Initial commit. draft
authorDamien Garaud <damien.garaud@logilab.fr>
Thu, 31 Oct 2013 15:19:50 +0100
changeset 0 93cab2942339
child 1 6422ee9797ec
Initial commit.
oecd-murder-rates.csv
oecd-unemployment.csv
quandl-data-with-pandas.html
quandl-data-with-pandas.ipynb
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/oecd-murder-rates.csv	Thu Oct 31 15:19:50 2013 +0100
@@ -0,0 +1,27 @@
+Date,USA,AUT,BEL,DNK,FIN,FRA,DEU,GRC,HUN,ISL,IRL,ITA,LUX,NLD,NOR,PRT,ESP,SWE,CHE,GBR,CZE,POL,SVK,SVN,EST,JPN,KOR,ISR,CAN,MEX,CHL,AUS,NZL
+1986-12-31,8.5,1.3,2.1,1.2,3.2,1.2,,1.0,2.9,1.2,0.9,1.3,2.2,1.0,1.6,1.5,1.1,1.5,1.0,0.8,1.1,1.7,,1.7,6.2,0.9,1.0,2.0,2.0,29.1,3.5,1.9,2.1
+1987-12-31,8.2,1.3,1.6,1.0,2.7,1.1,,0.8,2.6,0.8,1.1,1.6,1.6,0.9,1.5,1.4,1.2,1.2,1.2,1.0,1.1,1.9,,1.5,5.7,0.8,1.2,1.5,2.2,28.2,3.2,2.1,1.9
+1988-12-31,8.4,1.2,1.6,1.1,2.8,1.0,,1.1,2.8,1.8,0.9,1.8,1.3,0.9,1.2,1.5,0.9,1.4,1.2,1.0,0.8,1.9,,1.6,6.2,0.8,1.1,1.5,1.8,26.7,3.4,2.0,1.6
+1989-12-31,8.7,1.1,1.3,1.2,3.1,1.2,,0.8,3.0,0.0,0.6,2.2,3.0,1.0,1.4,1.6,0.9,1.5,1.2,1.1,1.0,2.2,,2.4,8.1,0.7,1.5,2.9,2.1,26.6,3.1,1.8,2.1
+1990-12-31,9.4,1.6,1.4,1.0,3.2,1.1,1.0,1.1,3.2,0.6,0.7,2.6,3.0,0.8,1.2,1.7,1.0,1.3,1.5,0.7,2.0,3.3,,2.0,11.6,0.6,1.6,2.1,2.0,23.7,3.5,2.1,2.3
+1991-12-31,9.9,1.2,1.4,1.4,3.0,1.1,1.1,1.5,4.2,2.0,0.7,2.7,2.2,1.1,1.6,1.7,0.9,1.5,1.3,0.7,1.9,3.1,,2.5,11.6,0.6,1.6,1.5,2.2,23.4,3.7,1.9,1.9
+1992-12-31,9.4,1.5,1.7,1.3,3.4,1.0,1.2,1.2,4.1,1.2,0.8,2.2,1.9,1.2,1.0,1.5,0.9,1.4,1.4,0.9,2.0,3.2,2.6,2.5,20.1,0.6,1.3,1.3,2.0,24.3,3.3,1.7,2.3
+1993-12-31,9.6,1.3,2.0,1.2,3.2,1.1,1.2,1.3,4.2,0.3,0.7,1.7,0.2,1.2,1.0,1.5,1.0,1.3,1.6,1.2,2.2,2.9,2.5,1.3,27.1,0.7,1.7,2.5,1.7,23.1,2.8,1.9,1.4
+1994-12-31,9.1,1.1,1.8,1.3,3.2,1.1,1.2,1.1,3.6,0.0,0.7,1.5,1.3,1.1,0.8,1.5,0.9,1.2,1.3,1.0,2.3,3.2,2.4,2.2,30.2,0.6,1.7,2.4,1.7,22.4,3.2,1.8,1.9
+1995-12-31,8.4,1.0,1.6,1.2,2.9,1.1,1.1,1.3,3.6,0.0,0.8,1.4,0.7,1.2,1.0,1.7,0.9,1.0,0.9,1.0,1.8,3.0,2.3,2.3,23.4,0.6,1.8,1.5,1.6,21.3,3.5,1.6,1.2
+1996-12-31,7.6,1.2,2.0,1.1,3.3,1.0,1.1,1.6,3.2,0.6,1.0,1.4,1.1,1.3,1.0,1.3,0.8,1.2,1.1,0.8,1.7,2.8,2.2,2.1,21.6,0.6,1.9,1.1,1.7,19.2,3.2,1.7,1.8
+1997-12-31,7.2,0.9,1.7,1.2,2.7,0.9,0.9,1.5,3.4,1.0,0.9,1.2,0.7,1.3,0.9,1.2,0.8,1.1,1.4,0.7,1.6,,2.6,2.3,17.6,0.6,2.1,0.5,1.4,17.6,2.7,1.7,1.8
+1998-12-31,6.5,1.1,2.0,0.9,2.4,0.7,0.9,1.3,3.3,,1.1,1.2,0.7,1.1,1.0,1.3,0.9,1.1,0.8,0.7,1.6,,2.3,1.0,19.9,0.6,2.2,0.5,1.5,17.5,2.9,1.6,1.5
+1999-12-31,6.1,0.8,2.5,1.1,2.7,0.7,0.9,1.2,2.9,0.9,1.0,1.2,0.9,1.2,0.9,1.1,0.8,1.2,1.0,0.7,1.4,2.4,2.6,1.5,17.2,0.6,1.7,2.3,1.5,15.4,3.1,1.5,1.3
+2000-12-31,5.8,0.9,,1.2,2.6,0.9,0.7,1.1,2.6,2.1,0.9,1.0,1.1,1.1,1.2,0.9,1.0,1.0,0.8,,1.5,2.2,2.3,1.0,14.4,0.6,1.8,3.0,1.5,13.1,5.2,1.6,1.4
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+2004-12-31,5.8,0.7,1.7,0.7,2.4,0.8,0.6,0.9,2.1,1.3,0.6,,0.5,1.2,0.9,,1.3,1.1,0.9,0.5,1.2,1.5,1.8,1.8,8.0,0.5,1.9,3.7,1.6,10.4,5.4,0.8,1.1
+2005-12-31,6.0,0.8,1.5,1.0,1.9,0.7,0.5,1.0,1.9,1.0,0.9,,1.5,1.1,0.7,,0.9,0.9,0.9,0.4,0.9,1.5,1.6,1.1,9.1,0.5,1.8,3.3,1.7,11.0,5.8,0.9,1.7
+2006-12-31,6.0,0.8,1.4,0.7,2.0,0.7,0.6,0.8,1.9,0.3,0.9,0.9,1.4,0.8,1.0,,0.8,0.9,0.6,0.4,1.0,1.5,,0.6,7.3,0.5,1.6,3.4,1.5,11.3,5.5,0.7,1.5
+2007-12-31,6.0,0.6,1.5,,2.2,0.6,0.6,1.1,1.7,0.6,1.0,0.9,1.2,0.9,0.7,1.0,0.7,1.2,0.6,0.4,1.0,1.4,,1.0,6.9,0.4,1.5,2.2,1.5,8.8,4.5,1.0,1.3
+2008-12-31,5.7,0.6,1.2,,2.2,0.7,0.5,1.3,2.0,0.3,0.9,0.8,1.1,0.9,0.6,1.3,0.8,0.8,0.6,0.3,0.7,1.2,1.2,0.6,6.5,0.4,1.4,2.1,1.7,14.0,4.2,1.2,1.3
+2009-12-31,5.4,0.7,1.2,,1.9,0.8,0.5,1.4,1.4,0.3,1.0,0.8,0.9,0.9,0.6,0.9,0.7,0.9,0.7,0.3,0.8,1.1,1.1,0.5,5.9,0.4,1.4,2.1,1.7,18.4,5.2,1.2,1.9
+2010-12-31,5.2,0.5,,,1.9,,0.6,1.4,1.4,,0.8,0.7,2.1,0.9,0.7,1.2,0.7,1.0,0.5,0.3,0.8,0.9,1.2,0.4,4.5,0.3,1.2,2.2,,23.4,,1.1,
+2011-12-31,,0.5,,,1.8,,0.5,,1.5,,,,0.4,0.9,2.3,0.9,0.7,,,,0.8,1.0,,,4.7,,1.1,,,,,0.8,
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/oecd-unemployment.csv	Thu Oct 31 15:19:50 2013 +0100
@@ -0,0 +1,106 @@
+Date,AUT,BEL,FIN,FRA,DEU,HUN,LUX,NLD,NOR,PRT,ESP,SWE,GBR,CZE,POL,JPN,AUS
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+
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="retrieve-some-data-from-quandl-and-play-with-pandas">Retrieve some Data from Quandl and Play with Pandas</h1>
+<p>This notebook is related to the blog post <strong>LINK HERE</strong>.</p>
+<p>This IPython notebook tries to show some features of the Python Data library <a href="http://pandas.pydata.org/" title="pandas">Pandas</a>. We would like to use some numerical data from the <a href="http://www.quandl.com/" title="Quandl">Quandl</a> website.</p>
+<ul>
+<li><strong>date</strong>: 2013-10-30</li>
+<li><strong>author</strong>: Damien Garaud <script type="text/javascript">
+<!--
+h='&#108;&#x6f;&#x67;&#x69;&#108;&#x61;&#98;&#46;&#102;&#114;';a='&#64;';n='&#100;&#x61;&#x6d;&#x69;&#x65;&#110;&#46;&#x67;&#x61;&#114;&#x61;&#x75;&#100;';e=n+a+h;
+document.write('<a h'+'ref'+'="ma'+'ilto'+':'+e+'">'+'<code>'+e+'</code>'+'<\/'+'a'+'>');
+// -->
+</script><noscript>&#100;&#x61;&#x6d;&#x69;&#x65;&#110;&#46;&#x67;&#x61;&#114;&#x61;&#x75;&#100;&#32;&#x61;&#116;&#32;&#108;&#x6f;&#x67;&#x69;&#108;&#x61;&#98;&#32;&#100;&#x6f;&#116;&#32;&#102;&#114;</noscript>, <a href="http://twitter.com/jazzydag">@jazzydag</a></li>
+<li><strong>source</strong>: link HERE</li>
+</ul>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="short-intro">Short Intro</h2>
+<p>Three lines to import modules and configure the Matplotlib display options.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[1]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">options</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">mpl_style</span> <span class="o">=</span> <span class="s">&#39;default&#39;</span>
+</pre></div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[2]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Pandas version&#39;</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">__version__</span>
+<span class="k">print</span> <span class="s">&#39;Numpy version&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">__version__</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+Pandas version 0.12.0-820-gbdb43eb
+Numpy version 1.7.1
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Just plot ten Brownian motions.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[2]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s">&#39;Brownian Motion&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[2]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+
+<pre>
+&lt;matplotlib.axes.AxesSubplot at 0x37a6fd0&gt;
+</pre>
+
+</div>
+</div>
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_display_data">
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+">
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="retrieve-some-data-quandls-datasets">Retrieve some Data: Quandl's Datasets</h2>
+<p>See <a href="http://www.quandl.com/" title="Quandl">Quandl</a> and its <a href="http://www.quandl.com/help/packages/python" title="Quandl&#39;s Python Package">Python package</a> which depends on Numpy and Pandas.</p>
+<p>It's possible to get a huge amount of datasets with timeseries on different topics --- finance, education, health, etc.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[3]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">Quandl</span>
+<span class="n">TOKEN</span> <span class="o">=</span> <span class="bp">None</span> <span class="c"># Replace None by your own Quandl token</span>
+</pre></div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="single-quandl-data"> Single Quandl Data</h3>
+<p>Get a dataset about the murder rates per 100 000 person in Great Britain (from OECD stats).</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[4]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">uk_df</span> <span class="o">=</span> <span class="n">Quandl</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">&#39;OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_GBR&#39;</span><span class="p">,</span> <span class="n">authtoken</span><span class="o">=</span><span class="n">TOKEN</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+Token RhzkBPMM2sM6ovJo4Tox activated and saved for later use.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_GBR
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[5]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">uk_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[5]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<div class="output_html rendered_html">
+<div style="max-height:1000px;max-width:1500px;overflow:auto;">
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Value</th>
+    </tr>
+    <tr>
+      <th>Date</th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>1986-12-31</th>
+      <td> 0.8</td>
+    </tr>
+    <tr>
+      <th>1987-12-31</th>
+      <td> 1.0</td>
+    </tr>
+    <tr>
+      <th>1988-12-31</th>
+      <td> 1.0</td>
+    </tr>
+    <tr>
+      <th>1989-12-31</th>
+      <td> 1.1</td>
+    </tr>
+    <tr>
+      <th>1990-12-31</th>
+      <td> 0.7</td>
+    </tr>
+    <tr>
+      <th>1991-12-31</th>
+      <td> 0.7</td>
+    </tr>
+    <tr>
+      <th>1992-12-31</th>
+      <td> 0.9</td>
+    </tr>
+    <tr>
+      <th>1993-12-31</th>
+      <td> 1.2</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Note that the Quandl website allows you to make less than 50 calls per day. You can sign up if you want more. See http://www.quandl.com/help/authorization-token</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[11]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">uk_df</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[11]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+
+<pre>
+&lt;matplotlib.axes.AxesSubplot at 0x3b36fd0&gt;
+</pre>
+
+</div>
+</div>
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_display_data">
+
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+qX3Fnm07fvw45s6di8jISIf3FXu27/jx47jlllucqi3X533xxRdITk6Gr68vJBIJpFIpmpqa0NPT
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+ySeIiIjA/Pnz0dfXh+bmZrs/YtmT9+mnnyI8PBwLFiyATqezzHGlvNGVd+7cOUgkEtx00004c+YM
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+trY2y0dyuVzucOGhPGHnzZw5E9OnT4dYLIZYLIZSqURCQgJCQ0NRVFQEoH8a5MCJHWKx2KlCznUm
+19tHef15CQkJMJvNlvaOqzBezMViMcLDwxEVFYXIyEicP3/e8vOKigr8/ve/h0gksvsjD+VRHhMG
++rRyuRzNzc2QyWRYunQpfHx88Oabb6KystJyARamcJnJ9fZRXn8eF+v0jITVi1M0NDTgzJkzCAgI
+QGxsLJRKJZqbm1k7pZXyKM9WFy5cQFhYGLy9vdHU1IQJEyagp6cH3t7ejGe5IpPr7aM812N1bRZv
+b2+Ul5ejtbUVGzduRHJy8ogXhKY8yuMir6WlBcePH4fRaMSqVass+WziMpPr7aM812O1mJeWlmLi
+xIlYunQppFL21/SiPMqz1c8//4yUlBSsWbOGk23jOpPr7aM812O1zeLs4kqUR3lsGbyCH1e4zOR6
++yjP9eiCzoQQ4gb4/aeGEEKITaiYE0KIG6BiTgghboCfX8sSwoCdO3eip6cHQP8iXb/85S9tnk52
++PBhLF682KFFtQhxBToyJ25LJBLhsccew6uvvoqkpCS88cYbNj/2yJEjrF+AlxAm0ZE5GRWWLFmC
