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analisis-de-datos.atom.xml
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<?xml version="1.0" encoding="utf-8"?>
<feed xmlns="http://www.w3.org/2005/Atom"><title>Raul E. Lopez Briega</title><link href="http://relopezbriega.github.io/" rel="alternate"></link><link href="/feeds/analisis-de-datos.atom.xml" rel="self"></link><id>http://relopezbriega.github.io/</id><updated>2016-09-18T00:00:00-03:00</updated><entry><title>Visualizaciones de datos con Python</title><link href="http://relopezbriega.github.io/blog/2016/09/18/visualizaciones-de-datos-con-python/" rel="alternate"></link><published>2016-09-18T00:00:00-03:00</published><author><name>Raul E. Lopez Briega</name></author><id>tag:relopezbriega.github.io,2016-09-18:blog/2016/09/18/visualizaciones-de-datos-con-python/</id><summary type="html"><p>
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<p><img alt="Visualizaciones de datos con Python" title="Visualizaciones de datos con Python" src="http://relopezbriega.github.io/images/DataViz.png" high=400px width=600px></p>
</div>
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<h2 id="Introducci&#243;n">Introducci&#243;n<a class="anchor-link" href="#Introducci&#243;n">&#182;</a></h2><p>Las visualizaciones son una herramienta fundamental para entender y compartir ideas sobre los datos. La visualización correcta puede ayudar a expresar una idea central, o abrir un espacio para una más profunda investigación; con ella se puede conseguir que todo el mundo hable sobre un <a href="https://es.wikipedia.org/wiki/Conjunto_de_datos">conjunto de datos</a>, o compartir una visión sobre lo que los datos nos quieren decir.</p>
<p>Una buena visualización puede dar a quien la observa un sentido rico y amplio de un <a href="https://es.wikipedia.org/wiki/Conjunto_de_datos">conjunto de datos</a>. Puede comunicar los datos de manera precisa a la vez que expone los lugares en dónde se necesita más información o dónde una hipótesis no se sostiene. Por otra parte, la visualización nos proporciona un lienzo para aplicar nuestras propias ideas, experiencias y conocimientos cuando observamos y analizamos datos, permitiendo realizar múltiples interpretaciones. Si como dice el dicho <em>"una imagen vale más que mil palabras"</em>, un gráfico interactivo bien elegido entonces podría valer cientos de <a href="https://es.wikipedia.org/wiki/Contraste_de_hip%C3%B3tesis">pruebas estadísticas</a>.</p>
<h2 id="Librer&#237;as-para-visualizar-datos-en-Python">Librer&#237;as para visualizar datos en Python<a class="anchor-link" href="#Librer&#237;as-para-visualizar-datos-en-Python">&#182;</a></h2><p>Como bien sabemos, la comunidad de <a href="http://python.org/">Python</a> es muy grande, por lo tanto vamos a poder encontrar un gran número de librerías para visualizar datos. Al tener tanta variedad de opciones, a veces se hace realmente difícil determinar cuando utilizar cada una de ellas. En este artículo yo voy a presentar solo cuatro que creo que cubren un gran abanico de casos:</p>
<ul>
<li><strong><a href="http://matplotlib.org/gallery.html">Matplotlib</a></strong>: Que es la más antigua y se convirtió en la librería por defecto para visualizaciones de datos; muchas otras están basadas en ella. Es extremadamente potente, pero con ese poder viene aparejada la complejidad. Se puede hacer prácticamente de todo con <a href="http://matplotlib.org/gallery.html">Matplotlib</a> pero no siempre es tan fácil de averiguar como hacerlo. Los que siguen el <a href="http://relopezbriega.github.io/">blog</a> me habrán visto utilizarla en varios artículos.</li>
</ul>
<ul>
<li><strong><a href="http://bokeh.pydata.org/en/latest/">Bokeh</a></strong>: Una de las más jóvenes librerías de visualizaciones, pero no por ello menos potente. <a href="http://bokeh.pydata.org/en/latest/">Bokeh</a> es una librería para visualizaciones interactivas diseñada para funcionar en los navegadores web modernos. Su objetivo es proporcionar una construcción elegante y concisa de gráficos modernos al estilo de <a href="https://d3js.org/">D3.js</a>, y para ampliar esta capacidad con la interactividad y buen rendimiento sobre grandes volúmenes de datos. <a href="http://bokeh.pydata.org/en/latest/">Bokeh</a> puede ayudar a cualquier persona a crear en forma rápida y sencilla gráficos interactivos, <em>dashboards</em> y aplicaciones de datos. Puede crear tanto gráficos estáticos como gráficos interactivos en el servidor de <a href="http://bokeh.pydata.org/en/latest/docs/user_guide/server.html">Bokeh</a>.</li>
