Nombres.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f458a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bfe8180",
   "metadata": {},
   "source": [
    "En este caso de estudio analizaremos la distribución de nombres en Argentina desde 1912 a 2015. Los datos fueron tomados de [datos.gob.ar](https://datos.gob.ar/dataset/otros-nombres-personas-fisicas/archivo/otros_2.1).\n",
    "\n",
    "El set de datos contiene el listado histórico de los nombres registrados ante el Registro Nacional de las Personas agrupados por nombre y año. Por ejemplo \"Maria\" y \"Maria Luisa\" son entradas diferentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9937465b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nombre</th>\n",
       "      <th>cantidad</th>\n",
       "      <th>año</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>314</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rosa</td>\n",
       "      <td>203</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jose</td>\n",
       "      <td>163</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maria Luisa</td>\n",
       "      <td>127</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carmen</td>\n",
       "      <td>117</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        nombre  cantidad   año\n",
       "0        Maria       314  1922\n",
       "1         Rosa       203  1922\n",
       "2         Jose       163  1922\n",
       "3  Maria Luisa       127  1922\n",
       "4       Carmen       117  1922"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datos/nombres.zip')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b7235d7",
   "metadata": {},
   "source": [
    "Podemos usar describe para obtener un resúmen de los datos, como por ejemplo que tenemos más de 9.7 millones de entradas. Y que el nombre más popular, en un año, fue registrado 7939 veces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "09b3e7bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cantidad</th>\n",
       "      <th>año</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.761609e+06</td>\n",
       "      <td>9.761609e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.177199e+00</td>\n",
       "      <td>1.980137e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.336806e+01</td>\n",
       "      <td>2.429556e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.922000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.961000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.985000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.001000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.939000e+03</td>\n",
       "      <td>2.015000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cantidad           año\n",
       "count  9.761609e+06  9.761609e+06\n",
       "mean   6.177199e+00  1.980137e+03\n",
       "std    4.336806e+01  2.429556e+01\n",
       "min    1.000000e+00  1.922000e+03\n",
       "25%    1.000000e+00  1.961000e+03\n",
       "50%    2.000000e+00  1.985000e+03\n",
       "75%    3.000000e+00  2.001000e+03\n",
       "max    7.939000e+03  2.015000e+03"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a89cb3d8",
   "metadata": {},
   "source": [
    "Veamos cual fue ese nombre y en que año se dio el registro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33fe2d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>nombre</th>\n",
       "      <th>cantidad</th>\n",
       "      <th>año</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3673344</th>\n",
       "      <td>Leonel Hernan</td>\n",
       "      <td>7939</td>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                nombre  cantidad   año\n",
       "3673344  Leonel Hernan      7939  1975"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"cantidad\"] == 7939]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c040418e",
   "metadata": {},
   "source": [
    "Antes de continuar nos vamos a asegurar de unificar nombres con y sin tilde y vamos a asegurarnos que solo la primer letra de cada nombre es mayúscula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d17fb71",
   "metadata": {},
   "outputs": [],
   "source": [
    "trans = str.maketrans('áéíóúäëïöüâêîôûàèìòùãẽĩõũ', 'aeiou'*5) # this can be done with the unidecode library\n",
    "\n",
    "\n",
    "df[\"nombre\"] = (df[\"nombre\"].str.replace(\"[\\(].*?[\\)]\", \"\", regex=True)\n",
    "                            .str.lower()\n",
    "                            .str.translate(trans)\n",
    "                            .str.title()\n",
    "                            .str.rstrip()\n",
    "               )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "394def91",
   "metadata": {},
   "source": [
    "Vamos a quedarnos solo con los datos cuyos nombres tengan una longitud mayor a 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a197970",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"nombre\"].str.len() > 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d865e07",
   "metadata": {},
   "source": [
    "Y vamos a revisar si tenemos entradas marcadas como datos perdidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e968841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nombre      0\n",
       "cantidad    0\n",
       "año         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b421cec",
   "metadata": {},
   "source": [
    "El set de datos original contenía algunos valores, pero estos fueron eliminados en la liempieza que hicimos anterioremente. Si todavía tuvieramos datos faltantes podríamos haberlos esliminado haciendo\n",
    "\n",
    "```python\n",
    "df.dropna(inplace=True)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d956fbf1",
   "metadata": {},
   "source": [
    "Es posible seguir normalizando y limpiando estos datos, ya que hay varias anomalías en los nombres. Cómo números, nombres incompletos etc. A los fines de este ejemplo consideraremos que todas esas anomalías ocurren con suficiente baja probabilidad como para no afectar el resultado de nuestro análisis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d712b239",