+06dPo6KiAhEREdi/fz/a2trQ3NyMWbNm4Z577gHQv9ZGWloa1Go1XnnlFUgkEjzzzDOWizRUVFTg
+wIEDMJlM8PX1xaOPPsrqiVSE2IqKORk1xowZg5qaGiQmJuLBBx+Er68v9Ho9nn76aSxbtgwKhQIy
+mQxpaWl48sknsW3btmuW2O3r68Pbb7+N3/72t1AoFPjhhx/w8ccf4/HHH3fhVhHSj4o5GTUGn1Ih
+Fotx8eJFNDc3w8PDA2q1GgqFwurj6+rq0NLSgj179gDoP5GEeuqEL6iYk1GjvLwct956K6qrq/HW
+W29h8eLFiI+Ph7+/v00X2pVIJAgLC8NLL73EwWgJsQ99AUpGhePHj8Pb2xuJiYm4fPkypk2bhiVL
+llhWwbueTCazXMpuoNBHRkbCYDDg3LlzlvvRCdSEL+h0fuK2du3aBY1GA7PZjISEBDz00EPw9vZG
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+kQi33HILli1b5qpNJMSCijkhhLgBarMQQogboGJOCCFugIo5IYS4ASrmhBDiBqiYE0KIG6BiTggh
+buD/A0ZXvhhQNagsAAAAAElFTkSuQmCC
+">
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="murder-rates-data-of-33-countries"> Murder Rates Data of 33 Countries</h3>
+<p>I would like to get this for the 33 other countries. The pattern looks like <code>OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_[A-Z]+</code>. At the end, you have the &quot;country code&quot;.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[22]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">re</span>
+<span class="kn">import</span> <span class="nn">urllib2</span>
+
+<span class="k">def</span> <span class="nf">get_quandl_country_code</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">pattern</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Get the country code from an URL and a pattern.</span>
+<span class="sd">    </span>
+<span class="sd">    Suppose your pattern have the &#39;country&#39; group name.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">rawtext</span> <span class="o">=</span> <span class="n">urllib2</span><span class="o">.</span><span class="n">urlopen</span><span class="p">(</span><span class="n">url</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+    <span class="n">regexp</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">pattern</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="s">&#39;country&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">regexp</span><span class="o">.</span><span class="n">finditer</span><span class="p">(</span><span class="n">rawtext</span><span class="p">)]</span>
+</pre></div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[23]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">URL</span> <span class="o">=</span> <span class="s">&#39;http://www.quandl.com/society/oecd-murder-rates&#39;</span>
+<span class="n">PATTERN</span> <span class="o">=</span> <span class="s">r&#39;OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_(?P&lt;country&gt;[A-Z]+)&#39;</span>
+<span class="n">country_code</span> <span class="o">=</span> <span class="n">get_quandl_country_code</span><span class="p">(</span><span class="n">URL</span><span class="p">,</span> <span class="n">PATTERN</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;Nb countries&#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">country_code</span><span class="p">),</span> <span class="s">&#39;--- first 5: &#39;</span><span class="p">,</span> <span class="n">country_code</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+Nb countries 33 --- first 5:  [&apos;USA&apos;, &apos;AUT&apos;, &apos;BEL&apos;, &apos;DNK&apos;, &apos;FIN&apos;]
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now, let's retrieve each data per country and concat them into a unique <em>DataFrame</em>. Note that the time index of each <em>DataFrame</em> is not the same for each country.</p>
+<p>Retrieve two datasets and try the <em>pd.concat</em> function.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[8]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">usa_df</span> <span class="o">=</span> <span class="n">Quandl</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">&#39;OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_USA&#39;</span><span class="p">,</span> <span class="n">authtoken</span><span class="o">=</span><span class="n">TOKEN</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+Token RhzkBPMM2sM6ovJo4Tox activated and saved for later use.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_USA
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[16]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">uk_df</span><span class="p">,</span> <span class="n">usa_df</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">df</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[16]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<div class="output_html rendered_html">
+<div style="max-height:1000px;max-width:1500px;overflow:auto;">
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Value</th>
+      <th>Value</th>
+    </tr>
+    <tr>
+      <th>Date</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>1986-12-31</th>
+      <td> 0.8</td>
+      <td> 8.5</td>
+    </tr>
+    <tr>
+      <th>1987-12-31</th>
+      <td> 1.0</td>
+      <td> 8.2</td>
+    </tr>
+    <tr>
+      <th>1988-12-31</th>
+      <td> 1.0</td>
+      <td> 8.4</td>
+    </tr>
+    <tr>
+      <th>1989-12-31</th>
+      <td> 1.1</td>
+      <td> 8.7</td>
+    </tr>
+    <tr>
+      <th>1990-12-31</th>
+      <td> 0.7</td>
+      <td> 9.4</td>
+    </tr>
+    <tr>
+      <th>1991-12-31</th>
+      <td> 0.7</td>
+      <td> 9.9</td>
+    </tr>
+    <tr>
+      <th>1992-12-31</th>
+      <td> 0.9</td>
+      <td> 9.4</td>
+    </tr>
+    <tr>
+      <th>1993-12-31</th>
+      <td> 1.2</td>
+      <td> 9.6</td>
+    </tr>
+    <tr>
+      <th>1994-12-31</th>
+      <td> 1.0</td>
+      <td> 9.1</td>
+    </tr>
+    <tr>
+      <th>1995-12-31</th>
+      <td> 1.0</td>
+      <td> 8.4</td>
+    </tr>
+    <tr>
+      <th>1996-12-31</th>
+      <td> 0.8</td>
+      <td> 7.6</td>
+    </tr>
+    <tr>
+      <th>1997-12-31</th>
+      <td> 0.7</td>
+      <td> 7.2</td>
+    </tr>
+    <tr>
+      <th>1998-12-31</th>
+      <td> 0.7</td>
+      <td> 6.5</td>
+    </tr>
+    <tr>
+      <th>1999-12-31</th>
+      <td> 0.7</td>
+      <td> 6.1</td>
+    </tr>
+    <tr>
+      <th>2000-12-31</th>
+      <td> NaN</td>
+      <td> 5.8</td>
+    </tr>
+    <tr>
+      <th>2001-12-31</th>
+      <td> 0.4</td>
+      <td> 7.0</td>
+    </tr>
+    <tr>
+      <th>2002-12-31</th>
+      <td> 0.4</td>
+      <td> 6.0</td>
+    </tr>
+    <tr>
+      <th>2003-12-31</th>
+      <td> 0.5</td>
+      <td> 5.9</td>
+    </tr>
+    <tr>
+      <th>2004-12-31</th>
+      <td> 0.5</td>
+      <td> 5.8</td>
+    </tr>
+    <tr>
+      <th>2005-12-31</th>
+      <td> 0.4</td>
+      <td> 6.0</td>
+    </tr>
+    <tr>
+      <th>2006-12-31</th>
+      <td> 0.4</td>
+      <td> 6.0</td>
+    </tr>
+    <tr>
+      <th>2007-12-31</th>
+      <td> 0.4</td>
+      <td> 6.0</td>
+    </tr>
+    <tr>
+      <th>2008-12-31</th>
+      <td> 0.3</td>
+      <td> 5.7</td>
+    </tr>
+    <tr>
+      <th>2009-12-31</th>
+      <td> 0.3</td>
+      <td> 5.4</td>
+    </tr>
+    <tr>
+      <th>2010-12-31</th>
+      <td> 0.3</td>
+      <td> 5.2</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As you can see, there are a few <code>NaN</code> values. Moreover, I don't have the name of the country as the column label. Let's fix this and get the datasets for the 33 countries.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[24]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">def</span> <span class="nf">get_quandl_dataset</span><span class="p">(</span><span class="n">code</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Get a Pandas DataFrame from the Quandl website according to the code.</span>
+<span class="sd">    </span>
+<span class="sd">    name: str or list</span>
+<span class="sd">        The name(s) of the col label.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">Quandl</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">code</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">name</span><span class="p">:</span>
+        <span class="n">name</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="n">name</span>
+        <span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">df</span>
+</pre></div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Sorry but I can't avoid the annoying printings from the Quandl's <code>get</code> function...</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[13]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">code</span> <span class="o">=</span> <span class="n">PATTERN</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">r&quot;(?P&lt;country&gt;[A-Z]+)&quot;</span><span class="p">,</span> <span class="s">&quot;</span><span class="si">%s</span><span class="s">&quot;</span><span class="p">)</span>
+<span class="n">murder</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">get_quandl_dataset</span><span class="p">(</span><span class="n">code</span> <span class="o">%</span> <span class="n">name</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">country_code</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_USA
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_AUT
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_BEL
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_DNK
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_FIN
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_FRA
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_DEU
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_GRC
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_HUN
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_ISL
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_IRL
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_ITA
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_LUX
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_NLD
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_NOR
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_PRT
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_ESP
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_SWE
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_CHE
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_GBR
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_CZE
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_POL
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_SVK
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_SVN
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_EST
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_JPN
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_KOR
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_ISR
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_CAN
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_MEX
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_CHL
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_AUS
+Using cached token RhzkBPMM2sM6ovJo4Tox for authentication.
