Sesion-06/Ejemplo-07/graficas_de_barras_apiladas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejemplo 7: Gráficas de barras apiladas\n",
    "\n",
    "### 1. Objetivos:\n",
    "    - Aprender a generar e interpretar gráficas de barras apiladas\n",
    " \n",
    "---\n",
    "    \n",
    "### 2. Desarrollo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>team</th>\n",
       "      <th>noc</th>\n",
       "      <th>games</th>\n",
       "      <th>year</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>sport</th>\n",
       "      <th>event</th>\n",
       "      <th>medal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>athlete_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 1,000 metres</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>25.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1992 Winter</td>\n",
       "      <td>1992</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Albertville</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name sex   age  height  weight         team  \\\n",
       "athlete_id                                                                    \n",
       "1                          A Dijiang   M  24.0   180.0    80.0        China   \n",
       "2                           A Lamusi   M  23.0   170.0    60.0        China   \n",
       "5           Christine Jacoba Aaftink   F  21.0   185.0    82.0  Netherlands   \n",
       "5           Christine Jacoba Aaftink   F  21.0   185.0    82.0  Netherlands   \n",
       "5           Christine Jacoba Aaftink   F  25.0   185.0    82.0  Netherlands   \n",
       "\n",
       "            noc        games  year  season         city          sport  \\\n",
       "athlete_id                                                               \n",
       "1           CHN  1992 Summer  1992  Summer    Barcelona     Basketball   \n",
       "2           CHN  2012 Summer  2012  Summer       London           Judo   \n",
       "5           NED  1988 Winter  1988  Winter      Calgary  Speed Skating   \n",
       "5           NED  1988 Winter  1988  Winter      Calgary  Speed Skating   \n",
       "5           NED  1992 Winter  1992  Winter  Albertville  Speed Skating   \n",
       "\n",
       "                                         event medal  \n",
       "athlete_id                                            \n",
       "1                  Basketball Men's Basketball  None  \n",
       "2                 Judo Men's Extra-Lightweight  None  \n",
       "5             Speed Skating Women's 500 metres  None  \n",
       "5           Speed Skating Women's 1,000 metres  None  \n",
       "5             Speed Skating Women's 500 metres  None  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../Datasets/athlete_olympic_events-clean.csv', index_col=0)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por ejemplo, digamos que queremos graficar la frecuencia de los 4 deportes que analizamos anteriormente. Queremos ver cuántas veces aparece cada uno en nuestro dataset para compararlos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grouped = df.groupby('name')[['sex', 'sport']].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ball = df_grouped[df_grouped['sport'].isin(['Basketball', 'Volleyball', 'Football', 'Baseball'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Football      3993\n",
       "Basketball    2607\n",
       "Volleyball    2374\n",
       "Baseball       703\n",
       "Name: sport, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counts_all = df_ball['sport'].value_counts()\n",
    "\n",
    "value_counts_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ramon/Env/data_science/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "sns.barplot(value_counts_all.index, value_counts_all, ax=ax, palette='Blues');\n",
    "\n",
    "ax.set_ylabel('count')\n",
    "ax.set_title('Conteo de frecuencia de 4 deportes Olímpicos', fontsize=13, pad=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¡Listo! Pero qué pasa si ahora además queremos segmentar nuestros conteos utilizando la variable 'sex'. Esto nos servirá para saber cómo es que cada género contribuye al conteo total de atletas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>sex</th>\n",
       "      <th>sport</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Th Anh</th>\n",
       "      <td>F</td>\n",
       "      <td>Fencing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Th Ngn Thng</th>\n",
       "      <td>F</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tin Tun</th>\n",
       "      <td>M</td>\n",
       "      <td>Boxing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A Dijiang</th>\n",
       "      <td>M</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A Lamusi</th>\n",
       "      <td>M</td>\n",
       "      <td>Judo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sex       sport  count\n",
       "name                               \n",
       " Th Anh        F     Fencing      1\n",
       " Th Ngn Thng   F  Gymnastics      1\n",
       " Tin Tun       M      Boxing      1\n",
       "A Dijiang      M  Basketball      1\n",
       "A Lamusi       M        Judo      1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grouped['count'] = 1\n",
    "\n",
    "df_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ball_with_count = df_grouped[df_grouped['sport'].isin(['Basketball', 'Volleyball', 'Football', 'Baseball'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sport       sex\n",
       "Baseball    M       703\n",
       "Basketball  F       877\n",
       "            M      1730\n",
       "Football    F       703\n",
       "            M      3290\n",
       "Volleyball  F      1074\n",
       "            M      1300\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counts = df_ball_with_count.groupby(['sport', 'sex'])['count'].sum()\n",
    "\n",
    "value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sport</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baseball</th>\n",
       "      <td>NaN</td>\n",
       "      <td>703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basketball</th>\n",
       "      <td>877.0</td>\n",
       "      <td>1730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Football</th>\n",
       "      <td>703.0</td>\n",
       "      <td>3290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volleyball</th>\n",
       "      <td>1074.0</td>\n",
       "      <td>1300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex              F       M\n",
       "sport                     \n",
       "Baseball       NaN   703.0\n",
       "Basketball   877.0  1730.0\n",
       "Football     703.0  3290.0\n",
       "Volleyball  1074.0  1300.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counts = value_counts.unstack(1)\n",
    "\n",
    "value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "value_counts = value_counts.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "\n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "plt1 = ax.bar(value_counts.index, value_counts['M'], label='M',\n",
    "              color=[\"#7788AA\",\"#4E638E\",\"#2E4372\",\"#152A55\"])\n",
    "plt2 = ax.bar(value_counts.index, value_counts['F'], bottom=value_counts['M'],\n",
    "              color=[\"#FFD0AA\", \"#D4996A\", \"#AA6B39\", \"#804415\"])\n",
    "\n",
    "ax.set_ylabel('count')\n",
    "ax.set_title('Conteo de frecuencia de 4 deportes Olímpicos', fontsize=13, pad=15);\n",
    "plt.legend((plt1[0], plt2[0]), ('Men', 'Women'));\n",
    "ax.set_ylim(0, 4500);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con esta gráfica se vuelve muy evidente la disparidad que existe en casi todos estos deportes. Excepto por volleyball, donde la participación de hombres y mujeres es similar, en los demás deportes la participación es femenina es mucho menor o incluso nula."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (data_science)",
   "language": "python",
   "name": "data_science"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}