   "metadata": {},
   "source": [
    "## Algunos nombres con patrones particulares"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64a49bc",
   "metadata": {},
   "source": [
    "Es común que la mayoría de los nombres sean populares durante solo un periodo relativamente corto de tiempo. Muchas veces la popularidad de los nombres se relaciona con eventos históricos o de la cultura popular.\n",
    "\n",
    "Veamos por ejemplo como cambio la frecuencia del nombre \"Diego Armando\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3eff4241",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_diego = df[df[\"nombre\"] == \"Diego Armando\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e938eec",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df_diego.plot(x=\"año\", y=\"cantidad\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a43d1c",
   "metadata": {},
   "source": [
    "No es sorpresa que la popularidad de este nombre esté estrechamente vinculada a la popularidad de Diego Armándo Maradona y a su trayectoria profesional como futbolísta, por ejemplo vemos como el primer pico se vincula con su entrada al equipo de Boca Juniors (1981-1982) y el segundo pico al mundial de fútbol celebrado en México en el año 1986."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63d9050b",
   "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>nombre</th>\n",
       "      <th>cantidad</th>\n",
       "      <th>año</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4331462</th>\n",
       "      <td>Diego Armando</td>\n",
       "      <td>1699</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4989636</th>\n",
       "      <td>Diego Armando</td>\n",
       "      <td>1496</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4259954</th>\n",
       "      <td>Diego Armando</td>\n",
       "      <td>1133</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4458306</th>\n",
       "      <td>Diego Armando</td>\n",
       "      <td>1078</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4135435</th>\n",
       "      <td>Diego Armando</td>\n",
       "      <td>991</td>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                nombre  cantidad   año\n",
       "4331462  Diego Armando      1699  1981\n",
       "4989636  Diego Armando      1496  1986\n",
       "4259954  Diego Armando      1133  1980\n",
       "4458306  Diego Armando      1078  1982\n",
       "4135435  Diego Armando       991  1979"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diego_sorted = df_diego.sort_values(\"cantidad\", ascending=False)\n",
    "df_diego_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4d4397",
   "metadata": {},
   "source": [
    "Otro nombre que muestra un perfil muy particular es \"Malvina\". En este caso en vez de buscar exactamente el nombre \"Malvina\", vamos a buscar cualquier nombre que lo contenga, es decir contaremos como válida \"Malvina\" pero tambien nombres como \"Maria Malvina\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "89b89d7c",
   "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>nombre</th>\n",
       "      <th>cantidad</th>\n",
       "      <th>año</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>Maria Malvina</td>\n",
       "      <td>2</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4072</th>\n",
       "      <td>Malvina Maria Gracia</td>\n",
       "      <td>1</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8944</th>\n",
       "      <td>Malvina</td>\n",
       "      <td>1</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16978</th>\n",
       "      <td>Erminda Malvina</td>\n",
       "      <td>1</td>\n",
       "      <td>1923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17099</th>\n",
       "      <td>Malvina Olga</td>\n",
       "      <td>1</td>\n",
       "      <td>1923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     nombre  cantidad   año\n",
       "1489          Maria Malvina         2  1922\n",
       "4072   Malvina Maria Gracia         1  1922\n",
       "8944                Malvina         1  1922\n",
       "16978       Erminda Malvina         1  1923\n",
       "17099          Malvina Olga         1  1923"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_malvina = df[df[\"nombre\"].str.contains(\"Malvina\")]\n",
    "df_malvina.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26268294",
   "metadata": {},
   "source": [
    "Como en cada año tenemos más de una instancia de \"Malvina\", necesitamos agrupar por año y sumar las cantidades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2bcbc86f",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df_malvina = df_malvina.groupby(\"año\").sum().reset_index()\n",
    "df_malvina.plot(x=\"año\", y=\"cantidad\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c86a6f",
   "metadata": {},
   "source": [
    "## Cuáles han sido los nombres más populares por año?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02670e12",
   "metadata": {},
   "source": [
    "Ahora veremos cual es el nombre más popular en cada año, para ello creamos una función que ordena las cantidades de forma descendente y luego toma los primeros `num` elementos.\n",
    "\n",
    "Luego agrupamos por año y aplicamos la función. Por último reseteamos el índice (hacemos que vuelva a ser de 0 a la cantidad de años -1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "233b88da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_n(group, num):\n",
    "    return group.sort_values(\"cantidad\", ascending=False)[:num]\n",
    "\n",
    "top1 = df.groupby([\"año\"]).apply(top_n, 1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44005a0b",
   "metadata": {},
   "source": [
