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<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">&lt;p&gt;
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&lt;p&gt;&lt;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&gt;&lt;/p&gt;

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&lt;h2 id="Introducci&amp;#243;n"&gt;Introducci&amp;#243;n&lt;a class="anchor-link" href="#Introducci&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;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 &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;conjunto de datos&lt;/a&gt;, o compartir una visión sobre lo que los datos nos quieren decir.&lt;/p&gt;
&lt;p&gt;Una buena visualización puede dar a quien la observa un sentido rico y amplio de un &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;conjunto de datos&lt;/a&gt;. 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 &lt;em&gt;"una imagen vale más que mil palabras"&lt;/em&gt;, un gráfico interactivo bien elegido entonces podría valer cientos de &lt;a href="https://es.wikipedia.org/wiki/Contraste_de_hip%C3%B3tesis"&gt;pruebas estadísticas&lt;/a&gt;.&lt;/p&gt;
&lt;h2 id="Librer&amp;#237;as-para-visualizar-datos-en-Python"&gt;Librer&amp;#237;as para visualizar datos en Python&lt;a class="anchor-link" href="#Librer&amp;#237;as-para-visualizar-datos-en-Python"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Como bien sabemos, la comunidad de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; 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:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;&lt;a href="http://matplotlib.org/gallery.html"&gt;Matplotlib&lt;/a&gt;&lt;/strong&gt;: 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 &lt;a href="http://matplotlib.org/gallery.html"&gt;Matplotlib&lt;/a&gt; pero no siempre es tan fácil de averiguar como hacerlo. Los que siguen el &lt;a href="http://relopezbriega.github.io/"&gt;blog&lt;/a&gt; me habrán visto utilizarla en varios artículos.&lt;/li&gt;
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&lt;li&gt;&lt;strong&gt;&lt;a href="http://bokeh.pydata.org/en/latest/"&gt;Bokeh&lt;/a&gt;&lt;/strong&gt;: Una de las más jóvenes librerías de visualizaciones, pero no por ello menos potente. &lt;a href="http://bokeh.pydata.org/en/latest/"&gt;Bokeh&lt;/a&gt; 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 &lt;a href="https://d3js.org/"&gt;D3.js&lt;/a&gt;, y para ampliar esta capacidad con la interactividad y buen rendimiento sobre grandes volúmenes de datos. &lt;a href="http://bokeh.pydata.org/en/latest/"&gt;Bokeh&lt;/a&gt; puede ayudar a cualquier persona a crear en forma rápida y sencilla gráficos interactivos, &lt;em&gt;dashboards&lt;/em&gt; y aplicaciones de datos. Puede crear tanto gráficos estáticos como gráficos interactivos en el servidor de &lt;a href="http://bokeh.pydata.org/en/latest/docs/user_guide/server.html"&gt;Bokeh&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;&lt;a href="https://stanford.edu/~mwaskom/software/seaborn/"&gt;Seaborn&lt;/a&gt;&lt;/strong&gt;: Si de gráficos estadísticos se trata, &lt;a href="https://stanford.edu/~mwaskom/software/seaborn/"&gt;Seaborn&lt;/a&gt; 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 &lt;a href="http://matplotlib.org/gallery.html"&gt;Matplotlib&lt;/a&gt; pero nos ofrece varias características interesantes tales como temas, paletas de colores, funciones y herramientas para visualizar &lt;a href="http://relopezbriega.github.io/blog/2016/06/29/distribuciones-de-probabilidad-con-python/"&gt;distribuciones&lt;/a&gt; de una o varias &lt;a href="https://es.wikipedia.org/wiki/Variable_aleatoria"&gt;variables aleatorias&lt;/a&gt;, &lt;a href="https://es.wikipedia.org/wiki/Regresi%C3%B3n_lineal"&gt;regresiones lineales&lt;/a&gt;, &lt;a href="https://es.wikipedia.org/wiki/Serie_temporal"&gt;series de tiempo&lt;/a&gt;, entre muchas otras. Con ella podemos construir visualizaciones complejas en forma sencilla.&lt;/li&gt;
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&lt;li&gt;&lt;strong&gt;&lt;a href="https://folium.readthedocs.io/en/latest/"&gt;Folium&lt;/a&gt;&lt;/strong&gt;: Si lo que necesitamos es visualizar datos de &lt;a href="https://es.wikipedia.org/wiki/Geolocalizaci%C3%B3n"&gt;geolocalización&lt;/a&gt; en mapas interactivos, entonces &lt;a href="https://folium.readthedocs.io/en/latest/"&gt;Folium&lt;/a&gt; es una muy buena opción. Esta librería de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; es una herramienta sumamente poderosa para realizar mapas al estilo &lt;a href="http://leafletjs.com/"&gt;leaflet.js&lt;/a&gt;. El hecho de que los resultados de &lt;a href="https://folium.readthedocs.io/en/latest/"&gt;Folium&lt;/a&gt; son interactivos hace que esta librería sea útil para la construcción de &lt;em&gt;dashboards&lt;/em&gt;.&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="&amp;#191;C&amp;#243;mo-elegir-la-visualizaci&amp;#243;n-adecuada?"&gt;&amp;#191;C&amp;#243;mo elegir la visualizaci&amp;#243;n adecuada?&lt;a class="anchor-link" href="#&amp;#191;C&amp;#243;mo-elegir-la-visualizaci&amp;#243;n-adecuada?"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;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:&lt;/p&gt;
&lt;p&gt;&lt;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&gt;&lt;/p&gt;
&lt;p&gt;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:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;&lt;a href="http://relopezbriega.github.io/blog/2016/06/29/distribuciones-de-probabilidad-con-python/"&gt;Distribuciones&lt;/a&gt;&lt;/strong&gt;: 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 &lt;a href="http://relopezbriega.github.io/blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/"&gt;cuantitativas&lt;/a&gt; y &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;categóricas&lt;/a&gt;. Dependiendo del tipo y cantidad de variables, el método de visualización que vamos a utilizar.&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Comparaciones&lt;/strong&gt;: 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 &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_barras"&gt;diagramas de barras&lt;/a&gt; para cuando estamos comparando elementos o categorías y los &lt;a href="https://en.wikipedia.org/wiki/Line_chart"&gt;diagramas de puntos y líneas&lt;/a&gt; cuando comparamos variables &lt;a href="http://relopezbriega.github.io/blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/"&gt;cuantitativas&lt;/a&gt;.&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Relaciones&lt;/strong&gt;: Aquí el objetivo es comprender la relación entre dos o más variables. La visualización más utilizada en esta categoría es el &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_dispersi%C3%B3n"&gt;gráfico de dispersión&lt;/a&gt;.&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Composiciones&lt;/strong&gt;: 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 &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_barras"&gt;diagramas de barras&lt;/a&gt; y los &lt;a href="https://es.wikipedia.org/wiki/Gr%C3%A1fico_circular"&gt;gráficos de tortas&lt;/a&gt;.&lt;/p&gt;
&lt;/li&gt;
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&lt;h2 id="Ejemplos-en-Python"&gt;Ejemplos en Python&lt;a class="anchor-link" href="#Ejemplos-en-Python"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;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 &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; para visualización de datos. Obviamente los ejemplos van a ser sencillos ya que un tutorial exhaustivo sobre cada herramienta requeriría mucho más espacio.&lt;/p&gt;
&lt;h3 id="Matplotlib"&gt;Matplotlib&lt;a class="anchor-link" href="#Matplotlib"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Comencemos con &lt;a href="http://matplotlib.org/gallery.html"&gt;Matplotlib&lt;/a&gt;; como les comentaba, es tal vez la librería más utilizada para gráficos en 2d. El objeto &lt;code&gt;pyplot&lt;/code&gt; nos proporciona la interfase principal sobre la que podemos crear las visualizaciones de datos con esta librería.&lt;/p&gt;

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&lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importando modulos necesarios&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;numpy&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;np&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;pandas&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;pd&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;pydataset&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;data&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;re&lt;/span&gt;

&lt;span class="c1"&gt;# librerías de visualizaciones&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;seaborn&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;sns&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;matplotlib.pyplot&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;plt&lt;/span&gt; 
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;bokeh.io&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;output_notebook&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;show&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;bokeh.charts&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;Histogram&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;Scatter&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;folium&lt;/span&gt;

&lt;span class="c1"&gt;# graficos incrustados&lt;/span&gt;
&lt;span class="o"&gt;%&lt;/span&gt;&lt;span class="k"&gt;matplotlib&lt;/span&gt; inline
&lt;span class="n"&gt;output_notebook&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Cargamos algunos datasets de ejemplo&lt;/span&gt;
&lt;span class="n"&gt;iris&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;data&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;iris&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;tips&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;data&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;tips&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[3]:&lt;/div&gt;

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    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo matplotlib&lt;/span&gt;
&lt;span class="c1"&gt;# graficanco funciones seno y coseno&lt;/span&gt;
&lt;span class="n"&gt;X&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;linspace&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;pi&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;pi&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;256&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;endpoint&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="kc"&gt;True&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;S&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cos&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sin&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# configurando el tamaño de la figura&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;figure&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;figsize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;6&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="c1"&gt;# dibujando las curvas&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;blue&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;linewidth&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;2.5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;linestyle&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;-&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;coseno&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;S&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;red&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;  &lt;span class="n"&gt;linewidth&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;2.5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;linestyle&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;-&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;seno&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="c1"&gt;# personalizando los valores de los ejes&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xticks&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;pi&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;pi&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;pi&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;pi&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
          &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="sa"&gt;r&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;$-\pi$&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="sa"&gt;r&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;$-\pi/2$&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="sa"&gt;r&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;$0$&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="sa"&gt;r&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;$+\pi/2$&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="sa"&gt;r&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;$+\pi$&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;

&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;yticks&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
          &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="sa"&gt;r&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;$-1$&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="sa"&gt;r&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;$0$&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="sa"&gt;r&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;$+1$&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
&lt;span class="c1"&gt;# agregando la leyenda&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;loc&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;upper left&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="c1"&gt;# moviendo los ejes de coordenadas&lt;/span&gt;
&lt;span class="n"&gt;ax&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;gca&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;  &lt;span class="c1"&gt;# get current axis&lt;/span&gt;
&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;spines&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;right&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_color&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;none&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;spines&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;top&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_color&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;none&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xaxis&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_ticks_position&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;bottom&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;spines&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;bottom&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_position&lt;/span&gt;&lt;span class="p"&gt;((&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;data&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;yaxis&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_ticks_position&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;left&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;spines&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;left&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_position&lt;/span&gt;&lt;span class="p"&gt;((&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;data&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="c1"&gt;# mostrando el resultado&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing text_cell rendered"&gt;
&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;
&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;En este primer ejemplo vemos como podemos acceder a la API de &lt;a href="http://matplotlib.org/gallery.html"&gt;Matplotlib&lt;/a&gt; desde el objeto &lt;code&gt;pyplot&lt;/code&gt; e ir dando forma al gráfico. Veamos ahora unos ejemplos con el dataset iris.&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[4]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo con iris&lt;/span&gt;
&lt;span class="c1"&gt;# histograma de Petal.Length&lt;/span&gt;
&lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;head&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[4]:&lt;/div&gt;


&lt;div class="output_html rendered_html output_subarea output_execute_result"&gt;
&lt;div&gt;
&lt;table border="1" class="dataframe"&gt;
  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;Sepal.Length&lt;/th&gt;
      &lt;th&gt;Sepal.Width&lt;/th&gt;
      &lt;th&gt;Petal.Length&lt;/th&gt;
      &lt;th&gt;Petal.Width&lt;/th&gt;
      &lt;th&gt;Species&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;1&lt;/th&gt;
      &lt;td&gt;5.1&lt;/td&gt;
      &lt;td&gt;3.5&lt;/td&gt;
      &lt;td&gt;1.4&lt;/td&gt;
      &lt;td&gt;0.2&lt;/td&gt;
      &lt;td&gt;setosa&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;2&lt;/th&gt;
      &lt;td&gt;4.9&lt;/td&gt;
      &lt;td&gt;3.0&lt;/td&gt;
      &lt;td&gt;1.4&lt;/td&gt;
      &lt;td&gt;0.2&lt;/td&gt;
      &lt;td&gt;setosa&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;3&lt;/th&gt;
      &lt;td&gt;4.7&lt;/td&gt;
      &lt;td&gt;3.2&lt;/td&gt;
      &lt;td&gt;1.3&lt;/td&gt;
      &lt;td&gt;0.2&lt;/td&gt;
      &lt;td&gt;setosa&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;4&lt;/th&gt;
      &lt;td&gt;4.6&lt;/td&gt;
      &lt;td&gt;3.1&lt;/td&gt;
      &lt;td&gt;1.5&lt;/td&gt;
      &lt;td&gt;0.2&lt;/td&gt;
      &lt;td&gt;setosa&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;5&lt;/th&gt;
      &lt;td&gt;5.0&lt;/td&gt;
      &lt;td&gt;3.6&lt;/td&gt;
      &lt;td&gt;1.4&lt;/td&gt;
      &lt;td&gt;0.2&lt;/td&gt;
      &lt;td&gt;setosa&lt;/td&gt;
    &lt;/tr&gt;
  &lt;/tbody&gt;
&lt;/table&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[5]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# separo en especies&lt;/span&gt;
&lt;span class="n"&gt;setosa&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Species&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;setosa&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;span class="n"&gt;versicolor&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Species&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;versicolor&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;span class="n"&gt;virginica&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Species&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;virginica&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[6]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# crear histograma&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;figure&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;figsize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="n"&gt;n&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;bins&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;patches&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;hist&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;setosa&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Length&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="mi"&gt;12&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                            &lt;span class="n"&gt;facecolor&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;red&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;setosa&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;n&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;bins&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;patches&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;hist&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;versicolor&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Length&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="mi"&gt;12&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                            &lt;span class="n"&gt;facecolor&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;green&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;versicolor&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;n&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;bins&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;patches&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;hist&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;virginica&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Length&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="mi"&gt;12&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                            &lt;span class="n"&gt;facecolor&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;blue&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;virginica&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;loc&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;top_right&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Histograma largo del pétalo&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;largo del pétalo&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;cuenta largo del pétalo&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

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&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
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&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[7]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo diagrama de dispersion entre Petal.Length y Petal.Width&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;figure&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;figsize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;scatter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;setosa&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Length&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;setosa&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Width&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; 
            &lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;red&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;setosa&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;scatter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;versicolor&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Length&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;versicolor&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Width&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; 
            &lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;green&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;versicolor&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;scatter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;virginica&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Length&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;virginica&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Width&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; 
            &lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;blue&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;virginica&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Tamaño del pétalo&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Largo del pétalo (cm)&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Ancho del pétalo (cm)&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;loc&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;top_left&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

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&gt;
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&lt;h3 id="Bokeh"&gt;Bokeh&lt;a class="anchor-link" href="#Bokeh"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;&lt;a href="http://bokeh.pydata.org/en/latest/"&gt;Bokeh&lt;/a&gt; además de generar unos hermosos gráficos interactivos nos permite realizar gráficos complejos en forma muy sencilla. La interfase de alto nivel con la que vamos a trabajar principalmente para generar visualizaciones con esta librería es &lt;code&gt;bokeh.charts&lt;/code&gt;. Repitamos los ejemplos que realizamos anteriormente sobre el dataset iris y veamos que sencillo que es realizarlos con &lt;a href="http://bokeh.pydata.org/en/latest/"&gt;Bokeh&lt;/a&gt;.&lt;/p&gt;

&lt;/div&gt;
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&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[8]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo de histograma de Petal.Length&lt;/span&gt;
&lt;span class="c1"&gt;# solo 2 lineas de código&lt;/span&gt;
&lt;span class="n"&gt;hist&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;Histogram&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;values&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Petal.Length&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Species&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                 &lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;top_right&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;bins&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;12&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;hist&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo diagrama de dispersion entre Petal.Length y Petal.Width&lt;/span&gt;
&lt;span class="c1"&gt;# solo 2 lineas de código&lt;/span&gt;
&lt;span class="n"&gt;disp&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;Scatter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Length&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Petal.Width&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Species&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
            &lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;top_left&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;marker&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Species&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Tamaño del petalo&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;disp&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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&lt;h3 id="Seaborn"&gt;Seaborn&lt;a class="anchor-link" href="#Seaborn"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;&lt;a href="https://stanford.edu/~mwaskom/software/seaborn/"&gt;Seaborn&lt;/a&gt; tiene su énfasis en los gráficos estadísticos. Nos permite realizar fácilmente gráficos de regresión y de las principales distribuciones; pero donde realmente brilla &lt;a href="https://stanford.edu/~mwaskom/software/seaborn/"&gt;Seaborn&lt;/a&gt; es en su capacidad de visualizar muchas características diferentes a la vez. Veamos algunos ejemplos&lt;/p&gt;

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&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[10]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo gráfico de distribuciones&lt;/span&gt;
&lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;random&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;normal&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;size&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;dist&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;distplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[11]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo gráfico regresión con tips dataset&lt;/span&gt;
&lt;span class="n"&gt;tips&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;head&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[11]:&lt;/div&gt;


&lt;div class="output_html rendered_html output_subarea output_execute_result"&gt;
&lt;div&gt;
&lt;table border="1" class="dataframe"&gt;
  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;total_bill&lt;/th&gt;
      &lt;th&gt;tip&lt;/th&gt;
      &lt;th&gt;sex&lt;/th&gt;
      &lt;th&gt;smoker&lt;/th&gt;
      &lt;th&gt;day&lt;/th&gt;
      &lt;th&gt;time&lt;/th&gt;
      &lt;th&gt;size&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;1&lt;/th&gt;
      &lt;td&gt;16.99&lt;/td&gt;
      &lt;td&gt;1.01&lt;/td&gt;
      &lt;td&gt;Female&lt;/td&gt;
      &lt;td&gt;No&lt;/td&gt;
      &lt;td&gt;Sun&lt;/td&gt;
      &lt;td&gt;Dinner&lt;/td&gt;
      &lt;td&gt;2&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;2&lt;/th&gt;
      &lt;td&gt;10.34&lt;/td&gt;
      &lt;td&gt;1.66&lt;/td&gt;
      &lt;td&gt;Male&lt;/td&gt;
      &lt;td&gt;No&lt;/td&gt;
      &lt;td&gt;Sun&lt;/td&gt;
      &lt;td&gt;Dinner&lt;/td&gt;
      &lt;td&gt;3&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;3&lt;/th&gt;
      &lt;td&gt;21.01&lt;/td&gt;
      &lt;td&gt;3.50&lt;/td&gt;
      &lt;td&gt;Male&lt;/td&gt;
      &lt;td&gt;No&lt;/td&gt;
      &lt;td&gt;Sun&lt;/td&gt;
      &lt;td&gt;Dinner&lt;/td&gt;
      &lt;td&gt;3&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;4&lt;/th&gt;
      &lt;td&gt;23.68&lt;/td&gt;
      &lt;td&gt;3.31&lt;/td&gt;
      &lt;td&gt;Male&lt;/td&gt;
      &lt;td&gt;No&lt;/td&gt;
      &lt;td&gt;Sun&lt;/td&gt;
      &lt;td&gt;Dinner&lt;/td&gt;
      &lt;td&gt;2&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;5&lt;/th&gt;
      &lt;td&gt;24.59&lt;/td&gt;
      &lt;td&gt;3.61&lt;/td&gt;
      &lt;td&gt;Female&lt;/td&gt;
      &lt;td&gt;No&lt;/td&gt;
      &lt;td&gt;Sun&lt;/td&gt;
      &lt;td&gt;Dinner&lt;/td&gt;
      &lt;td&gt;4&lt;/td&gt;
    &lt;/tr&gt;
  &lt;/tbody&gt;
&lt;/table&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[12]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;reg&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;regplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;total_bill&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;tip&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;data&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;tips&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt"&gt;&lt;/div&gt;