+Returning Dataframe for  OECD/HEALTH_STAT_CICDHOCD_TXCMILTX_NZL
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>OK ! I've got a <code>murder</code> <em>DataFrame</em> with 33 columns labelised with the country codename and 26 dates.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[19]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="n">murder</span><span class="o">.</span><span class="n">shape</span>
+<span class="k">print</span> <span class="n">murder</span><span class="o">.</span><span class="n">dtypes</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
+<span class="k">print</span> <span class="n">murder</span><span class="p">[</span><span class="n">country_code</span><span class="p">[:</span><span class="mi">5</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+(26, 33)
+USA    float64
+AUT    float64
+BEL    float64
+DNK    float64
+FIN    float64
+dtype: object
+            USA  AUT  BEL  DNK  FIN
+Date                               
+1986-12-31  8.5  1.3  2.1  1.2  3.2
+1987-12-31  8.2  1.3  1.6  1.0  2.7
+1988-12-31  8.4  1.2  1.6  1.1  2.8
+1989-12-31  8.7  1.1  1.3  1.2  3.1
+1990-12-31  9.4  1.6  1.4  1.0  3.2
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Here, you have some missing values represented by the type <code>NaN</code>, i.e. <em>Not a Number</em>. You can count the number of no NaN values per country (i.e. for each column).</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[20]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">murder</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[20]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+
+<pre>
+USA    25
+AUT    26
+BEL    21
+DNK    21
+FIN    26
+FRA    24
+DEU    22
+GRC    25
+HUN    26
+ISL    22
+IRL    25
+ITA    23
+LUX    26
+NLD    26
+NOR    26
+PRT    23
+ESP    26
+SWE    25
+CHE    25
+GBR    24
+CZE    26
+POL    24
+SVK    17
+SVN    25
+EST    26
+JPN    25
+KOR    26
+ISR    25
+CAN    24
+MEX    25
+CHL    24
+AUS    26
+NZL    24
+dtype: int64
+</pre>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>You can try to plot all data according to time.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[21]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">murder</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[21]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+
+<pre>
+&lt;matplotlib.axes.AxesSubplot at 0x4086c90&gt;
+</pre>
+
+</div>
+</div>
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_display_data">
+
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+">
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Well... The legend is a quite big.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="unemployment-data">Unemployment Data</h3>
+<p>I would like to get an other kind of datasets with the quite same countries and try to join and compare the data. Take another look at the Quandl's website. I found &quot;The Registered Unemployment and Job Vacancies (MEI) dataset&quot; from OECD stats. Let's go to retrieve an other <em>DataFrame</em>.</p>
+<p>Take a look at the datasets from France http://www.quandl.com/OECD-Organisation-for-Economic-Co-operation-and-Development/MEI_LAB_REG1_FRA_EMECJV_ST_Q-France-Unfilled-Job-Vacancies-Level-Rate-Or-Quantity-Series-Quarterly</p>
+<p>Loop on the country code and concat the result.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[25]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">PATTERN2</span> <span class="o">=</span> <span class="s">r&#39;OECD/MEI_LAB_REG1_</span><span class="si">%(country)s</span><span class="s">_EMECJV_ST_Q&#39;</span>
+<span class="n">df_can</span> <span class="o">=</span> <span class="n">get_quandl_dataset</span><span class="p">(</span><span class="n">PATTERN2</span> <span class="o">%</span> <span class="p">{</span><span class="s">&#39;country&#39;</span><span class="p">:</span> <span class="s">&#39;CAN&#39;</span><span class="p">},</span> <span class="s">&#39;CAN&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">Exception</span>                                 Traceback (most recent call last)
+<span class="ansigreen">&lt;ipython-input-25-091e11b7a758&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">      1</span> PATTERN2 <span class="ansiblue">=</span> <span class="ansiblue">r&apos;OECD/MEI_LAB_REG1_%(country)s_EMECJV_ST_Q&apos;</span><span class="ansiblue"></span>
+<span class="ansigreen">----&gt; 2</span><span class="ansired"> </span>df_can <span class="ansiblue">=</span> get_quandl_dataset<span class="ansiblue">(</span>PATTERN2 <span class="ansiblue">%</span> <span class="ansiblue">{</span><span class="ansiblue">&apos;country&apos;</span><span class="ansiblue">:</span> <span class="ansiblue">&apos;CAN&apos;</span><span class="ansiblue">}</span><span class="ansiblue">,</span> <span class="ansiblue">&apos;CAN&apos;</span><span class="ansiblue">)</span><span class="ansiblue"></span>
+
+<span class="ansigreen">&lt;ipython-input-24-165e2948d6b0&gt;</span> in <span class="ansicyan">get_quandl_dataset</span><span class="ansiblue">(code, name)</span>
+<span class="ansigreen">      5</span>         The name<span class="ansiblue">(</span>s<span class="ansiblue">)</span> of the col label<span class="ansiblue">.</span><span class="ansiblue"></span>
+<span class="ansigreen">      6</span>     &quot;&quot;&quot;
+<span class="ansigreen">----&gt; 7</span><span class="ansired">     </span>df <span class="ansiblue">=</span> Quandl<span class="ansiblue">.</span>get<span class="ansiblue">(</span>code<span class="ansiblue">)</span><span class="ansiblue"></span>
+<span class="ansigreen">      8</span>     <span class="ansigreen">if</span> name<span class="ansiblue">:</span><span class="ansiblue"></span>
+<span class="ansigreen">      9</span>         name <span class="ansiblue">=</span> <span class="ansiblue">[</span>name<span class="ansiblue">]</span> <span class="ansigreen">if</span> isinstance<span class="ansiblue">(</span>name<span class="ansiblue">,</span> str<span class="ansiblue">)</span> <span class="ansigreen">else</span> name<span class="ansiblue"></span>
+
+<span class="ansigreen">/home/dag/doc/work/blogpost/2013/pandas/Quandl/Quandl.pyc</span> in <span class="ansicyan">get</span><span class="ansiblue">(dataset, **kwargs)</span>
+<span class="ansigreen">    134</span>             <span class="ansigreen">elif</span> str<span class="ansiblue">(</span>e<span class="ansiblue">)</span> <span class="ansiblue">==</span> <span class="ansiblue">&apos;HTTP Error 404: Not Found&apos;</span><span class="ansiblue">:</span><span class="ansiblue"></span>
+<span class="ansigreen">    135</span>                 error <span class="ansiblue">=</span> <span class="ansiblue">&quot;Dataset not found. Check Quandl code: {} for errors&quot;</span><span class="ansiblue">.</span>format<span class="ansiblue">(</span>dataset<span class="ansiblue">)</span><span class="ansiblue"></span>
+<span class="ansigreen">--&gt; 136</span><span class="ansired">                 </span><span class="ansigreen">raise</span> Exception<span class="ansiblue">(</span>error<span class="ansiblue">)</span><span class="ansiblue"></span>
+<span class="ansigreen">    137</span> <span class="ansiblue"></span>
+<span class="ansigreen">    138</span>             <span class="ansired">#Catch all</span><span class="ansiblue"></span><span class="ansiblue"></span>
+
+<span class="ansired">Exception</span>: Dataset not found. Check Quandl code: OECD/MEI_LAB_REG1_CAN_EMECJV_ST_Q for errors</pre>
+</div>
+</div>
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>So bad. You can see that the kind of datasets does not exist for each previous countries. Thus, I make a loop over my country codenames with <code>try..except</code>. A more specific exception type would have been appreciated here. &quot;If you want, make a patch&quot; ... you're right but for now,</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[26]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">dflist</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">country</span> <span class="ow">in</span> <span class="n">country_code</span><span class="p">:</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">get_quandl_dataset</span><span class="p">(</span><span class="n">PATTERN2</span> <span class="o">%</span> <span class="p">{</span><span class="s">&#39;country&#39;</span><span class="p">:</span> <span class="n">country</span><span class="p">},</span> <span class="n">country</span><span class="p">)</span>
+        <span class="n">dflist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="k">continue</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_AUT_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_BEL_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_FIN_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_FRA_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_DEU_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_HUN_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_LUX_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_NLD_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_NOR_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_PRT_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_ESP_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_SWE_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_GBR_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_CZE_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_POL_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_JPN_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+Returning Dataframe for  OECD/MEI_LAB_REG1_AUS_EMECJV_ST_Q
+No authentication tokens found: usage will be limited.
+See www.quandl.com/api for more information.
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[27]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="nb">len</span><span class="p">(</span><span class="n">dflist</span><span class="p">)</span>
+<span class="n">unemploy</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">dflist</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="k">print</span> <span class="n">unemploy</span><span class="o">.</span><span class="n">columns</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+17
+Index([u&apos;AUT&apos;, u&apos;BEL&apos;, u&apos;FIN&apos;, u&apos;FRA&apos;, u&apos;DEU&apos;, u&apos;HUN&apos;, u&apos;LUX&apos;, u&apos;NLD&apos;, u&apos;NOR&apos;, u&apos;PRT&apos;, u&apos;ESP&apos;, u&apos;SWE&apos;, u&apos;GBR&apos;, u&apos;CZE&apos;, u&apos;POL&apos;, u&apos;JPN&apos;, u&apos;AUS&apos;], dtype=object)
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>17 countries which occur in my previous &quot;murder rates&quot; <em>DataFrame</em> with more dates and a different time step.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[28]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">unemploy</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[28]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<div class="output_html rendered_html">
+<pre>
+&lt;class 'pandas.core.frame.DataFrame'&gt;
+DatetimeIndex: 105 entries, 1986-03-31 00:00:00 to 2012-03-31 00:00:00
+Freq: Q-DEC
+Data columns (total 17 columns):
+AUT    105  non-null values
+BEL    73  non-null values
+FIN    105  non-null values
+FRA    93  non-null values
+DEU    105  non-null values
+HUN    81  non-null values
+LUX    105  non-null values
+NLD    88  non-null values
+NOR    105  non-null values
+PRT    105  non-null values
+ESP    77  non-null values
+SWE    105  non-null values
+GBR    105  non-null values