    "A fin de facilitar la visualización del resultado omitimos la columna `cantidad`, transponemos el DataFrame (cambiamos filas por columnas) y ocultamos las columnas (que antes era el índice)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19062fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_b212a\">\n",
       "  <thead>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b212a_level0_row0\" class=\"row_heading level0 row0\" >nombre</th>\n",
       "      <td id=\"T_b212a_row0_col0\" class=\"data row0 col0\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col1\" class=\"data row0 col1\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col2\" class=\"data row0 col2\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col3\" class=\"data row0 col3\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col4\" class=\"data row0 col4\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col5\" class=\"data row0 col5\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col6\" class=\"data row0 col6\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col7\" class=\"data row0 col7\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col8\" class=\"data row0 col8\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col9\" class=\"data row0 col9\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col10\" class=\"data row0 col10\" >Maria</td>\n",
       "      <td id=\"T_b212a_row0_col11\" class=\"data row0 col11\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col12\" class=\"data row0 col12\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col13\" class=\"data row0 col13\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col14\" class=\"data row0 col14\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col15\" class=\"data row0 col15\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col16\" class=\"data row0 col16\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col17\" class=\"data row0 col17\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col18\" class=\"data row0 col18\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col19\" class=\"data row0 col19\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col20\" class=\"data row0 col20\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col21\" class=\"data row0 col21\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col22\" class=\"data row0 col22\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col23\" class=\"data row0 col23\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col24\" class=\"data row0 col24\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col25\" class=\"data row0 col25\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col26\" class=\"data row0 col26\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col27\" class=\"data row0 col27\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col28\" class=\"data row0 col28\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col29\" class=\"data row0 col29\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col30\" class=\"data row0 col30\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col31\" class=\"data row0 col31\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col32\" class=\"data row0 col32\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col33\" class=\"data row0 col33\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col34\" class=\"data row0 col34\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col35\" class=\"data row0 col35\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col36\" class=\"data row0 col36\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col37\" class=\"data row0 col37\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col38\" class=\"data row0 col38\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col39\" class=\"data row0 col39\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col40\" class=\"data row0 col40\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col41\" class=\"data row0 col41\" >Norma Beatriz</td>\n",
       "      <td id=\"T_b212a_row0_col42\" class=\"data row0 col42\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col43\" class=\"data row0 col43\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col44\" class=\"data row0 col44\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col45\" class=\"data row0 col45\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col46\" class=\"data row0 col46\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col47\" class=\"data row0 col47\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col48\" class=\"data row0 col48\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col49\" class=\"data row0 col49\" >Miguel Angel</td>\n",
       "      <td id=\"T_b212a_row0_col50\" class=\"data row0 col50\" >Juan Carlos</td>\n",
       "      <td id=\"T_b212a_row0_col51\" class=\"data row0 col51\" >Miguel Angel</td>\n",
       "      <td id=\"T_b212a_row0_col52\" class=\"data row0 col52\" >Miguel Angel</td>\n",
       "      <td id=\"T_b212a_row0_col53\" class=\"data row0 col53\" >Leonel Hernan</td>\n",
       "      <td id=\"T_b212a_row0_col54\" class=\"data row0 col54\" >Miguel Angel</td>\n",
       "      <td id=\"T_b212a_row0_col55\" class=\"data row0 col55\" >Miguel Angel</td>\n",
       "      <td id=\"T_b212a_row0_col56\" class=\"data row0 col56\" >Miguel Angel</td>\n",
       "      <td id=\"T_b212a_row0_col57\" class=\"data row0 col57\" >Maria Laura</td>\n",
       "      <td id=\"T_b212a_row0_col58\" class=\"data row0 col58\" >Maria Laura</td>\n",
       "      <td id=\"T_b212a_row0_col59\" class=\"data row0 col59\" >Maria Laura</td>\n",
       "      <td id=\"T_b212a_row0_col60\" class=\"data row0 col60\" >Juan Pablo</td>\n",
       "      <td id=\"T_b212a_row0_col61\" class=\"data row0 col61\" >Maria Laura</td>\n",
       "      <td id=\"T_b212a_row0_col62\" class=\"data row0 col62\" >Maria Laura</td>\n",
       "      <td id=\"T_b212a_row0_col63\" class=\"data row0 col63\" >Juan Manuel</td>\n",