&lt;div class="output_png output_subarea "&gt;
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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[13]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo pairplot con datase iris&lt;/span&gt;
&lt;span class="n"&gt;g&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;pairplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;hue&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Species&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;diag_kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;hist&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;  
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt"&gt;&lt;/div&gt;



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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[14]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo FacetGrid con iris&lt;/span&gt;
&lt;span class="n"&gt;g&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;FacetGrid&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;col&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Species&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;g&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;g&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;map&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;scatter&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s2"&gt;&amp;quot;Petal.Length&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s2"&gt;&amp;quot;Petal.Width&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
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"
&gt;
&lt;/div&gt;

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&lt;h3 id="Folium"&gt;Folium&lt;a class="anchor-link" href="#Folium"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Por último, veamos un ejemplo de como utilizar &lt;a href="https://folium.readthedocs.io/en/latest/"&gt;Folium&lt;/a&gt;. Ya que yo soy adepto al uso de la bicicleta para moverme por la ciudad, y muchas veces se hace difícil encontrar una bicicletería en donde poder encontrar repuestos o reparar la bicicleta; en este ejemplo vamos a crear un mapa interactivo del barrio de &lt;a href="https://es.wikipedia.org/wiki/Palermo_(Buenos_Aires)"&gt;Palermo&lt;/a&gt; en donde vamos a marcar la ubicación de los negocios de bicicleterías. Esta información la podemos extraer del padrón que ofrece el &lt;a href="http://www.buenosaires.gob.ar/"&gt;gobierno de la Ciudad de Buenos Aires&lt;/a&gt; en su &lt;a href="http://data.buenosaires.gob.ar/"&gt;portal de datos&lt;/a&gt;.&lt;/p&gt;

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&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[15]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# dataset de bicicleterías de Ciudad de Buenos Aires&lt;/span&gt;
&lt;span class="c1"&gt;# descargado desde http://data.buenosaires.gob.ar/dataset/bicicleterias&lt;/span&gt;
&lt;span class="n"&gt;bici&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;read_csv&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;data/bicicleterias.csv&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;sep&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;;&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;bici&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;head&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[15]:&lt;/div&gt;


&lt;div class="output_html rendered_html output_subarea output_execute_result"&gt;
&lt;div&gt;
&lt;table border="1" class="dataframe"&gt;
  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;WKT&lt;/th&gt;
      &lt;th&gt;ID&lt;/th&gt;
      &lt;th&gt;NOMBRE&lt;/th&gt;
      &lt;th&gt;DIRECCION&lt;/th&gt;
      &lt;th&gt;TELEFONO&lt;/th&gt;
      &lt;th&gt;EMAIL&lt;/th&gt;
      &lt;th&gt;WEB&lt;/th&gt;
      &lt;th&gt;MECANICA_S&lt;/th&gt;
      &lt;th&gt;HORARIO_DE&lt;/th&gt;
      &lt;th&gt;CALLE&lt;/th&gt;
      &lt;th&gt;ALTURA&lt;/th&gt;
      &lt;th&gt;DIRECCION_&lt;/th&gt;
      &lt;th&gt;BARRIO&lt;/th&gt;
      &lt;th&gt;COMUNA&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;0&lt;/th&gt;
      &lt;td&gt;POINT (-58.466041249451614 -34.557060215984805)&lt;/td&gt;
      &lt;td&gt;52&lt;/td&gt;
      &lt;td&gt;11 A FONDO&lt;/td&gt;
      &lt;td&gt;CONGRESO 2757&lt;/td&gt;
      &lt;td&gt;45421835&lt;/td&gt;
      &lt;td&gt;info@11afondo.com&lt;/td&gt;
      &lt;td&gt;HTTP://WWW.11AFONDO.COM&lt;/td&gt;
      &lt;td&gt;NO&lt;/td&gt;
      &lt;td&gt;LUN A VIE DE 10 A 14 Y DE 16 A 20/SAB DE 10 A 14&lt;/td&gt;
      &lt;td&gt;CONGRESO AV.&lt;/td&gt;
      &lt;td&gt;2757&lt;/td&gt;
      &lt;td&gt;CONGRESO AV. 2757&lt;/td&gt;
      &lt;td&gt;NUÑEZ&lt;/td&gt;
      &lt;td&gt;COMUNA 13&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;1&lt;/th&gt;
      &lt;td&gt;POINT (-58.41279876038783 -34.591915372813645)&lt;/td&gt;
      &lt;td&gt;32&lt;/td&gt;
      &lt;td&gt;AMERICAN BIKE&lt;/td&gt;
      &lt;td&gt;AV. CNEL. DIAZ 1664&lt;/td&gt;
      &lt;td&gt;48220889&lt;/td&gt;
      &lt;td&gt;info@americanbike.com.ar&lt;/td&gt;
      &lt;td&gt;HTTP://WWW.AMERICANBIKE.COM.AR/&lt;/td&gt;
      &lt;td&gt;SI&lt;/td&gt;
      &lt;td&gt;LUN A VIER DE 10 A 14 Y DE 15 A 20.30 / SAB DE 10 A 14 Y DE 15 A 20&lt;/td&gt;
      &lt;td&gt;DIAZ, CNEL. AV.&lt;/td&gt;
      &lt;td&gt;1664&lt;/td&gt;
      &lt;td&gt;DIAZ, CNEL. AV. 1664&lt;/td&gt;
      &lt;td&gt;PALERMO&lt;/td&gt;
      &lt;td&gt;COMUNA 14&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;2&lt;/th&gt;
      &lt;td&gt;POINT (-58.425646989945932 -34.580365554062418)&lt;/td&gt;
      &lt;td&gt;30&lt;/td&gt;
      &lt;td&gt;ANDINO BIKES&lt;/td&gt;
      &lt;td&gt;GUEMES 4818&lt;/td&gt;
      &lt;td&gt;47753677&lt;/td&gt;
      &lt;td&gt;andino_bike@hotmail.com&lt;/td&gt;
      &lt;td&gt;HTTP://WWW.ANDINOBIKE.COM.AR/&lt;/td&gt;
      &lt;td&gt;NO&lt;/td&gt;
      &lt;td&gt;LUN A VIER DE 9 A 19 / SAB DE 10:00 - 17:00&lt;/td&gt;
      &lt;td&gt;GUEMES&lt;/td&gt;
      &lt;td&gt;4818&lt;/td&gt;
      &lt;td&gt;GUEMES 4818&lt;/td&gt;
      &lt;td&gt;PALERMO&lt;/td&gt;
      &lt;td&gt;COMUNA 14&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;3&lt;/th&gt;
      &lt;td&gt;POINT (-58.437608880680997 -34.6045094278806)&lt;/td&gt;
      &lt;td&gt;107&lt;/td&gt;
      &lt;td&gt;BABE BIKES&lt;/td&gt;
      &lt;td&gt;WARNES 10&lt;/td&gt;
      &lt;td&gt;48549862&lt;/td&gt;
      &lt;td&gt;info@babebikes.com.ar&lt;/td&gt;
      &lt;td&gt;NaN&lt;/td&gt;
      &lt;td&gt;NO&lt;/td&gt;
      &lt;td&gt;NaN&lt;/td&gt;
      &lt;td&gt;WARNES&lt;/td&gt;
      &lt;td&gt;10&lt;/td&gt;
      &lt;td&gt;WARNES AV. 10&lt;/td&gt;
      &lt;td&gt;VILLA CRESPO&lt;/td&gt;
      &lt;td&gt;COMUNA 15&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;4&lt;/th&gt;
      &lt;td&gt;POINT (-58.439598908303168 -34.58547499220991)&lt;/td&gt;
      &lt;td&gt;118&lt;/td&gt;
      &lt;td&gt;BELGRAVIA TAILOR MADE BICYLES&lt;/td&gt;
      &lt;td&gt;BONPLAND 1459&lt;/td&gt;
      &lt;td&gt;1544291001&lt;/td&gt;
      &lt;td&gt;hola@belgravia.com.ar&lt;/td&gt;
      &lt;td&gt;WWW.FACEBOOK.COM/BELGRAVIABIKES&lt;/td&gt;
      &lt;td&gt;NO&lt;/td&gt;
      &lt;td&gt;NaN&lt;/td&gt;
      &lt;td&gt;BONPLAND&lt;/td&gt;
      &lt;td&gt;1459&lt;/td&gt;
      &lt;td&gt;BONPLAND 1459&lt;/td&gt;
      &lt;td&gt;PALERMO&lt;/td&gt;
      &lt;td&gt;COMUNA 14&lt;/td&gt;
    &lt;/tr&gt;
  &lt;/tbody&gt;
&lt;/table&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
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&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[16]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# corregimos el campo de coordenadas del dataset.&lt;/span&gt;
&lt;span class="k"&gt;def&lt;/span&gt; &lt;span class="nf"&gt;coord&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;):&lt;/span&gt;
    &lt;span class="n"&gt;coor&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;re&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;findall&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="sa"&gt;r&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;-?\d+\.\d&lt;/span&gt;&lt;span class="si"&gt;{7}&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;coords&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="nb"&gt;float&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;s&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;s&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="n"&gt;coor&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
    &lt;span class="k"&gt;return&lt;/span&gt; &lt;span class="n"&gt;coords&lt;/span&gt;&lt;span class="p"&gt;[::&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;

&lt;span class="n"&gt;bici&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;WKT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;bici&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;WKT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;apply&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;coord&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# filtramos solo las bicicleterías de palermo&lt;/span&gt;
&lt;span class="n"&gt;bici_palermo&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;bici&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;bici&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;BARRIO&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;PALERMO&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;][[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;WKT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;NOMBRE&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]]&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[17]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# creamos el mapa con folium&lt;/span&gt;
&lt;span class="n"&gt;mapa&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;folium&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Map&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;location&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mf"&gt;34.588889&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mf"&gt;58.430556&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;zoom_start&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;13&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[18]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# agregamos los markers con el nombre de cada bicicletería.&lt;/span&gt;
&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;index&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;row&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="n"&gt;bici_palermo&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;iterrows&lt;/span&gt;&lt;span class="p"&gt;():&lt;/span&gt;
    &lt;span class="n"&gt;mapa&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;simple_marker&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;row&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;WKT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; 
                   &lt;span class="n"&gt;popup&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;row&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;NOMBRE&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;marker_color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;red&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                   &lt;span class="n"&gt;marker_icon&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;info-sign&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[19]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# visualizamos el mapa con los markers&lt;/span&gt;
&lt;span class="n"&gt;mapa&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[19]:&lt;/div&gt;


&lt;div class="output_html rendered_html output_subarea output_execute_result"&gt;
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&lt;p&gt;Aquí concluye este artículo, ya no hay excusas para graficar sus datos, como vimos &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; cuenta con herramientas que son fáciles de usar y muy poderosas. A divertirse!&lt;/p&gt;
&lt;p&gt;Saludos!&lt;/p&gt;
&lt;p&gt;&lt;em&gt;Este post fue escrito utilizando IPython notebook. Pueden descargar este &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/DataViz.ipynb"&gt;notebook&lt;/a&gt; o ver su version estática en &lt;a href="http://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/DataViz.ipynb"&gt;nbviewer&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

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&lt;/p&gt;</summary><category term="python"></category><category term="programacion"></category><category term="analisis de datos"></category><category term="Machine Learning"></category><category term="matematica"></category><category term="probabilidad"></category><category term="estadistica"></category><category term="distribuciones"></category><category term="visualizaciones"></category><category term="bokeh"></category><category term="folium"></category><category term="seaborn"></category><category term="matplotlib"></category></entry><entry><title>Análisis de datos cuantitativos con Python</title><link href="http://relopezbriega.github.io/blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/" rel="alternate"></link><published>2016-03-13T00:00:00-03:00</published><author><name>Raul E. Lopez Briega</name></author><id>tag:relopezbriega.github.io,2016-03-13:blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/</id><summary type="html">&lt;p&gt;
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&lt;p&gt;&lt;img alt="Datos cuantitativos con Python" title="Datos cuantitativos con Python" src="http://relopezbriega.github.io/images/categorical_data.jpg" high=400px width=600px&gt;&lt;/p&gt;

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&lt;h2 id="Introducci&amp;#243;n"&gt;Introducci&amp;#243;n&lt;a class="anchor-link" href="#Introducci&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;En mi artículo anterior, &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;Análisis de datos categóricos con Python&lt;/a&gt;, mencione la importancia de reconocer los distintos tipos de &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; con que nos podemos encontrar al realizar análisis &lt;a href="http://relopezbriega.github.io/tag/estadistica.html"&gt;estadísticos&lt;/a&gt; y vimos también como podemos trabajar con los &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;datos categóricos&lt;/a&gt;. En esta oportunidad, vamos a ver como podemos manipular, interpretar y obtener información de los &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;datos cuantitativos&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;Recordemos que las &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variables cuantitativas&lt;/a&gt; son variables medidas en una escala numérica. Altura, peso, tiempo de respuesta, la calificación subjetiva del dolor, la temperatura, y la puntuación en un examen, son ejemplos de &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variables cuantitativas&lt;/a&gt;. Las &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variables cuantitativas&lt;/a&gt; se distinguen de las &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;variables categóricas&lt;/a&gt; (también llamadas cualitativas) como el color favorito, religión, ciudad de nacimiento, y el deporte favorito; en las que no hay un orden o medida involucrados.&lt;/p&gt;
&lt;h2 id="Analizando-datos-cuantitativos-con-Python"&gt;Analizando datos cuantitativos con Python&lt;a class="anchor-link" href="#Analizando-datos-cuantitativos-con-Python"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Para los ejemplos de este artículo, vamos a trabajar con el &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; &lt;a href="http://www.stat.cmu.edu/~larry/all-of-statistics/=data/faithful.dat"&gt;faithful&lt;/a&gt;, el cual consiste en una colección de observaciones sobre las erupciones del &lt;a href="https://es.wikipedia.org/wiki/G%C3%A9iser"&gt;géiser&lt;/a&gt; &lt;a href="https://en.wikipedia.org/wiki/Old_Faithful"&gt;Old Faithful&lt;/a&gt; en el &lt;a href="https://es.wikipedia.org/wiki/Parque_nacional_de_Yellowstone"&gt;parque nacional Yellowstone&lt;/a&gt; de los Estados Unidos. La información que contiene este &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; es la siguiente:&lt;/p&gt;

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&lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importando modulos necesarios&lt;/span&gt;
&lt;span class="o"&gt;%&lt;/span&gt;&lt;span class="k"&gt;matplotlib&lt;/span&gt; inline

&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;matplotlib.pyplot&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;plt&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;numpy&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;np&lt;/span&gt; 
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;scipy&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;stats&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;pandas&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;pd&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;seaborn&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;sns&lt;/span&gt; 
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;pydataset&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;data&lt;/span&gt;

&lt;span class="c1"&gt;# parametros esteticos de seaborn&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_palette&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;deep&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;desat&lt;/span&gt;&lt;span class="o"&gt;=.&lt;/span&gt;&lt;span class="mi"&gt;6&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_context&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;rc&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;{&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;figure.figsize&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;4&lt;/span&gt;&lt;span class="p"&gt;)})&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;faithful&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;data&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;faithful&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;head&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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      &lt;th&gt;1&lt;/th&gt;
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&lt;p&gt;Como podemos ver, &lt;a href="http://www.stat.cmu.edu/~larry/all-of-statistics/=data/faithful.dat"&gt;faithful&lt;/a&gt; es un &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; bastante simple que solo contiene observaciones de dos variables; la primera, que se llama &lt;code&gt;eruptions&lt;/code&gt;, contiene la información de la duración de la erupción del &lt;a href="https://es.wikipedia.org/wiki/G%C3%A9iser"&gt;géiser&lt;/a&gt;; mientras que la segunda, se llama &lt;code&gt;waiting&lt;/code&gt; y contiene la información sobre el tiempo de espera para la siguiente erupción del &lt;a href="https://es.wikipedia.org/wiki/G%C3%A9iser"&gt;géiser&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;Al igual de como comentábamos cuando analizamos &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;datos categóricos&lt;/a&gt;, lo primero que deberíamos intentar hacer es crear una imagen que represente de la mejor manera posible a nuestros &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;, ya que nuestro cerebro tiende a procesar mejor la información visual. Para el caso de las &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variables cuantitativas&lt;/a&gt;, un buen candidato para comenzar a  hacernos una imagen de lo que nuestros datos representan, son los &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histogramas&lt;/a&gt;.&lt;/p&gt;
&lt;h3 id="Histogramas"&gt;Histogramas&lt;a class="anchor-link" href="#Histogramas"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Para las &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variables cuantitativas&lt;/a&gt;, a diferencia de lo que pasaba con las &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;variables categóricas&lt;/a&gt;, no existe una forma obvia de agrupar los datos; por tal motivo lo que se suele hacer es, dividir los posibles valores en diferentes &lt;em&gt;contenedores&lt;/em&gt; del mismo tamaño y luego contar el número de casos que cae dentro de cada uno de los &lt;em&gt;contenedores&lt;/em&gt;. Estos &lt;em&gt;contenedores&lt;/em&gt; junto con sus recuentos, nos proporcionan una imagen de la &lt;a href="https://es.wikipedia.org/wiki/Distribuci%C3%B3n_de_probabilidad"&gt;distribución&lt;/a&gt; de la &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variable cuantitativa&lt;/a&gt; y constituyen la base para poder graficar el &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histograma&lt;/a&gt;. Para construir el gráfico, simplemente  debemos representar a los recuentos como barras y graficarlas contra los valores de cada uno de los &lt;em&gt;contenedores&lt;/em&gt;.&lt;/p&gt;
&lt;p&gt;Con &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; podemos representar fácilmente el &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histograma&lt;/a&gt; de la variable &lt;code&gt;eruptions&lt;/code&gt; utilizando el método &lt;code&gt;hist&lt;/code&gt; del &lt;code&gt;DataFrame&lt;/code&gt; de &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt; del siguiente modo:&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# histograma duración de erupciones con 8 barras&lt;/span&gt;
&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;hist&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;bins&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; 
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&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Frecuencia&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

