{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejemplo con library catalogs\n",
    "## Preparación y carga de datos\n",
    "En primer lugar se cargan las bibliotecas `pandas`, `ipywidgets` y la función propia `bar_scatter_plot` del archivo `plot_function.py`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interactive, fixed\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from plot_function import bar_scatter_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En siguiente lugar cargamos los datos, el fichero `data.csv` ubicado en el directorio `data`. Tras ello se previsualizan las primeras instancias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos = pandas.read_csv(\"data/datos.csv\")\n",
    "datos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último, modifico los nombres de la columnas para evitar problemas con los espacios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos.columns = ['Worldcat_entity', 'Works', 'Publications', 'Library_Holdings', 'Google_Scholar_Citations']\n",
    "datos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualización de los datos\n",
    "En primer lugar hago una tabla interactiva con la que poder filtrar los datos de las distintas columnas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@interact\n",
    "def show_entities_more_than(column=['Works', 'Publications', 'Library_Holdings', 'Google_Scholar_Citations'], x=100):\n",
    "    return datos.loc[datos[column] > x]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tras ello un gráfico de barras interactivo en el que aparecen dos series de datos. En este caso el mismo pero de dos formas diferentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pandas\n",
    "def plot_entities_more_than(Serie1=['Works', 'Publications', 'Library_Holdings', 'Google_Scholar_Citations'],\n",
    "                            Serie2=['Publications', 'Works', 'Library_Holdings', 'Google_Scholar_Citations']):\n",
    "    grafico_barras = datos.plot.bar(x='Worldcat_entity', y=[Serie1, Serie2],\n",
    "                                figsize=(20,10),\n",
    "                                rot=45)\n",
    "    grafico_barras = grafico_barras.set_xticklabels(datos.Worldcat_entity,\n",
    "                                                fontdict={'horizontalalignment': 'right', 'size':12})\n",
    "    return grafico_barras\n",
    "\n",
    "w=interactive(plot_entities_more_than)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# matplotlib\n",
    "def plot_entities_more_than(Serie1=['Works', 'Publications', 'Library_Holdings', 'Google_Scholar_Citations'],\n",
    "                            Serie2=['Publications', 'Works', 'Library_Holdings', 'Google_Scholar_Citations']):\n",
    "    \n",
    "    labels = datos['Worldcat_entity']\n",
    "    x = np.arange(len(labels))\n",
    "    width = 0.25\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(20,10), dpi= 100)\n",
    "    rects1 = ax.bar(x - width/2, datos[Serie1], width, label=Serie1)\n",
    "    rects2 = ax.bar(x + width/2, datos[Serie2], width, label=Serie2)\n",
    "    \n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xlabel('Worldcat_entity', fontdict={'size':14})\n",
    "    ax.set_xticklabels(labels, rotation=45, fontdict={'horizontalalignment': 'right', 'size':12})\n",
    "    ax.legend()\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    \n",
    "    return plt.show()\n",
    "\n",
    "w=interactive(plot_entities_more_than)\n",
    "w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último, ejecuto la función que he preparado para hacer el ranking de autores por library holdings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar_scatter_plot(datos)"
   ]
  }
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