       "      <td id=\"T_b212a_row0_col64\" class=\"data row0 col64\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col65\" class=\"data row0 col65\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col66\" class=\"data row0 col66\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col67\" class=\"data row0 col67\" >Maria Florencia</td>\n",
       "      <td id=\"T_b212a_row0_col68\" class=\"data row0 col68\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col69\" class=\"data row0 col69\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col70\" class=\"data row0 col70\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col71\" class=\"data row0 col71\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col72\" class=\"data row0 col72\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col73\" class=\"data row0 col73\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col74\" class=\"data row0 col74\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col75\" class=\"data row0 col75\" >Maria Belen</td>\n",
       "      <td id=\"T_b212a_row0_col76\" class=\"data row0 col76\" >Agustin</td>\n",
       "      <td id=\"T_b212a_row0_col77\" class=\"data row0 col77\" >Valentina</td>\n",
       "      <td id=\"T_b212a_row0_col78\" class=\"data row0 col78\" >Valentina</td>\n",
       "      <td id=\"T_b212a_row0_col79\" class=\"data row0 col79\" >Valentina</td>\n",
       "      <td id=\"T_b212a_row0_col80\" class=\"data row0 col80\" >Valentina</td>\n",
       "      <td id=\"T_b212a_row0_col81\" class=\"data row0 col81\" >Valentina</td>\n",
       "      <td id=\"T_b212a_row0_col82\" class=\"data row0 col82\" >Valentina</td>\n",
       "      <td id=\"T_b212a_row0_col83\" class=\"data row0 col83\" >Valentina</td>\n",
       "      <td id=\"T_b212a_row0_col84\" class=\"data row0 col84\" >Valentina</td>\n",
       "      <td id=\"T_b212a_row0_col85\" class=\"data row0 col85\" >Valentina</td>\n",
       "      <td id=\"T_b212a_row0_col86\" class=\"data row0 col86\" >Valentina</td>\n",
       "      <td id=\"T_b212a_row0_col87\" class=\"data row0 col87\" >Benjamin</td>\n",
       "      <td id=\"T_b212a_row0_col88\" class=\"data row0 col88\" >Benjamin</td>\n",
       "      <td id=\"T_b212a_row0_col89\" class=\"data row0 col89\" >Benjamin</td>\n",
       "      <td id=\"T_b212a_row0_col90\" class=\"data row0 col90\" >Benjamin</td>\n",
       "      <td id=\"T_b212a_row0_col91\" class=\"data row0 col91\" >Benjamin</td>\n",
       "      <td id=\"T_b212a_row0_col92\" class=\"data row0 col92\" >Benjamin</td>\n",
       "      <td id=\"T_b212a_row0_col93\" class=\"data row0 col93\" >Benjamin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b212a_level0_row1\" class=\"row_heading level0 row1\" >año</th>\n",
       "      <td id=\"T_b212a_row1_col0\" class=\"data row1 col0\" >1922</td>\n",
       "      <td id=\"T_b212a_row1_col1\" class=\"data row1 col1\" >1923</td>\n",
       "      <td id=\"T_b212a_row1_col2\" class=\"data row1 col2\" >1924</td>\n",
       "      <td id=\"T_b212a_row1_col3\" class=\"data row1 col3\" >1925</td>\n",
       "      <td id=\"T_b212a_row1_col4\" class=\"data row1 col4\" >1926</td>\n",
       "      <td id=\"T_b212a_row1_col5\" class=\"data row1 col5\" >1927</td>\n",
       "      <td id=\"T_b212a_row1_col6\" class=\"data row1 col6\" >1928</td>\n",
       "      <td id=\"T_b212a_row1_col7\" class=\"data row1 col7\" >1929</td>\n",
       "      <td id=\"T_b212a_row1_col8\" class=\"data row1 col8\" >1930</td>\n",
       "      <td id=\"T_b212a_row1_col9\" class=\"data row1 col9\" >1931</td>\n",
       "      <td id=\"T_b212a_row1_col10\" class=\"data row1 col10\" >1932</td>\n",
       "      <td id=\"T_b212a_row1_col11\" class=\"data row1 col11\" >1933</td>\n",
       "      <td id=\"T_b212a_row1_col12\" class=\"data row1 col12\" >1934</td>\n",
       "      <td id=\"T_b212a_row1_col13\" class=\"data row1 col13\" >1935</td>\n",
       "      <td id=\"T_b212a_row1_col14\" class=\"data row1 col14\" >1936</td>\n",
       "      <td id=\"T_b212a_row1_col15\" class=\"data row1 col15\" >1937</td>\n",
       "      <td id=\"T_b212a_row1_col16\" class=\"data row1 col16\" >1938</td>\n",
       "      <td id=\"T_b212a_row1_col17\" class=\"data row1 col17\" >1939</td>\n",
       "      <td id=\"T_b212a_row1_col18\" class=\"data row1 col18\" >1940</td>\n",
       "      <td id=\"T_b212a_row1_col19\" class=\"data row1 col19\" >1941</td>\n",
       "      <td id=\"T_b212a_row1_col20\" class=\"data row1 col20\" >1942</td>\n",
       "      <td id=\"T_b212a_row1_col21\" class=\"data row1 col21\" >1943</td>\n",
       "      <td id=\"T_b212a_row1_col22\" class=\"data row1 col22\" >1944</td>\n",
       "      <td id=\"T_b212a_row1_col23\" class=\"data row1 col23\" >1945</td>\n",
       "      <td id=\"T_b212a_row1_col24\" class=\"data row1 col24\" >1946</td>\n",
       "      <td id=\"T_b212a_row1_col25\" class=\"data row1 col25\" >1947</td>\n",
       "      <td id=\"T_b212a_row1_col26\" class=\"data row1 col26\" >1948</td>\n",
       "      <td id=\"T_b212a_row1_col27\" class=\"data row1 col27\" >1949</td>\n",
       "      <td id=\"T_b212a_row1_col28\" class=\"data row1 col28\" >1950</td>\n",
       "      <td id=\"T_b212a_row1_col29\" class=\"data row1 col29\" >1951</td>\n",
       "      <td id=\"T_b212a_row1_col30\" class=\"data row1 col30\" >1952</td>\n",
       "      <td id=\"T_b212a_row1_col31\" class=\"data row1 col31\" >1953</td>\n",
       "      <td id=\"T_b212a_row1_col32\" class=\"data row1 col32\" >1954</td>\n",
       "      <td id=\"T_b212a_row1_col33\" class=\"data row1 col33\" >1955</td>\n",
       "      <td id=\"T_b212a_row1_col34\" class=\"data row1 col34\" >1956</td>\n",
       "      <td id=\"T_b212a_row1_col35\" class=\"data row1 col35\" >1957</td>\n",
       "      <td id=\"T_b212a_row1_col36\" class=\"data row1 col36\" >1958</td>\n",
       "      <td id=\"T_b212a_row1_col37\" class=\"data row1 col37\" >1959</td>\n",
       "      <td id=\"T_b212a_row1_col38\" class=\"data row1 col38\" >1960</td>\n",