+CZE    86  non-null values
+POL    89  non-null values
+JPN    105  non-null values
+AUS    105  non-null values
+dtypes: float64(17)
+</pre>
+</div>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's dump these two <em>DataFrame</em> into CSV files. In that way, I will be able to get back these datasets without calling Quandl and using the concat function.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[1]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">murder</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s">&#39;oecd-murder-rates.csv&#39;</span><span class="p">)</span>
+<span class="n">unemploy</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s">&#39;oecd-unemployment.csv&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>To load the <em>DataFrames</em> from these CSV files, just do:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[2]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">murder</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s">&#39;oecd-murder-rates.csv&#39;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">unemploy</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s">&#39;oecd-unemployment.csv&#39;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>See the other <code>read_csv</code> arguments to fit with your case: delimiter, header, date format, skip{row,footer}, dealing with missing values, etc.</p>
+<p>Maybe you saw that the two <em>DataFrames</em> do not have the same shape:</p>
+<ul>
+<li>number of dates in not the same (moreover, you don't have the same time frequency) ;</li>
+<li>number of countries is not the same.</li>
+</ul>
+<p>I didn't mention it before: unlike Numpy, you can 'attach' an Index to your data. Here, I use a timeseries which is a powerfull pandas feature. You can use integers, strings or floats as Index if you want to.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[31]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="n">murder</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">unemploy</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+(26, 33) (105, 17)
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>First, I would like to select countries which occur in my two <em>DataFrames</em>. I know there are less countries in <code>unemploy</code> that <code>murder</code>.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[5]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">sub_murder</span> <span class="o">=</span> <span class="n">murder</span><span class="p">[</span><span class="n">unemploy</span><span class="o">.</span><span class="n">columns</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Then, I would like to have the same time frequency for my two <em>DataFrames</em>. Let's go to get some unemployment values for each dates which occur in my &quot;murder&quot; <em>DataFrame</em>.</p>
+<ul>
+<li>murder DataFrame: last day of each year, i.e. yearly freq ;</li>
+<li>unemployement DataFrame: last day of each quarter, i.e. quaterly freq.</li>
+</ul>
+<p>Here, I use <code>DateOffset</code> pandas object to deal with different frequencies.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[3]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">pandas.tseries.offsets</span> <span class="kn">as</span> <span class="nn">offs</span>
+</pre></div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[6]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">sub_murder</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">offset</span> <span class="o">=</span> <span class="n">offs</span><span class="o">.</span><span class="n">YearEnd</span><span class="p">()</span>
+<span class="n">unemploy</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">offset</span> <span class="o">=</span> <span class="n">offs</span><span class="o">.</span><span class="n">QuarterEnd</span><span class="p">()</span>
+
+<span class="n">sub_murder</span><span class="o">.</span><span class="n">index</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[6]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+
+<pre>
+&lt;class &apos;pandas.tseries.index.DatetimeIndex&apos;&gt;
+[1986-12-31 00:00:00, ..., 2011-12-31 00:00:00]
+Length: 26, Freq: A-DEC, Timezone: None
+</pre>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[7]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">unemploy</span><span class="o">.</span><span class="n">index</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[7]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+
+<pre>
+&lt;class &apos;pandas.tseries.index.DatetimeIndex&apos;&gt;
+[1986-03-31 00:00:00, ..., 2012-03-31 00:00:00]
+Length: 105, Freq: Q-MAR, Timezone: None
+</pre>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As you can see, the <code>__repr__</code> of my Index object has changed. There is <code>Freq: A-DEC</code> which means <em>calendar year end</em>.</p>
+<p>Note that these lines are not mandatory if you get <em>DataFrames</em> from Quandl since <code>Quandl.get(your_data_code)</code> can return a <em>DataFrame</em> with the well frequency.</p>
+<p>See pandas doc about offsets (i.e. frequency) aliases http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases</p>
+<p>Well. Now, how can you get unemployment values (yearly average for instance) with the same frequency as the &quot;murder&quot; <em>DataFrame</em>? It's simple, just use the <code>resample</code> method.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[8]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">year_unempl</span> <span class="o">=</span> <span class="n">unemploy</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="s">&#39;A-DEC&#39;</span><span class="p">)</span>
+<span class="n">year_unempl</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[8]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<div class="output_html rendered_html">
+<div style="max-height:1000px;max-width:1500px;overflow:auto;">
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>AUT</th>
+      <th>BEL</th>
+      <th>FIN</th>
+      <th>FRA</th>
+      <th>DEU</th>
+      <th>HUN</th>
+      <th>LUX</th>
+      <th>NLD</th>
+      <th>NOR</th>
+      <th>PRT</th>
+      <th>ESP</th>
+      <th>SWE</th>
+      <th>GBR</th>
+      <th>CZE</th>
+      <th>POL</th>
+      <th>JPN</th>
+      <th>AUS</th>
+    </tr>
+    <tr>
+      <th>Date</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>1986-12-31</th>
+      <td> 24692.6650</td>
+      <td> 17739.3325</td>
+      <td> 12438.58075</td>
+      <td>        NaN</td>
+      <td> 142974.775</td>
+      <td>        NaN</td>
+      <td>  235.000000</td>
+      <td>    NaN</td>
+      <td> 10493.41675</td>
+      <td>  4275.00000</td>
+      <td>  46104.4975</td>
+      <td> 39000.00000</td>
+      <td> 186850</td>
+      <td>         NaN</td>
+      <td>         NaN</td>
+      <td> 380499.975</td>
+      <td>  64725</td>
+    </tr>
+    <tr>
+      <th>1987-12-31</th>
+      <td> 26836.1650</td>
+      <td> 14739.1675</td>
+      <td> 12430.83250</td>
+      <td>        NaN</td>
+      <td> 157548.725</td>
+      <td>        NaN</td>
+      <td>  245.333350</td>
+      <td>    NaN</td>
+      <td> 12400.41500</td>
+      <td>  7625.00000</td>
+      <td>  46297.4200</td>
+      <td> 46141.66750</td>
+      <td> 234800</td>
+      <td>         NaN</td>
+      <td>         NaN</td>
+      <td> 436333.325</td>
+      <td>  68675</td>
+    </tr>
+    <tr>
+      <th>1988-12-31</th>
+      <td> 31184.0850</td>
+      <td> 21508.8300</td>
+      <td> 17358.33250</td>
+      <td>        NaN</td>
+      <td> 174979.050</td>
+      <td>        NaN</td>
+      <td>  216.250000</td>
+      <td>  92625</td>
+      <td>  8686.08350</td>
+      <td>  9983.33400</td>
+      <td>  55248.5850</td>
+      <td> 52108.33500</td>
+      <td> 245850</td>
+      <td>         NaN</td>
+      <td>         NaN</td>
+      <td> 558666.650</td>
+      <td>  77750</td>
+    </tr>
+    <tr>
+      <th>1989-12-31</th>
+      <td> 45598.6650</td>
+      <td> 23157.1675</td>
+      <td> 30380.00000</td>
+      <td>  99923.160</td>
+      <td> 235447.675</td>
+      <td>        NaN</td>
+      <td>  201.000000</td>
+      <td>  92625</td>
+      <td>  6906.00100</td>
+      <td> 10908.33250</td>
+      <td>  58452.7475</td>
+      <td> 49950.00000</td>
+      <td> 217525</td>
+      <td>         NaN</td>
+      <td>         NaN</td>
+      <td> 618166.650</td>
+      <td>  83675</td>
+    </tr>
+    <tr>
+      <th>1990-12-31</th>
+      <td> 55621.5000</td>
+      <td> 18805.3325</td>
+      <td> 26925.75000</td>
+      <td> 100990.065</td>
+      <td> 296489.150</td>
+      <td>        NaN</td>
+      <td>  177.666675</td>
+      <td> 115400</td>
+      <td>  6595.66675</td>
+      <td>  9458.33350</td>
+      <td>  51657.3350</td>
+      <td> 41525.00250</td>
+      <td> 172425</td>
+      <td>  61912.6700</td>
+      <td> 44291.66500</td>
+      <td> 644500.000</td>
+      <td>  58900</td>
+    </tr>
+    <tr>
+      <th>1991-12-31</th>
+      <td> 49447.5825</td>
+      <td> 16227.7525</td>
+      <td> 13380.16750</td>
+      <td>  93521.750</td>
+      <td> 335004.200</td>
+      <td>        NaN</td>
+      <td>  153.749975</td>
+      <td>  95550</td>
+      <td>  6515.91650</td>
+      <td>  8508.33350</td>
+      <td>  41445.5000</td>
+      <td> 18550.00000</td>
+      <td> 116025</td>
+      <td>  41269.6650</td>
+      <td> 44208.33250</td>
+      <td> 634750.000</td>
+      <td>  31600</td>
+    </tr>
+    <tr>
+      <th>1992-12-31</th>
+      <td> 44125.8325</td>
+      <td> 16886.2500</td>
+      <td>  7051.41675</td>
+      <td> 100831.400</td>
+      <td> 326219.100</td>
+      <td> 21751.6650</td>
+      <td>  159.000000</td>
+      <td>  67200</td>
+      <td>  6421.83325</td>
+      <td>  7983.33325</td>
+      <td>  31678.9150</td>
+      <td> 10566.66650</td>
+      <td> 116175</td>
+      <td>  77535.2475</td>
+      <td> 31708.33250</td>
+      <td> 554083.350</td>
+      <td>  33875</td>
+    </tr>
+    <tr>
+      <th>1993-12-31</th>
+      <td> 32906.0850</td>
+      <td> 14583.3350</td>
+      <td>  5935.16650</td>
+      <td>  99241.065</td>
+      <td> 255561.825</td>
+      <td> 34375.4150</td>
+      <td>  125.583335</td>
+      <td>  39275</td>
+      <td>  7417.58325</td>
+      <td>  6483.33350</td>
+      <td>  20745.0850</td>
+      <td>  8616.66675</td>
+      <td> 126975</td>
+      <td>  68332.0000</td>
+      <td> 32033.33250</td>
+      <td> 472750.025</td>
+      <td>  43525</td>
+    </tr>
+    <tr>
+      <th>1994-12-31</th>
+      <td> 30196.6675</td>
+      <td> 19049.0850</td>
+      <td>  7362.33325</td>
+      <td> 132958.250</td>
+      <td> 264042.800</td>
+      <td> 35485.7475</td>
+      <td>  271.250025</td>
+      <td>  41750</td>
+      <td>  7666.00000</td>
+      <td>  5911.66675</td>
+      <td>  19956.4150</td>
+      <td> 11833.33325</td>
+      <td> 157425</td>
+      <td>  74074.9975</td>
+      <td> 39050.00250</td>
+      <td> 455000.000</td>
+      <td>  74000</td>
+    </tr>
+    <tr>
+      <th>1995-12-31</th>
+      <td> 24986.1675</td>
+      <td> 19652.4175</td>
+      <td>  8305.33425</td>
+      <td> 167718.575</td>
+      <td> 295787.875</td>
+      <td> 28679.9150</td>
+      <td>  437.666650</td>
+      <td>  57000</td>
+      <td>  8789.83325</td>
+      <td>  7968.33350</td>
+      <td>  28216.0825</td>
+      <td> 14966.66500</td>
+      <td> 181350</td>
+      <td>  91248.1700</td>
+      <td> 38525.00000</td>
+      <td> 474333.350</td>
