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Update florida counties, adding basic statistics doc pages
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‎_posts/plotly_js/choropleth-maps/2016-08-04-florida-counties.html

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---
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name: Choropleth Map of Florida Counties Colored by Political Party
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plot_url: https://codepen.io/plotly/embed/vKWqxA/?height=505&theme-id=15263&default-tab=result\
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plot_url: https://codepen.io/plotly/embed/vKWqxA/?height=510&theme-id=15263&default-tab=result\
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language: plotly_js
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suite: choropleth-maps
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order: 6

‎_posts/python/statistics/basic-statistics/2015-06-30-python_Basic_Statistics.html

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---
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permalink: python/basic_statistics/
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description: Learn how to to perform basic statistical operations using Python.
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name: Basic Statistics | plotly
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has_thumbnail: false
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thumbnail: /images/static-image
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layout: user-guide
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name: Basic Statistics
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language: python
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title: Basic Statistics in Python | plotly
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display_as: statistics
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has_thumbnail: false
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page_type: example_index
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order: 0
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---
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{% raw %}
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<div class="cell border-box-sizing text_cell rendered">
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<h4 id="New-to-Plotly?">New to Plotly?<a class="anchor-link" href="#New-to-Plotly?">&#182;</a></h4><p>Plotly's Python library is free and open source! <a href="https://plot.ly/python/getting-started/">Get started</a> by dowloading the client and <a href="https://plot.ly/python/getting-started/">reading the primer</a>.
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<br>You can set up Plotly to work in <a href="https://plot.ly/python/getting-started/#initialization-for-online-plotting">online</a> or <a href="https://plot.ly/python/getting-started/#initialization-for-offline-plotting">offline</a> mode, or in <a href="https://plot.ly/python/getting-started/#start-plotting-online">jupyter notebooks</a>.
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<br>We also have a quick-reference <a href="https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf">cheatsheet</a> (new!) to help you get started!</p>
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<h4 id="Imports">Imports<a class="anchor-link" href="#Imports">&#182;</a></h4><p>The tutorial below imports <a href="http://www.numpy.org/">numpy</a>, <a href="https://plot.ly/pandas/intro-to-pandas-tutorial/">pandas</a>, and <a href="https://www.scipy.org/">scipy</a></p>
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<div class="prompt input_prompt">In&nbsp;[1]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">plotly.plotly</span> <span class="kn">as</span> <span class="nn">py</span>
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<span class="kn">from</span> <span class="nn">plotly.tools</span> <span class="kn">import</span> <span class="n">FigureFactory</span> <span class="k">as</span> <span class="n">FF</span>
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<span class="kn">import</span> <span class="nn">plotly.graph_objs</span> <span class="kn">as</span> <span class="nn">go</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
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<span class="kn">import</span> <span class="nn">scipy</span>
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</pre></div>
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<p>Let us import a dataset to perform our statistics. We will be looking at the consumption of alcohol by country in 2010.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">data</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;https://raw.githubusercontent.com/plotly/datasets/master/2010_alcohol_consumption_by_country.csv&#39;</span><span class="p">)</span>
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<span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
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</pre></div>
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<div class="output_area"><div class="prompt output_prompt">Out[2]:</div>
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>location</th>
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<th>alcohol</th>
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</tr>
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</thead>
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<tbody>
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<th>0</th>
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<td>Belarus</td>
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<td>17.5</td>
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</tr>
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<th>1</th>
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<td>Moldova</td>
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<td>16.8</td>
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</tr>
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<th>2</th>
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<td>Lithuania</td>
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<td>15.4</td>
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</tr>
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<th>3</th>
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<td>Russia</td>
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<td>15.1</td>
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</tr>
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<th>4</th>
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<td>Romania</td>
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<td>14.4</td>
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</tr>
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<th>5</th>
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<td>Ukraine</td>
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<td>13.9</td>
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</tr>
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<th>6</th>
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<td>Andorra</td>
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<td>13.8</td>
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<th>7</th>
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<td>Hungary</td>
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<td>13.3</td>
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</tr>
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<th>8</th>
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<td>Czech Republic</td>
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<td>13.0</td>
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</tr>
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<th>9</th>
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<td>Slovakia</td>
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<td>13.0</td>
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<h4 id="Mean-and-Variance">Mean and Variance<a class="anchor-link" href="#Mean-and-Variance">&#182;</a></h4>
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<p>Two of the most basic statistical operations are the <code>mean</code> $\mu$ and <code>standard deviation</code> $\sigma$ of a one-dimension array of data, that is, a sequence of numeric values. The <code>mean</code> of a set of numbers $x_1, ..., x_N$ is defined as:</p>
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$$\begin{align*}
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\mu = \sum_{i=1}^N{x_i}
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\end{align*}
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$$<p>The mean is used colloquially as the <em>average</em> of a set of values. The standard deviation on the other hand is a statistical metric that describes the spread of the data, or how far the values are from the mean. The <code>standard deviation</code> of a set of data is defined as:</p>
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$$\begin{align*}
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\sigma = \sqrt{\frac{1}{N-1}\sum_{i=1}^{N}{(x_i-\mu)^2}}
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\end{align*}
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$$
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<div class=" highlight hl-ipython2"><pre><span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s">&#39;alcohol&#39;</span><span class="p">])</span>
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<span class="n">st_dev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s">&#39;alcohol&#39;</span><span class="p">])</span>
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<span class="k">print</span> <span class="s">&quot;The mean is </span><span class="si">%r</span><span class="s">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">mean</span><span class="p">)</span>
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<span class="k">print</span> <span class="s">&quot;The standard deviation is </span><span class="si">%r</span><span class="s">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">st_dev</span><span class="p">)</span>
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</pre></div>
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<pre>The mean is 6.2083769633507835
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The standard deviation is 4.130671000635401
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</pre>
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<h4 id="Median,-Maximum-and-Minimum">Median, Maximum and Minimum<a class="anchor-link" href="#Median,-Maximum-and-Minimum">&#182;</a></h4>
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<p>We can also compute other statistics such as the <code>median</code>, <code>maximum</code> and <code>minimum</code></p>
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<div class=" highlight hl-ipython2"><pre><span class="n">median</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s">&#39;alcohol&#39;</span><span class="p">])</span>
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<span class="n">maximum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s">&#39;alcohol&#39;</span><span class="p">])</span>
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<span class="n">minimum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s">&#39;alcohol&#39;</span><span class="p">])</span>
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<span class="k">print</span> <span class="s">&quot;The median is </span><span class="si">%r</span><span class="s">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">median</span><span class="p">)</span>
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<span class="k">print</span> <span class="s">&quot;The maximum is </span><span class="si">%r</span><span class="s">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">maximum</span><span class="p">)</span>
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<span class="k">print</span> <span class="s">&quot;The minimum is </span><span class="si">%r</span><span class="s">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">minimum</span><span class="p">)</span>
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</pre></div>
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<pre>The median is 6.4000000000000004
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The maximum is 17.5
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The minimum is 0.1
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</pre>
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</div>{% endraw %}

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