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&lt;p&gt;Como podemos ver con este gráfico, la duración más frecuente de las erupciones del &lt;a href="https://es.wikipedia.org/wiki/G%C3%A9iser"&gt;géiser&lt;/a&gt; ronda en alrededor de cuatro minutos y medio. Una cosa que debemos hacer notar es que en los &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histogramas&lt;/a&gt;, los &lt;em&gt;contenedores&lt;/em&gt; dividen a todos los valores de la &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variable cuantitativa&lt;/a&gt;, por lo que no deberíamos encontrar espacios entre las barras (a diferencia de lo que pasaba con los &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_barras"&gt;gráficos de barras&lt;/a&gt; que vimos en el &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;artículo anterior&lt;/a&gt;). Cualquier espacio entre las barras es una brecha en los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;, que nos indica un región para la que no existen valores.&lt;/p&gt;
&lt;h4 id="Distribuci&amp;#243;n-de-frecuencia"&gt;Distribuci&amp;#243;n de frecuencia&lt;a class="anchor-link" href="#Distribuci&amp;#243;n-de-frecuencia"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;Un tema íntimamente relacionado con los &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histogramas&lt;/a&gt; son las tablas de &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;distribución de frecuencia&lt;/a&gt;, en definitiva los &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histogramas&lt;/a&gt; no son más que gráficos de tablas de &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;distribución de frecuencia&lt;/a&gt;. La &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;distribución de frecuencia&lt;/a&gt; de una &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variable cuantitativa&lt;/a&gt; consiste en un resumen de la ocurrencia de un &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;dato&lt;/a&gt; dentro de una colección de categorías que no se superponen. Estas categorías las vamos a poder armar según nuestra conveniencia y lo que queramos analizar. Por ejemplo si quisiéramos armar la &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;distribución de frecuencia&lt;/a&gt; de la variable &lt;code&gt;eruptions&lt;/code&gt; podríamos realizar las siguiente manipulaciones con &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt;:&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[4]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Distribución de frecuencia.&lt;/span&gt;
&lt;span class="c1"&gt;# 1ro creamos un rango para las categorías.&lt;/span&gt;
&lt;span class="n"&gt;contenedores&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;arange&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mf"&gt;1.5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;6.&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;0.5&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# luego cortamos los datos en cada contenedor &lt;/span&gt;
&lt;span class="n"&gt;frec&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cut&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;contenedores&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# por último hacemos el recuento de los contenedores&lt;/span&gt;
&lt;span class="c1"&gt;# para armar la tabla de frecuencia.&lt;/span&gt;
&lt;span class="n"&gt;tabla_frec&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;value_counts&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;frec&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;tabla_frec&lt;/span&gt;
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&lt;pre class="ipynb"&gt;(4, 4.5]    75
(1.5, 2]    55
(4.5, 5]    54
(2, 2.5]    37
(3.5, 4]    34
(3, 3.5]     9
(2.5, 3]     5
(5, 5.5]     3
dtype: int64&lt;/pre&gt;
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&lt;p&gt;Como nos nuestra esta tabla de &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;distribución de frecuencia&lt;/a&gt;, la duración que más veces ocurre para las erupciones, se encuentran en el rango de 4 a 4.5 minutos.&lt;/p&gt;
&lt;h3 id="Diagrama-de-tallos-y-hojas"&gt;Diagrama de tallos y hojas&lt;a class="anchor-link" href="#Diagrama-de-tallos-y-hojas"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Los &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histogramas&lt;/a&gt; nos permiten apreciar la &lt;a href="https://es.wikipedia.org/wiki/Distribuci%C3%B3n_de_probabilidad"&gt;distribución&lt;/a&gt; de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; de una forma sencilla, pero los mismos no nos muestran los valores del &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; en sí mismos. Para solucionar esto, existe el &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_tallos_y_hojas"&gt;diagrama de tallos y hojas&lt;/a&gt;, el cual es similar al &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histograma&lt;/a&gt; pero nos muestra los valores individuales de nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt;. Para que quede más claro, vemos un ejemplo sencillo. Supongamos que tenemos una muestra sobre el ritmo cardíaco de 24 mujeres, las observaciones son las siguientes:&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;pulso&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;88&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;80&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;76&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;72&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;68&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;56&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;64&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;60&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;64&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;68&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;64&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;68&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
         &lt;span class="mi"&gt;72&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;76&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;80&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;84&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;68&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;80&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;76&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;72&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;84&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;80&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;72&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;76&lt;/span&gt; &lt;span class="p"&gt;]&lt;/span&gt;
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&lt;p&gt;Podríamos graficar el &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histograma&lt;/a&gt; de estas observaciones del siguiente modo:&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;hist&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;pulso&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;bins&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;7&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;latidos por minuto&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;N° de mujeres&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;p&gt;Este &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histograma&lt;/a&gt; nos muestra la forma en que se distribuyen los datos, pero no nos muestra los datos individuales. Para esto podríamos graficar su &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_tallos_y_hojas"&gt;diagrama de tallos y hojas&lt;/a&gt; de la siguiente manera:&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Diagrama de tallos y hojas&lt;/span&gt;
&lt;span class="k"&gt;def&lt;/span&gt; &lt;span class="nf"&gt;tallos&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;d&lt;/span&gt;&lt;span class="p"&gt;):&lt;/span&gt;
    &lt;span class="s2"&gt;&amp;quot;Genera un simple diagramas de tallos y hojas&amp;quot;&lt;/span&gt;
    &lt;span class="n"&gt;l&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="n"&gt;t&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sort&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;d&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;
    &lt;span class="n"&gt;O&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="nb"&gt;range&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nb"&gt;int&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;l&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;l&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;%&lt;/span&gt;&lt;span class="k"&gt;t&lt;/span&gt;),int(l[-1]+11),t)
    &lt;span class="n"&gt;I&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;searchsorted&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;l&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="n"&gt;O&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;e&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="n"&gt;a&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="n"&gt;f&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="nb"&gt;zip&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;I&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="n"&gt;I&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;:],&lt;/span&gt;&lt;span class="n"&gt;O&lt;/span&gt;&lt;span class="p"&gt;):&lt;/span&gt; 
        &lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&lt;/span&gt;&lt;span class="si"&gt;%3d&lt;/span&gt;&lt;span class="s1"&gt;|&amp;#39;&lt;/span&gt;&lt;span class="o"&gt;%&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;f&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="n"&gt;t&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;&lt;span class="o"&gt;*&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;l&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;e&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;&lt;span class="n"&gt;a&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;f&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;&lt;span class="n"&gt;sep&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
        
&lt;span class="n"&gt;tallos&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;pulso&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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&lt;pre class="ipynb"&gt;  5|6
  6|04448888
  7|22226666
  8|0000448
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&lt;p&gt;Como vemos, la distribución del &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_tallos_y_hojas"&gt;diagrama de tallos y hojas&lt;/a&gt; es similar a la del  &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histograma&lt;/a&gt;, pero en este caso si podemos ver los valores de nuestras observaciones. El diagrama se lee así: Por un lado tenemos las decenas de los latidos, las cuales constituyen los &lt;em&gt;tallos&lt;/em&gt; de nuestro diagrama (los valores antes del &lt;em&gt;pipe&lt;/em&gt; o barra vertical "|") y luego vamos agrando &lt;em&gt;hojas&lt;/em&gt; a estos &lt;em&gt;tallos&lt;/em&gt; , representadas por las unidades de cada latido. De esta forma 5|6, significa que solo aparece el valor 56 una sola vez, en cambio 8|0000, significa que tenemos el valor 80 observado en 4 oportunidades.&lt;/p&gt;
&lt;h3 id="Diagrama-de-dispersi&amp;#243;n"&gt;Diagrama de dispersi&amp;#243;n&lt;a class="anchor-link" href="#Diagrama-de-dispersi&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Hasta aquí venimos graficando únicamente una sola &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variable cuantitativa&lt;/a&gt; pero ¿qué pasa si queremos trabajar con dos variables? Para estos casos existe el &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_dispersi%C3%B3n"&gt;diagrama de dispersión&lt;/a&gt;. El &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_dispersi%C3%B3n"&gt;diagrama de dispersión&lt;/a&gt; es una de las formas más comunes que existen para visualizar &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; y constituye una de las mejores forma de observar relaciones entre dos &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variables cuantitativas&lt;/a&gt;. Veremos que se puede observar un montón de cosas por el solo hecho de mirar. Este diagrama es una de las mejores formas de visualizar las asociaciones que pueden existir entre nuestros datos.&lt;/p&gt;
&lt;p&gt;El &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_dispersi%C3%B3n"&gt;diagrama de dispersión&lt;/a&gt; empareja los valores de dos &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variables cuantitativas&lt;/a&gt; y luego los representa como puntos geométricos dentro de un diagrama cartesiano. Por ejemplo, volviendo a nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; &lt;a href="http://www.stat.cmu.edu/~larry/all-of-statistics/=data/faithful.dat"&gt;faithful&lt;/a&gt;, podríamos emparejar a las variables &lt;code&gt;eruptions&lt;/code&gt; y &lt;code&gt;waiting&lt;/code&gt; en la misma observación como coordenadas (x, y) y luego graficarlas en el eje cartesiano. Con la ayuda de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; podríamos generar el &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_dispersi%C3%B3n"&gt;diagrama de dispersión&lt;/a&gt; del siguiente modo:&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# diagrama de dispersión&lt;/span&gt;
&lt;span class="n"&gt;disp&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;scatter&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;waiting&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
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&lt;/div&gt;
&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Como podemos ver con solo observar la dispersión de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; parece existir una &lt;em&gt;relación lineal&lt;/em&gt; entre los datos de este &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt;.&lt;/p&gt;
&lt;h3 id="Medidas-de-tendencia-central"&gt;Medidas de tendencia central&lt;a class="anchor-link" href="#Medidas-de-tendencia-central"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Una vez que ya nos dimos una buena idea visual de como se distribuyen los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; y de las relaciones que pueden existir entre los mismos, podemos pasar a calcular medidas numéricas propias de la &lt;a href="https://es.wikipedia.org/wiki/Estad%C3%ADstica_descriptiva"&gt;estadística descriptiva&lt;/a&gt;. En general, suele ser interesante conocer cual puede ser el &lt;em&gt;promedio o valor central&lt;/em&gt; al que tiende la &lt;a href="https://es.wikipedia.org/wiki/Distribuci%C3%B3n_de_probabilidad"&gt;distribución&lt;/a&gt; de nuestros &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;, para esto se utilizan las &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_tendencia_central"&gt;medidas de tendencia central&lt;/a&gt;, entre las que podemos encontrar a:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;La &lt;a href="https://es.wikipedia.org/wiki/Media_aritm%C3%A9tica"&gt;media aritmética&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;La &lt;a href="https://es.wikipedia.org/wiki/Media_ponderada"&gt;media ponderada&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;La &lt;a href="https://es.wikipedia.org/wiki/Media_geom%C3%A9trica"&gt;media geométrica&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;La &lt;a href="https://es.wikipedia.org/wiki/Media_arm%C3%B3nica"&gt;media armónica&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;La &lt;a href="https://es.wikipedia.org/wiki/Mediana_(estad%C3%ADstica)"&gt;mediana&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;La &lt;a href="https://es.wikipedia.org/wiki/Media_truncada"&gt;media truncada&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;La &lt;a href="https://es.wikipedia.org/wiki/Moda_(estad%C3%ADstica)"&gt;moda&lt;/a&gt;&lt;/li&gt;
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&lt;p&gt;Veamos como podemos calcularlas con &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;:&lt;/p&gt;
&lt;h4 id="Media-aritm&amp;#233;tica"&gt;Media aritm&amp;#233;tica&lt;a class="anchor-link" href="#Media-aritm&amp;#233;tica"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;La &lt;a href="https://es.wikipedia.org/wiki/Media_aritm%C3%A9tica"&gt;media aritmética&lt;/a&gt; es el valor obtenido al sumar todos los datos y dividir el resultado entre el número total elementos. La calculamos con el método &lt;code&gt;mean&lt;/code&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# media de variable eruptions&lt;/span&gt;
&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;h4 id="Media-ponderada"&gt;Media ponderada&lt;a class="anchor-link" href="#Media-ponderada"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;La &lt;a href="https://es.wikipedia.org/wiki/Media_ponderada"&gt;media ponderada&lt;/a&gt; es apropiada cuando en un &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; cada dato tiene una importancia relativa (o peso) respecto de los demás. Como esta media no aplica para nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; no la vamos a calcular.&lt;/p&gt;
&lt;h4 id="Media-geom&amp;#233;trica"&gt;Media geom&amp;#233;trica&lt;a class="anchor-link" href="#Media-geom&amp;#233;trica"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;La &lt;a href="https://es.wikipedia.org/wiki/Media_geom%C3%A9trica"&gt;media geométrica&lt;/a&gt; es útil cuando queremos comparar cosas con propiedades muy diferentes; también es es recomendada para datos de progresión geométrica, para promediar razones, interés compuesto y números índices. Se calcula tomando la &lt;a href="https://es.wikipedia.org/wiki/Radicaci%C3%B3n"&gt;raíz n-ésima&lt;/a&gt; del producto de todos los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;. La calculamos con la función &lt;code&gt;gmean&lt;/code&gt; de &lt;a href="http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html"&gt;SciPy&lt;/a&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# media geometrica&lt;/span&gt;
&lt;span class="n"&gt;stats&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;gmean&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
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&lt;h4 id="Media-arm&amp;#243;nica"&gt;Media arm&amp;#243;nica&lt;a class="anchor-link" href="#Media-arm&amp;#243;nica"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;La &lt;a href="https://es.wikipedia.org/wiki/Media_arm%C3%B3nica"&gt;media armónica&lt;/a&gt; promedia el número de elementos y los divide por la suma de sus inversos. La &lt;a href="https://es.wikipedia.org/wiki/Media_arm%C3%B3nica"&gt;media armónica&lt;/a&gt; es siempre la media más baja y es recomendada para promediar velocidades. La calculamos con la función &lt;code&gt;hmean&lt;/code&gt; de &lt;a href="http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html"&gt;SciPy&lt;/a&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# media armónica&lt;/span&gt;
&lt;span class="n"&gt;stats&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;hmean&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
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&lt;h4 id="Mediana"&gt;Mediana&lt;a class="anchor-link" href="#Mediana"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;La &lt;a href="https://es.wikipedia.org/wiki/Mediana"&gt;mediana&lt;/a&gt; representa el valor de posición central en un &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;conjunto de datos&lt;/a&gt; ordenados. La podemos calcular utilizando el método &lt;code&gt;median&lt;/code&gt; de &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt;&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# mediana&lt;/span&gt;
&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;median&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;pre class="ipynb"&gt;4.0&lt;/pre&gt;
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&lt;h4 id="Media-truncada"&gt;Media truncada&lt;a class="anchor-link" href="#Media-truncada"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;La &lt;a href="https://es.wikipedia.org/wiki/Media_truncada"&gt;media truncada&lt;/a&gt; es una mezcla entre la &lt;a href="https://es.wikipedia.org/wiki/Media_aritm%C3%A9tica"&gt;media aritmética&lt;/a&gt; y la &lt;a href="https://es.wikipedia.org/wiki/Mediana"&gt;mediana&lt;/a&gt;. Para calcular el promedio previamente se descartan porciones en el extremo inferior y superior de la &lt;a href="https://es.wikipedia.org/wiki/Distribuci%C3%B3n_de_probabilidad"&gt;distribución&lt;/a&gt; de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;. En &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; podemos utilizar la función &lt;code&gt;trim_mean&lt;/code&gt; de &lt;a href="http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html"&gt;SciPy&lt;/a&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# media truncada, recortando el 10 superior e inferior&lt;/span&gt;
&lt;span class="n"&gt;stats&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;trim_mean&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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&lt;h4 id="Moda"&gt;Moda&lt;a class="anchor-link" href="#Moda"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;Por último, la &lt;a href="https://es.wikipedia.org/wiki/Moda_(estad%C3%ADstica)"&gt;moda&lt;/a&gt; es el valor que tiene mayor frecuencia absoluta. Son los picos que vemos en el &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histograma&lt;/a&gt;. Dependiendo de la la &lt;a href="https://es.wikipedia.org/wiki/Distribuci%C3%B3n_de_probabilidad"&gt;distribución&lt;/a&gt; de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; puede existir más de una, como en el caso de la variable &lt;code&gt;eruptions&lt;/code&gt;. La calculamos con el método &lt;code&gt;mode&lt;/code&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# moda&lt;/span&gt;
&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mode&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;h3 id="Medidas-de-dispersi&amp;#243;n"&gt;Medidas de dispersi&amp;#243;n&lt;a class="anchor-link" href="#Medidas-de-dispersi&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Las &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_tendencia_central"&gt;medidas de tendencia central&lt;/a&gt; no son las únicas medidas de resumen &lt;a href="http://relopezbriega.github.io/tag/estadistica.html"&gt;estadístico&lt;/a&gt; que podemos calcular; otras medidas también de gran importancia son las &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_dispersi%C3%B3n"&gt;medidas de dispersión&lt;/a&gt;. Las &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_dispersi%C3%B3n"&gt;medidas de dispersión&lt;/a&gt;, también llamadas medidas de variabilidad, muestran la variabilidad de una &lt;a href="https://es.wikipedia.org/wiki/Distribuci%C3%B3n_de_probabilidad"&gt;distribución&lt;/a&gt;, indicando por medio de un número si las diferentes puntuaciones de una variable están muy alejadas de la &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_tendencia_central"&gt;media&lt;/a&gt;. Cuanto mayor sea ese valor, mayor será la variabilidad, y cuanto menor sea, más homogénea será a la &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_tendencia_central"&gt;media&lt;/a&gt;. Así se sabe si todos los casos son parecidos o varían mucho entre ellos. Las principales &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_dispersi%C3%B3n"&gt;medidas de dispersión&lt;/a&gt; son:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;La &lt;a href="https://es.wikipedia.org/wiki/Varianza"&gt;varianza&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;El &lt;a href="https://es.wikipedia.org/wiki/Desviaci%C3%B3n_t%C3%ADpica"&gt;desvío estándar&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Los &lt;a href="https://es.wikipedia.org/wiki/Cuartil"&gt;cuartiles&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;La &lt;a href="https://es.wikipedia.org/wiki/Covarianza"&gt;covarianza&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;El &lt;a href="https://es.wikipedia.org/wiki/Coeficiente_de_correlaci%C3%B3n_de_Pearson"&gt;coeficiente de correlación&lt;/a&gt;&lt;/li&gt;
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&lt;p&gt;Analicemos cada uno de ellos:&lt;/p&gt;
&lt;h4 id="Varianza"&gt;Varianza&lt;a class="anchor-link" href="#Varianza"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;La &lt;a href="https://es.wikipedia.org/wiki/Varianza"&gt;varianza&lt;/a&gt; intenta describir la dispersión de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;. Se define como la &lt;a href="https://es.wikipedia.org/wiki/Esperanza_matem%C3%A1tica"&gt;esperanza&lt;/a&gt; del cuadrado de la desviación de dicha variable respecto a su &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_tendencia_central"&gt;media&lt;/a&gt;. Una &lt;a href="https://es.wikipedia.org/wiki/Varianza"&gt;varianza&lt;/a&gt; pequeña indica que los puntos de datos tienden a estar muy cerca de la &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_tendencia_central"&gt;media&lt;/a&gt; y por lo tanto el uno al otro, mientras que una &lt;a href="https://es.wikipedia.org/wiki/Varianza"&gt;varianza&lt;/a&gt; alta indica que los puntos de datos están muy distribuidos alrededor de la &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_tendencia_central"&gt;media&lt;/a&gt; y la una de la otra. La podemos calcular con el método &lt;code&gt;var&lt;/code&gt;.&lt;/p&gt;