       "      <td id=\"T_b212a_row1_col39\" class=\"data row1 col39\" >1961</td>\n",
       "      <td id=\"T_b212a_row1_col40\" class=\"data row1 col40\" >1962</td>\n",
       "      <td id=\"T_b212a_row1_col41\" class=\"data row1 col41\" >1963</td>\n",
       "      <td id=\"T_b212a_row1_col42\" class=\"data row1 col42\" >1964</td>\n",
       "      <td id=\"T_b212a_row1_col43\" class=\"data row1 col43\" >1965</td>\n",
       "      <td id=\"T_b212a_row1_col44\" class=\"data row1 col44\" >1966</td>\n",
       "      <td id=\"T_b212a_row1_col45\" class=\"data row1 col45\" >1967</td>\n",
       "      <td id=\"T_b212a_row1_col46\" class=\"data row1 col46\" >1968</td>\n",
       "      <td id=\"T_b212a_row1_col47\" class=\"data row1 col47\" >1969</td>\n",
       "      <td id=\"T_b212a_row1_col48\" class=\"data row1 col48\" >1970</td>\n",
       "      <td id=\"T_b212a_row1_col49\" class=\"data row1 col49\" >1971</td>\n",
       "      <td id=\"T_b212a_row1_col50\" class=\"data row1 col50\" >1972</td>\n",
       "      <td id=\"T_b212a_row1_col51\" class=\"data row1 col51\" >1973</td>\n",
       "      <td id=\"T_b212a_row1_col52\" class=\"data row1 col52\" >1974</td>\n",
       "      <td id=\"T_b212a_row1_col53\" class=\"data row1 col53\" >1975</td>\n",
       "      <td id=\"T_b212a_row1_col54\" class=\"data row1 col54\" >1976</td>\n",
       "      <td id=\"T_b212a_row1_col55\" class=\"data row1 col55\" >1977</td>\n",
       "      <td id=\"T_b212a_row1_col56\" class=\"data row1 col56\" >1978</td>\n",
       "      <td id=\"T_b212a_row1_col57\" class=\"data row1 col57\" >1979</td>\n",
       "      <td id=\"T_b212a_row1_col58\" class=\"data row1 col58\" >1980</td>\n",
       "      <td id=\"T_b212a_row1_col59\" class=\"data row1 col59\" >1981</td>\n",
       "      <td id=\"T_b212a_row1_col60\" class=\"data row1 col60\" >1982</td>\n",
       "      <td id=\"T_b212a_row1_col61\" class=\"data row1 col61\" >1983</td>\n",
       "      <td id=\"T_b212a_row1_col62\" class=\"data row1 col62\" >1984</td>\n",
       "      <td id=\"T_b212a_row1_col63\" class=\"data row1 col63\" >1985</td>\n",
       "      <td id=\"T_b212a_row1_col64\" class=\"data row1 col64\" >1986</td>\n",
       "      <td id=\"T_b212a_row1_col65\" class=\"data row1 col65\" >1987</td>\n",
       "      <td id=\"T_b212a_row1_col66\" class=\"data row1 col66\" >1988</td>\n",
       "      <td id=\"T_b212a_row1_col67\" class=\"data row1 col67\" >1989</td>\n",
       "      <td id=\"T_b212a_row1_col68\" class=\"data row1 col68\" >1990</td>\n",
       "      <td id=\"T_b212a_row1_col69\" class=\"data row1 col69\" >1991</td>\n",
       "      <td id=\"T_b212a_row1_col70\" class=\"data row1 col70\" >1992</td>\n",
       "      <td id=\"T_b212a_row1_col71\" class=\"data row1 col71\" >1993</td>\n",
       "      <td id=\"T_b212a_row1_col72\" class=\"data row1 col72\" >1994</td>\n",
       "      <td id=\"T_b212a_row1_col73\" class=\"data row1 col73\" >1995</td>\n",
       "      <td id=\"T_b212a_row1_col74\" class=\"data row1 col74\" >1996</td>\n",
       "      <td id=\"T_b212a_row1_col75\" class=\"data row1 col75\" >1997</td>\n",
       "      <td id=\"T_b212a_row1_col76\" class=\"data row1 col76\" >1998</td>\n",
       "      <td id=\"T_b212a_row1_col77\" class=\"data row1 col77\" >1999</td>\n",
       "      <td id=\"T_b212a_row1_col78\" class=\"data row1 col78\" >2000</td>\n",
       "      <td id=\"T_b212a_row1_col79\" class=\"data row1 col79\" >2001</td>\n",
       "      <td id=\"T_b212a_row1_col80\" class=\"data row1 col80\" >2002</td>\n",
       "      <td id=\"T_b212a_row1_col81\" class=\"data row1 col81\" >2003</td>\n",
       "      <td id=\"T_b212a_row1_col82\" class=\"data row1 col82\" >2004</td>\n",
       "      <td id=\"T_b212a_row1_col83\" class=\"data row1 col83\" >2005</td>\n",
       "      <td id=\"T_b212a_row1_col84\" class=\"data row1 col84\" >2006</td>\n",
       "      <td id=\"T_b212a_row1_col85\" class=\"data row1 col85\" >2007</td>\n",
       "      <td id=\"T_b212a_row1_col86\" class=\"data row1 col86\" >2008</td>\n",
       "      <td id=\"T_b212a_row1_col87\" class=\"data row1 col87\" >2009</td>\n",
       "      <td id=\"T_b212a_row1_col88\" class=\"data row1 col88\" >2010</td>\n",
       "      <td id=\"T_b212a_row1_col89\" class=\"data row1 col89\" >2011</td>\n",
       "      <td id=\"T_b212a_row1_col90\" class=\"data row1 col90\" >2012</td>\n",
       "      <td id=\"T_b212a_row1_col91\" class=\"data row1 col91\" >2013</td>\n",
       "      <td id=\"T_b212a_row1_col92\" class=\"data row1 col92\" >2014</td>\n",
       "      <td id=\"T_b212a_row1_col93\" class=\"data row1 col93\" >2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f21b907fc10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1[[\"nombre\", \"año\"]].T.style.hide(axis=\"columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f598dbc0",
   "metadata": {},
   "source": [
    "Ahora vamos a agregar una columna \"prop\" a nuestro DataFrame, indicando la proporción que cada nombre representa del total de nombres por año."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "65a2138c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_prop(grupo):\n",
    "    grupo[\"prop\"] = grupo[\"cantidad\"] / grupo[\"cantidad\"].sum()\n",
    "    return grupo\n",
    "\n",
    "df = df.groupby(\"año\").apply(add_prop)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a282701f",
   "metadata": {},
   "source": [
    "Revisamos que las proporciones den 1 por año, para todos los años"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8802c830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby(\"año\").sum()[\"prop\"] == 1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "483eef24",
   "metadata": {},
   "source": [
    "Ahora vamos a graficar la distribución a los largo del tiempo de 4 de los nombres más populares, \"Maria\", \"Juan Carlos\", \"Valentina\" y \"Benjamin\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a3c0337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nombres = [\"Maria\", \"Juan Carlos\", \"Valentina\", \"Benjamin\"]\n",
