plotly-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotly\n",
    "\n",
    "Librería de visualización interactiva.\n",
    "\n",
    "## Instalación en Jupyter\n",
    "\n",
    "* Con anaconda:\n",
    "```bash\n",
    "conda install plotly\n",
    "```\n",
    "* Sin anaconda\n",
    "```bash\n",
    "pip install plotly\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algunos ejemplos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.750935</td>\n",
       "      <td>0.821012</td>\n",
       "      <td>0.146340</td>\n",
       "      <td>-0.903430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.789729</td>\n",
       "      <td>1.362872</td>\n",
       "      <td>-0.172060</td>\n",
       "      <td>0.540172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.013976</td>\n",
       "      <td>0.380672</td>\n",
       "      <td>-1.302765</td>\n",
       "      <td>-0.446996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.792818</td>\n",
       "      <td>0.699287</td>\n",
       "      <td>0.651938</td>\n",
       "      <td>0.033268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.143245</td>\n",
       "      <td>-0.114720</td>\n",
       "      <td>0.281870</td>\n",
       "      <td>0.462295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>-0.817023</td>\n",
       "      <td>-0.886313</td>\n",
       "      <td>1.739226</td>\n",
       "      <td>0.453168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.151985</td>\n",
       "      <td>-1.766156</td>\n",
       "      <td>-1.004610</td>\n",
       "      <td>-0.810747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>-0.534421</td>\n",
       "      <td>-0.412663</td>\n",
       "      <td>-0.004431</td>\n",
       "      <td>0.768475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.696892</td>\n",
       "      <td>0.200851</td>\n",
       "      <td>2.136898</td>\n",
       "      <td>0.404393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>-0.433134</td>\n",
       "      <td>0.445632</td>\n",
       "      <td>-1.019441</td>\n",
       "      <td>0.412332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           a         b         c         d\n",
       "0   1.750935  0.821012  0.146340 -0.903430\n",
       "1   0.789729  1.362872 -0.172060  0.540172\n",
       "2  -1.013976  0.380672 -1.302765 -0.446996\n",
       "3  -0.792818  0.699287  0.651938  0.033268\n",
       "4  -0.143245 -0.114720  0.281870  0.462295\n",
       "..       ...       ...       ...       ...\n",
       "95 -0.817023 -0.886313  1.739226  0.453168\n",
       "96  0.151985 -1.766156 -1.004610 -0.810747\n",
       "97 -0.534421 -0.412663 -0.004431  0.768475\n",
       "98  0.696892  0.200851  2.136898  0.404393\n",
       "99 -0.433134  0.445632 -1.019441  0.412332\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# añadimos esta directiva para poder visualizar los graficos en jupyter\n",
    "%matplotlib inline \n",
    "\n",
    "# importamos cufflinks y la utilizaremos sin conexión\n",
    "import cufflinks as cf\n",
    "cf.go_offline()\n",
    "\n",
    "# importar las versiones fuera de línea de los módulos de Plotly \n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    " \n",
    "# Como Plotly Plots son interactivos, utilizan JavaScript detrás de escena. Conectamos JavaScript a nuestro Notebook. \n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "# randn(filas, columnas)\n",
    "dataframe = pd.DataFrame(np.random.randn(100,4), columns=['a','b','c','d'])\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f281889cb10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grafico no interactivo\n",
    "dataframe.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
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       "                            \n",
       "var gd = document.getElementById('852b746a-304a-4807-be77-d5455bb207b6');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "// Listen for the removal of the full notebook cells\n",
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       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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       "                        })\n",
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       "                });\n",
       "            </script>\n",
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     "metadata": {},
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   ],
   "source": [
    "# Gráfico interactivo\n",
    "dataframe.iplot()"
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   "execution_count": 4,
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    "dataframe.iplot(kind='scatter', x='a', y='b', mode='markers')"
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# diagrama de tipo barras\n",
    "dataframe.iplot(kind='bar')"
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  },
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('63f973cc-8709-4aaa-b5a1-2fdb0be69419');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "            Plotly.purge(gd);\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "            </script>\n",
       "        </div>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sumamos las barras\n",
    "dataframe.sum().iplot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('bf933fe9-5929-4f9e-84f2-0a8fb0532392');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# diagramas de caja\n",
    "dataframe.iplot(kind='box')"
   ]
  },
  {
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    "dataframe['a'].iplot(kind='hist',bins=30)"
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   "source": [
    "# histograma superpuesto de las 4 columnas\n",
    "dataframe.iplot(kind='hist',bins=30)"
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   "metadata": {},
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      "/home/ikari/anaconda3/lib/python3.7/site-packages/cufflinks/plotlytools.py:849: FutureWarning:\n",
      "\n",
      "The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead\n",
      "\n",
      "/home/ikari/anaconda3/lib/python3.7/site-packages/cufflinks/plotlytools.py:850: FutureWarning:\n",
      "\n",
      "The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead\n",
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('f753be1c-6102-4549-b2c3-3e62690027f8');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico que nos añade uno abajo simétrico par acomparar entre 2 columnas\n",
    "dataframe[['a','b']].iplot(kind='spread')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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   "source": [
    "# Grafico de tipo burbujas: columna 'a' se representa en el eje x, columna 'b' se representa en el eje y.\n",
    "# Tamaño de las burbujas: columna c\n",
    "dataframe.iplot(kind='bubble', x='a', y='b', size='c')"
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   "execution_count": 12,
   "metadata": {},
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th>c</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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