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&lt;h4 id="Desv&amp;#237;o-est&amp;#225;ndar"&gt;Desv&amp;#237;o est&amp;#225;ndar&lt;a class="anchor-link" href="#Desv&amp;#237;o-est&amp;#225;ndar"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;El &lt;a href="https://es.wikipedia.org/wiki/Desviaci%C3%B3n_t%C3%ADpica"&gt;desvío estándar&lt;/a&gt; o desviación típica es una medida que se utiliza para cuantificar la cantidad de variación o dispersión de un conjunto de valores de datos. Un &lt;a href="https://es.wikipedia.org/wiki/Desviaci%C3%B3n_t%C3%ADpica"&gt;desvío estándar&lt;/a&gt; cerca de 0 indica que los puntos de datos tienden a estar muy cerca de la &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_tendencia_central"&gt;media&lt;/a&gt; del conjunto, mientras que un alto &lt;a href="https://es.wikipedia.org/wiki/Desviaci%C3%B3n_t%C3%ADpica"&gt;desvío estándar&lt;/a&gt; indica que los puntos de datos se extienden a lo largo de un rango amplio de valores. Se calcula como la raíz cuadrada de la &lt;a href="https://es.wikipedia.org/wiki/Varianza"&gt;varianza&lt;/a&gt; y con &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt; lo podemos obtener con el método &lt;code&gt;std&lt;/code&gt;.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[16]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# desvio estándar&lt;/span&gt;
&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;std&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;pre class="ipynb"&gt;1.1413712511052092&lt;/pre&gt;
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&lt;h4 id="Cuartiles"&gt;Cuartiles&lt;a class="anchor-link" href="#Cuartiles"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;Los &lt;a href="https://es.wikipedia.org/wiki/Cuartil"&gt;cuartiles&lt;/a&gt; son los tres puntos que dividen el conjunto de datos en cuatro grupos iguales, cada grupo comprende un cuarto de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;.El (Q1) se define como el número medio entre el número más pequeño y la &lt;a href="https://es.wikipedia.org/wiki/Mediana"&gt;mediana&lt;/a&gt; del &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;conjunto de datos&lt;/a&gt;. El segundo cuartil (Q2) es la &lt;a href="https://es.wikipedia.org/wiki/Mediana"&gt;mediana&lt;/a&gt; de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;. El tercer cuartil (Q3) es el valor medio entre la &lt;a href="https://es.wikipedia.org/wiki/Mediana"&gt;mediana&lt;/a&gt; y el valor más alto del &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;conjunto de datos&lt;/a&gt;. Para dividir nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; en sus &lt;a href="https://es.wikipedia.org/wiki/Cuartil"&gt;cuartiles&lt;/a&gt; utilizamos el método &lt;code&gt;quantile&lt;/code&gt;.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[17]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# cuartiles&lt;/span&gt;
&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;quantile&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="mi"&gt;25&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="mi"&gt;75&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;pre class="ipynb"&gt;0.25    2.16275
0.50    4.00000
0.75    4.45425
dtype: float64&lt;/pre&gt;
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&lt;p&gt;Un gráfico relacionado a los &lt;a href="https://es.wikipedia.org/wiki/Cuartil"&gt;cuartiles&lt;/a&gt; y describe varias características importantes al mismo tiempo, tales como la dispersión y simetría es el &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_caja"&gt;diagrama de caja&lt;/a&gt;. Para su realización se representan los tres &lt;a href="https://es.wikipedia.org/wiki/Cuartil"&gt;cuartiles&lt;/a&gt; y los valores mínimo y máximo de los datos, sobre un rectángulo, alineado horizontal o verticalmente. Podemos utilizar la función &lt;code&gt;boxplot&lt;/code&gt; de &lt;a href="http://stanford.edu/~mwaskom/software/seaborn/"&gt;Seaborn&lt;/a&gt; para generarlo.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[18]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# diagrama de cajas&lt;/span&gt;
&lt;span class="n"&gt;cajas&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;boxplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nb"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]))&lt;/span&gt;
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&lt;p&gt;Hasta aquí hemos calculado &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_dispersi%C3%B3n"&gt;medidas de dispersión&lt;/a&gt; para una sola variable, pero nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; tiene dos &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variables cuantitativas&lt;/a&gt;; veamos como podemos calcular medidas combinadas para la dos variables.&lt;/p&gt;
&lt;h4 id="Covarianza"&gt;Covarianza&lt;a class="anchor-link" href="#Covarianza"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;La &lt;a href="https://es.wikipedia.org/wiki/Covarianza"&gt;covarianza&lt;/a&gt; es el equivalente de la &lt;a href="https://es.wikipedia.org/wiki/Varianza"&gt;varianza&lt;/a&gt; aplicado a una variable bidimensional. Es la &lt;a href="https://es.wikipedia.org/wiki/Media_aritm%C3%A9tica"&gt;media aritmética&lt;/a&gt; de los productos de las desviaciones de cada una de las variables respecto a sus &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_tendencia_central"&gt;medias&lt;/a&gt;.La covarianza indica el sentido de la &lt;a href="https://es.wikipedia.org/wiki/Correlaci%C3%B3n"&gt;correlación&lt;/a&gt; entre las variables; Si es mayor que cero la correlación es directa, en caso de ser menor, la &lt;a href="https://es.wikipedia.org/wiki/Correlaci%C3%B3n"&gt;correlación&lt;/a&gt; es inversa. La podemos calcular utilizando el método &lt;code&gt;cov&lt;/code&gt;.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[19]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# covarianza&lt;/span&gt;
&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cov&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;div class="prompt output_prompt"&gt;Out[19]:&lt;/div&gt;


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&lt;table border="1" class="dataframe"&gt;
  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;eruptions&lt;/th&gt;
      &lt;th&gt;waiting&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;eruptions&lt;/th&gt;
      &lt;td&gt;1.302728&lt;/td&gt;
      &lt;td&gt;13.977808&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;waiting&lt;/th&gt;
      &lt;td&gt;13.977808&lt;/td&gt;
      &lt;td&gt;184.823312&lt;/td&gt;
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&lt;h4 id="Correlaci&amp;#243;n"&gt;Correlaci&amp;#243;n&lt;a class="anchor-link" href="#Correlaci&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;Por último, el &lt;a href="https://es.wikipedia.org/wiki/Coeficiente_de_correlaci%C3%B3n_de_Pearson"&gt;coeficiente de correlación&lt;/a&gt; es una medida del grado de &lt;em&gt;dependencia lineal&lt;/em&gt; entre dos variables. El &lt;a href="https://es.wikipedia.org/wiki/Coeficiente_de_correlaci%C3%B3n_de_Pearson"&gt;coeficiente de correlación&lt;/a&gt; oscila entre -1 y 1. Un valor de 1 significa que una &lt;a href="https://es.wikipedia.org/wiki/Ecuaci%C3%B3n_de_primer_grado"&gt;ecuación lineal&lt;/a&gt; describe la relación entre las dos variables a la perfección, con todos los puntos de datos cayendo sobre una línea recta de pendiente positiva. Un valor de -1 implica que todos los puntos de datos se encuentran en una línea con pendiente negativa. Un valor de 0 implica que no existe una correlación lineal entre las variables. Lo podemos calcular con el método &lt;code&gt;corr&lt;/code&gt;.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[20]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# coeficiente de correlación&lt;/span&gt;
&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;corr&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;div style="max-height:1000px;max-width:1500px;overflow:auto;"&gt;
&lt;table border="1" class="dataframe"&gt;
  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;eruptions&lt;/th&gt;
      &lt;th&gt;waiting&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;eruptions&lt;/th&gt;
      &lt;td&gt;1.000000&lt;/td&gt;
      &lt;td&gt;0.900811&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;waiting&lt;/th&gt;
      &lt;td&gt;0.900811&lt;/td&gt;
      &lt;td&gt;1.000000&lt;/td&gt;
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&lt;p&gt;Como podemos ver las dos variables tienen una correlación bastante alta, lo que sugiere que están íntimamente relacionadas; a la misma conclusión habíamos llegado al observar el &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_dispersi%C3%B3n"&gt;diagrama de dispersión&lt;/a&gt;.&lt;/p&gt;
&lt;h3 id="Resumen-estad&amp;#237;stico"&gt;Resumen estad&amp;#237;stico&lt;a class="anchor-link" href="#Resumen-estad&amp;#237;stico"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Hasta aquí hemos calculado tanto las &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_tendencia_central"&gt;medidas de tendencia central&lt;/a&gt; como las &lt;a href="https://es.wikipedia.org/wiki/Medidas_de_dispersi%C3%B3n"&gt;medidas de dispersión&lt;/a&gt; una por una, pero ¿no sería más conveniente que con un simple comando podemos obtener un resumen &lt;a href="http://relopezbriega.github.io/tag/estadistica.html"&gt;estadístico&lt;/a&gt; con las principales medidas? Es por esto que &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt; nos ofrece el método &lt;code&gt;describe&lt;/code&gt;, un comando para gobernarlos a todos!, el cual nos ofrece un resumen con las principales medidas &lt;a href="http://relopezbriega.github.io/tag/estadistica.html"&gt;estadísticas&lt;/a&gt;.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[21]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# resumen estadístico&lt;/span&gt;
&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;eruptions&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;describe&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;pre class="ipynb"&gt;count    272.000000
mean       3.487783
std        1.141371
min        1.600000
25%        2.162750
50%        4.000000
75%        4.454250
max        5.100000
Name: eruptions, dtype: float64&lt;/pre&gt;
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&lt;p&gt;Siguiendo la misma línea; &lt;a href="http://stanford.edu/~mwaskom/software/seaborn/"&gt;Seaborn&lt;/a&gt; nos ofrece la función &lt;code&gt;pairplot&lt;/code&gt; que nos proporciona un resumen gráfico con &lt;a href="https://es.wikipedia.org/wiki/Histograma"&gt;histogramas&lt;/a&gt; y &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_dispersi%C3%B3n"&gt;diagramas de dispersión&lt;/a&gt; de las variables de nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt;.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[22]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;par&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;pairplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;faithful&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;p&gt;Con esto concluye este artículo; ahora ya deberían estar en condiciones de poder analizar tanto &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;variables cuantitativas&lt;/a&gt; como &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;variables categóricas&lt;/a&gt;. A practicar!&lt;/p&gt;
&lt;p&gt;Saludos!&lt;/p&gt;
&lt;p&gt;&lt;em&gt;Este post fue escrito utilizando Jupyter notebook. Pueden descargar este &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/quantitativePython.ipynb"&gt;notebook&lt;/a&gt; o ver su version estática en &lt;a href="http://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/quantitativePython.ipynb"&gt;nbviewer&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

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&lt;/p&gt;</summary><category term="python"></category><category term="estadistica"></category><category term="programacion"></category><category term="matematica"></category><category term="analisis de datos"></category></entry><entry><title>Análisis de datos categóricos con Python</title><link href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/" rel="alternate"></link><published>2016-02-29T00:00:00-03:00</published><author><name>Raul E. Lopez Briega</name></author><id>tag:relopezbriega.github.io,2016-02-29:blog/2016/02/29/analisis-de-datos-categoricos-con-python/</id><summary type="html">&lt;p&gt;
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&lt;p&gt;&lt;img alt="Datos categóricos con Python" title="Datos categóricos con Python" src="http://relopezbriega.github.io/images/categorical_data.jpg" high=400px width=600px&gt;&lt;/p&gt;

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&lt;h2 id="Introducci&amp;#243;n"&gt;Introducci&amp;#243;n&lt;a class="anchor-link" href="#Introducci&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Cuando trabajamos con &lt;a href="http://relopezbriega.github.io/tag/estadistica.html"&gt;estadísticas&lt;/a&gt;, es importante reconocer los diferentes tipos de &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;: numéricos (&lt;a href="https://es.wikipedia.org/wiki/Variable_discreta_y_variable_continua"&gt;discretos y continuos&lt;/a&gt;), &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;categóricos&lt;/a&gt; y ordinales. Los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; no son más que observaciones del mundo en que vivimos, por tanto, los mismos pueden venir en diferentes formas, no solo numérica. Por ejemplo, si le preguntáramos a nuestros amigos ¿cuántas mascotas tienen? nos podrían responder: &lt;code&gt;0, 1, 2, 4, 3, 8&lt;/code&gt;; esta información por sí misma puede ser útil, pero para nuestro análisis de mascotas, nos podría servir también otro tipo de información, como por ejemplo el &lt;em&gt;género&lt;/em&gt; de cada uno de nuestros amigos; de esta forma obtendríamos la siguiente información: &lt;code&gt;hombre, mujer, mujer, mujer, hombre, mujer&lt;/code&gt;. Como vemos, podemos incluir a los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; dentro de tres categorías fundamentales: &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;datos cuantitativos&lt;/a&gt; o numéricos, &lt;a href="https://es.wikipedia.org/wiki/Cualidad"&gt;datos cualitativos&lt;/a&gt; o &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;categóricos&lt;/a&gt; y datos ordinales.&lt;/p&gt;
&lt;h3 id="Datos-cuantitativos"&gt;Datos cuantitativos&lt;a class="anchor-link" href="#Datos-cuantitativos"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Los &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;datos cuantitativos&lt;/a&gt; son representados por números; estos números van a ser significativos si representan la medida o la cantidad observada de cierta característica. Dentro de esta categoría podemos encontrar por ejemplo: cantidades de dólares, cuentas, tamaños, número de empleados, y kilómetros por hora. Con los &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;datos cuantitativos&lt;/a&gt;, se puede hacer todo tipo de tareas de procesamiento de datos numéricos, tales como sumarlos, calcular promedios, o medir su variabilidad. Asimismo, vamos a poder dividir a los &lt;a href="https://es.wikipedia.org/wiki/Cantidad"&gt;datos cuantitativos&lt;/a&gt; en &lt;a href="https://es.wikipedia.org/wiki/Variable_discreta_y_variable_continua"&gt;discretos y continuos&lt;/a&gt;, dependiendo de los valores potencialmente observables.&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Los datos &lt;strong&gt;&lt;em&gt;discretos&lt;/em&gt;&lt;/strong&gt; solo van a poder asumir un valor de una lista de números específicos. Representan ítems que pueden ser &lt;em&gt;contados&lt;/em&gt;; todos sus posibles valores pueden ser listados. Suele ser relativamente fácil trabajar con este tipo de &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;dato&lt;/a&gt;.&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Los datos &lt;strong&gt;&lt;em&gt;continuos&lt;/em&gt;&lt;/strong&gt; representan &lt;em&gt;mediciones&lt;/em&gt;; sus posibles valores no pueden ser contados y sólo pueden ser descritos usando intervalos en la recta de los &lt;a href="https://es.wikipedia.org/wiki/N%C3%BAmero_real"&gt;números reales&lt;/a&gt;. Por ejemplo, la cantidad de kilómetros recorridos no puede ser medida con exactitud, puede ser que hayamos recorrido 1.7 km o 1.6987 km; en cualquier medida que tomemos del mundo real, siempre pueden haber pequeñas o grandes variaciones. Generalmente, los &lt;em&gt;datos continuos&lt;/em&gt; se suelen redondear a un número fijo de decimales para facilitar su manipulación.&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;h3 id="Datos-cualitativos"&gt;Datos cualitativos&lt;a class="anchor-link" href="#Datos-cualitativos"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Si los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; nos dicen en cual de determinadas categorías no numéricas nuestros ítems van a caer, entonces estamos hablando de &lt;a href="https://es.wikipedia.org/wiki/Cualidad"&gt;datos cualitativos&lt;/a&gt; o &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;categóricos&lt;/a&gt;; ya que los mismos van a representar determinada &lt;em&gt;cualidad&lt;/em&gt; que los ítems poseen. Dentro de esta categoría vamos a encontrar &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; como: el sexo de una persona, el estado civil, la ciudad natal, o los tipos de películas que le gustan. Los &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;datos categóricos&lt;/a&gt; pueden tomar valores numéricos (por ejemplo, "1" para indicar "masculino" y "2" para indicar "femenino"), pero esos números no tienen un sentido matemático.&lt;/p&gt;
&lt;h3 id="Datos-ordinales"&gt;Datos ordinales&lt;a class="anchor-link" href="#Datos-ordinales"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Una categoría intermedia entre los dos tipos de &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; anteriores, son los &lt;em&gt;datos ordinales&lt;/em&gt;. En este tipo de &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;, va a existir un &lt;em&gt;orden&lt;/em&gt; significativo, vamos a poder clasificar un primero, segundo, tercero, etc. es decir, que podemos establecer un &lt;em&gt;ranking&lt;/em&gt; para estos &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;, el cual posiblemente luego tenga un rol importante en la etapa de análisis. Los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; se dividen en categorías, pero los números colocados en cada categoría tienen un significado. Por ejemplo, la calificación de un restaurante en una escala de 0 (bajo) a 5 (más alta) estrellas representa &lt;em&gt;datos ordinales&lt;/em&gt;. Los &lt;em&gt;datos ordinales&lt;/em&gt; son a menudo tratados como &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;datos categóricos&lt;/a&gt;, en el sentido que se suelen agrupar y ordenar. Sin embargo, a diferencia de los &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;datos categóricos&lt;/a&gt;, los números sí tienen un significado matemático.&lt;/p&gt;
&lt;p&gt;En este artículo me voy a centrar en el segundo grupo, los &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;datos categóricos&lt;/a&gt;; veremos como podemos manipular fácilmente con la ayuda de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; estos &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; para poder encontrar &lt;em&gt;patrones&lt;/em&gt;, &lt;em&gt;relaciones&lt;/em&gt;, &lt;em&gt;tendencias&lt;/em&gt; y &lt;em&gt;excepciones&lt;/em&gt;.&lt;/p&gt;