    "_, axes = plt.subplots(len(nombres), sharex=True, sharey=True, figsize=(12, 6), constrained_layout=True)\n",
    "\n",
    "for nombre, ax in zip(nombres, axes):\n",
    "    (df[df[\"nombre\"].str.contains(nombre)].groupby(\"año\")\n",
    "                                          .sum()\n",
    "                                          .reset_index()\n",
    "                                          .plot(\"año\", \n",
    "                                                \"prop\", ax=ax, legend=False, title=nombre))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924ca4b4",
   "metadata": {},
   "source": [
    "En la figura anterior hemos indicado que queremos compartir el `eje-y` entre los distintos subplots. Esto tiene la ventaja de simplificar la comparación entre subplots. Pero hay veces que puede ser preferible dejar que cada subplot tenga sus propios valores para el `eje-y`. En esos casos es conveniente indicar que la escala no es compartida."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63da9047",
   "metadata": {},
   "source": [
    "## Diversidad de nombres"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c570351",
   "metadata": {},
   "source": [
    "No solo la popularidad de cada nombre sigue modas y refleja cambios culturales. La diversidad de los nombres también puede variar en el tiempo. Hay mayor diversidad de nombres ahora que hace un siglo? \n",
    "\n",
    "Para responder a este interrogante empecemos por calcular la proporción de cada nombre por año"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4012c0b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_prop(group):\n",
    "    group[\"prop\"] = group[\"cantidad\"] / group[\"cantidad\"].sum()\n",
    "    return group\n",
    "\n",
    "df = df.groupby([\"año\"]).apply(add_prop)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0fcbdb",
   "metadata": {},
   "source": [
    "Podemos corroborar que para cada año la suma da 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fc81f836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    94.0\n",
       "mean      1.0\n",
       "std       0.0\n",
       "min       1.0\n",
       "25%       1.0\n",
       "50%       1.0\n",
       "75%       1.0\n",
       "max       1.0\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"año\"])[\"prop\"].sum().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86cba9ce",
   "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>nombre</th>\n",
       "      <th>cantidad</th>\n",
       "      <th>año</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>314</td>\n",
       "      <td>1922</td>\n",
       "      <td>0.013269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rosa</td>\n",
       "      <td>203</td>\n",
       "      <td>1922</td>\n",
       "      <td>0.008578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jose</td>\n",
       "      <td>163</td>\n",
       "      <td>1922</td>\n",
       "      <td>0.006888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maria Luisa</td>\n",
       "      <td>127</td>\n",
       "      <td>1922</td>\n",
       "      <td>0.005367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carmen</td>\n",
       "      <td>117</td>\n",
       "      <td>1922</td>\n",
       "      <td>0.004944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        nombre  cantidad   año      prop\n",
       "0        Maria       314  1922  0.013269\n",
       "1         Rosa       203  1922  0.008578\n",
       "2         Jose       163  1922  0.006888\n",
       "3  Maria Luisa       127  1922  0.005367\n",
       "4       Carmen       117  1922  0.004944"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c753fcf3",
   "metadata": {},
   "source": [
    "Ahora calcularemos cuales son los 1000 nombres más populares por año."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f33cd75",
   "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>nombre</th>\n",
       "      <th>cantidad</th>\n",
       "      <th>año</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>314</td>\n",
       "      <td>1922</td>\n",
       "      <td>0.013269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rosa</td>\n",
       "      <td>203</td>\n",
       "      <td>1922</td>\n",
       "      <td>0.008578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jose</td>\n",
       "      <td>163</td>\n",
       "      <td>1922</td>\n",
       "      <td>0.006888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maria Luisa</td>\n",
       "      <td>127</td>\n",
       "      <td>1922</td>\n",
       "      <td>0.005367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carmen</td>\n",
       "      <td>117</td>\n",
       "      <td>1922</td>\n",
       "      <td>0.004944</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>93995</th>\n",
       "      <td>Martin Ezequiel</td>\n",
       "      <td>71</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.000090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93996</th>\n",
       "      <td>Ian Maximiliano</td>\n",
       "      <td>71</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.000090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93997</th>\n",
       "      <td>Zoe Yazmin</td>\n",
       "      <td>71</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.000090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93998</th>\n",
       "      <td>Isabella Francesca</td>\n",
       "      <td>71</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.000090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93999</th>\n",
       "      <td>Zaira</td>\n",
       "      <td>71</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.000090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   nombre  cantidad   año      prop\n",