+      <td>  74100</td>
+    </tr>
+    <tr>
+      <th>1996-12-31</th>
+      <td> 19430.4975</td>
+      <td> 21262.5025</td>
+      <td> 10131.50000</td>
+      <td> 188449.250</td>
+      <td> 302137.600</td>
+      <td> 38297.2500</td>
+      <td> 1107.250175</td>
+      <td>  68825</td>
+      <td>  9990.24850</td>
+      <td>  8622.83325</td>
+      <td>  49616.1650</td>
+      <td> 14041.66675</td>
+      <td> 223925</td>
+      <td>  98727.4900</td>
+      <td> 30733.33250</td>
+      <td> 530916.675</td>
+      <td>  79450</td>
+    </tr>
+    <tr>
+      <th>1997-12-31</th>
+      <td> 19018.5850</td>
+      <td> 24568.6675</td>
+      <td> 13690.33425</td>
+      <td> 209466.650</td>
+      <td> 311882.825</td>
+      <td> 42543.9150</td>
+      <td>  931.416750</td>
+      <td>  89500</td>
+      <td> 14057.33500</td>
+      <td> 10998.66675</td>
+      <td>  49468.1675</td>
+      <td> 16150.00000</td>
+      <td> 279850</td>
+      <td>  77260.5850</td>
+      <td> 26008.33000</td>
+      <td> 558583.325</td>
+      <td>  84750</td>
+    </tr>
+    <tr>
+      <th>1998-12-31</th>
+      <td> 23088.1675</td>
+      <td> 36319.5850</td>
+      <td> 16842.25250</td>
+      <td> 226316.650</td>
+      <td> 390079.575</td>
+      <td> 48260.0000</td>
+      <td> 1443.250000</td>
+      <td> 125725</td>
+      <td> 18608.33000</td>
+      <td> 12930.83250</td>
+      <td>  75107.7475</td>
+      <td> 23075.00000</td>
+      <td> 296625</td>
+      <td>  55423.5025</td>
+      <td> 16041.66750</td>
+      <td> 492333.325</td>
+      <td>  98325</td>
+    </tr>
+    <tr>
+      <th>1999-12-31</th>
+      <td> 31216.4175</td>
+      <td> 43985.9175</td>
+      <td> 14627.91575</td>
+      <td> 246874.975</td>
+      <td> 422541.850</td>
+      <td> 51271.3325</td>
+      <td> 1529.666750</td>
+      <td> 163150</td>
+      <td> 17878.16750</td>
+      <td> 13732.50000</td>
+      <td> 143101.8350</td>
+      <td> 25775.00000</td>
+      <td> 307725</td>
+      <td>  35563.3325</td>
+      <td> 12374.99825</td>
+      <td> 488416.675</td>
+      <td> 101100</td>
+    </tr>
+    <tr>
+      <th>2000-12-31</th>
+      <td> 35495.2525</td>
+      <td> 53144.4150</td>
+      <td> 17120.16500</td>
+      <td> 263016.650</td>
+      <td> 452102.250</td>
+      <td> 49999.7525</td>
+      <td> 1364.333250</td>
+      <td> 190150</td>
+      <td> 18424.50000</td>
+      <td> 15141.00000</td>
+      <td> 106791.6600</td>
+      <td> 35108.33250</td>
+      <td> 357975</td>
+      <td>  46700.5000</td>
+      <td> 12358.33425</td>
+      <td> 585833.325</td>
+      <td> 115825</td>
+    </tr>
+    <tr>
+      <th>2001-12-31</th>
+      <td> 29669.8325</td>
+      <td> 46386.0850</td>
+      <td> 19637.83500</td>
+      <td> 252341.650</td>
+      <td> 430995.425</td>
+      <td> 43366.4175</td>
+      <td> 1380.916500</td>
+      <td> 176550</td>
+      <td> 15191.41500</td>
+      <td> 12220.25250</td>
+      <td> 116460.9250</td>
+      <td> 27350.00250</td>
+      <td> 586400</td>
+      <td>  58177.5000</td>
+      <td>  9866.66750</td>
+      <td> 594916.650</td>
+      <td>  92925</td>
+    </tr>
+    <tr>
+      <th>2002-12-31</th>
+      <td> 23189.0025</td>
+      <td> 41156.7500</td>
+      <td> 21591.58000</td>
+      <td> 245508.350</td>
+      <td> 372452.325</td>
+      <td> 44602.5000</td>
+      <td>  940.000000</td>
+      <td> 130000</td>
+      <td> 12225.41750</td>
+      <td>  9727.83425</td>
+      <td> 130919.8500</td>
+      <td> 29209.08500</td>
+      <td> 605500</td>
+      <td>  48117.0850</td>
+      <td> 11941.66825</td>
+      <td> 598583.325</td>
+      <td>  96825</td>
+    </tr>
+    <tr>
+      <th>2003-12-31</th>
+      <td> 21716.4150</td>
+      <td> 40471.0825</td>
+      <td> 23320.83250</td>
+      <td> 241716.650</td>
+      <td> 266281.150</td>
+      <td> 47239.4150</td>
+      <td>  841.833350</td>
+      <td>  96250</td>
+      <td> 11114.50000</td>
+      <td> 10227.33500</td>
+      <td> 149534.2250</td>
+      <td> 25263.00000</td>
+      <td> 588250</td>
+      <td>  42488.5000</td>
+      <td> 16983.33500</td>
+      <td> 670166.675</td>
+      <td> 106325</td>
+    </tr>
+    <tr>
+      <th>2004-12-31</th>
+      <td> 23774.2500</td>
+      <td> 47681.3300</td>
+      <td> 23479.25000</td>
+      <td> 255758.350</td>
+      <td> 206031.575</td>
+      <td> 48223.4150</td>
+      <td> 1014.083500</td>
+      <td> 111650</td>
+      <td> 10707.74900</td>
+      <td>  9693.24825</td>
+      <td> 150802.9000</td>
+      <td> 20859.41500</td>
+      <td> 637750</td>
+      <td>  46054.0025</td>
+      <td> 22108.33500</td>
+      <td> 761666.675</td>
+      <td> 123275</td>
+    </tr>
+    <tr>
+      <th>2005-12-31</th>
+      <td> 26208.5000</td>
+      <td>        NaN</td>
+      <td> 29101.75000</td>
+      <td> 276575.000</td>
+      <td> 255758.250</td>
+      <td> 41613.5000</td>
+      <td> 1210.416750</td>
+      <td> 143525</td>
+      <td> 13321.08250</td>
+      <td>  9057.83250</td>
+      <td> 161163.0000</td>
+      <td> 22348.41500</td>
+      <td> 621500</td>
+      <td>  55691.9150</td>
+      <td> 29708.33250</td>
+      <td> 825583.325</td>
+      <td> 140050</td>
+    </tr>
+    <tr>
+      <th>2006-12-31</th>
+      <td> 32912.4175</td>
+      <td>        NaN</td>
+      <td> 33991.92000</td>
+      <td> 283608.350</td>
+      <td> 354286.750</td>
+      <td> 41676.4975</td>
+      <td> 1750.750000</td>
+      <td> 191750</td>
+      <td> 18894.83250</td>
+      <td> 10359.75175</td>
+      <td>         NaN</td>
+      <td> 32414.83250</td>
+      <td> 601500</td>
+      <td>  84235.0825</td>
+      <td> 47491.66750</td>
+      <td> 860750.000</td>
+      <td> 153850</td>
+    </tr>
+    <tr>
+      <th>2007-12-31</th>
+      <td> 38213.9175</td>
+      <td>        NaN</td>
+      <td> 40167.25250</td>
+      <td> 302358.350</td>
+      <td> 423431.600</td>
+      <td> 29933.3350</td>
+      <td> 2473.333250</td>
+      <td> 227725</td>
+      <td> 24302.00250</td>
+      <td> 14402.58500</td>
+      <td>         NaN</td>
+      <td> 42312.66500</td>
+      <td> 660500</td>
+      <td> 123978.1750</td>
+      <td> 66650.00250</td>
+      <td> 805666.650</td>
+      <td> 171525</td>
+    </tr>
+    <tr>
+      <th>2008-12-31</th>
+      <td> 37497.9175</td>
+      <td>        NaN</td>
+      <td> 37493.08250</td>
+      <td> 292975.000</td>
+      <td> 389045.000</td>
+      <td> 25386.0000</td>
+      <td> 2080.500000</td>
+      <td> 213325</td>
+      <td> 26571.16750</td>
+      <td> 16043.83500</td>
+      <td>         NaN</td>
+      <td> 37980.41750</td>
+      <td> 617500</td>
+      <td> 139698.0000</td>
+      <td> 65799.99750</td>
+      <td> 678666.650</td>
+      <td>  90600</td>
+    </tr>
+    <tr>
+      <th>2009-12-31</th>
+      <td> 27165.2500</td>
+      <td>        NaN</td>
+      <td> 26944.33250</td>
+      <td> 240158.325</td>
+      <td> 225648.175</td>
+      <td> 20739.5850</td>
+      <td> 1269.833250</td>
+      <td> 114825</td>
+      <td> 23875.83500</td>
+      <td> 17869.75000</td>
+      <td>         NaN</td>
+      <td> 26995.75000</td>
+      <td> 442750</td>
+      <td>  46043.1675</td>
+      <td> 38400.00000</td>
+      <td> 522750.025</td>
+      <td>  37700</td>
+    </tr>
+    <tr>
+      <th>2010-12-31</th>
+      <td> 31008.6650</td>
+      <td>        NaN</td>
+      <td> 28496.91750</td>
+      <td> 265891.650</td>
+      <td> 359347.650</td>
+      <td> 22240.5850</td>
+      <td> 1959.250000</td>
+      <td>    NaN</td>
+      <td> 24323.74750</td>
+      <td> 19841.67000</td>
+      <td>         NaN</td>
+      <td> 33264.00000</td>
+      <td> 471250</td>
+      <td>  33141.3350</td>
+      <td> 44966.66750</td>
+      <td> 571583.325</td>
+      <td> 177200</td>
+    </tr>
+    <tr>
+      <th>2011-12-31</th>
+      <td> 32310.0850</td>
+      <td>        NaN</td>
+      <td> 35072.41750</td>
+      <td> 280016.675</td>
+      <td> 466288.475</td>
+      <td> 41304.8325</td>
+      <td> 2856.666500</td>
+      <td>    NaN</td>
+      <td> 26703.58500</td>
+      <td> 12567.66750</td>
+      <td>         NaN</td>
+      <td> 45255.91500</td>
+      <td> 467750</td>
+      <td>  36700.4175</td>
+      <td> 40841.66750</td>
+      <td> 655500.000</td>
+      <td> 184800</td>
+    </tr>
+    <tr>
+      <th>2012-12-31</th>
+      <td> 27586.3300</td>
+      <td>        NaN</td>
+      <td> 55361.00000</td>
+      <td> 262300.000</td>
+      <td> 471999.700</td>
+      <td> 45270.6700</td>
+      <td> 2883.333000</td>
+      <td>    NaN</td>
+      <td> 25564.00000</td>
+      <td>  8844.33300</td>
+      <td>         NaN</td>
+      <td> 68413.00000</td>
+      <td> 441000</td>
+      <td>  37016.0000</td>
+      <td> 37000.00000</td>
+      <td> 760666.700</td>
+      <td> 182200</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>If you want, you can specify the 'aggregation' method with the <code>how</code> argument with a str or a function.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="data-shaking-murder-rates-and-unemployment-dataframes">Data Shaking: Murder Rates and Unemployment <em>DataFrames</em></h3>
+<p>Let's try to bring datasets together using <code>pd.merge</code> function.</p>
+<p>First, I would like to add a name to the Index used to store country codes.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[9]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">sub_murder</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s">u&quot;Country&quot;</span>
+<span class="n">year_unempl</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s">u&quot;Country&quot;</span>
+</pre></div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[14]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="n">sub_murder</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+Country     AUT  BEL  FIN  FRA  DEU  HUN  LUX  NLD  NOR  PRT  ESP  SWE  GBR  \
+Date                                                                          
+1986-12-31  1.3  2.1  3.2  1.2  NaN  2.9  2.2  1.0  1.6  1.5  1.1  1.5  0.8   
+1987-12-31  1.3  1.6  2.7  1.1  NaN  2.6  1.6  0.9  1.5  1.4  1.2  1.2  1.0   
+1988-12-31  1.2  1.6  2.8  1.0  NaN  2.8  1.3  0.9  1.2  1.5  0.9  1.4  1.0   
+1989-12-31  1.1  1.3  3.1  1.2  NaN  3.0  3.0  1.0  1.4  1.6  0.9  1.5  1.1   
+1990-12-31  1.6  1.4  3.2  1.1  1.0  3.2  3.0  0.8  1.2  1.7  1.0  1.3  0.7   
+1991-12-31  1.2  1.4  3.0  1.1  1.1  4.2  2.2  1.1  1.6  1.7  0.9  1.5  0.7   
+1992-12-31  1.5  1.7  3.4  1.0  1.2  4.1  1.9  1.2  1.0  1.5  0.9  1.4  0.9   
+1993-12-31  1.3  2.0  3.2  1.1  1.2  4.2  0.2  1.2  1.0  1.5  1.0  1.3  1.2   
+1994-12-31  1.1  1.8  3.2  1.1  1.2  3.6  1.3  1.1  0.8  1.5  0.9  1.2  1.0   
+1995-12-31  1.0  1.6  2.9  1.1  1.1  3.6  0.7  1.2  1.0  1.7  0.9  1.0  1.0   
+1996-12-31  1.2  2.0  3.3  1.0  1.1  3.2  1.1  1.3  1.0  1.3  0.8  1.2  0.8   
+1997-12-31  0.9  1.7  2.7  0.9  0.9  3.4  0.7  1.3  0.9  1.2  0.8  1.1  0.7   
+1998-12-31  1.1  2.0  2.4  0.7  0.9  3.3  0.7  1.1  1.0  1.3  0.9  1.1  0.7   
+1999-12-31  0.8  2.5  2.7  0.7  0.9  2.9  0.9  1.2  0.9  1.1  0.8  1.2  0.7   
+2000-12-31  0.9  NaN  2.6  0.9  0.7  2.6  1.1  1.1  1.2  0.9  1.0  1.0  NaN   
+2001-12-31  1.0  NaN  2.9  0.8  0.7  2.4  1.9  1.2  0.7  1.3  1.0  1.0  0.4   
+2002-12-31  0.9  NaN  2.5  0.8  0.7  2.5  1.8  1.2  0.9  1.7  1.0  1.2  0.4   
+2003-12-31  0.6  1.4  1.8  0.7  0.7  2.0  0.9  1.2  1.0  1.6  1.0  0.9  0.5   
+2004-12-31  0.7  1.7  2.4  0.8  0.6  2.1  0.5  1.2  0.9  NaN  1.3  1.1  0.5   