</ul>
<ul>
<li><strong><a href="https://stanford.edu/~mwaskom/software/seaborn/">Seaborn</a></strong>: Si de gráficos estadísticos se trata, <a href="https://stanford.edu/~mwaskom/software/seaborn/">Seaborn</a> es la librería que deberíamos utilizar, con ella podemos crear gráficos estadísticos informativos y atractivos de forma muy sencilla. Es una de las tantas librerías que se basan en <a href="http://matplotlib.org/gallery.html">Matplotlib</a> pero nos ofrece varias características interesantes tales como temas, paletas de colores, funciones y herramientas para visualizar <a href="http://relopezbriega.github.io/blog/2016/06/29/distribuciones-de-probabilidad-con-python/">distribuciones</a> de una o varias <a href="https://es.wikipedia.org/wiki/Variable_aleatoria">variables aleatorias</a>, <a href="https://es.wikipedia.org/wiki/Regresi%C3%B3n_lineal">regresiones lineales</a>, <a href="https://es.wikipedia.org/wiki/Serie_temporal">series de tiempo</a>, entre muchas otras. Con ella podemos construir visualizaciones complejas en forma sencilla.</li>
</ul>
<ul>
<li><strong><a href="https://folium.readthedocs.io/en/latest/">Folium</a></strong>: Si lo que necesitamos es visualizar datos de <a href="https://es.wikipedia.org/wiki/Geolocalizaci%C3%B3n">geolocalización</a> en mapas interactivos, entonces <a href="https://folium.readthedocs.io/en/latest/">Folium</a> es una muy buena opción. Esta librería de <a href="http://python.org/">Python</a> es una herramienta sumamente poderosa para realizar mapas al estilo <a href="http://leafletjs.com/">leaflet.js</a>. El hecho de que los resultados de <a href="https://folium.readthedocs.io/en/latest/">Folium</a> son interactivos hace que esta librería sea útil para la construcción de <em>dashboards</em>.</li>
</ul>
<h2 id="&#191;C&#243;mo-elegir-la-visualizaci&#243;n-adecuada?">&#191;C&#243;mo elegir la visualizaci&#243;n adecuada?<a class="anchor-link" href="#&#191;C&#243;mo-elegir-la-visualizaci&#243;n-adecuada?">&#182;</a></h2><p>Una de las primeras preguntas que nos debemos realizar al explorar datos es ¿qué método de visualización es más efectivo?. Para intentar responder esta pregunta podemos utilizar la siguiente guía:</p>
<p><img alt="Visualizaciones de datos con Python" title="Visualizaciones de datos con Python" src="http://relopezbriega.github.io/images/chartchooserincolor.jpg" high=400px width=600px></p>
<p>Como podemos ver, la guía se divide en cuatro categorías principales y luego se clasifican los distintos métodos de visualización que mejor representan cada una de esas categorías. Veamos un poco más en detalle cada una de ellas:</p>
<ul>
<li><p><strong><a href="http://relopezbriega.github.io/blog/2016/06/29/distribuciones-de-probabilidad-con-python/">Distribuciones</a></strong>: En esta categoría intentamos comprender como los datos se distribuyen. Se suelen utilizar en el comienzo de la etapa de exploración de datos, cuando queremos comprender las variables. Aquí también nos vamos a encontrar con variables de dos tipos <a href="http://relopezbriega.github.io/blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/">cuantitativas</a> y <a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/">categóricas</a>. Dependiendo del tipo y cantidad de variables, el método de visualización que vamos a utilizar.</p>
</li>
<li><p><strong>Comparaciones</strong>: En esta categoría el objetivo es comparar valores a través de diferentes categorías y con el tiempo (tendencia). Los tipos de gráficos más comunes en esta categoría son los <a href="https://es.wikipedia.org/wiki/Diagrama_de_barras">diagramas de barras</a> para cuando estamos comparando elementos o categorías y los <a href="https://en.wikipedia.org/wiki/Line_chart">diagramas de puntos y líneas</a> cuando comparamos variables <a href="http://relopezbriega.github.io/blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/">cuantitativas</a>.</p>
</li>
<li><p><strong>Relaciones</strong>: Aquí el objetivo es comprender la relación entre dos o más variables. La visualización más utilizada en esta categoría es el <a href="https://es.wikipedia.org/wiki/Diagrama_de_dispersi%C3%B3n">gráfico de dispersión</a>.</p>
</li>