       "0                   Maria       314  1922  0.013269\n",
       "1                    Rosa       203  1922  0.008578\n",
       "2                    Jose       163  1922  0.006888\n",
       "3             Maria Luisa       127  1922  0.005367\n",
       "4                  Carmen       117  1922  0.004944\n",
       "...                   ...       ...   ...       ...\n",
       "93995     Martin Ezequiel        71  2015  0.000090\n",
       "93996     Ian Maximiliano        71  2015  0.000090\n",
       "93997          Zoe Yazmin        71  2015  0.000090\n",
       "93998  Isabella Francesca        71  2015  0.000090\n",
       "93999               Zaira        71  2015  0.000090\n",
       "\n",
       "[94000 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000 = df.groupby([\"año\"]).apply(top_n, 1000).reset_index(drop=True)\n",
    "top1000  # podemos ver que la cantidad total de filas es 94 * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "daadfdb0",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "prop_año = top1000.groupby(\"año\")[\"prop\"].sum()\n",
    "prop_año.plot(title=\"Proporción anual de los 1000 nombres más comunes\",\n",
    "           #yticks=np.linspace(0, 0.5, 5)\n",
    "          );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "202e507b",
   "metadata": {},
   "source": [
    "Una forma alternativa de explorar esta pregunta es calcular cual es la cantidad mínima de nombres para obtener el 50% de la proporción total de nombres por año.\n",
    "\n",
    "\n",
    "Para calcular esta cantidad, primero tenemos que ordenar las proporciones en orden descendente. Luego calculamos la suma acumulada de la proporción (que va de 0 a 1 siempre creciendo). Por último contamos cuantos valores de la suma acumulada son menores o iguales a 0.5 y los sumamos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b2bcfab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cum_count(grupo, q=0.5):\n",
    "    grupo = grupo.sort_values(\"prop\", ascending=False)\n",
    "    return (grupo.prop.cumsum() <= 0.5).sum() \n",
    "\n",
    "diversidad = df.groupby([\"año\"]).apply(cum_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eb96ed08",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "diversidad.plot(title=\"Cantidad de nombres para obtener el 50%\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba670a0",
   "metadata": {},
   "source": [
    "## Longitud de los nombres\n",
    "\n",
    "Ahora exploraremos la longitud de los nombres y su cambio con lo años"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "32c9a3b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def longitud(group):\n",
    "    group[\"len\"] = group[\"nombre\"].str.len()\n",
    "    return group\n",
    "\n",
    "df = df.groupby([\"año\"]).apply(longitud)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b34f2dc",
   "metadata": {},
   "source": [
    "Y el nombre más largo registrado en Argentina es..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "41c25c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Josina Livina Dolores Lucia Carmen Balduina Yolanda Maria Inmaculada Gislena Javiera'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"len\"] == df[\"len\"].max()][\"nombre\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07760287",
   "metadata": {},
   "source": [
    "Ahora veamos cual es la distribución de la longitud de nombres, para esto usaremos un histograma junto un con \"rug plot\" (o gráfico de alfombra). Hacemos esto para ayudar a destacar la larga cola de la distribución de nombres. Dado que las longitudes son discretas hemos decidido usar un bin para cada longitud observada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "63e56d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEDCAYAAAAlRP8qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAASXUlEQVR4nO3dfbBcd13H8feHFFAetEAuTk1aU5xQ7DC0wrVFRQ0gkraOGWdQW3kQppjpDMWH8YEwjkDHf8r4MOhYyGRq7OBDOyIVY1upDgJVsJpbLDVpCcS2tteiuRQoI8wIKV//2BO6XW7u7k02OXt/eb9mdu6ec3579tO9N5+e+9uz56aqkCStfU/oO4AkaTosdElqhIUuSY2w0CWpERa6JDXCQpekRvRa6El2JzmUZN+E4386yd1J9if58xOdT5LWkvR5HnqSHwb+F3hvVT1/zNjNwF8AL6uqLyR5dlUdOhk5JWkt6PUIvapuAz4/vC7Jdyf5YJI7kvxjkud1m34euKaqvtA91jKXpCGzOIe+C3hzVb0I+FXg3d365wLPTfKxJLcn2dpbQkmaQaf1HWBYkqcBPwC8L8mR1U/uvp4GbAa2ABuBf0zy/Kr64kmOKUkzaaYKncFvDF+sqvOX2bYI3F5VXwPuS3KAQcHvPYn5JGlmzdSUS1V9iUFZ/xRABs7rNn8AeGm3fj2DKZh7+8gpSbOo79MWrwf+GTgnyWKSy4FXA5cn+SSwH9jWDb8VeDjJ3cCHgV+rqof7yC1Js6jX0xYlSdMzU1MukqRj19ubouvXr69Nmzb19fSStCbdcccdn6uqueW29VbomzZtYmFhoa+nl6Q1Kcl/Hm2bUy6S1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktSIWbse+pq2acfNj1u+/+pLekoi6VTkEbokNcJCl6RGWOiS1Ajn0E8g59QlnUweoUtSIyx0SWqEhS5JjbDQJakRFrokNWJsoSfZneRQkn1jxn1fkkeTvGp68SRJk5rkCP06YOtKA5KsA94J3DqFTJKkYzC20KvqNuDzY4a9GXg/cGgaoSRJq3fcc+hJNgA/CeycYOz2JAtJFpaWlo73qSVJQ6bxpui7gLdU1aPjBlbVrqqar6r5ubm5KTy1JOmIaXz0fx64IQnAeuDiJIer6gNT2LckaULHXehVdfaR+0muA26yzCXp5Btb6EmuB7YA65MsAm8HnghQVWPnzSVJJ8fYQq+qyybdWVW9/rjSSJKOmZ8UlaRGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUiLGFnmR3kkNJ9h1l+6uT3NXdPp7kvOnHlCSNM8kR+nXA1hW23wf8SFW9APgtYNcUckmSVmmSPxJ9W5JNK2z/+NDi7cDGKeSSJK3S2EJfpcuBv53yPmfWph039x1Bkr5haoWe5KUMCv0lK4zZDmwHOOuss6b11JIkpnSWS5IXANcC26rq4aONq6pdVTVfVfNzc3PTeGpJUue4Cz3JWcCNwGur6tPHH0mSdCzGTrkkuR7YAqxPsgi8HXgiQFXtBN4GPAt4dxKAw1U1f6ICr2XDc+73X31Jj0kktWiSs1wuG7P9jcAbp5ZIknRM/KSoJDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1IixhZ5kd5JDSfYdZXuS/EGSg0nuSvLC6ceUJI0zyRH6dcDWFbZfBGzubtuB9xx/LEnSao0t9Kq6Dfj8CkO2Ae+tgduB05OcMa2AkqTJTGMOfQPw4NDyYrfumyTZnmQhycLS0tIUnlqSdMQ0Cj3LrKvlBlbVrqqar6r5ubm5KTy1JOmIaRT6InDm0PJG4KEp7FeStArTKPQ9wOu6s11eDDxSVZ+dwn4lSatw2rgBSa4HtgDrkywCbweeCFBVO4FbgIuBg8BXgDecqLCSpKMbW+hVddmY7QW8aWqJJEnHxE+KSlIjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2YqNCTbE1yIMnBJDuW2f7tSf4mySeT7E/iH4qWpJNsbKEnWQdcA1wEnAtcluTckWFvAu6uqvOALcDvJnnSlLNKklYwyRH6BcDBqrq3qr4K3ABsGxlTwNOTBHga8Hng8FSTSpJWdNoEYzYADw4tLwIXjoz5Q2AP8BDwdOBnqurroztKsh3YDnDWWWcdS95mbNpx8+OW77/6kp6SSGrFJEfoWWZdjSy/ErgT+E7gfOAPk3zbNz2oaldVzVfV/Nzc3CqjSpJWMkmhLwJnDi1vZHAkPuwNwI01cBC4D3jedCJKkiYxSaHvBTYnObt7o/NSBtMrwx4AXg6Q5DuAc4B7pxlUkrSysXPoVXU4yZXArcA6YHdV7U9yRbd9J/BbwHVJ/p3BFM1bqupzJzC3JGnEJG+KUlW3ALeMrNs5dP8h4MemG02StBp+UlSSGmGhS1IjLHRJasREc+gaGP0wkCTNEo/QJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJasREhZ5ka5IDSQ4m2XGUMVuS3Jlkf5KPTjemJGmcsX/gIsk64BrgFcAisDfJnqq6e2jM6cC7ga1V9UCSZ5+gvJKko5jkCP0C4GBV3VtVXwVuALaNjPlZ4MaqegCgqg5NN6YkaZxJCn0D8ODQ8mK3bthzgWck+UiSO5K8brkdJdmeZCHJwtLS0rElliQta5JCzzLramT5NOBFwCXAK4HfTPLcb3pQ1a6qmq+q+bm5uVWHlSQd3SR/JHoROHNoeSPw0DJjPldVXwa+nOQ24Dzg01NJKUkaa5Ij9L3A5iRnJ3kScCmwZ2TMXwM/lOS0JE8BLgTumW5USdJKxh6hV9XhJFcCtwLrgN1VtT/JFd32nVV1T5IPAncBXweurap9JzK4JOnxJplyoapuAW4ZWbdzZPm3gd+eXjRJ0mr4SVFJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyb6YJFOvE07bn7c8v1XX9JTEklrlUfoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUiIkKPcnWJAeSHEyyY4Vx35fk0SSvml5ESdIkxhZ6knXANcBFwLnAZUnOPcq4dzL4Y9KSpJNskiP0C4CDVXVvVX0VuAHYtsy4NwPvBw5NMZ8kaUKTFPoG4MGh5cVu3Tck2QD8JLBzpR0l2Z5kIcnC0tLSarNKklYwSaFnmXU1svwu4C1V9ehKO6qqXVU1X1Xzc3NzE0aUJE1iksvnLgJnDi1vBB4aGTMP3JAEYD1wcZLDVfWBaYSUJI03SaHvBTYnORv4L+BS4GeHB1TV2UfuJ7kOuMkyl6STa2yhV9XhJFcyOHtlHbC7qvYnuaLbvuK8uSTp5JjoLxZV1S3ALSPrli3yqnr98ceSJK2Wf4JujNE/DSdJs8qP/ktSIyx0SWqEhS5JjbDQJakRvik6o0bfjL3/6kt6SiJprfAIXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1Ij/Oj/GjF8KQAvAyBpOR6hS1IjLHRJasREhZ5ka5IDSQ4m2bHM9lcnuau7fTzJedOPKklaydg59CTrgGuAVwCLwN4ke6rq7qFh9wE/UlVfSHIRsAu48EQElpfWlbS8SY7QLwAOVtW9VfVV4AZg2/CAqvp4VX2hW7wd2DjdmJKkcSYp9A3Ag0PLi926o7kc+NvlNiTZnmQhycLS0tLkKSVJY01y2mKWWVfLDkxeyqDQX7Lc9qraxWA6hvn5+WX3odVzCkYSTFboi8CZQ8sbgYdGByV5AXAtcFFVPTydeJKkSU0y5bIX2Jzk7CRPAi4F9gwPSHIWcCPw2qr69PRjSpLGGXuEXlWHk1wJ3AqsA3ZX1f4kV3TbdwJvA54FvDsJwOGqmj9xsSVJo1LVz1T2/Px8LSws9PLcqzE6P70WOacutSPJHUc7YPaTopLUCC/ONaKFI3JJpyaP0CWpERa6JDXCQpekRjiHfgrwj2NIpwaP0CWpERa6JDXilJty8UJWklp1ShT6SueWe965pFacEoWux/gbitQu59AlqRFrstC7KzquuLxpx83fuP3nO3/8G9uG7692+XgeO819TfOxk7yWxzL2VH8swBOe8IRl7y839vTTTz/q9i1btjxu2+jyO97xDo5m3GNHrbSv47Ga/U4zw7h9DW8/Uf/t4553mtZkoUtrwfCVTMdd1fSRRx456raPfvSjKy5fddVVx/zYUSvt63isZr/TzDBuX8PbT9R/+7jnnSYLXZIa4Zui8kwfqREeoUtSIyx0SWpEM1MuThtMj6+ltDZNVOhJtgK/z+CPRF9bVVePbE+3/WLgK8Drq+oTU86qnljw0towttCTrAOuAV4BLAJ7k+ypqruHhl0EbO5uFwLv6b6qMZa7NLsmOUK/ADhYVfcCJLkB2AYMF/o24L01ONn29iSnJzmjqj479cQdi2U2jH4f/L5I/cm4DzwkeRWwtare2C2/Friwqq4cGnMTcHVV/VO3/CHgLVW1MLKv7cD2bvEc4MDQ5vXA5ybM/SLgjmNc7uuxs5LDx7b//eYYx06y/VitZr+jY1fTDat93pVe53FOZK6VfFdVzS23YZIj9CyzbvT/ApOMoap2AbuWfZJkoarmJ8hzUs1irlnMBLOZaxYzgblWYxYzwWzmmuS0xUXgzKHljcBDxzBGknQCTVLoe4HNSc5O8iTgUmDPyJg9wOsy8GLgkRM5fy5J+mZjp1yq6nCSK4FbGZy2uLuq9ie5otu+E7iFwSmLBxmctviGY8iy7FTMDJjFXLOYCWYz1yxmAnOtxixmghnMNfZNUUnS2uBH/yWpERa6JDViJgo9ydYkB5IcTLKjpwy7kxxKsm9o3TOT/H2Sz3Rfn9FDrjOTfDjJPUn2J/nFvrMl+ZYk/5rkk12mq/rONJJvXZJ/6z4f0XuuJPcn+fckdyZZmIVMXYbTk/xlkk91P1/f33euJOd0r9OR25eS/NIM5Prl7md9X5Lru38DvX8PR/Ve6EOXFrgIOBe4LMm5PUS5Dtg6sm4H8KGq2gx8qFs+2Q4Dv1JV3wO8GHhT9/r0me3/gJdV1XnA+cDW7uymWXi9AH4RuGdoeRZyvbSqzh86b3kWMv0+8MGqeh5wHoPXrNdcVXWge53OZ/Dhm68Af9VnriQbgF8A5qvq+QxODrm0z0xHVVW93oDvB24dWn4r8NaesmwC9g0tHwDO6O6fARyYgdfrrxlcV2cmsgFPAT7B4No9vWdi8BmIDwEvA26ahe8jcD+wfmRd35m+DbiP7sSIWck1kuXHgI/1nQvYADwIPJPBmYE3ddlm5rU6cuv9CJ3HXqwjFrt1s+A7qjufvvv67D7DJNkEfC/wL/ScrZvWuBM4BPx9VfWeqfMu4NeBrw+t6ztXAX+X5I7u8hezkOk5wBLwx9301LVJnjoDuYZdClzf3e8tV1X9F/A7wAPAZxl8zubv+sx0NLNQ6BNdNuBUl+RpwPuBX6qqL/Wdp6oercGvxRuBC5I8v+dIJPlx4FBVnYhrkRyPH6yqFzKYVnxTkh/uOxCDI80XAu+pqu8FvswsTBl0ug8x/gTwvhnI8gwGFyA8G/hO4KlJXtNvquXNQqHP8mUD/ifJGQDd10N9hEjyRAZl/mdVdeMsZauqLwIfYfD+Q9+ZfhD4iST3AzcAL0vyp33nqqqHuq+HGMwHX9B3Jgb/7ha736wA/pJBwfed64iLgE9U1f90y33m+lHgvqpaqqqvATcCP9BzpmXNQqFPcmmBvuwBfq67/3MM5q9PqiQB/gi4p6p+bxayJZlLcnp3/1sZ/MB/qs9MAFX11qraWFWbGPwc/UNVvabPXEmemuTpR+4zmHvd12cmgKr6b+DBJOd0q17O4JLYvf/Mdy7jsekW6DfXA8CLkzyl+/f4cgZvIM/Ka/WYvifxuzcULgY+DfwH8Bs9ZbiewfzY1xgcvVwOPIvBG2yf6b4+s4dcL2EwBXUXcGd3u7jPbMALgH/rMu0D3tat7/31Gsq4hcfeFO3ztXoO8Mnutv/Iz/csvFYMzlBa6L6PHwCeMSO5ngI8DHz70LpecwFXMTho2Qf8CfDkvjMtd/Oj/5LUiFmYcpEkTYGFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhrx/6C0yO+0N6w9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "longitudes = df[\"len\"].values\n",
    "plt.hist(\"len\", data=df, bins=np.unique(longitudes))\n",
    "plt.plot(longitudes, np.zeros_like(longitudes), \"k|\", ms=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ac7a36",
   "metadata": {},
   "source": [
    "Ahora vemos el cambio en el tiempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3cf88422",
   "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>año</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1922</td>\n",
       "      <td>12.850658</td>\n",
       "      <td>3.766880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1923</td>\n",
       "      <td>12.932770</td>\n",
       "      <td>3.763134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1924</td>\n",
       "      <td>13.045494</td>\n",
       "      <td>3.778390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1925</td>\n",
       "      <td>13.079113</td>\n",
       "      <td>3.707827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1926</td>\n",
       "      <td>13.185431</td>\n",
       "      <td>3.648110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    año       mean       std\n",
       "0  1922  12.850658  3.766880\n",
       "1  1923  12.932770  3.763134\n",
       "2  1924  13.045494  3.778390\n",
       "3  1925  13.079113  3.707827\n",
       "4  1926  13.185431  3.648110"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_len_summary = df.groupby('año').agg(\n",
    "                                       mean=('len', np.mean),\n",
    "                                       std=('len', np.std)\n",
    "                                       ).reset_index()\n",
    "\n",
    "\n",
    "df_len_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "15eb0069",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.errorbar(\"año\", \"mean\", \"std\", data=df_len_summary);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}