+2005-12-31  0.8  1.5  1.9  0.7  0.5  1.9  1.5  1.1  0.7  NaN  0.9  0.9  0.4   
+2006-12-31  0.8  1.4  2.0  0.7  0.6  1.9  1.4  0.8  1.0  NaN  0.8  0.9  0.4   
+2007-12-31  0.6  1.5  2.2  0.6  0.6  1.7  1.2  0.9  0.7  1.0  0.7  1.2  0.4   
+2008-12-31  0.6  1.2  2.2  0.7  0.5  2.0  1.1  0.9  0.6  1.3  0.8  0.8  0.3   
+2009-12-31  0.7  1.2  1.9  0.8  0.5  1.4  0.9  0.9  0.6  0.9  0.7  0.9  0.3   
+2010-12-31  0.5  NaN  1.9  NaN  0.6  1.4  2.1  0.9  0.7  1.2  0.7  1.0  0.3   
+2011-12-31  0.5  NaN  1.8  NaN  0.5  1.5  0.4  0.9  2.3  0.9  0.7  NaN  NaN   
+
+Country     CZE  POL  JPN  AUS  
+Date                            
+1986-12-31  1.1  1.7  0.9  1.9  
+1987-12-31  1.1  1.9  0.8  2.1  
+1988-12-31  0.8  1.9  0.8  2.0  
+1989-12-31  1.0  2.2  0.7  1.8  
+1990-12-31  2.0  3.3  0.6  2.1  
+1991-12-31  1.9  3.1  0.6  1.9  
+1992-12-31  2.0  3.2  0.6  1.7  
+1993-12-31  2.2  2.9  0.7  1.9  
+1994-12-31  2.3  3.2  0.6  1.8  
+1995-12-31  1.8  3.0  0.6  1.6  
+1996-12-31  1.7  2.8  0.6  1.7  
+1997-12-31  1.6  NaN  0.6  1.7  
+1998-12-31  1.6  NaN  0.6  1.6  
+1999-12-31  1.4  2.4  0.6  1.5  
+2000-12-31  1.5  2.2  0.6  1.6  
+2001-12-31  1.3  1.8  0.6  1.6  
+2002-12-31  1.3  1.8  0.6  1.4  
+2003-12-31  1.3  1.5  0.5  1.3  
+2004-12-31  1.2  1.5  0.5  0.8  
+2005-12-31  0.9  1.5  0.5  0.9  
+2006-12-31  1.0  1.5  0.5  0.7  
+2007-12-31  1.0  1.4  0.4  1.0  
+2008-12-31  0.7  1.2  0.4  1.2  
+2009-12-31  0.8  1.1  0.4  1.2  
+2010-12-31  0.8  0.9  0.3  1.1  
+2011-12-31  0.8  1.0  NaN  0.8  
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[15]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="n">year_unempl</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+Country            AUT         BEL          FIN         FRA         DEU  \
+Date                                                                      
+1986-12-31  24692.6650  17739.3325  12438.58075         NaN  142974.775   
+1987-12-31  26836.1650  14739.1675  12430.83250         NaN  157548.725   
+1988-12-31  31184.0850  21508.8300  17358.33250         NaN  174979.050   
+1989-12-31  45598.6650  23157.1675  30380.00000   99923.160  235447.675   
+1990-12-31  55621.5000  18805.3325  26925.75000  100990.065  296489.150   
+1991-12-31  49447.5825  16227.7525  13380.16750   93521.750  335004.200   
+1992-12-31  44125.8325  16886.2500   7051.41675  100831.400  326219.100   
+1993-12-31  32906.0850  14583.3350   5935.16650   99241.065  255561.825   
+1994-12-31  30196.6675  19049.0850   7362.33325  132958.250  264042.800   
+1995-12-31  24986.1675  19652.4175   8305.33425  167718.575  295787.875   
+1996-12-31  19430.4975  21262.5025  10131.50000  188449.250  302137.600   
+1997-12-31  19018.5850  24568.6675  13690.33425  209466.650  311882.825   
+1998-12-31  23088.1675  36319.5850  16842.25250  226316.650  390079.575   
+1999-12-31  31216.4175  43985.9175  14627.91575  246874.975  422541.850   
+2000-12-31  35495.2525  53144.4150  17120.16500  263016.650  452102.250   
+2001-12-31  29669.8325  46386.0850  19637.83500  252341.650  430995.425   
+2002-12-31  23189.0025  41156.7500  21591.58000  245508.350  372452.325   
+2003-12-31  21716.4150  40471.0825  23320.83250  241716.650  266281.150   
+2004-12-31  23774.2500  47681.3300  23479.25000  255758.350  206031.575   
+2005-12-31  26208.5000         NaN  29101.75000  276575.000  255758.250   
+2006-12-31  32912.4175         NaN  33991.92000  283608.350  354286.750   
+2007-12-31  38213.9175         NaN  40167.25250  302358.350  423431.600   
+2008-12-31  37497.9175         NaN  37493.08250  292975.000  389045.000   
+2009-12-31  27165.2500         NaN  26944.33250  240158.325  225648.175   
+2010-12-31  31008.6650         NaN  28496.91750  265891.650  359347.650   
+2011-12-31  32310.0850         NaN  35072.41750  280016.675  466288.475   
+2012-12-31  27586.3300         NaN  55361.00000  262300.000  471999.700   
+
+Country            HUN          LUX     NLD          NOR          PRT  \
+Date                                                                    
+1986-12-31         NaN   235.000000     NaN  10493.41675   4275.00000   
+1987-12-31         NaN   245.333350     NaN  12400.41500   7625.00000   
+1988-12-31         NaN   216.250000   92625   8686.08350   9983.33400   
+1989-12-31         NaN   201.000000   92625   6906.00100  10908.33250   
+1990-12-31         NaN   177.666675  115400   6595.66675   9458.33350   
+1991-12-31         NaN   153.749975   95550   6515.91650   8508.33350   
+1992-12-31  21751.6650   159.000000   67200   6421.83325   7983.33325   
+1993-12-31  34375.4150   125.583335   39275   7417.58325   6483.33350   
+1994-12-31  35485.7475   271.250025   41750   7666.00000   5911.66675   
+1995-12-31  28679.9150   437.666650   57000   8789.83325   7968.33350   
+1996-12-31  38297.2500  1107.250175   68825   9990.24850   8622.83325   
+1997-12-31  42543.9150   931.416750   89500  14057.33500  10998.66675   
+1998-12-31  48260.0000  1443.250000  125725  18608.33000  12930.83250   
+1999-12-31  51271.3325  1529.666750  163150  17878.16750  13732.50000   
+2000-12-31  49999.7525  1364.333250  190150  18424.50000  15141.00000   
+2001-12-31  43366.4175  1380.916500  176550  15191.41500  12220.25250   
+2002-12-31  44602.5000   940.000000  130000  12225.41750   9727.83425   
+2003-12-31  47239.4150   841.833350   96250  11114.50000  10227.33500   
+2004-12-31  48223.4150  1014.083500  111650  10707.74900   9693.24825   
+2005-12-31  41613.5000  1210.416750  143525  13321.08250   9057.83250   
+2006-12-31  41676.4975  1750.750000  191750  18894.83250  10359.75175   
+2007-12-31  29933.3350  2473.333250  227725  24302.00250  14402.58500   
+2008-12-31  25386.0000  2080.500000  213325  26571.16750  16043.83500   
+2009-12-31  20739.5850  1269.833250  114825  23875.83500  17869.75000   
+2010-12-31  22240.5850  1959.250000     NaN  24323.74750  19841.67000   
+2011-12-31  41304.8325  2856.666500     NaN  26703.58500  12567.66750   
+2012-12-31  45270.6700  2883.333000     NaN  25564.00000   8844.33300   
+
+Country             ESP          SWE     GBR          CZE          POL  \
+Date                                                                     
+1986-12-31   46104.4975  39000.00000  186850          NaN          NaN   
+1987-12-31   46297.4200  46141.66750  234800          NaN          NaN   
+1988-12-31   55248.5850  52108.33500  245850          NaN          NaN   
+1989-12-31   58452.7475  49950.00000  217525          NaN          NaN   
+1990-12-31   51657.3350  41525.00250  172425   61912.6700  44291.66500   
+1991-12-31   41445.5000  18550.00000  116025   41269.6650  44208.33250   
+1992-12-31   31678.9150  10566.66650  116175   77535.2475  31708.33250   
+1993-12-31   20745.0850   8616.66675  126975   68332.0000  32033.33250   
+1994-12-31   19956.4150  11833.33325  157425   74074.9975  39050.00250   
+1995-12-31   28216.0825  14966.66500  181350   91248.1700  38525.00000   
+1996-12-31   49616.1650  14041.66675  223925   98727.4900  30733.33250   
+1997-12-31   49468.1675  16150.00000  279850   77260.5850  26008.33000   
+1998-12-31   75107.7475  23075.00000  296625   55423.5025  16041.66750   
+1999-12-31  143101.8350  25775.00000  307725   35563.3325  12374.99825   
+2000-12-31  106791.6600  35108.33250  357975   46700.5000  12358.33425   
+2001-12-31  116460.9250  27350.00250  586400   58177.5000   9866.66750   
+2002-12-31  130919.8500  29209.08500  605500   48117.0850  11941.66825   
+2003-12-31  149534.2250  25263.00000  588250   42488.5000  16983.33500   
+2004-12-31  150802.9000  20859.41500  637750   46054.0025  22108.33500   
+2005-12-31  161163.0000  22348.41500  621500   55691.9150  29708.33250   
+2006-12-31          NaN  32414.83250  601500   84235.0825  47491.66750   
+2007-12-31          NaN  42312.66500  660500  123978.1750  66650.00250   
+2008-12-31          NaN  37980.41750  617500  139698.0000  65799.99750   
+2009-12-31          NaN  26995.75000  442750   46043.1675  38400.00000   
+2010-12-31          NaN  33264.00000  471250   33141.3350  44966.66750   
+2011-12-31          NaN  45255.91500  467750   36700.4175  40841.66750   
+2012-12-31          NaN  68413.00000  441000   37016.0000  37000.00000   
+
+Country            JPN     AUS  
+Date                            
+1986-12-31  380499.975   64725  
+1987-12-31  436333.325   68675  
+1988-12-31  558666.650   77750  
+1989-12-31  618166.650   83675  
+1990-12-31  644500.000   58900  
+1991-12-31  634750.000   31600  
+1992-12-31  554083.350   33875  
+1993-12-31  472750.025   43525  
+1994-12-31  455000.000   74000  
+1995-12-31  474333.350   74100  
+1996-12-31  530916.675   79450  
+1997-12-31  558583.325   84750  
+1998-12-31  492333.325   98325  
+1999-12-31  488416.675  101100  
+2000-12-31  585833.325  115825  
+2001-12-31  594916.650   92925  
+2002-12-31  598583.325   96825  
+2003-12-31  670166.675  106325  
+2004-12-31  761666.675  123275  
+2005-12-31  825583.325  140050  
+2006-12-31  860750.000  153850  
+2007-12-31  805666.650  171525  
+2008-12-31  678666.650   90600  
+2009-12-31  522750.025   37700  
+2010-12-31  571583.325  177200  
+2011-12-31  655500.000  184800  
+2012-12-31  760666.700  182200  
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Then, use <code>pd.merge</code> function to create a single <em>DataFrame</em> with all values.</p>
+<ol style="list-style-type: decimal">
+<li>You want to merge the data along their Index</li>
+<li>You want to make the difference between the values from &quot;murder rates&quot; and &quot;unemployment&quot; for each countries</li>
+</ol>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[10]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">dfa</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">sub_murder</span><span class="p">,</span> <span class="n">year_unempl</span><span class="p">,</span> <span class="n">left_index</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">right_index</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">dfa</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[10]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<div class="output_html rendered_html">
+<pre>
+&lt;class 'pandas.core.frame.DataFrame'&gt;
+DatetimeIndex: 26 entries, 1986-12-31 00:00:00 to 2011-12-31 00:00:00
+Freq: A-DEC
+Data columns (total 34 columns):
+AUT_x    26  non-null values
+BEL_x    21  non-null values
+FIN_x    26  non-null values
+FRA_x    24  non-null values
+DEU_x    22  non-null values
+HUN_x    26  non-null values
+LUX_x    26  non-null values
+NLD_x    26  non-null values
+NOR_x    26  non-null values
+PRT_x    23  non-null values
+ESP_x    26  non-null values
+SWE_x    25  non-null values
+GBR_x    24  non-null values
+CZE_x    26  non-null values
+POL_x    24  non-null values
+JPN_x    25  non-null values
+AUS_x    26  non-null values
+AUT_y    26  non-null values
+BEL_y    19  non-null values
+FIN_y    26  non-null values
+FRA_y    23  non-null values
+DEU_y    26  non-null values
+HUN_y    20  non-null values
+LUX_y    26  non-null values
+NLD_y    22  non-null values
+NOR_y    26  non-null values
+PRT_y    26  non-null values
+ESP_y    20  non-null values
+SWE_y    26  non-null values
+GBR_y    26  non-null values
+CZE_y    22  non-null values
+POL_y    22  non-null values
+JPN_y    26  non-null values
+AUS_y    26  non-null values
+dtypes: float64(34)
+</pre>
