transformer to the dataset, you can access the principal c
 components variable (note that it contains the PCs as horizontal vectors, so, for example, the first
 principal component is equal to 
 !bc pycod
-pca.components_.T[:, 0].
+pca.components_.T[:, 0]
 !ec
 Another very useful piece of information is the explained variance ratio of each principal component,
 available via the $explained\_variance\_ratio$ variable. It indicates the proportion of the dataset’s