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&lt;h2 id="An&amp;#225;lisis-de-datos-categ&amp;#243;ricos-con-Python"&gt;An&amp;#225;lisis de datos categ&amp;#243;ricos con Python&lt;a class="anchor-link" href="#An&amp;#225;lisis-de-datos-categ&amp;#243;ricos-con-Python"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Para ejemplificar el análisis, vamos a utilizar nuestras habituales librerías científicas &lt;a href="http://www.numpy.org/"&gt;NumPy&lt;/a&gt;, &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt;,  &lt;a href="http://matplotlib.org/"&gt;Matplotlib&lt;/a&gt; y &lt;a href="http://stanford.edu/~mwaskom/software/seaborn/"&gt;Seaborn&lt;/a&gt;. También vamos a utilizar la librería &lt;a href="https://github.com/iamaziz/PyDataset"&gt;pydataset&lt;/a&gt;, la cual nos facilita cargar los diferentes &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; para analizar.&lt;/p&gt;
&lt;p&gt;La idea es realizar un análisis &lt;a href="http://relopezbriega.github.io/tag/estadistica.html"&gt;estadístico&lt;/a&gt; sobre los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; de los sobrevivientes a la tragedia del &lt;a href="https://es.wikipedia.org/wiki/RMS_Titanic"&gt;Titanic&lt;/a&gt;.&lt;/p&gt;
&lt;h3 id="La-tragedia-del-Titanic"&gt;La tragedia del Titanic&lt;a class="anchor-link" href="#La-tragedia-del-Titanic"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;El hundimiento del &lt;a href="https://es.wikipedia.org/wiki/RMS_Titanic"&gt;Titanic&lt;/a&gt; es uno de los naufragios más infames de la historia. El 15 de abril de 1912, durante su viaje inaugural, el &lt;a href="https://es.wikipedia.org/wiki/RMS_Titanic"&gt;Titanic&lt;/a&gt; se hundió después de chocar con un iceberg, matando a miles de personas. Esta tragedia sensacional conmocionó a la comunidad internacional y condujo a mejores normas de seguridad aplicables a los buques. 
Una de las razones por las que el naufragio dio lugar a semejante cantidad de muertes fue que no había suficientes botes salvavidas para los pasajeros y la tripulación. Aunque hubo algún elemento de suerte involucrada en sobrevivir al hundimiento, algunos grupos de personas tenían más probabilidades de sobrevivir que otros, como las mujeres, los niños y la clase alta.&lt;/p&gt;
&lt;p&gt;El siguiente &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; proporciona información sobre el destino de los pasajeros en el viaje fatal del trasatlántico &lt;a href="https://es.wikipedia.org/wiki/RMS_Titanic"&gt;Titanic&lt;/a&gt;, que se resume de acuerdo con el nivel económico (clase), el sexo, la edad y la supervivencia.&lt;/p&gt;

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&lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importando modulos necesarios&lt;/span&gt;
&lt;span class="o"&gt;%&lt;/span&gt;&lt;span class="k"&gt;matplotlib&lt;/span&gt; inline

&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;matplotlib.pyplot&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;plt&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;numpy&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;np&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;pandas&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;pd&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;seaborn&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;sns&lt;/span&gt; 
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;pydataset&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;data&lt;/span&gt;

&lt;span class="c1"&gt;# parametros esteticos de seaborn&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_palette&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;deep&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;desat&lt;/span&gt;&lt;span class="o"&gt;=.&lt;/span&gt;&lt;span class="mi"&gt;6&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_context&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;rc&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;{&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;figure.figsize&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;4&lt;/span&gt;&lt;span class="p"&gt;)})&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importando dataset&lt;/span&gt;
&lt;span class="n"&gt;titanic&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;data&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;titanic&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# ver primeros 10 registros&lt;/span&gt;
&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;head&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;class&lt;/th&gt;
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      &lt;th&gt;sex&lt;/th&gt;
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      &lt;th&gt;1&lt;/th&gt;
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      &lt;th&gt;3&lt;/th&gt;
      &lt;td&gt;1st class&lt;/td&gt;
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      &lt;td&gt;1st class&lt;/td&gt;
      &lt;td&gt;adults&lt;/td&gt;
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      &lt;th&gt;5&lt;/th&gt;
      &lt;td&gt;1st class&lt;/td&gt;
      &lt;td&gt;adults&lt;/td&gt;
      &lt;td&gt;man&lt;/td&gt;
      &lt;td&gt;yes&lt;/td&gt;
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      &lt;th&gt;6&lt;/th&gt;
      &lt;td&gt;1st class&lt;/td&gt;
      &lt;td&gt;adults&lt;/td&gt;
      &lt;td&gt;man&lt;/td&gt;
      &lt;td&gt;yes&lt;/td&gt;
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      &lt;th&gt;7&lt;/th&gt;
      &lt;td&gt;1st class&lt;/td&gt;
      &lt;td&gt;adults&lt;/td&gt;
      &lt;td&gt;man&lt;/td&gt;
      &lt;td&gt;yes&lt;/td&gt;
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      &lt;th&gt;8&lt;/th&gt;
      &lt;td&gt;1st class&lt;/td&gt;
      &lt;td&gt;adults&lt;/td&gt;
      &lt;td&gt;man&lt;/td&gt;
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      &lt;th&gt;9&lt;/th&gt;
      &lt;td&gt;1st class&lt;/td&gt;
      &lt;td&gt;adults&lt;/td&gt;
      &lt;td&gt;man&lt;/td&gt;
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      &lt;th&gt;10&lt;/th&gt;
      &lt;td&gt;1st class&lt;/td&gt;
      &lt;td&gt;adults&lt;/td&gt;
      &lt;td&gt;man&lt;/td&gt;
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&lt;p&gt;El problema con &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; como estos, y en general con la mayoría de las &lt;em&gt;tablas de &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;&lt;/em&gt;, es que nos presentan mucha información y no nos permiten ver que es lo que realmente sucede o sucedió. Por tanto, deberíamos procesarla de alguna manera para hacernos una imagen de lo que los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; realmente representan y nos quieren decir; y que mejor manera para hacernos una imagen de algo que utilizar &lt;em&gt;visualizaciones&lt;/em&gt;. Una buena &lt;em&gt;visualización de los datos&lt;/em&gt; puede revelar cosas que es probable que no podamos ver en una tabla de números y nos ayudará a pensar con claridad acerca de los patrones y relaciones que pueden estar escondidos en los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;. También nos va a ayudar a encontrar las características y patrones más importantes o los casos que son realmente excepcionales y no deberíamos de encontrar.&lt;/p&gt;
&lt;h3 id="Tablas-de-frecuencia"&gt;Tablas de frecuencia&lt;a class="anchor-link" href="#Tablas-de-frecuencia"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Para &lt;em&gt;hacernos una imagen&lt;/em&gt; de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;, lo primero que tenemos que hacer es agruparlos. Al armar diferentes grupos nos vamos acercando a la comprensión de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;. La idea es ir amontonamos las cosas que parecen ir juntas, para poder ver como se distribuyen a través de las diferentes categorías. Para los &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;datos categóricos&lt;/a&gt;, agrupar es fácil; simplemente debemos contar el número de
ítems que corresponden a cada categoría y apilarlos.
Una forma en la que podemos agrupar nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; del &lt;a href="https://es.wikipedia.org/wiki/RMS_Titanic"&gt;Titanic&lt;/a&gt; es contando las diferentes &lt;em&gt;clases&lt;/em&gt; de pasajeros. Podemos organizar estos conteos en una &lt;em&gt;tabla de &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;frecuencia&lt;/a&gt;&lt;/em&gt;, que registra los totales y los nombres de las categorías utilizando la función &lt;code&gt;value_counts&lt;/code&gt; que nos proporciona &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt; del siguiente modo:&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# tabla de frecuencia de clases de pasajeros&lt;/span&gt;
&lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;value_counts&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
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&lt;pre class="ipynb"&gt;3rd class    706
1st class    325
2nd class    285
dtype: int64&lt;/pre&gt;
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&lt;p&gt;Contar las cantidad de apariciones de cada categoría puede ser útil, pero a veces puede resultar más útil saber la &lt;em&gt;fracción o proporción&lt;/em&gt; de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; de cada categoría, así que podríamos entonces dividir los recuentos por el total de casos para obtener los porcentajes que representa cada categoría.&lt;/p&gt;
&lt;p&gt;Una &lt;em&gt;tabla de &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;frecuencia&lt;/a&gt; relativa&lt;/em&gt; muestra los porcentajes, en lugar de los recuentos de los valores en cada categoría. Ambos tipos de tablas muestran cómo los
casos se distribuyen a través de las categorías. De esta manera, ellas describen la &lt;em&gt;distribución&lt;/em&gt; de una &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;variable categórica&lt;/a&gt;, ya que enumeran las posibles categorías y nos dicen con qué frecuencia se produce cada una de ellas.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# tabla de frecuencia relativa de pasajeros&lt;/span&gt;
&lt;span class="mi"&gt;100&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;value_counts&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;/&lt;/span&gt; &lt;span class="nb"&gt;len&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;3rd class    53.647416
1st class    24.696049
2nd class    21.656535
dtype: float64&lt;/pre&gt;
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&lt;h3 id="Gr&amp;#225;ficos-de-tartas-y-barras"&gt;Gr&amp;#225;ficos de tartas y barras&lt;a class="anchor-link" href="#Gr&amp;#225;ficos-de-tartas-y-barras"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Ahora que ya conocemos a las &lt;em&gt;tablas de &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;frecuencia&lt;/a&gt;&lt;/em&gt; ya estamos en condiciones de crear visualizaciones que realmente nos den una imagen de los &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;, sus propiedades y sus relaciones. En este punto, debemos ser sumamente cuidadosos, ya que una mala visualización puede llegar a distorsionar nuestra comprensión, en lugar de ayudarnos. 
Las mejores &lt;em&gt;visualizaciones de datos&lt;/em&gt; siguen un principio fundamental llamado el &lt;strong&gt;principio del área&lt;/strong&gt;. Este principio nos dice que el área ocupada por cada parte del gráfico se debe corresponder con la magnitud del valor que representa. Violaciones del &lt;em&gt;principio de área&lt;/em&gt; son una forma común de mentir con &lt;a href="http://relopezbriega.github.io/tag/estadistica.html"&gt;estadísticas&lt;/a&gt;. Dos gráficos útiles que podemos utilizar para representar nuestros &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt; y que cumplen con este principio son el &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_barras"&gt;gráfico de barras&lt;/a&gt; y el &lt;a href="https://es.wikipedia.org/wiki/Gr%C3%A1fico_circular"&gt;gráfico de tarta&lt;/a&gt;.&lt;/p&gt;
&lt;h4 id="Gr&amp;#225;fico-de-barras"&gt;Gr&amp;#225;fico de barras&lt;a class="anchor-link" href="#Gr&amp;#225;fico-de-barras"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;El &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_barras"&gt;gráfico de barras&lt;/a&gt; nos ayuda a darnos una impresión visual más precisa de la distribución de nuestros &lt;a href="https://es.wikipedia.org/wiki/Dato"&gt;datos&lt;/a&gt;. La altura de cada barra muestra el recuento de su categoría. Los barras tienen el  mismo ancho, por lo que sus alturas determinan sus áreas, y estas áreas son proporcionales a los recuentos en cada categoría. De esta forma, podemos ver fácilmente que había más del doble de pasajeros de tercera clase, que de primera o segunda clase. Los &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_barras"&gt;gráficos de barras&lt;/a&gt; hacen que este tipo de comparaciones sean fáciles y naturales. Veamos como podemos crearlos de forma sencilla utilizando el método &lt;code&gt;plot&lt;/code&gt; dentro de un &lt;code&gt;DataFrame&lt;/code&gt; de &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt;.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[6]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Gráfico de barras de pasajeros del Titanic&lt;/span&gt;
&lt;span class="n"&gt;plot&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;value_counts&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;bar&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                            &lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Pasajeros del Titanic&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;p&gt;Si quisiéramos enfocarnos en la &lt;em&gt;proporción relativa&lt;/em&gt; de los pasajeros de cada una de las clases, simplemente podemos sustituir a los recuentos con porcentajes y utilizar un &lt;em&gt;gráfico de barras de frecuencias relativas&lt;/em&gt;.&lt;/p&gt;

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&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[7]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# gráfico de barras de frecuencias relativas.&lt;/span&gt;
&lt;span class="n"&gt;plot&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;100&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;value_counts&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;/&lt;/span&gt; &lt;span class="nb"&gt;len&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]))&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;
&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;bar&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Pasajeros del Titanic %&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;h4 id="Gr&amp;#225;fico-de-tartas"&gt;Gr&amp;#225;fico de tartas&lt;a class="anchor-link" href="#Gr&amp;#225;fico-de-tartas"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;&lt;p&gt;El &lt;a href="https://es.wikipedia.org/wiki/Gr%C3%A1fico_circular"&gt;gráfico de tarta&lt;/a&gt; muestra el total de casos como un círculo y luego corta este círculo en piezas cuyos tamaños son proporcionales a la fracción que cada categoría representa sobre el total de casos. Los &lt;a href="https://es.wikipedia.org/wiki/Gr%C3%A1fico_circular"&gt;gráfico de tarta&lt;/a&gt; dan una impresión rápida de cómo todo un grupo se divide en grupos más pequeños. Lo podríamos graficar del siguiente modo, también utilizando el método &lt;code&gt;plot&lt;/code&gt;:&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[8]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Gráfico de tarta de pasajeros del Titanic&lt;/span&gt;
&lt;span class="n"&gt;plot&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;value_counts&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;pie&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;autopct&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&lt;/span&gt;&lt;span class="si"&gt;%.2f&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                                            &lt;span class="n"&gt;figsize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;6&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;6&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;
                                            &lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Pasajeros del Titanic&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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SUVORK5CYII=
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&lt;p&gt;Como se puede apreciar, con el &lt;a href="https://es.wikipedia.org/wiki/Gr%C3%A1fico_circular"&gt;gráfico de tarta&lt;/a&gt; no es tan fácil determinar que los pasajeros de tercera clase son más que el doble que los de primera clase; tampoco es fácil determinar si hay más pasajeros de primera o de segunda clase. Para este tipo de comparaciones, son mucho más útiles los &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_barras"&gt;gráficos de barras&lt;/a&gt;.&lt;/p&gt;

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&lt;h3 id="Relacionando-variables-categ&amp;#243;ricas"&gt;Relacionando variables categ&amp;#243;ricas&lt;a class="anchor-link" href="#Relacionando-variables-categ&amp;#243;ricas"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Al analizar la tragedia del &lt;a href="https://es.wikipedia.org/wiki/RMS_Titanic"&gt;Titanic&lt;/a&gt;, una de las preguntas que podríamos hacer es ¿existe alguna relación entre la clase de pasajeros y la posibilidad de alcanzar un bote salvavidas y sobrevivir a la tragedia? Para poder responder a esta pregunta, vamos a necesitar analizar a las variables &lt;em&gt;class&lt;/em&gt; y &lt;em&gt;survived&lt;/em&gt; de nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; en forma conjunta. Una buena forma de analizar dos &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;variables categóricas&lt;/a&gt; en forma conjunta, es agrupar los recuentos en una tabla de &lt;em&gt;doble entrada&lt;/em&gt;; este tipo de tablas se conocen en &lt;a href="http://relopezbriega.github.io/tag/estadistica.html"&gt;estadística&lt;/a&gt; con el nombre de &lt;a href="https://es.wikipedia.org/wiki/Tabla_de_contingencia"&gt;tabla de contingencia&lt;/a&gt;. Veamos como podemos crear esta tabla utilizando la función &lt;code&gt;crosstab&lt;/code&gt; de &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt;.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[9]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Tabla de contingencia class / survived&lt;/span&gt;
&lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;crosstab&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;index&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;survived&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
            &lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;margins&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="kc"&gt;True&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;div class="prompt output_prompt"&gt;Out[9]:&lt;/div&gt;


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&lt;div style="max-height:1000px;max-width:1500px;overflow:auto;"&gt;
&lt;table border="1" class="dataframe"&gt;
  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;class&lt;/th&gt;
      &lt;th&gt;1st class&lt;/th&gt;
      &lt;th&gt;2nd class&lt;/th&gt;
      &lt;th&gt;3rd class&lt;/th&gt;
      &lt;th&gt;All&lt;/th&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;survived&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;no&lt;/th&gt;
      &lt;td&gt;122&lt;/td&gt;
      &lt;td&gt;167&lt;/td&gt;
      &lt;td&gt;528&lt;/td&gt;
      &lt;td&gt;817&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;yes&lt;/th&gt;
      &lt;td&gt;203&lt;/td&gt;
      &lt;td&gt;118&lt;/td&gt;
      &lt;td&gt;178&lt;/td&gt;
      &lt;td&gt;499&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;All&lt;/th&gt;
      &lt;td&gt;325&lt;/td&gt;
      &lt;td&gt;285&lt;/td&gt;
      &lt;td&gt;706&lt;/td&gt;
      &lt;td&gt;1316&lt;/td&gt;
    &lt;/tr&gt;
  &lt;/tbody&gt;
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&lt;p&gt;Los márgenes de la tabla, tanto en la derecha y en la parte inferior, nos muestran los totales. La línea inferior de la tabla representa la distribución de &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;frecuencia&lt;/a&gt; de la clase de pasajeros. La columna derecha de la tabla es la distribución de &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;frecuencia&lt;/a&gt; de la variable supervivencia. 
Cuando se presenta la información de este modo, cada celda de cada uno de los márgenes de la tabla representa la &lt;em&gt;distribución marginal&lt;/em&gt; de esa variable en particular. Cada celda nos va a mostrar el recuento para la combinación de los valores de nuestras dos &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;variables categóricas&lt;/a&gt;, en este caso &lt;em&gt;class&lt;/em&gt; y &lt;em&gt;survived&lt;/em&gt;.&lt;/p&gt;
&lt;p&gt;Al igual de como habíamos visto con las &lt;em&gt;tablas de &lt;a href="https://es.wikipedia.org/wiki/Frecuencia_estad%C3%ADstica"&gt;frecuencia&lt;/a&gt;&lt;/em&gt;, también nos podría ser útil representar a las &lt;a href="https://es.wikipedia.org/wiki/Tabla_de_contingencia"&gt;tablas de contingencia&lt;/a&gt; con porcentajes relativos; esto lo podríamos realizar utilizando el método &lt;code&gt;apply&lt;/code&gt; del siguiente modo:&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[10]:&lt;/div&gt;