<li><p><strong>Composiciones</strong>: En esta categoría el objetivo es comprender como esta compuesta o distribuida una variable; ya sea a través del tiempo o en forma estática. Las visualizaciones más comunes aquí son los <a href="https://es.wikipedia.org/wiki/Diagrama_de_barras">diagramas de barras</a> y los <a href="https://es.wikipedia.org/wiki/Gr%C3%A1fico_circular">gráficos de tortas</a>.</p>
</li>
</ul>
<h2 id="Ejemplos-en-Python">Ejemplos en Python<a class="anchor-link" href="#Ejemplos-en-Python">&#182;</a></h2><p>Luego de esta introducción es hora de ensuciarse las manos y ponerse a jugar con algunos ejemplos en el uso de cada una de estas 4 librerías que nos ofrece <a href="http://python.org/">Python</a> para visualización de datos. Obviamente los ejemplos van a ser sencillos ya que un tutorial exhaustivo sobre cada herramienta requeriría mucho más espacio.</p>
<h3 id="Matplotlib">Matplotlib<a class="anchor-link" href="#Matplotlib">&#182;</a></h3><p>Comencemos con <a href="http://matplotlib.org/gallery.html">Matplotlib</a>; como les comentaba, es tal vez la librería más utilizada para gráficos en 2d. El objeto <code>pyplot</code> nos proporciona la interfase principal sobre la que podemos crear las visualizaciones de datos con esta librería.</p>
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<div class="highlight-ipynb"><pre class="ipynb"><span></span><span class="c1"># importando modulos necesarios</span>
<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="kn">from</span> <span class="nn">pydataset</span> <span class="kn">import</span> <span class="n">data</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="c1"># librerías de visualizaciones</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="kn">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">bokeh.io</span> <span class="kn">import</span> <span class="n">output_notebook</span><span class="p">,</span> <span class="n">show</span>
<span class="kn">from</span> <span class="nn">bokeh.charts</span> <span class="kn">import</span> <span class="n">Histogram</span><span class="p">,</span> <span class="n">Scatter</span>
<span class="kn">import</span> <span class="nn">folium</span>
<span class="c1"># graficos incrustados</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">output_notebook</span><span class="p">()</span>
</pre></div>
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<div class="prompt input_prompt">In&nbsp;[2]:</div>
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<div class="highlight-ipynb"><pre class="ipynb"><span></span><span class="c1"># Cargamos algunos datasets de ejemplo</span>
<span class="n">iris</span> <span class="o">=</span> <span class="n">data</span><span class="p">(</span><span class="s1">&#39;iris&#39;</span><span class="p">)</span>
<span class="n">tips</span> <span class="o">=</span> <span class="n">data</span><span class="p">(</span><span class="s1">&#39;tips&#39;</span><span class="p">)</span>
</pre></div>
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<div class="prompt input_prompt">In&nbsp;[3]:</div>
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<div class="highlight-ipynb"><pre class="ipynb"><span></span><span class="c1"># Ejemplo matplotlib</span>
<span class="c1"># graficanco funciones seno y coseno</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">C</span><span class="p">,</span> <span class="n">S</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="c1"># configurando el tamaño de la figura</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="c1"># dibujando las curvas</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;-&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;coseno&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;-&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;seno&quot;</span><span class="p">)</span>
<span class="c1"># personalizando los valores de los ejes</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">],</span>
<span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$-\pi$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$-\pi/2$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$0$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$+\pi/2$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$+\pi$&#39;</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$-1$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$0$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$+1$&#39;</span><span class="p">])</span>
