From 452db0c6b1a320b9b26ac3044738fe8dcd7d7243 Mon Sep 17 00:00:00 2001
From: Liam Connors <connorsl@tcd.ie>
Date: Fri, 5 Apr 2024 16:09:20 -0400
Subject: [PATCH] remove links

---
 .../2015-06-30-baltimore.html                  | 10 ++++------
 .../ipython-notebooks/baltimore.ipynb          | 18 +++++-------------
 2 files changed, 9 insertions(+), 19 deletions(-)

diff --git a/_posts/python-v3/ipython-notebooks/2015-06-30-baltimore.html b/_posts/python-v3/ipython-notebooks/2015-06-30-baltimore.html
index 4c2a3f67e..732306eee 100755
--- a/_posts/python-v3/ipython-notebooks/2015-06-30-baltimore.html
+++ b/_posts/python-v3/ipython-notebooks/2015-06-30-baltimore.html
@@ -14,8 +14,8 @@
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="Baltimore-Vital-Signs">Baltimore Vital Signs<a class="anchor-link" href="#Baltimore-Vital-Signs">&#182;</a></h4><p>About the author:
-This notebook was forked from <a href="https://github.com/jtelszasz/baltimore_vital_signs">https://github.com/jtelszasz/baltimore_vital_signs</a>. The original author is Justin Elszasz. You can follow Justin on Twitter <a href="http://twitter.com/TheTrainingSet">@TheTrainingSet</a> or read his <a href="http://www.thetrainingset.com">blog</a>.</p>
+<h4 id="Baltimore-Vital-Signs">Baltimore Vital Signs<a class="anchor-link" href="#Baltimore-Vital-Signs">&#182;</a></h4><p>
+This notebook was forked from <a href="https://github.com/jtelszasz/baltimore_vital_signs">https://github.com/jtelszasz/baltimore_vital_signs</a>.</p>
 <h4 id="Introduction">Introduction<a class="anchor-link" href="#Introduction">&#182;</a></h4><p>The <a href="http://bniajfi.org/indicators/all">Baltimore Neighborhoods Indicators Alliance -- Jacob France Institute (BNIA)</a> at the University of Baltimore has made it their mission to provide a clean, concise set of indicators that illustrate the health and wealth of the city. There are 152 socio-economic indicators in the Vital Signs dataset, and some are reported for multiple years which results in 295 total variables for each of the 56 Baltimore neighborhoods captured.  The indicators are dug up from a number of sources, including the U.S. Census Bureau and its American Community Survey, the FBI and Baltimore Police Department, Baltimore departments of city housing, health, and education.</p>
 <h4 id="Imports">Imports<a class="anchor-link" href="#Imports">&#182;</a></h4>
 </div>
@@ -56,7 +56,7 @@ <h4 id="Imports">Imports<a class="anchor-link" href="#Imports">&#182;</a></h4>
         <span class="n">df</span> <span class="o">=</span> <span class="n">df_file</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">df_file</span><span class="p">)</span>
-        
+
 <span class="n">df</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;CSA2010&#39;</span><span class="p">]</span>
 <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;CSA2010&#39;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
 <span class="k">print</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
@@ -742,8 +742,6 @@ <h1 id="Principal-Component-Analysis">Principal Component Analysis<a class="anch
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<p>Read this <a href="http://www.thetrainingset.com/articles/A-City-Divided-In-N-Dimensions">post</a> at The Training Set for purpose of the following analyses (this section and the next one).</p>
-
 </div>
 </div>
 </div>
@@ -1399,6 +1397,6 @@ <h1 id="K-means-Clustering">K-means Clustering<a class="anchor-link" href="#K-me
 </div>
 </div>
 </div>
- 
+
 
 {% endraw %}
diff --git a/_posts/python-v3/ipython-notebooks/baltimore.ipynb b/_posts/python-v3/ipython-notebooks/baltimore.ipynb
index 85cd70849..68c2b0a6d 100755
--- a/_posts/python-v3/ipython-notebooks/baltimore.ipynb
+++ b/_posts/python-v3/ipython-notebooks/baltimore.ipynb
@@ -5,8 +5,7 @@
    "metadata": {},
    "source": [
     "#### Baltimore Vital Signs\n",
-    "About the author:\n",
-    "This notebook was forked from [https://github.com/jtelszasz/baltimore_vital_signs](https://github.com/jtelszasz/baltimore_vital_signs). The original author is Justin Elszasz. You can follow Justin on Twitter [@TheTrainingSet](http://twitter.com/TheTrainingSet) or read his [blog](http://www.thetrainingset.com).\n",
+    "This notebook was forked from [https://github.com/jtelszasz/baltimore_vital_signs](https://github.com/jtelszasz/baltimore_vital_signs).\n",
     "#### Introduction\n",
     "The [Baltimore Neighborhoods Indicators Alliance -- Jacob France Institute (BNIA)](http://bniajfi.org/indicators/all) at the University of Baltimore has made it their mission to provide a clean, concise set of indicators that illustrate the health and wealth of the city. There are 152 socio-economic indicators in the Vital Signs dataset, and some are reported for multiple years which results in 295 total variables for each of the 56 Baltimore neighborhoods captured.  The indicators are dug up from a number of sources, including the U.S. Census Bureau and its American Community Survey, the FBI and Baltimore Police Department, Baltimore departments of city housing, health, and education.\n",
     "#### Imports"
@@ -48,7 +47,7 @@
     "        df = df_file\n",
     "    else:\n",
     "        df = pd.merge(df, df_file)\n",
-    "        \n",
+    "\n",
     "df.index = df['CSA2010']\n",
     "df.drop('CSA2010', inplace=True)\n",
     "print len(df.columns)\n",
@@ -544,7 +543,7 @@
     }
    ],
    "source": [
-    "# Delete misplaced legend annotations. \n",
+    "# Delete misplaced legend annotations.\n",
     "py_fig['layout'].pop('annotations', None)"
    ]
   },
@@ -604,13 +603,6 @@
     "# Principal Component Analysis"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read this [post](http://www.thetrainingset.com/articles/A-City-Divided-In-N-Dimensions) at The Training Set for purpose of the following analyses (this section and the next one)."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 32,
@@ -830,7 +822,7 @@
     "                    y=df_X_reduced[1],\n",
     "                    text=df.index,\n",
     "                    mode='markers',\n",
-    "                    # Size by total population of each neighborhood. \n",
+    "                    # Size by total population of each neighborhood.\n",
     "                    marker=pgo.Marker(size=df['tpop10'],\n",
     "                                      sizemode='diameter',\n",
     "                                      sizeref=df['tpop10'].max()/50,\n",
@@ -1184,7 +1176,7 @@
     "! pip install publisher --upgrade\n",
     "import publisher\n",
     "publisher.publish(\n",
-    "    'baltimore.ipynb', 'ipython-notebooks/baltimore-vital-signs/', 'Baltimore Vital Signs', \n",
+    "    'baltimore.ipynb', 'ipython-notebooks/baltimore-vital-signs/', 'Baltimore Vital Signs',\n",
     "    'PCA and k-means clustering on dataset with Baltimore neighborhood indicators', name='Clustering')"
    ]
   },