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    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# tabla de contingencia en porcentajes relativos total&lt;/span&gt;
&lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;crosstab&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;index&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;survived&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
            &lt;span class="n"&gt;margins&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="kc"&gt;True&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;apply&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="k"&gt;lambda&lt;/span&gt; &lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="nb"&gt;len&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt;&lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;div style="max-height:1000px;max-width:1500px;overflow:auto;"&gt;
&lt;table border="1" class="dataframe"&gt;
  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;class&lt;/th&gt;
      &lt;th&gt;1st class&lt;/th&gt;
      &lt;th&gt;2nd class&lt;/th&gt;
      &lt;th&gt;3rd class&lt;/th&gt;
      &lt;th&gt;All&lt;/th&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;survived&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;no&lt;/th&gt;
      &lt;td&gt;9.270517&lt;/td&gt;
      &lt;td&gt;12.689970&lt;/td&gt;
      &lt;td&gt;40.121581&lt;/td&gt;
      &lt;td&gt;62.082067&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;yes&lt;/th&gt;
      &lt;td&gt;15.425532&lt;/td&gt;
      &lt;td&gt;8.966565&lt;/td&gt;
      &lt;td&gt;13.525836&lt;/td&gt;
      &lt;td&gt;37.917933&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;All&lt;/th&gt;
      &lt;td&gt;24.696049&lt;/td&gt;
      &lt;td&gt;21.656535&lt;/td&gt;
      &lt;td&gt;53.647416&lt;/td&gt;
      &lt;td&gt;100.000000&lt;/td&gt;
    &lt;/tr&gt;
  &lt;/tbody&gt;
&lt;/table&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

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&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Con esta tabla podemos ver fácilmente que solo el 37.91% de los pasajeros sobrevivió a la tragedia y que este 37% se compone de la siguiente forma: del total de pasajeros sobrevivió un 15.42% de pasajeros que eran de primera clase, un 8.97% que eran de segunda clase y un 13.52% que eran pasajeros de tercera clase.&lt;/p&gt;
&lt;p&gt;Volviendo a nuestra pregunta inicial sobre la posibilidad de sobrevivir según la clase de pasajero, podría ser más útil armar la tabla de porcentajes como un porcentaje relativo sobre el total de cada fila, es decir calcular el porcentaje relativo que cada clase tiene sobre haber sobrevivido o no. Esto lo podemos realizar del siguiente modo:&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[11]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# tabla de contingencia en porcentajes relativos segun sobreviviente&lt;/span&gt;
&lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;crosstab&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;index&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;survived&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
           &lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;apply&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="k"&gt;lambda&lt;/span&gt; &lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sum&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt;&lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

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&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[11]:&lt;/div&gt;


&lt;div class="output_html rendered_html output_subarea output_execute_result"&gt;
&lt;div style="max-height:1000px;max-width:1500px;overflow:auto;"&gt;
&lt;table border="1" class="dataframe"&gt;
  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;class&lt;/th&gt;
      &lt;th&gt;1st class&lt;/th&gt;
      &lt;th&gt;2nd class&lt;/th&gt;
      &lt;th&gt;3rd class&lt;/th&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;survived&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;no&lt;/th&gt;
      &lt;td&gt;14.932681&lt;/td&gt;
      &lt;td&gt;20.440636&lt;/td&gt;
      &lt;td&gt;64.626683&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;yes&lt;/th&gt;
      &lt;td&gt;40.681363&lt;/td&gt;
      &lt;td&gt;23.647295&lt;/td&gt;
      &lt;td&gt;35.671343&lt;/td&gt;
    &lt;/tr&gt;
  &lt;/tbody&gt;
&lt;/table&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
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&lt;div class="prompt input_prompt"&gt;
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&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Aquí podemos ver que de los pasajeros que sobrevivieron a la tragedia, el 40.68% correspondían a primera clase, el 35.67% a tercera clase y el 23.65% a segunda clase. Por tanto podríamos inferir que los pasajeros de primera clase tenían más posibilidades de sobrevivir.&lt;/p&gt;
&lt;p&gt;Es más, también podríamos armar la tabla de porcentaje relativos en relación al total de cada clase de pasajero y así podríamos ver que de los pasajeros de primera clase, logró sobrevivir un 62.46%.&lt;/p&gt;

&lt;/div&gt;
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&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[12]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# tabla de contingencia en porcentajes relativos segun clase&lt;/span&gt;
&lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;crosstab&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;index&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;survived&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
           &lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;apply&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="k"&gt;lambda&lt;/span&gt; &lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sum&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt;&lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[12]:&lt;/div&gt;


&lt;div class="output_html rendered_html output_subarea output_execute_result"&gt;
&lt;div style="max-height:1000px;max-width:1500px;overflow:auto;"&gt;
&lt;table border="1" class="dataframe"&gt;
  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;class&lt;/th&gt;
      &lt;th&gt;1st class&lt;/th&gt;
      &lt;th&gt;2nd class&lt;/th&gt;
      &lt;th&gt;3rd class&lt;/th&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;survived&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;no&lt;/th&gt;
      &lt;td&gt;37.538462&lt;/td&gt;
      &lt;td&gt;58.596491&lt;/td&gt;
      &lt;td&gt;74.787535&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;yes&lt;/th&gt;
      &lt;td&gt;62.461538&lt;/td&gt;
      &lt;td&gt;41.403509&lt;/td&gt;
      &lt;td&gt;25.212465&lt;/td&gt;
    &lt;/tr&gt;
  &lt;/tbody&gt;
&lt;/table&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
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&lt;p&gt;Este último resultado lo podríamos representar visualmente con simples &lt;a href="https://es.wikipedia.org/wiki/Diagrama_de_barras"&gt;gráfico de barras&lt;/a&gt; del siguiente modo:&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[13]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Gráfico de barras de sobreviviviente segun clase&lt;/span&gt;
&lt;span class="n"&gt;plot&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;crosstab&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;index&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
            &lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;survived&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;apply&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="k"&gt;lambda&lt;/span&gt; &lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sum&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt;&lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                              &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;bar&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[14]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Gráfico de barras de sobreviviviente segun clase&lt;/span&gt;
&lt;span class="n"&gt;plot&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;crosstab&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;index&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;survived&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
            &lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;titanic&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;class&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
                  &lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;apply&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="k"&gt;lambda&lt;/span&gt; &lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="n"&gt;r&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sum&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt;&lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                          &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;bar&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;stacked&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="kc"&gt;True&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt"&gt;&lt;/div&gt;



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&lt;p&gt;Estas mismas manipulaciones las podemos realizar para otro tipo de combinación de &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable"&gt;variables categóricas&lt;/a&gt;, como podría ser el &lt;em&gt;sexo&lt;/em&gt; o la &lt;em&gt;edad&lt;/em&gt; de los pasajeros, pero eso ya se los dejo a ustedes para que se entretengan y practiquen un rato.&lt;/p&gt;
&lt;p&gt;Con este termina esta artículo, si les gustó y están interesados en la &lt;a href="http://relopezbriega.github.io/tag/estadistica.html"&gt;estadísticas&lt;/a&gt;, no duden en visitar mi anterior artículo &lt;a href="http://relopezbriega.github.io/blog/2015/06/27/probabilidad-y-estadistica-con-python/"&gt;Probabilidad y Estadística con Python&lt;/a&gt; y seguir la novedades del blog!&lt;/p&gt;
&lt;p&gt;Saludos!&lt;/p&gt;
&lt;p&gt;&lt;em&gt;Este post fue escrito utilizando Jupyter notebook. Pueden descargar este &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/CategoricalPython.ipynb"&gt;notebook&lt;/a&gt; o ver su version estática en &lt;a href="http://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/CategoricalPython.ipynb"&gt;nbviewer&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

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&lt;/p&gt;</summary><category term="python"></category><category term="estadistica"></category><category term="programacion"></category><category term="matematica"></category><category term="analisis de datos"></category></entry><entry><title>Ipython y Spark para el analisis de datos</title><link href="http://relopezbriega.github.io/blog/2014/08/22/ipython-y-spark-para-el-analisis-de-datos/" rel="alternate"></link><published>2014-08-22T00:00:00-03:00</published><author><name>Raul E. Lopez Briega</name></author><id>tag:relopezbriega.github.io,2014-08-22:blog/2014/08/22/ipython-y-spark-para-el-analisis-de-datos/</id><summary type="html">&lt;p&gt;
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&lt;p&gt;Una de las nuevas estrellas en el análisis de datos masivos es &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt;. Desarrollado en &lt;a href="http://www.scala-lang.org/"&gt;Scala&lt;/a&gt;, &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt; es una plataforma de computación de código abierto para el análisis y procesamiento de grandes volúmenes de datos.&lt;/p&gt;
&lt;p&gt;Algunas de las ventajas que nos ofrece &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt; sobre otros &lt;a href="http://es.wikipedia.org/wiki/Framework"&gt;frameworks&lt;/a&gt;, son:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Velocidad:&lt;/strong&gt; Sin dudas la velocidad es una de las principales fortalezas de &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt;, como esta diseñado para soportar el &lt;a href="http://en.wikipedia.org/wiki/In-Memory_Processing"&gt;procesameinto en memoria&lt;/a&gt;, puede alcanzar una performance sorprendente en análisis avanzados de datos. Algunos programas escritos utilizando &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt;, pueden correr hasta 100x más rápido que utilizando &lt;a href="http://hadoop.apache.org/"&gt;Hadoop&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Fácil de usar:&lt;/strong&gt; Podemos escribir programas en &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;, &lt;a href="http://www.scala-lang.org/"&gt;Scala&lt;/a&gt; o &lt;a href="http://java.com/en/"&gt;Java&lt;/a&gt; que hagan uso de las herramientas que ofrece &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt;; asimismo nos permite trabajar en forma interactiva (con &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; o con &lt;a href="http://www.scala-lang.org/"&gt;Scala&lt;/a&gt;) y su &lt;a href="http://es.wikipedia.org/wiki/Interfaz_de_programaci%C3%B3n_de_aplicaciones"&gt;API&lt;/a&gt; es muy fácil de aprender. &lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Generalismo:&lt;/strong&gt; El mundo del análisis de datos incluye muchos subgrupos de distinta índole, están los que hacen un análisis investigativo, los que que realizan análisis exploratorios, los que construyen sistemas de procesamientos de datos, etc. Los usuarios de cada uno de esos subgrupos, al tener objetivos distintos, suelen utilizar una gran variedad de herramientas totalmente diferentes. &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt; nos proporciona un gran número de herramientas de alto nivel como &lt;a href="http://spark.apache.org/sql/"&gt;Spark SQL&lt;/a&gt;, &lt;a href="http://spark.apache.org/mllib/"&gt;MLlib&lt;/a&gt; para &lt;a href="http://es.wikipedia.org/wiki/Machine_learning"&gt;machine learning&lt;/a&gt;, &lt;a href="http://spark.apache.org/graphx/"&gt;GraphX&lt;/a&gt;, y &lt;a href="http://spark.apache.org/streaming/"&gt;Spark Streaming&lt;/a&gt;; las cuales pueden ser combinadas para crear aplicaciones multipropósito que ataquen los diferentes dominios del análisis de datos. &lt;/li&gt;
&lt;/ul&gt;

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&lt;h2 id="RDD-o--Resilient-Distributed-Datasets"&gt;RDD o  Resilient Distributed Datasets&lt;a class="anchor-link" href="#RDD-o--Resilient-Distributed-Datasets"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;En el corazón de &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt; se encuentran los &lt;a href="http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf"&gt;RDDs&lt;/a&gt;.
Los &lt;a href="http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf"&gt;RDDs&lt;/a&gt; son una abstracción distribuida que le permite a los programadores realizar cómputos &lt;a href="http://en.wikipedia.org/wiki/In-Memory_Processing"&gt;en memoria&lt;/a&gt;  sobre grandes &lt;a href='http://es.wikipedia.org/wiki/Cl%C3%BAster_(inform%C3%A1tica)' target='_blank'&gt;clusters&lt;/a&gt; de computadoras sin errores o pérdidas de información. Están especialmente diseñados para el análisis de datos interactivo (&lt;a href="http://es.wikipedia.org/wiki/Miner%C3%ADa_de_datos"&gt;data mining&lt;/a&gt;) y para la aplicación de algoritmos iterativos (&lt;a href="http://es.wikipedia.org/wiki/MapReduce"&gt;MapReduce&lt;/a&gt;). En ambos casos, mantener los datos en la memoria puede mejorar el rendimiento en una gran proporción. Para lograr la tolerancia a fallos de manera eficiente, &lt;a href="http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf"&gt;RDDs&lt;/a&gt; utiliza una forma restringida de memoria compartida. 
Los &lt;a href="http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf"&gt;RDDs&lt;/a&gt; son los suficientemente expresivos como para capturar una gran variedad de cálculos.&lt;/p&gt;

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&lt;h2 id="Instalando-Apache-Spark"&gt;Instalando Apache Spark&lt;a class="anchor-link" href="#Instalando-Apache-Spark"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;Para instalar &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt; en forma local y poder comenzar a utilizarlo, pueden seguir los siguientes pasos:&lt;/p&gt;

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&lt;li&gt;&lt;p&gt;En primer lugar, necesitamos tener instalado &lt;a href="http://www.oracle.com/technetwork/java/javase/downloads/jdk7-downloads-1880260.html"&gt;Oracle JDK&lt;/a&gt;. Para instalarlo en &lt;a href="http://www.ubuntu.com/"&gt;Ubuntu&lt;/a&gt; podemos utilizar los siguientes comandos:&lt;/p&gt;
&lt;pre&gt;
$ sudo add-apt-repository ppa:webupd8team/java
$ sudo apt-get update
$ sudo apt-get install oracle-jdk7-installer
&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Luego nos instalamos las herramientas para trabajar con &lt;a href="http://www.scala-lang.org/"&gt;Scala&lt;/a&gt;, &lt;a href="http://www.scala-sbt.org/"&gt;SBT&lt;/a&gt; con el siguiente comando:&lt;/p&gt;
&lt;pre&gt;
$ sudo apt-get install sbt
&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Después nos descargamos la última versión de &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt; desde &lt;a href="http://d3kbcqa49mib13.cloudfront.net/spark-1.0.2.tgz"&gt;aquí&lt;/a&gt;&lt;/p&gt;
&lt;pre&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Ahora descomprimimos el archivo:&lt;/p&gt;
&lt;pre&gt;
$ tar -xvf spark-1.0.2.tgz 
&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Nos movemos a la carpeta recién descomprimida y realizamos la compilación de &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt; utilizando &lt;a href="http://www.scala-sbt.org/"&gt;SBT&lt;/a&gt;. (tener paciencia, es sabido que &lt;a href="http://www.scala-lang.org/"&gt;Scala&lt;/a&gt; tarda mucho en compilar)&lt;/p&gt;
&lt;pre&gt;
$ cd spark-1.0.2
$ sbt/sbt assembly
&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Opcionalmente, para facilitar la utilización de &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt; desde la línea de comando, yo modifique mi archivo .bashrc para incluir los siguientes alias:&lt;/p&gt;
&lt;pre&gt;
$ echo "alias ipyspark='IPYTHON_OPTS="notebook --pylab inline" ~/spark-1.0.2/bin/pyspark'" &gt;&gt; ~/.bashrc
$ echo "alias pyspark='~/spark-1.0.2/bin/pyspark'" &gt;&gt; ~/.bashrc
$ echo "alias spark='~/spark-1.0.2/bin/spark-shell'" &gt;&gt; ~/.bashrc
&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Ahora simplemente tipeando &lt;code&gt;pyspark&lt;/code&gt; nos abre el interprete interactivo de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; con &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt;, tipeando &lt;code&gt;spark&lt;/code&gt; nos abre el interprete interactivo de &lt;a href="http://www.scala-lang.org/"&gt;Scala&lt;/a&gt;, y tipeando &lt;code&gt;ipyspark&lt;/code&gt; nos abre el &lt;a href="http://ipython.org/notebook.html"&gt;notebook de Ipython&lt;/a&gt; integrado con &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt;!&lt;/p&gt;
&lt;pre&gt;$ ./bin/pyspark #o pyspark si creamos el alias&lt;/pre&gt;&lt;/li&gt;
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&lt;h2 id="Ejemplo-de-utilizaci&amp;#243;n-de-Spark-con-Ipython"&gt;Ejemplo de utilizaci&amp;#243;n de Spark con Ipython&lt;a class="anchor-link" href="#Ejemplo-de-utilizaci&amp;#243;n-de-Spark-con-Ipython"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;Ahora llegó el momento de ensuciarse las manos y probar &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt;, vamos a hacer el típico ejercicio de wordcounts; en este caso vamos a contar todas las palabras que posee la Biblia en su versión en inglés y vamos a ver cuantas veces aparece la palabra Dios(God). Para esto nos descargamos la versión de la Biblia en texto plano del &lt;a href="http://www.gutenberg.org/ebooks/10"&gt;proyecto Gutemberg&lt;/a&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Comenzamos con algunos imports.&lt;/span&gt;
&lt;span class="c1"&gt;# No necesitamos importar pyspark porque ya se autoimporta como sc.&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;operator&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;add&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;pandas&lt;/span&gt; &lt;span class="k"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;pd&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;lineas&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;sc&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;textFile&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;/home/raul/spark-1.0.2/examples/data/Bible.txt&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# usamos la función textFile para subir el texto a Spark&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Mapeamos las funciones para realizar la cuenta de las palabras y generamos el RDD.&lt;/span&gt;
&lt;span class="n"&gt;cuenta&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;lineas&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;flatMap&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="k"&gt;lambda&lt;/span&gt; &lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;split&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39; &amp;#39;&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt; \
                  &lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;map&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="k"&gt;lambda&lt;/span&gt; &lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;replace&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;,&amp;#39;&lt;/span&gt; &lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;upper&lt;/span&gt;&lt;span class="p"&gt;(),&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt; \
                  &lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;reduceByKey&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;add&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Creamos la lista con las palabras y su respectiva frecuencia.&lt;/span&gt;
&lt;span class="n"&gt;lista&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;cuenta&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;collect&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Creamos un DataFrame de Pandas para facilitar el manejo de los datos.&lt;/span&gt;
&lt;span class="n"&gt;dataframe&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DataFrame&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;lista&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;palabras&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;cuenta&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Nos quedamos solo con las palabras que hacen referencia a Dios&lt;/span&gt;
&lt;span class="n"&gt;god&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;dataframe&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;dataframe&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;palabras&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;apply&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="k"&gt;lambda&lt;/span&gt; &lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;upper&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;GOD&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;LORD&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;JESUS&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;FATHER&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])]&lt;/span&gt;
&lt;span class="n"&gt;god&lt;/span&gt;
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      &lt;th&gt;2867 &lt;/th&gt;
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      &lt;td&gt;  814&lt;/td&gt;
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      &lt;th&gt;7329 &lt;/th&gt;
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      &lt;th&gt;8902 &lt;/th&gt;
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    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;21404&lt;/th&gt;
      &lt;td&gt;  JESUS&lt;/td&gt;
      &lt;td&gt;  893&lt;/td&gt;
    &lt;/tr&gt;
  &lt;/tbody&gt;
&lt;/table&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[7]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Realizamos un gráfico de barras sobre los datos&lt;/span&gt;
&lt;span class="n"&gt;god&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_index&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;palabras&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;bar&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[7]:&lt;/div&gt;



&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;&amp;lt;matplotlib.axes.AxesSubplot at 0x7f071e10ea10&amp;gt;&lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_area"&gt;
&lt;div class="prompt"&gt;&lt;/div&gt;