<span class="c1"># agregando la leyenda</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper left&#39;</span><span class="p">)</span>
<span class="c1"># moviendo los ejes de coordenadas</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span> <span class="c1"># get current axis</span>
<span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">&#39;right&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">&#39;top&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">&#39;bottom&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="s1">&#39;data&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">&#39;left&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="s1">&#39;data&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
<span class="c1"># mostrando el resultado</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<p>En este primer ejemplo vemos como podemos acceder a la API de <a href="http://matplotlib.org/gallery.html">Matplotlib</a> desde el objeto <code>pyplot</code> e ir dando forma al gráfico. Veamos ahora unos ejemplos con el dataset iris.</p>
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<div class="highlight-ipynb"><pre class="ipynb"><span></span><span class="c1"># Ejemplo con iris</span>
<span class="c1"># histograma de Petal.Length</span>
<span class="n">iris</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Sepal.Length</th>
<th>Sepal.Width</th>
<th>Petal.Length</th>
<th>Petal.Width</th>
<th>Species</th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>5.1</td>
<td>3.5</td>
<td>1.4</td>
<td>0.2</td>
<td>setosa</td>
</tr>
<tr>
<th>2</th>
<td>4.9</td>
<td>3.0</td>
<td>1.4</td>
<td>0.2</td>
<td>setosa</td>
</tr>
<tr>
<th>3</th>
<td>4.7</td>
<td>3.2</td>
<td>1.3</td>
<td>0.2</td>
<td>setosa</td>
</tr>
<tr>
<th>4</th>
<td>4.6</td>
<td>3.1</td>
<td>1.5</td>
<td>0.2</td>
<td>setosa</td>
</tr>
<tr>
<th>5</th>
<td>5.0</td>
<td>3.6</td>
<td>1.4</td>
<td>0.2</td>
<td>setosa</td>
</tr>
</tbody>
</table>
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<div class="highlight-ipynb"><pre class="ipynb"><span></span><span class="c1"># separo en especies</span>
<span class="n">setosa</span> <span class="o">=</span> <span class="n">iris</span><span class="p">[</span><span class="n">iris</span><span class="o">.</span><span class="n">Species</span> <span class="o">==</span> <span class="s1">&#39;setosa&#39;</span><span class="p">]</span>
<span class="n">versicolor</span> <span class="o">=</span> <span class="n">iris</span><span class="p">[</span><span class="n">iris</span><span class="o">.</span><span class="n">Species</span> <span class="o">==</span> <span class="s1">&#39;versicolor&#39;</span><span class="p">]</span>
<span class="n">virginica</span> <span class="o">=</span> <span class="n">iris</span><span class="p">[</span><span class="n">iris</span><span class="o">.</span><span class="n">Species</span> <span class="o">==</span> <span class="s1">&#39;virginica&#39;</span><span class="p">]</span>
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<div class="highlight-ipynb"><pre class="ipynb"><span></span><span class="c1"># crear histograma</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">setosa</span><span class="p">[</span><span class="s1">&#39;Petal.Length&#39;</span><span class="p">],</span> <span class="mi">12</span><span class="p">,</span>
<span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;setosa&#39;</span><span class="p">)</span>
<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">versicolor</span><span class="p">[</span><span class="s1">&#39;Petal.Length&#39;</span><span class="p">],</span> <span class="mi">12</span><span class="p">,</span>
<span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;versicolor&#39;</span><span class="p">)</span>
<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">virginica</span><span class="p">[</span><span class="s1">&#39;Petal.Length&#39;</span><span class="p">],</span> <span class="mi">12</span><span class="p">,</span>
<span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;virginica&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;top_right&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Histograma largo del pétalo&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;largo del pétalo&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;cuenta largo del pétalo&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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