+</div>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As you can see, pandas merging makes the difference between the left and right labels with the suffixes <code>(_x, _y)</code> by default. Be more explicit.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[11]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">dfa</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">sub_murder</span><span class="p">,</span> <span class="n">year_unempl</span><span class="p">,</span> <span class="n">left_index</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">right_index</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+               <span class="n">suffixes</span><span class="o">=</span><span class="p">(</span><span class="s">&#39;_MUR&#39;</span><span class="p">,</span> <span class="s">&#39;_UNP&#39;</span><span class="p">))</span>
+<span class="n">dfa</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[11]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<div class="output_html rendered_html">
+<pre>
+&lt;class 'pandas.core.frame.DataFrame'&gt;
+DatetimeIndex: 26 entries, 1986-12-31 00:00:00 to 2011-12-31 00:00:00
+Freq: A-DEC
+Data columns (total 34 columns):
+AUT_MUR    26  non-null values
+BEL_MUR    21  non-null values
+FIN_MUR    26  non-null values
+FRA_MUR    24  non-null values
+DEU_MUR    22  non-null values
+HUN_MUR    26  non-null values
+LUX_MUR    26  non-null values
+NLD_MUR    26  non-null values
+NOR_MUR    26  non-null values
+PRT_MUR    23  non-null values
+ESP_MUR    26  non-null values
+SWE_MUR    25  non-null values
+GBR_MUR    24  non-null values
+CZE_MUR    26  non-null values
+POL_MUR    24  non-null values
+JPN_MUR    25  non-null values
+AUS_MUR    26  non-null values
+AUT_UNP    26  non-null values
+BEL_UNP    19  non-null values
+FIN_UNP    26  non-null values
+FRA_UNP    23  non-null values
+DEU_UNP    26  non-null values
+HUN_UNP    20  non-null values
+LUX_UNP    26  non-null values
+NLD_UNP    22  non-null values
+NOR_UNP    26  non-null values
+PRT_UNP    26  non-null values
+ESP_UNP    20  non-null values
+SWE_UNP    26  non-null values
+GBR_UNP    26  non-null values
+CZE_UNP    22  non-null values
+POL_UNP    22  non-null values
+JPN_UNP    26  non-null values
+AUS_UNP    26  non-null values
+dtypes: float64(34)
+</pre>
+</div>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now you have all values in the same <em>DataFrame</em>. You would like to sort labels in order to have <code>COUNTRY_CODE_MUR</code> close to <code>COUNTRY_CODE_UNP</code>.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[19]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">dfa</span><span class="o">.</span><span class="n">sort_index</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[19]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<div class="output_html rendered_html">
+<pre>
+&lt;class 'pandas.core.frame.DataFrame'&gt;
+DatetimeIndex: 26 entries, 1986-12-31 00:00:00 to 2011-12-31 00:00:00
+Freq: A-DEC
+Data columns (total 34 columns):
+AUS_MUR    26  non-null values
+AUS_UNP    26  non-null values
+AUT_MUR    26  non-null values
+AUT_UNP    26  non-null values
+BEL_MUR    21  non-null values
+BEL_UNP    19  non-null values
+CZE_MUR    26  non-null values
+CZE_UNP    22  non-null values
+DEU_MUR    22  non-null values
+DEU_UNP    26  non-null values
+ESP_MUR    26  non-null values
+ESP_UNP    20  non-null values
+FIN_MUR    26  non-null values
+FIN_UNP    26  non-null values
+FRA_MUR    24  non-null values
+FRA_UNP    23  non-null values
+GBR_MUR    24  non-null values
+GBR_UNP    26  non-null values
+HUN_MUR    26  non-null values
+HUN_UNP    20  non-null values
+JPN_MUR    25  non-null values
+JPN_UNP    26  non-null values
+LUX_MUR    26  non-null values
+LUX_UNP    26  non-null values
+NLD_MUR    26  non-null values
+NLD_UNP    22  non-null values
+NOR_MUR    26  non-null values
+NOR_UNP    26  non-null values
+POL_MUR    24  non-null values
+POL_UNP    22  non-null values
+PRT_MUR    23  non-null values
+PRT_UNP    26  non-null values
+SWE_MUR    25  non-null values
+SWE_UNP    26  non-null values
+dtypes: float64(34)
+</pre>
+</div>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Take 4 arbitrary countries and display the values.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[12]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">clist</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;NOR&#39;</span><span class="p">,</span> <span class="s">&#39;FRA&#39;</span><span class="p">,</span> <span class="s">&#39;GBR&#39;</span><span class="p">,</span> <span class="s">&#39;DEU&#39;</span><span class="p">]</span>
+<span class="n">clist</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;_&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">c</span><span class="p">,</span> <span class="s">&#39;MUR&#39;</span><span class="p">])</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">clist</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="s">&#39;_&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">c</span><span class="p">,</span> <span class="s">&#39;UNP&#39;</span><span class="p">])</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">clist</span><span class="p">]</span>
+<span class="n">clist_sorted</span> <span class="o">=</span> <span class="n">sort</span><span class="p">(</span><span class="n">clist</span><span class="p">)</span>
+<span class="k">print</span> <span class="n">clist</span>
+<span class="k">print</span> <span class="n">clist_sorted</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+[&apos;NOR_MUR&apos;, &apos;FRA_MUR&apos;, &apos;GBR_MUR&apos;, &apos;DEU_MUR&apos;, &apos;NOR_UNP&apos;, &apos;FRA_UNP&apos;, &apos;GBR_UNP&apos;, &apos;DEU_UNP&apos;]
+[&apos;DEU_MUR&apos; &apos;DEU_UNP&apos; &apos;FRA_MUR&apos; &apos;FRA_UNP&apos; &apos;GBR_MUR&apos; &apos;GBR_UNP&apos; &apos;NOR_MUR&apos;
+ &apos;NOR_UNP&apos;]
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[23]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">dfa</span><span class="p">[</span><span class="n">clist_sorted</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[23]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<div class="output_html rendered_html">
+<div style="max-height:1000px;max-width:1500px;overflow:auto;">
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th>Country</th>
+      <th>DEU_MUR</th>
+      <th>DEU_UNP</th>
+      <th>FRA_MUR</th>
+      <th>FRA_UNP</th>
+      <th>GBR_MUR</th>
+      <th>GBR_UNP</th>
+      <th>NOR_MUR</th>
+      <th>NOR_UNP</th>
+    </tr>
+    <tr>
+      <th>Date</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>1986-12-31</th>
+      <td> NaN</td>
+      <td> 142974.775</td>
+      <td> 1.2</td>
+      <td>        NaN</td>
+      <td> 0.8</td>
+      <td> 186850</td>
+      <td> 1.6</td>
+      <td> 10493.41675</td>
+    </tr>
+    <tr>
+      <th>1987-12-31</th>
+      <td> NaN</td>
+      <td> 157548.725</td>
+      <td> 1.1</td>
+      <td>        NaN</td>
+      <td> 1.0</td>
+      <td> 234800</td>
+      <td> 1.5</td>
+      <td> 12400.41500</td>
+    </tr>
+    <tr>
+      <th>1988-12-31</th>
+      <td> NaN</td>
+      <td> 174979.050</td>
+      <td> 1.0</td>
+      <td>        NaN</td>
+      <td> 1.0</td>
+      <td> 245850</td>
+      <td> 1.2</td>
+      <td>  8686.08350</td>
+    </tr>
+    <tr>
+      <th>1989-12-31</th>
+      <td> NaN</td>
+      <td> 235447.675</td>
+      <td> 1.2</td>
+      <td>  99923.160</td>
+      <td> 1.1</td>
+      <td> 217525</td>
+      <td> 1.4</td>
+      <td>  6906.00100</td>
+    </tr>
+    <tr>
+      <th>1990-12-31</th>
+      <td> 1.0</td>
+      <td> 296489.150</td>
+      <td> 1.1</td>
+      <td> 100990.065</td>
+      <td> 0.7</td>
+      <td> 172425</td>
+      <td> 1.2</td>
+      <td>  6595.66675</td>
+    </tr>
+    <tr>
+      <th>1991-12-31</th>
+      <td> 1.1</td>
+      <td> 335004.200</td>
+      <td> 1.1</td>
+      <td>  93521.750</td>
+      <td> 0.7</td>
+      <td> 116025</td>
+      <td> 1.6</td>
+      <td>  6515.91650</td>
+    </tr>
+    <tr>
+      <th>1992-12-31</th>
+      <td> 1.2</td>
+      <td> 326219.100</td>
+      <td> 1.0</td>
+      <td> 100831.400</td>
+      <td> 0.9</td>
+      <td> 116175</td>
+      <td> 1.0</td>
+      <td>  6421.83325</td>
+    </tr>
+    <tr>
+      <th>1993-12-31</th>
+      <td> 1.2</td>
+      <td> 255561.825</td>
+      <td> 1.1</td>
+      <td>  99241.065</td>
+      <td> 1.2</td>
+      <td> 126975</td>
+      <td> 1.0</td>
+      <td>  7417.58325</td>
+    </tr>
+    <tr>
+      <th>1994-12-31</th>
+      <td> 1.2</td>
+      <td> 264042.800</td>
+      <td> 1.1</td>
+      <td> 132958.250</td>
+      <td> 1.0</td>
+      <td> 157425</td>
+      <td> 0.8</td>
+      <td>  7666.00000</td>
+    </tr>
+    <tr>
+      <th>1995-12-31</th>
+      <td> 1.1</td>
+      <td> 295787.875</td>
+      <td> 1.1</td>
+      <td> 167718.575</td>
+      <td> 1.0</td>
+      <td> 181350</td>
+      <td> 1.0</td>
+      <td>  8789.83325</td>
+    </tr>
+    <tr>
+      <th>1996-12-31</th>
+      <td> 1.1</td>
+      <td> 302137.600</td>
+      <td> 1.0</td>
+      <td> 188449.250</td>
+      <td> 0.8</td>
+      <td> 223925</td>
+      <td> 1.0</td>
+      <td>  9990.24850</td>
+    </tr>
+    <tr>
+      <th>1997-12-31</th>
+      <td> 0.9</td>
+      <td> 311882.825</td>
+      <td> 0.9</td>
+      <td> 209466.650</td>
+      <td> 0.7</td>
+      <td> 279850</td>
+      <td> 0.9</td>
+      <td> 14057.33500</td>
+    </tr>
+    <tr>
+      <th>1998-12-31</th>
+      <td> 0.9</td>
+      <td> 390079.575</td>
+      <td> 0.7</td>
+      <td> 226316.650</td>
+      <td> 0.7</td>
+      <td> 296625</td>
+      <td> 1.0</td>
+      <td> 18608.33000</td>
+    </tr>
+    <tr>
+      <th>1999-12-31</th>
+      <td> 0.9</td>
+      <td> 422541.850</td>
+      <td> 0.7</td>
+      <td> 246874.975</td>
+      <td> 0.7</td>
+      <td> 307725</td>
+      <td> 0.9</td>
+      <td> 17878.16750</td>
+    </tr>
+    <tr>
+      <th>2000-12-31</th>
+      <td> 0.7</td>
+      <td> 452102.250</td>
+      <td> 0.9</td>
+      <td> 263016.650</td>
+      <td> NaN</td>
+      <td> 357975</td>
+      <td> 1.2</td>
+      <td> 18424.50000</td>
+    </tr>
+    <tr>
+      <th>2001-12-31</th>
+      <td> 0.7</td>
+      <td> 430995.425</td>
+      <td> 0.8</td>
+      <td> 252341.650</td>
+      <td> 0.4</td>
+      <td> 586400</td>
+      <td> 0.7</td>
+      <td> 15191.41500</td>
+    </tr>
+    <tr>
+      <th>2002-12-31</th>
+      <td> 0.7</td>
+      <td> 372452.325</td>
+      <td> 0.8</td>
+      <td> 245508.350</td>
+      <td> 0.4</td>
+      <td> 605500</td>
+      <td> 0.9</td>
+      <td> 12225.41750</td>
+    </tr>
+    <tr>
+      <th>2003-12-31</th>
+      <td> 0.7</td>
+      <td> 266281.150</td>
+      <td> 0.7</td>
+      <td> 241716.650</td>
+      <td> 0.5</td>
+      <td> 588250</td>
+      <td> 1.0</td>
+      <td> 11114.50000</td>
+    </tr>
+    <tr>
+      <th>2004-12-31</th>
+      <td> 0.6</td>
+      <td> 206031.575</td>
+      <td> 0.8</td>
+      <td> 255758.350</td>
+      <td> 0.5</td>
+      <td> 637750</td>
+      <td> 0.9</td>
+      <td> 10707.74900</td>
+    </tr>
+    <tr>
+      <th>2005-12-31</th>
+      <td> 0.5</td>
+      <td> 255758.250</td>
+      <td> 0.7</td>
+      <td> 276575.000</td>
+      <td> 0.4</td>
+      <td> 621500</td>
+      <td> 0.7</td>
+      <td> 13321.08250</td>
+    </tr>
+    <tr>
+      <th>2006-12-31</th>
+      <td> 0.6</td>
+      <td> 354286.750</td>
+      <td> 0.7</td>
+      <td> 283608.350</td>
+      <td> 0.4</td>
+      <td> 601500</td>
+      <td> 1.0</td>
+      <td> 18894.83250</td>
+    </tr>
+    <tr>
+      <th>2007-12-31</th>
+      <td> 0.6</td>
+      <td> 423431.600</td>