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6xOYmjnAAUypZP4Ajxk7xDpVwAI7PQ84BPTw3tODjAP22+nbKIximwy8CTxqbx8s6d2IJKByjYTs
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OAb02uUiIpJAM4UdoyIVS+UaCVkx49pFRERERERERERERERERERERESkIv1/oW2weLM2q/EAAAAA
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Realizamos la sumatoria de las 4 palabras combinadas&lt;/span&gt;
&lt;span class="n"&gt;god&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sum&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;pre class="ipynb"&gt;palabras    FATHERGODLORDJESUS
cuenta                   11485
dtype: object&lt;/pre&gt;
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&lt;p&gt;Como demuestra el ejemplo, Dios sería nombrado en la Biblia, ya sea como lord, god, jesus o father; unas 11485 veces!&lt;/p&gt;

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&lt;h2 id="Conclusi&amp;#243;n"&gt;Conclusi&amp;#243;n&lt;a class="anchor-link" href="#Conclusi&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;&lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt; es realmente una herramienta muy prometedora, con ella podemos analizar datos con un rendimiento muy alto y combinado con otras herramientas como &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;, &lt;a href="http://www.numpy.org/"&gt;Numpy&lt;/a&gt;, &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt; e &lt;a href="http://ipython.org/"&gt;IPython&lt;/a&gt;; se convierten en un &lt;a href="http://es.wikipedia.org/wiki/Framework"&gt;framework&lt;/a&gt; sumamente completo y efectivo para el análisis de grandes volúmenes de datos en forma muy sencilla.&lt;/p&gt;
&lt;p&gt;Para más información sobre &lt;a href="http://spark.apache.org/"&gt;Apache Spark&lt;/a&gt;, pueden visitar su la sección de &lt;a href="http://spark.apache.org/examples.html"&gt;ejemplos&lt;/a&gt; de su página oficial.&lt;/p&gt;
&lt;p&gt;Espero les haya sido de utilidad este &lt;a href="(http://ipython.org/notebook.html"&gt;notebook&lt;/a&gt;).&lt;/p&gt;
&lt;p&gt;Saludos!!&lt;/p&gt;

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&lt;p&gt;&lt;em&gt;Este post fue escrito utilizando IPython notebook. Pueden descargar este &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/pyspark.ipynb"&gt;notebook&lt;/a&gt; o ver su version estática en &lt;a href="http://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/pyspark.ipynb"&gt;nbviewer&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

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&lt;/p&gt;</summary><category term="python"></category><category term="analisis de datos"></category><category term="map-reduce"></category></entry><entry><title>Python - Librerías esenciales para el análisis de datos</title><link href="http://relopezbriega.github.io/blog/2014/05/28/python-librerias-esenciales-para-el-analisis-de-datos/" rel="alternate"></link><published>2014-05-28T00:00:00-03:00</published><author><name>Raul E. Lopez Briega</name></author><id>tag:relopezbriega.github.io,2014-05-28:blog/2014/05/28/python-librerias-esenciales-para-el-analisis-de-datos/</id><summary type="html">&lt;p&gt;
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&lt;p&gt;En mi &lt;a href="http://relopezbriega.github.io/blog/2014/05/25/mi-python-blog-introduccion-a-python/"&gt;artículo anterior&lt;/a&gt; hice una breve introducción al mundo de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;, hoy voy a detallar algunas de las librerías que son esenciales para trabajar con &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; en la comunidad científica o en el análisis de datos.&lt;/p&gt;
&lt;p&gt;Una de las grandes ventajas que ofrece &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; sobre otros lenguajes de programación, además de que es que es mucho más fácil de aprender; es lo grande y prolifera que es la comunidad de desarrolladores que lo rodean; comunidad que ha contribuido con una gran variedad de librerías de primer nivel que extienden la funcionalidades del lenguaje. Vamos a poder encontrar una librería en &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; para prácticamente cualquier cosa que se nos ocurra.&lt;/p&gt;
&lt;p&gt;Algunas de las librerías que se han vuelto esenciales y ya forman casi parte del lenguaje en sí mismo son las siguientes:&lt;/p&gt;

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&lt;h2 id="Numpy"&gt;Numpy&lt;a class="anchor-link" href="#Numpy"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;&lt;a href="http://www.numpy.org/"&gt;Numpy&lt;/a&gt;, abreviatura de Numerical &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; , es el paquete fundamental para la computación científica en &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;. Dispone, entre otras cosas de:&lt;/p&gt;
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&lt;li&gt;Un objeto &lt;a href='http://es.wikipedia.org/wiki/Matriz_(matem%C3%A1ticas)' target='_blank'&gt;matriz&lt;/a&gt; multidimensional &lt;em&gt;ndarray&lt;/em&gt;,rápido y eficiente.&lt;/li&gt;
&lt;li&gt;Funciones para realizar cálculos elemento a elemento u otras operaciones matemáticas con &lt;a href='http://es.wikipedia.org/wiki/Matriz_(matem%C3%A1ticas)' target='_blank'&gt;matrices&lt;/a&gt;. &lt;/li&gt;
&lt;li&gt;Herramientas para la lectura y escritura de los conjuntos de datos basados &lt;a href='http://es.wikipedia.org/wiki/Matriz_(matem%C3%A1ticas)' target='_blank'&gt;matrices&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;Operaciones de &lt;a href="http://es.wikipedia.org/wiki/%C3%81lgebra_lineal"&gt;álgebra lineal&lt;/a&gt;, &lt;a href="http://es.wikipedia.org/wiki/Transformada_de_Fourier"&gt;transformaciones de Fourier&lt;/a&gt;, y generación de números aleatorios.&lt;/li&gt;
&lt;li&gt;Herramientas de integración para conectar &lt;a href="http://es.wikipedia.org/wiki/C_(lenguaje_de_programaci%C3%B3n"&gt;C&lt;/a&gt;, &lt;a href="http://es.wikipedia.org/wiki/C%2B%2B"&gt;C++&lt;/a&gt; y &lt;a href="http://es.wikipedia.org/wiki/Fortran"&gt;Fortran&lt;/a&gt; con &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Más allá de las capacidades de procesamiento rápido de &lt;a href='http://es.wikipedia.org/wiki/Matriz_(matem%C3%A1ticas)' target='_blank'&gt;matrices&lt;/a&gt; que &lt;a href="http://www.numpy.org/"&gt;Numpy&lt;/a&gt; añade a &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;, uno de sus
propósitos principales con respecto al análisis de datos es la utilización de sus &lt;a href="http://es.wikipedia.org/wiki/Estructura_de_datos"&gt;estructuras de datos&lt;/a&gt; como contenedores para transmitir los datos entre diferentes algoritmos. Para datos numéricos , las &lt;a href='http://es.wikipedia.org/wiki/Matriz_(matem%C3%A1ticas)' target='_blank'&gt;matrices&lt;/a&gt; de &lt;a href="http://www.numpy.org/"&gt;Numpy&lt;/a&gt; son una forma mucho más eficiente de almacenar y manipular datos que cualquier otra de las &lt;a href="http://es.wikipedia.org/wiki/Estructura_de_datos"&gt;estructuras de datos&lt;/a&gt; estándar incorporadas en &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;. Asimismo, librerías escritas en un lenguaje de bajo nivel, como &lt;a href="http://es.wikipedia.org/wiki/C_(lenguaje_de_programaci%C3%B3n"&gt;C&lt;/a&gt; o &lt;a href="http://es.wikipedia.org/wiki/Fortran"&gt;Fortran&lt;/a&gt;, pueden operar en los datos almacenados en &lt;a href='http://es.wikipedia.org/wiki/Matriz_(matem%C3%A1ticas)' target='_blank'&gt;matrices&lt;/a&gt; de &lt;a href="http://www.numpy.org/"&gt;Numpy&lt;/a&gt; sin necesidad de copiar o modificar ningún dato.&lt;/p&gt;
&lt;p&gt;Como nomenclatura general, cuando importamos la librería &lt;a href="http://www.numpy.org/"&gt;Numpy&lt;/a&gt; en nuestro programa &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; se suele utilizar la siguiente:&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;numpy&lt;/span&gt; &lt;span class="k"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;np&lt;/span&gt;
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&lt;h3 id="Creando-matrices-en-Numpy"&gt;Creando matrices en Numpy&lt;a class="anchor-link" href="#Creando-matrices-en-Numpy"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;
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&lt;p&gt;Existen varias maneras de crear matrices en Numpy, por ejemplo desde:&lt;/p&gt;
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&lt;li&gt;Una &lt;em&gt;lista&lt;/em&gt; o &lt;em&gt;tuple&lt;/em&gt; de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Funciones específicas para crear matrices como &lt;code&gt;arange&lt;/code&gt;, &lt;code&gt;linspace&lt;/code&gt;, etc.&lt;/li&gt;
&lt;li&gt;Archivos planos con datos, como por ejemplo archivos .csv&lt;/li&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#Creando un vector desde una lista de Python&lt;/span&gt;
&lt;span class="n"&gt;vector&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;array&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;4&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#Para crear una matriz, simplemente le pasamos una lista anidada al objeto array de Numpy&lt;/span&gt;
&lt;span class="n"&gt;matriz&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;array&lt;/span&gt;&lt;span class="p"&gt;([[&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
                   &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;4&lt;/span&gt;&lt;span class="p"&gt;]])&lt;/span&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#El tipo de objeto de tanto de los vectores como de las matrices es ndarray&lt;/span&gt;
&lt;span class="nb"&gt;type&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;vector&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt; &lt;span class="nb"&gt;type&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;matriz&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#Los objetos ndarray de Numpy cuentan con las propiedades shape y size que nos muestran sus dimensiones.&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt; &lt;span class="n"&gt;vector&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;shape&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;vector&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;size&lt;/span&gt;

&lt;span class="nb"&gt;print&lt;/span&gt; &lt;span class="n"&gt;matriz&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;shape&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;matriz&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;size&lt;/span&gt;
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&lt;pre class="ipynb"&gt;(4,) 4
(2, 2) 4
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&lt;h4 id="Utilizando-funciones-para-crear-matrices"&gt;Utilizando funciones para crear matrices&lt;a class="anchor-link" href="#Utilizando-funciones-para-crear-matrices"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#arange&lt;/span&gt;
&lt;span class="c1"&gt;#La funcion arange nos facilita la creación de matrices&lt;/span&gt;
&lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;arange&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;11&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# argumentos: start, stop, step&lt;/span&gt;

&lt;span class="n"&gt;x&lt;/span&gt;
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&lt;pre class="ipynb"&gt;array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#linspace&lt;/span&gt;
&lt;span class="c1"&gt;#linspace nos devuelve un vector con la cantidad de muestras que le ingresemos y separados uniformamente entre sí.&lt;/span&gt;
&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;linspace&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;25&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;25&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;  &lt;span class="c1"&gt;# argumentos: start, stop, samples&lt;/span&gt;
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&lt;pre class="ipynb"&gt;array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,
        12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,
        23.,  24.,  25.])&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#mgrid&lt;/span&gt;
&lt;span class="c1"&gt;#Con mgrid podemos crear arrays multimensionales.&lt;/span&gt;
&lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mgrid&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; 

&lt;span class="n"&gt;x&lt;/span&gt;
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&lt;pre class="ipynb"&gt;array([[0, 0, 0, 0, 0],
       [1, 1, 1, 1, 1],
       [2, 2, 2, 2, 2],
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&lt;pre class="ipynb"&gt;array([[0, 1, 2, 3, 4],
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#zeros y ones&lt;/span&gt;
&lt;span class="c1"&gt;#Estas funciones nos permiten crear matrices de ceros o de unos.&lt;/span&gt;
&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;zeros&lt;/span&gt;&lt;span class="p"&gt;((&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;array([[ 1.,  1.,  1.],
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#random.randn&lt;/span&gt;
&lt;span class="c1"&gt;#Esta funcion nos permite generar una matriz con una distribución estándar de números.&lt;/span&gt;
&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;random&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;randn&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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&lt;pre class="ipynb"&gt;array([[ 1.39342127,  0.27553779,  1.60499887,  0.49998319,  0.70528917],
       [ 0.77384386,  0.13082401, -0.94628073,  1.11938778, -0.03671148],
       [-1.26643358, -0.49647634,  0.02653584,  1.69748904,  0.83353017],
       [ 2.37892618, -1.21239237,  1.12638933,  1.70430737,  0.50932112],
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#diag&lt;/span&gt;
&lt;span class="c1"&gt;#Nos permite crear una matriz con la diagonal de números que le ingresemos.&lt;/span&gt;
&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;diag&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
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&lt;h2 id="Matplotlib"&gt;Matplotlib&lt;a class="anchor-link" href="#Matplotlib"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;&lt;a href="http://matplotlib.org/"&gt;Matplotlib&lt;/a&gt; es la librería más popular en &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; para visualizaciones y gráficos. &lt;a href="http://matplotlib.org/"&gt;Matplotlib&lt;/a&gt; puede producir gráficos de alta calidad dignos de cualquier publicación científica.&lt;/p&gt;
&lt;p&gt;Algunas de las muchas ventajas que nos ofrece &lt;a href="http://matplotlib.org/"&gt;Matplotlib&lt;/a&gt;, incluyen:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Es fácil de aprender.&lt;/li&gt;
&lt;li&gt;Soporta texto, títulos y etiquetas en formato $\LaTeX$.&lt;/li&gt;
&lt;li&gt;Proporciona un gran control sobre cada uno de los elementos de las figuras, como ser su tamaño, el trazado de sus líneas, etc.&lt;/li&gt;
&lt;li&gt;Nos permite crear gráficos y figuras de gran calidad que pueden ser guardados en varios formatos, como ser: PNG, PDF, SVG, EPS, y PGF.&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;a href="http://matplotlib.org/"&gt;Matplotlib&lt;/a&gt; se integra de maravilla con &lt;a href="http://ipython.org/"&gt;IPython&lt;/a&gt; (ver más abajo), lo que nos proporciona un ambiente confortable para las visualizaciones y la exploración de datos interactiva.&lt;/p&gt;

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&lt;h3 id="Algunos-gr&amp;#225;ficos-con-Matplotlib"&gt;Algunos gr&amp;#225;ficos con Matplotlib&lt;a class="anchor-link" href="#Algunos-gr&amp;#225;ficos-con-Matplotlib"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#Generalmente se suele importar matplotlib de la siguiente forma.&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;matplotlib.pyplot&lt;/span&gt; &lt;span class="k"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;plt&lt;/span&gt;
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&lt;p&gt;Ahora vamos a graficar la siguiente función.&lt;/p&gt;
$$f(x) = e^{-x^2}$$
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Definimos nuestra función.&lt;/span&gt;
&lt;span class="k"&gt;def&lt;/span&gt; &lt;span class="nf"&gt;f&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;):&lt;/span&gt;
    &lt;span class="k"&gt;return&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;exp&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="o"&gt;**&lt;/span&gt; &lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#Creamos un vector con los puntos que le pasaremos a la funcion previamente creada.&lt;/span&gt;
&lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;linspace&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;num&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;30&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[17]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#Representeamos la función utilizando el objeto plt de matplotlib&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Eje $x$&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;$f(x)$&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Funcion $f(x)$&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;grid&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="kc"&gt;True&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;fig&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;f&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Función f(x)&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[18]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;#Grafico de puntos con matplotlib&lt;/span&gt;
&lt;span class="n"&gt;N&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="mi"&gt;100&lt;/span&gt;
&lt;span class="n"&gt;x1&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;random&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;randn&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;N&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;#creando vector x&lt;/span&gt;
&lt;span class="n"&gt;y1&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;random&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;randn&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;N&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;#creando vector x&lt;/span&gt;

&lt;span class="n"&gt;s&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="mi"&gt;50&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="mi"&gt;50&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;random&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;randn&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;N&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;#variable para modificar el tamaño(size)&lt;/span&gt;
&lt;span class="n"&gt;c&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;random&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;randn&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;N&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;#variable para modificar el color(color)&lt;/span&gt;

&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;scatter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;s&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;s&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;cmap&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cm&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Blues&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; 
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;grid&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="kc"&gt;True&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;colorbar&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;

&lt;span class="n"&gt;fig&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;scatter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

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&lt;h3 id="Interfase-orientada-a-objetos-de-matplotlib"&gt;Interfase orientada a objetos de matplotlib&lt;a class="anchor-link" href="#Interfase-orientada-a-objetos-de-matplotlib"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;
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&lt;p&gt;La idea principal con la &lt;a href="http://es.wikipedia.org/wiki/Programaci%C3%B3n_orientada_a_objetos"&gt;programación orientada a objetos&lt;/a&gt; es que a los objetos que se pueden aplicar funciones y acciones, y ningún objeto debería tener un estado global (como en el caso de la interfase con plt que acabamos de utilizar). La verdadera ventaja de este enfoque se hace evidente cuando se crean más de una figura, o cuando una figura contiene más de una trama secundaria.&lt;/p&gt;
&lt;p&gt;Para utilizar la &lt;a href="http://es.wikipedia.org/wiki/API"&gt;API&lt;/a&gt; &lt;a href="http://es.wikipedia.org/wiki/Programaci%C3%B3n_orientada_a_objetos"&gt;orientada a objetos&lt;/a&gt; comenzamos de forma similar al ejemplo anterior, pero en lugar de crear una nueva instancia global de &lt;code&gt;plt&lt;/code&gt;, almacenamos una referencia a la recientemente creada figura en la variable &lt;code&gt;fig&lt;/code&gt;, y a partir de ella creamos un nuevo eje &lt;code&gt;ejes&lt;/code&gt; usando el método &lt;code&gt;add_axes&lt;/code&gt; de la instancia &lt;code&gt;Figure&lt;/code&gt;:&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[19]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;linspace&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# Conjunto de puntos&lt;/span&gt;
&lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="o"&gt;**&lt;/span&gt; &lt;span class="mi"&gt;2&lt;/span&gt; &lt;span class="c1"&gt;# Funcion&lt;/span&gt;

&lt;span class="n"&gt;fig&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;figure&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;

&lt;span class="n"&gt;axes1&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;fig&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;add_axes&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="mf"&gt;0.1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;0.1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;0.8&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;0.8&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt; &lt;span class="c1"&gt;# Eje principal&lt;/span&gt;
&lt;span class="n"&gt;axes2&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;fig&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;add_axes&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="mf"&gt;0.2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;0.5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;0.4&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;0.3&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt; &lt;span class="c1"&gt;# Eje secundario&lt;/span&gt;

&lt;span class="c1"&gt;# Figura principal&lt;/span&gt;
&lt;span class="n"&gt;axes1&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;r&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;axes1&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;x&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;axes1&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;y&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;axes1&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Ej OOP&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# Insertada&lt;/span&gt;
&lt;span class="n"&gt;axes2&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;g&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;axes2&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;y&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;axes2&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;x&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;axes2&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;insertado&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;);&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[20]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo con más de una figura.&lt;/span&gt;
&lt;span class="n"&gt;fig&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;axes&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;subplots&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;nrows&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;ncols&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;ax&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="n"&gt;axes&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;
    &lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;r&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;x&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;y&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;titulo&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    
&lt;span class="n"&gt;fig&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;tight_layout&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt"&gt;&lt;/div&gt;



