Digital House M1 Clase 7 Visualizacion-practica-independiente-starter.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Práctica independiente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly.tools as tls\n",
    "#tls.set_credentials_file(username='sebasggx', api_key='uLZskgQnsV7QPNGSOGbq')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importar los paquetes requeridos\n",
    "\n",
    "No nos olvidemos de ejecutar esta celda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Paquetes numéricos y estadísticos:\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Pandas maneja la carga y manipulación del dataset\n",
    "import pandas as pd\n",
    "\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Inicializar plotly en modo offline para la notebook\n",
    "plotly.offline.init_notebook_mode()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Dataset salary.csv: Una vez más\n",
    "\n",
    "Queremos generar un gráfico paracido a este utilizando el dataset salary.csv y ``plotly``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Grafico](grafico.png)\n",
    "![Este es el gráfico que queremos generar]()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "salary = pd.read_csv('salary.csv')\n",
    "salary.columns = ['gender', 'professor_rank', 'years_in_job', 'degree_level', 'years_since_degree', 'yearly_salary']\n",
    "salary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    ### Obtener los 6 subconjuntos del dataset que representan los cruces entre gender y professor_rank (hay 6 subconjuntos: 2 generos * 3 tipos de cargo de profesor = 6)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "genders = salary.gender.unique()\n",
    "ranks = salary.professor_rank.unique()\n",
    "\n",
    "symbols = [\"circle\", \"cross\", \"x\"]\n",
    "colors = [\"#F22\", \"#22F\"]\n",
    "\n",
    "traces = []\n",
    "for i, gender in enumerate(genders):\n",
    "    for j, rank in enumerate(ranks):\n",
    "        data = salary[(salary['gender'] == gender) & (salary['professor_rank'] == rank)]\n",
    "        \n",
    "        traces.append(go.Scatter(\n",
    "            name=gender + '-' + rank,\n",
    "            x=data.years_in_job.values,\n",
    "            y=data.yearly_salary.values,\n",
    "            mode='markers',\n",
    "            marker=dict(\n",
    "                color=colors[i],\n",
    "                symbol=symbols[j],\n",
    "                size=data.years_since_degree.values\n",
    "            )\n",
    "        ))\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Salaries'\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=traces, layout=layout)\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. NBA\n",
    "\n",
    "Vamos a utilizar el dataset 'nba.tsv' que representa la probabilidad que tiene un equipo dado en un minuto dado del partido de ganar. Cada fila representa un equipo y cada columna un minuto del partido (de 0 a 48)\n",
    "\n",
    "Queremos un gráfico que nos muestre en el eje x los minutos de un partido de NBA y en el eje Y la probabilidad de ganar que, en ese minuto de juego, tiene cada equipo.\n",
    "\n",
    "Utilizaremos la funcion ``Scatter`` de ``plotly``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nba = pd.read_csv('nba.tsv', sep='\\t')\n",
    "\n",
    "xaxis = nba.columns.values[1:]\n",
    "\n",
    "data = [go.Scatter(name=i[1].team, x=xaxis, y=i[1].values[1:]) for i in nba.iterrows()]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='NBA',\n",
    "    xaxis=dict(title='Minutes'),\n",
    "    yaxis=dict(title='Percentage')\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Mapas\n",
    "\n",
    "Utilizando el dataset del Desafío 1, generar un gráfico que muestre el mapa de Estados Unidos y mostrar por intensidad/divergencia de color las notas promedio de los estudiantes de cada estado para alguno de los tests.\n",
    "\n",
    "Utilizar la estrucutra ``choropleth`` y basarse en el ejemplo de la demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('sat_scores.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scl = [\n",
    "    [0.0, 'rgb(242,240,247)'],\n",
    "    [0.2, 'rgb(218,218,235)'],\n",
    "    [0.4, 'rgb(188,189,220)'],\n",
    "    [0.6, 'rgb(158,154,200)'],\n",
    "    [0.8, 'rgb(117,107,177)'],\n",
    "    [1.0, 'rgb(84,39,143)']\n",
    "]\n",
    "\n",
    "df['text'] =  df['State'] + '<br>' + \\\n",
    "    'Rate ' + df['Rate'].astype(str) + '<br>' + \\\n",
    "    'Math ' + df['Math'].astype(str)+ '<br>' + \\\n",
    "    'Verbal ' + df['Verbal'].astype(str)\n",
    "\n",
    "data = [\n",
    "    dict(\n",
    "        type='choropleth',\n",
    "        colorscale = scl,\n",
    "        autocolorscale = False,\n",
    "        locations = df['State'],\n",
    "        z = df['Rate'],\n",
    "        locationmode = 'USA-states',\n",
    "        text = df['text'],\n",
    "        marker = dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(255,255,255)',\n",
    "                width = 2\n",
    "            )\n",
    "        ),\n",
    "        colorbar = dict(\n",
    "            title = \"Rate\"\n",
    "        )\n",
    "    )\n",
    "]\n",
    "\n",
    "layout = dict(\n",
    "    title = '2011 SAT scores',\n",
    "    geo = dict(\n",
    "        scope='usa',\n",
    "        projection=dict(type='albers usa'),\n",
    "        showlakes = True,\n",
    "        lakecolor = 'rgb(255, 255, 255)'\n",
    "    ),\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "plotly.offline.iplot(fig)"
   ]
  }
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