+      <td> 0.6</td>
+      <td> 302358.350</td>
+      <td> 0.4</td>
+      <td> 660500</td>
+      <td> 0.7</td>
+      <td> 24302.00250</td>
+    </tr>
+    <tr>
+      <th>2008-12-31</th>
+      <td> 0.5</td>
+      <td> 389045.000</td>
+      <td> 0.7</td>
+      <td> 292975.000</td>
+      <td> 0.3</td>
+      <td> 617500</td>
+      <td> 0.6</td>
+      <td> 26571.16750</td>
+    </tr>
+    <tr>
+      <th>2009-12-31</th>
+      <td> 0.5</td>
+      <td> 225648.175</td>
+      <td> 0.8</td>
+      <td> 240158.325</td>
+      <td> 0.3</td>
+      <td> 442750</td>
+      <td> 0.6</td>
+      <td> 23875.83500</td>
+    </tr>
+    <tr>
+      <th>2010-12-31</th>
+      <td> 0.6</td>
+      <td> 359347.650</td>
+      <td> NaN</td>
+      <td> 265891.650</td>
+      <td> 0.3</td>
+      <td> 471250</td>
+      <td> 0.7</td>
+      <td> 24323.74750</td>
+    </tr>
+    <tr>
+      <th>2011-12-31</th>
+      <td> 0.5</td>
+      <td> 466288.475</td>
+      <td> NaN</td>
+      <td> 280016.675</td>
+      <td> NaN</td>
+      <td> 467750</td>
+      <td> 2.3</td>
+      <td> 26703.58500</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>You can specify the <code>how</code> argument if you don't want to drop any NaN values.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[22]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">sub_murder</span><span class="p">,</span> <span class="n">year_unempl</span><span class="p">,</span> <span class="n">left_index</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">right_index</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+               <span class="n">suffixes</span><span class="o">=</span><span class="p">(</span><span class="s">&#39;_MUR&#39;</span><span class="p">,</span> <span class="s">&#39;_UNP&#39;</span><span class="p">),</span> <span class="n">how</span><span class="o">=</span><span class="s">&#39;outer&#39;</span><span class="p">)[</span><span class="n">clist_sorted</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[22]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<div class="output_html rendered_html">
+<div style="max-height:1000px;max-width:1500px;overflow:auto;">
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th>Country</th>
+      <th>DEU_MUR</th>
+      <th>DEU_UNP</th>
+      <th>FRA_MUR</th>
+      <th>FRA_UNP</th>
+      <th>GBR_MUR</th>
+      <th>GBR_UNP</th>
+      <th>NOR_MUR</th>
+      <th>NOR_UNP</th>
+    </tr>
+    <tr>
+      <th>Date</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>1986-12-31</th>
+      <td> NaN</td>
+      <td> 142974.775</td>
+      <td> 1.2</td>
+      <td>        NaN</td>
+      <td> 0.8</td>
+      <td> 186850</td>
+      <td> 1.6</td>
+      <td> 10493.41675</td>
+    </tr>
+    <tr>
+      <th>1987-12-31</th>
+      <td> NaN</td>
+      <td> 157548.725</td>
+      <td> 1.1</td>
+      <td>        NaN</td>
+      <td> 1.0</td>
+      <td> 234800</td>
+      <td> 1.5</td>
+      <td> 12400.41500</td>
+    </tr>
+    <tr>
+      <th>1988-12-31</th>
+      <td> NaN</td>
+      <td> 174979.050</td>
+      <td> 1.0</td>
+      <td>        NaN</td>
+      <td> 1.0</td>
+      <td> 245850</td>
+      <td> 1.2</td>
+      <td>  8686.08350</td>
+    </tr>
+    <tr>
+      <th>1989-12-31</th>
+      <td> NaN</td>
+      <td> 235447.675</td>
+      <td> 1.2</td>
+      <td>  99923.160</td>
+      <td> 1.1</td>
+      <td> 217525</td>
+      <td> 1.4</td>
+      <td>  6906.00100</td>
+    </tr>
+    <tr>
+      <th>1990-12-31</th>
+      <td> 1.0</td>
+      <td> 296489.150</td>
+      <td> 1.1</td>
+      <td> 100990.065</td>
+      <td> 0.7</td>
+      <td> 172425</td>
+      <td> 1.2</td>
+      <td>  6595.66675</td>
+    </tr>
+    <tr>
+      <th>1991-12-31</th>
+      <td> 1.1</td>
+      <td> 335004.200</td>
+      <td> 1.1</td>
+      <td>  93521.750</td>
+      <td> 0.7</td>
+      <td> 116025</td>
+      <td> 1.6</td>
+      <td>  6515.91650</td>
+    </tr>
+    <tr>
+      <th>1992-12-31</th>
+      <td> 1.2</td>
+      <td> 326219.100</td>
+      <td> 1.0</td>
+      <td> 100831.400</td>
+      <td> 0.9</td>
+      <td> 116175</td>
+      <td> 1.0</td>
+      <td>  6421.83325</td>
+    </tr>
+    <tr>
+      <th>1993-12-31</th>
+      <td> 1.2</td>
+      <td> 255561.825</td>
+      <td> 1.1</td>
+      <td>  99241.065</td>
+      <td> 1.2</td>
+      <td> 126975</td>
+      <td> 1.0</td>
+      <td>  7417.58325</td>
+    </tr>
+    <tr>
+      <th>1994-12-31</th>
+      <td> 1.2</td>
+      <td> 264042.800</td>
+      <td> 1.1</td>
+      <td> 132958.250</td>
+      <td> 1.0</td>
+      <td> 157425</td>
+      <td> 0.8</td>
+      <td>  7666.00000</td>
+    </tr>
+    <tr>
+      <th>1995-12-31</th>
+      <td> 1.1</td>
+      <td> 295787.875</td>
+      <td> 1.1</td>
+      <td> 167718.575</td>
+      <td> 1.0</td>
+      <td> 181350</td>
+      <td> 1.0</td>
+      <td>  8789.83325</td>
+    </tr>
+    <tr>
+      <th>1996-12-31</th>
+      <td> 1.1</td>
+      <td> 302137.600</td>
+      <td> 1.0</td>
+      <td> 188449.250</td>
+      <td> 0.8</td>
+      <td> 223925</td>
+      <td> 1.0</td>
+      <td>  9990.24850</td>
+    </tr>
+    <tr>
+      <th>1997-12-31</th>
+      <td> 0.9</td>
+      <td> 311882.825</td>
+      <td> 0.9</td>
+      <td> 209466.650</td>
+      <td> 0.7</td>
+      <td> 279850</td>
+      <td> 0.9</td>
+      <td> 14057.33500</td>
+    </tr>
+    <tr>
+      <th>1998-12-31</th>
+      <td> 0.9</td>
+      <td> 390079.575</td>
+      <td> 0.7</td>
+      <td> 226316.650</td>
+      <td> 0.7</td>
+      <td> 296625</td>
+      <td> 1.0</td>
+      <td> 18608.33000</td>
+    </tr>
+    <tr>
+      <th>1999-12-31</th>
+      <td> 0.9</td>
+      <td> 422541.850</td>
+      <td> 0.7</td>
+      <td> 246874.975</td>
+      <td> 0.7</td>
+      <td> 307725</td>
+      <td> 0.9</td>
+      <td> 17878.16750</td>
+    </tr>
+    <tr>
+      <th>2000-12-31</th>
+      <td> 0.7</td>
+      <td> 452102.250</td>
+      <td> 0.9</td>
+      <td> 263016.650</td>
+      <td> NaN</td>
+      <td> 357975</td>
+      <td> 1.2</td>
+      <td> 18424.50000</td>
+    </tr>
+    <tr>
+      <th>2001-12-31</th>
+      <td> 0.7</td>
+      <td> 430995.425</td>
+      <td> 0.8</td>
+      <td> 252341.650</td>
+      <td> 0.4</td>
+      <td> 586400</td>
+      <td> 0.7</td>
+      <td> 15191.41500</td>
+    </tr>
+    <tr>
+      <th>2002-12-31</th>
+      <td> 0.7</td>
+      <td> 372452.325</td>
+      <td> 0.8</td>
+      <td> 245508.350</td>
+      <td> 0.4</td>
+      <td> 605500</td>
+      <td> 0.9</td>
+      <td> 12225.41750</td>
+    </tr>
+    <tr>
+      <th>2003-12-31</th>
+      <td> 0.7</td>
+      <td> 266281.150</td>
+      <td> 0.7</td>
+      <td> 241716.650</td>
+      <td> 0.5</td>
+      <td> 588250</td>
+      <td> 1.0</td>
+      <td> 11114.50000</td>
+    </tr>
+    <tr>
+      <th>2004-12-31</th>
+      <td> 0.6</td>
+      <td> 206031.575</td>
+      <td> 0.8</td>
+      <td> 255758.350</td>
+      <td> 0.5</td>
+      <td> 637750</td>
+      <td> 0.9</td>
+      <td> 10707.74900</td>
+    </tr>
+    <tr>
+      <th>2005-12-31</th>
+      <td> 0.5</td>
+      <td> 255758.250</td>
+      <td> 0.7</td>
+      <td> 276575.000</td>
+      <td> 0.4</td>
+      <td> 621500</td>
+      <td> 0.7</td>
+      <td> 13321.08250</td>
+    </tr>
+    <tr>
+      <th>2006-12-31</th>
+      <td> 0.6</td>
+      <td> 354286.750</td>
+      <td> 0.7</td>
+      <td> 283608.350</td>
+      <td> 0.4</td>
+      <td> 601500</td>
+      <td> 1.0</td>
+      <td> 18894.83250</td>
+    </tr>
+    <tr>
+      <th>2007-12-31</th>
+      <td> 0.6</td>
+      <td> 423431.600</td>
+      <td> 0.6</td>
+      <td> 302358.350</td>
+      <td> 0.4</td>
+      <td> 660500</td>
+      <td> 0.7</td>
+      <td> 24302.00250</td>
+    </tr>
+    <tr>
+      <th>2008-12-31</th>
+      <td> 0.5</td>
+      <td> 389045.000</td>
+      <td> 0.7</td>
+      <td> 292975.000</td>
+      <td> 0.3</td>
+      <td> 617500</td>
+      <td> 0.6</td>
+      <td> 26571.16750</td>
+    </tr>
+    <tr>
+      <th>2009-12-31</th>
+      <td> 0.5</td>
+      <td> 225648.175</td>
+      <td> 0.8</td>
+      <td> 240158.325</td>
+      <td> 0.3</td>
+      <td> 442750</td>
+      <td> 0.6</td>
+      <td> 23875.83500</td>
+    </tr>
+    <tr>
+      <th>2010-12-31</th>
+      <td> 0.6</td>
+      <td> 359347.650</td>
+      <td> NaN</td>
+      <td> 265891.650</td>
+      <td> 0.3</td>
+      <td> 471250</td>
+      <td> 0.7</td>
+      <td> 24323.74750</td>
+    </tr>
+    <tr>
+      <th>2011-12-31</th>
+      <td> 0.5</td>
+      <td> 466288.475</td>
+      <td> NaN</td>
+      <td> 280016.675</td>
+      <td> NaN</td>
+      <td> 467750</td>
+      <td> 2.3</td>
+      <td> 26703.58500</td>
+    </tr>
+    <tr>
+      <th>2012-12-31</th>
+      <td> NaN</td>
+      <td> 471999.700</td>
+      <td> NaN</td>
+      <td> 262300.000</td>
+      <td> NaN</td>
+      <td> 441000</td>
+      <td> NaN</td>
+      <td> 25564.00000</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>If you want to plot or better compare these data, you should normalize some values --- for instance getting total population (from OECD stats) and using it to normalize unemployment data.</p>
+<p>Maybe I'll continue to 'play' with these data to show you other interesting and powerfull pandas features: group-by, more plottings, etc.</p>
+<p>For now, let's end up to show you which country has the max murder rates according to time...</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[43]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="n">sub_murder</span><span class="o">.</span><span class="n">idxmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+Date
+1986-12-31    FIN
+1987-12-31    FIN
+1988-12-31    FIN
+1989-12-31    FIN
+1990-12-31    POL
+1991-12-31    HUN
+1992-12-31    HUN
+1993-12-31    HUN
+1994-12-31    HUN
+1995-12-31    HUN
+1996-12-31    FIN
+1997-12-31    HUN
+1998-12-31    HUN
+1999-12-31    HUN
+2000-12-31    FIN
+2001-12-31    FIN
+2002-12-31    FIN
+2003-12-31    HUN
+2004-12-31    FIN
+2005-12-31    FIN
+2006-12-31    FIN
+2007-12-31    FIN
+2008-12-31    FIN
+2009-12-31    FIN
+2010-12-31    LUX
+2011-12-31    NOR
+Freq: A-DEC, dtype: object
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>... and the occurrences of each 'max' country in this <em>Series</em>.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[44]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="n">sub_murder</span><span class="o">.</span><span class="n">idxmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+FIN    14
+HUN     9
+NOR     1
+POL     1
+LUX     1
+dtype: int64
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">
+In&nbsp;[13]:
+</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">sub_murder</span><span class="o">.</span><span class="n">idxmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;bar&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+
+<div class="vbox output_wrapper">
+<div class="output vbox">
+
+
+<div class="hbox output_area"><div class="prompt output_prompt">
+    Out[13]:</div>
+<div class="box-flex1 output_subarea output_pyout">
+
+
+<pre>
+&lt;matplotlib.axes.AxesSubplot at 0x7f2f14135050&gt;
+</pre>
+
+</div>
+</div>
+
+<div class="hbox output_area"><div class="prompt"></div>
+<div class="box-flex1 output_subarea output_display_data">
+
+
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAW0AAAEHCAYAAABlbhceAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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