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&lt;h2 id="IPython"&gt;IPython&lt;a class="anchor-link" href="#IPython"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;&lt;a href="http://ipython.org/"&gt;IPython&lt;/a&gt; promueve un ambiente de trabajo de &lt;em&gt;ejecutar-explorar&lt;/em&gt; en contraposición al tradicional modelo de desarrollo de software de &lt;em&gt;editar-compilar-ejecutar&lt;/em&gt;. Es decir, que el problema computacional a resolver es más visto como todo un proceso de ejecucion de tareas, en lugar del tradicional modelo de producir una respuesta(&lt;code&gt;output&lt;/code&gt;) a una pregunta(&lt;code&gt;input&lt;/code&gt;).  &lt;a href="http://ipython.org/"&gt;IPython&lt;/a&gt; también provee una estrecha integración con nuestro sistema operativo, permitiendo acceder fácilmente a todos nuestros archivos desde la misma herramienta.&lt;/p&gt;
&lt;p&gt;Algunas de las características sobresalientes de &lt;a href="http://ipython.org/"&gt;IPython&lt;/a&gt; son:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Su poderoso &lt;a href='http://es.wikipedia.org/wiki/Shell_(inform%C3%A1tica)' target='_blank'&gt;shell&lt;/a&gt; interactivo.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://ipython.org/notebook.html"&gt;Notebook&lt;/a&gt;, su interfase web con soporte para código, texto, expresiones matemáticas, gráficos en línea y multimedia.&lt;/li&gt;
&lt;li&gt;Su soporte para poder realizar visualizaciones de datos en forma interactiva. &lt;a href="http://ipython.org/"&gt;IPython&lt;/a&gt; esta totalmente integrado con &lt;a href="http://matplotlib.org/"&gt;matplotlib&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;Su simple y flexible interfase para trabajar con la &lt;a href="http://es.wikipedia.org/wiki/Computaci%C3%B3n_paralela"&gt;computación paralela&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;a href="http://ipython.org/"&gt;IPython&lt;/a&gt; es mucho más que una librería, es todo un ambiente de trabajo que nos facilita enormemente trabajar con  &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;; las mismas páginas de este blog están desarrolladas con la ayuda del fantástico &lt;a href="http://ipython.org/notebook.html"&gt;Notebook&lt;/a&gt; de &lt;a href="http://ipython.org/"&gt;IPython&lt;/a&gt;. (para ver el &lt;a href="http://ipython.org/notebook.html"&gt;Notebook&lt;/a&gt; en el que se basa este artículo, visiten el siguiente &lt;a href="http://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/Python-Librerias-esenciales.ipynb"&gt;enlace&lt;/a&gt;.)&lt;/p&gt;
&lt;p&gt;Para más información sobre &lt;a href="http://ipython.org/"&gt;IPython&lt;/a&gt; y algunas de sus funciones los invito también a visitar el &lt;a href="http://relopezbriega.com.ar/2014/python/ipython-y-la-computacion-interactiva/"&gt;artículo&lt;/a&gt; que escribí en mi otro &lt;a href="http://relopezbriega.com.ar/"&gt;blog&lt;/a&gt;.&lt;/p&gt;

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&lt;h2 id="Pandas"&gt;Pandas&lt;a class="anchor-link" href="#Pandas"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;&lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt; es una librería &lt;a href="http://es.wikipedia.org/wiki/C%C3%B3digo_abierto"&gt;open source&lt;/a&gt; que aporta a &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; unas estructuras de datos fáciles de user y de alta performance, junto con un gran número de funciones esenciales para el análisis de datos. Con la ayuda de &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt; podemos trabajar con &lt;em&gt;datos estructurados&lt;/em&gt; de una forma más rápida y expresiva.&lt;/p&gt;
&lt;p&gt;Algunas de las cosas sobresalientes que nos aporta &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt; son:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Un rápido y eficiente objeto &lt;strong&gt;&lt;code&gt;DataFrame&lt;/code&gt;&lt;/strong&gt; para manipular datos con indexación integrada;&lt;/li&gt;
&lt;li&gt;herramientas para la &lt;strong&gt;lectura y escritura de datos&lt;/strong&gt; entre estructuras de datos rápidas y eficientes manejadas en memoria, como el &lt;code&gt;DataFrame&lt;/code&gt;, con la mayoría de los formatos conocidos para el manejo de datos, como ser: CSV y archivos de texto, archivos Microsoft Excel, bases de datos &lt;a href="http://es.wikipedia.org/wiki/SQL"&gt;SQL&lt;/a&gt;, y el formato científico HDF5.&lt;/li&gt;
&lt;li&gt;Proporciona una &lt;strong&gt;alineación inteligente de datos&lt;/strong&gt; y un manejo integrado de los datos faltantes; con estas funciones podemos obtener una ganancia de performace en los cálculos entre &lt;code&gt;DataFrames&lt;/code&gt; y una fácil manipulación y ordenamiento de los datos de nuestro &lt;code&gt;data set&lt;/code&gt;;&lt;/li&gt;
&lt;li&gt;Flexibilidad para &lt;strong&gt;manipular y redimensionar&lt;/strong&gt; nuestro &lt;code&gt;data set&lt;/code&gt;, facilidad para construir &lt;a href="http://es.wikipedia.org/wiki/Tabla_pivote"&gt;tablas pivote&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;La posibilidad de &lt;strong&gt;filtrar los datos, agregar o eliminar columnas&lt;/strong&gt; de una forma sumamente expresiva;&lt;/li&gt;
&lt;li&gt;Operaciones de &lt;strong&gt;&lt;em&gt;merge&lt;/em&gt; y &lt;em&gt;join&lt;/em&gt;&lt;/strong&gt; altamente eficientes sobre nuestros conjuntos de datos;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Indexación jerárquica&lt;/strong&gt; que proporciona una forma intuitiva de trabajar con datos de alta dimensión en una estructura de datos de menor dimensión ;&lt;/li&gt;
&lt;li&gt;Posibilidad de realizar cálculos agregados o transformaciones de datos con el poderoso motor &lt;strong&gt;&lt;code&gt;group by&lt;/code&gt;&lt;/strong&gt; que nos permite dividir-aplicar-combinar nuestros conjuntos de datos;&lt;/li&gt;
&lt;li&gt;combina las &lt;strong&gt;características de las matrices de alto rendimiento de &lt;a href="http://www.numpy.org/"&gt;Numpy&lt;/a&gt; con las flexibles capacidades de manipulación de datos de las hojas de cálculo&lt;/strong&gt; y bases de datos relacionales (tales como &lt;a href="http://es.wikipedia.org/wiki/SQL"&gt;SQL&lt;/a&gt;);&lt;/li&gt;
&lt;li&gt;Gran número de funcionalidades para el manejo de &lt;strong&gt;&lt;a href="http://es.wikipedia.org/wiki/Serie_temporal"&gt;series de tiempo&lt;/a&gt;&lt;/strong&gt; ideales para el análisis financiero;&lt;/li&gt;
&lt;li&gt;Todas sus funciones y estructuras de datos están &lt;strong&gt;optimizadas para el alto rendimiento&lt;/strong&gt;, con las partes críticas del código escritas en &lt;a href="http://www.cython.org/"&gt;Cython&lt;/a&gt; o &lt;a href="http://es.wikipedia.org/wiki/C_(lenguaje_de_programaci%C3%B3n"&gt;C&lt;/a&gt;;&lt;/li&gt;
&lt;/ul&gt;

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&lt;h3 id="Estructuras-de-datos-de-Pandas"&gt;Estructuras de datos de Pandas&lt;a class="anchor-link" href="#Estructuras-de-datos-de-Pandas"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Importando pandas&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;pandas&lt;/span&gt; &lt;span class="k"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;pd&lt;/span&gt;
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&lt;h4 id="Series"&gt;Series&lt;a class="anchor-link" href="#Series"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Las series son matrices de una sola dimension similares a los vectores, pero con su propio indice.&lt;/span&gt;
&lt;span class="c1"&gt;# Creando una Serie&lt;/span&gt;
&lt;span class="n"&gt;serie&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Series&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;4&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
&lt;span class="n"&gt;serie&lt;/span&gt;
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&lt;pre class="ipynb"&gt;0    2
1    4
2   -8
3    3
dtype: int64&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# podemos ver tantos los índices como los valores de las Series.&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt; &lt;span class="n"&gt;serie&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;values&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt; &lt;span class="n"&gt;serie&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;index&lt;/span&gt;
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&lt;pre class="ipynb"&gt;[ 2  4 -8  3]
Int64Index([0, 1, 2, 3], dtype=&amp;#39;int64&amp;#39;)
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Creando Series con nuestros propios índices.&lt;/span&gt;
&lt;span class="n"&gt;serie2&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Series&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;4&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;index&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;d&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;b&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;a&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;c&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
&lt;span class="n"&gt;serie2&lt;/span&gt;
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&lt;pre class="ipynb"&gt;d    2
b    4
a   -8
c    3
dtype: int64&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Accediendo a los datos a través de los índices&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt; &lt;span class="n"&gt;serie2&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;a&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt; &lt;span class="n"&gt;serie2&lt;/span&gt;&lt;span class="p"&gt;[[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;b&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;c&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;d&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]]&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt; &lt;span class="n"&gt;serie2&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;serie2&lt;/span&gt; &lt;span class="o"&gt;&amp;gt;&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
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&lt;pre class="ipynb"&gt;-8
b    4
c    3
d    2
dtype: int64
d    2
b    4
c    3
dtype: int64
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&lt;h4 id="DataFrame"&gt;DataFrame&lt;a class="anchor-link" href="#DataFrame"&gt;&amp;#182;&lt;/a&gt;&lt;/h4&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# El DataFrame es una estructura de datos tabular similar a las hojas de cálculo de Excel.&lt;/span&gt;
&lt;span class="c1"&gt;# Posee tanto indices de columnas como de filas.&lt;/span&gt;

&lt;span class="c1"&gt;# Creando un DataFrame.&lt;/span&gt;
&lt;span class="n"&gt;data&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;{&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;state&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Ohio&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;Ohio&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;Ohio&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;Nevada&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;Nevada&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
        &lt;span class="s1"&gt;&amp;#39;year&amp;#39;&lt;/span&gt; &lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;2000&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;2001&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;2002&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;2001&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;2002&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
        &lt;span class="s1"&gt;&amp;#39;pop&amp;#39;&lt;/span&gt;  &lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mf"&gt;1.5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;1.7&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;3.6&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;2.4&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;2.9&lt;/span&gt;&lt;span class="p"&gt;]}&lt;/span&gt;
&lt;span class="n"&gt;frame&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DataFrame&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;data&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# Creando un DataFrame desde un diccionario&lt;/span&gt;
&lt;span class="n"&gt;frame&lt;/span&gt;
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      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;pop&lt;/th&gt;
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      &lt;th&gt;year&lt;/th&gt;
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      &lt;th&gt;0&lt;/th&gt;
      &lt;td&gt; 1.5&lt;/td&gt;
      &lt;td&gt;   Ohio&lt;/td&gt;
      &lt;td&gt; 2000&lt;/td&gt;
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      &lt;th&gt;1&lt;/th&gt;
      &lt;td&gt; 1.7&lt;/td&gt;
      &lt;td&gt;   Ohio&lt;/td&gt;
      &lt;td&gt; 2001&lt;/td&gt;
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    &lt;tr&gt;
      &lt;th&gt;2&lt;/th&gt;
      &lt;td&gt; 3.6&lt;/td&gt;
      &lt;td&gt;   Ohio&lt;/td&gt;
      &lt;td&gt; 2002&lt;/td&gt;
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      &lt;th&gt;3&lt;/th&gt;
      &lt;td&gt; 2.4&lt;/td&gt;
      &lt;td&gt; Nevada&lt;/td&gt;
      &lt;td&gt; 2001&lt;/td&gt;
    &lt;/tr&gt;
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      &lt;th&gt;4&lt;/th&gt;
      &lt;td&gt; 2.9&lt;/td&gt;
      &lt;td&gt; Nevada&lt;/td&gt;
      &lt;td&gt; 2002&lt;/td&gt;
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&lt;p&gt;5 rows × 3 columns&lt;/p&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Creando un DataFrame desde un archivo.&lt;/span&gt;
&lt;span class="o"&gt;!&lt;/span&gt;cat &lt;span class="s1"&gt;&amp;#39;dataset.csv&amp;#39;&lt;/span&gt; # ejemplo archivo csv.
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1.5,Ohio,2000
1.7,Ohio,2001
3.6,Ohio,2002
2.4,Nevada,2001
2.9,Nevada,2002
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Leyendo el archivo dataset.csv para crear el DataFrame&lt;/span&gt;
&lt;span class="n"&gt;frame2&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;read_csv&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;dataset.csv&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;header&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; 
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      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;pop&lt;/th&gt;
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      &lt;th&gt;year&lt;/th&gt;
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      &lt;th&gt;0&lt;/th&gt;
      &lt;td&gt; 1.5&lt;/td&gt;
      &lt;td&gt;   Ohio&lt;/td&gt;
      &lt;td&gt; 2000&lt;/td&gt;
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    &lt;tr&gt;
      &lt;th&gt;1&lt;/th&gt;
      &lt;td&gt; 1.7&lt;/td&gt;
      &lt;td&gt;   Ohio&lt;/td&gt;
      &lt;td&gt; 2001&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;2&lt;/th&gt;
      &lt;td&gt; 3.6&lt;/td&gt;
      &lt;td&gt;   Ohio&lt;/td&gt;
      &lt;td&gt; 2002&lt;/td&gt;
    &lt;/tr&gt;
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      &lt;th&gt;3&lt;/th&gt;
      &lt;td&gt; 2.4&lt;/td&gt;
      &lt;td&gt; Nevada&lt;/td&gt;
      &lt;td&gt; 2001&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;4&lt;/th&gt;
      &lt;td&gt; 2.9&lt;/td&gt;
      &lt;td&gt; Nevada&lt;/td&gt;
      &lt;td&gt; 2002&lt;/td&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Seleccionando una columna como una Serie&lt;/span&gt;
&lt;span class="n"&gt;frame&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;state&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
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&lt;pre class="ipynb"&gt;0      Ohio
1      Ohio
2      Ohio
3    Nevada
4    Nevada
Name: state, dtype: object&lt;/pre&gt;
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&lt;pre class="ipynb"&gt;pop       1.7
state    Ohio
year     2001
Name: 1, dtype: object&lt;/pre&gt;
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&lt;pre class="ipynb"&gt;Index([u&amp;#39;pop&amp;#39;, u&amp;#39;state&amp;#39;, u&amp;#39;year&amp;#39;], dtype=&amp;#39;object&amp;#39;)&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Verificando los índices.&lt;/span&gt;
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&lt;h2 id="Otras-librer&amp;#237;as-dignas-de-mencion"&gt;Otras librer&amp;#237;as dignas de mencion&lt;a class="anchor-link" href="#Otras-librer&amp;#237;as-dignas-de-mencion"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;Otras librerías que también son muy importantes para el análisis de datos con &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; son:&lt;/p&gt;

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&lt;h3 id="SciPy"&gt;SciPy&lt;a class="anchor-link" href="#SciPy"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;
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&lt;p&gt;&lt;a href="http://www.scipy.org/"&gt;SciPy&lt;/a&gt; es un conjunto de paquetes donde cada uno ellos ataca un problema distinto dentro de la computación científica y el análisis numérico. Algunos de los paquetes que incluye, son:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;&lt;code&gt;scipy.integrate&lt;/code&gt;&lt;/strong&gt;: que proporciona diferentes funciones para resolver problemas de integración numérica.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;&lt;code&gt;scipy.linalg&lt;/code&gt;&lt;/strong&gt;: que proporciona funciones para resolver problemas de álgebra lineal.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;&lt;code&gt;scipy.optimize&lt;/code&gt;&lt;/strong&gt;: para los problemas de optimización y minimización.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;&lt;code&gt;scipy.signal&lt;/code&gt;&lt;/strong&gt;: para el análisis y procesamiento de señales.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;&lt;code&gt;scipy.sparse&lt;/code&gt;&lt;/strong&gt;: para matrices dispersas y solucionar sistemas lineales dispersos&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;&lt;code&gt;scipy.stats&lt;/code&gt;&lt;/strong&gt;: para el análisis de estadística y probabilidades.&lt;/li&gt;
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&lt;h3 id="Scikit-learn"&gt;Scikit-learn&lt;a class="anchor-link" href="#Scikit-learn"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;
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&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/"&gt;Scikit-learn&lt;/a&gt; es una librería especializada en algoritmos para &lt;a href="http://es.wikipedia.org/wiki/Miner%C3%ADa_de_datos"&gt;data mining&lt;/a&gt; y &lt;a href="http://es.wikipedia.org/wiki/Machine_learning"&gt;machine learning&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;Algunos de los problemas que podemos resolver utilizando las herramientas de &lt;a href="http://scikit-learn.org/stable/"&gt;Scikit-learn&lt;/a&gt;, son:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href='http://scikit-learn.org/stable/supervised_learning.html#supervised-learning' target='_blank'&gt;Clasificaciones&lt;/a&gt;: Identificar las categorías a que cada observación del conjunto de datos pertenece.&lt;/li&gt;
&lt;li&gt;&lt;a href='http://scikit-learn.org/stable/supervised_learning.html#supervised-learning' target='_blank'&gt;Regresiones&lt;/a&gt;: Predecire el valor continuo para cada nuevo ejemplo.&lt;/li&gt;
&lt;li&gt;&lt;a href='http://scikit-learn.org/stable/modules/clustering.html#clustering' target='_blank'&gt;Agrupaciones&lt;/a&gt;: Agrupación automática de objetos similares en un conjunto.&lt;/li&gt;
&lt;li&gt;&lt;a href='http://scikit-learn.org/stable/modules/decomposition.html#decompositions' target='_blank'&gt;Reducción de dimensiones&lt;/a&gt;: Reducir el número de variables aleatorias a considerar.&lt;/li&gt;
&lt;li&gt;&lt;a href='http://scikit-learn.org/stable/model_selection.html#model-selection' target='_blank'&gt;Selección de Modelos&lt;/a&gt;: Comparar, validar y elegir parámetros y modelos.&lt;/li&gt;
&lt;li&gt;&lt;a href='http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing' target='_blank'&gt;Preprocesamiento&lt;/a&gt;: Extracción de características a analizar y normalización de datos.&lt;/li&gt;
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&lt;/p&gt;</summary><category term="python"></category><category term="estadistica"></category><category term="analisis de datos"></category></entry></feed>