1_encabezados_columnas_son_valores.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encabezados de las columnas son valores\n",
    "\n",
    "Este notebook muestra dos ejemplos de como los encabezados o nombres de las columnas muestran valores. Este tipo de \"messy datasets\" tienen uso práctico en dos tipos de situaciones:\n",
    "\n",
    "1. Presentaciones\n",
    "2. Registro de observaciones espaciadas regularmente en el tiempo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"Housekeeping\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext lab_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import re\n",
    "\n",
    "import pandas as pd\n",
    "import savReaderWriter as spss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejemplo 1: Religion vs. Income\n",
    "\n",
    "> Un tipo de dataset messy común son los datos tabulares diseñados para **presentación**, donde las variables forman tanto filas y columnas, y los encabezados de las columnas son valores, y no nombres de las variables.\n",
    "\n",
    "El [Pew Research Center](http://www.pewresearch.org/) es un centro de estudios muy prolífico e influyente en investigación sobre todo tipo de aspectos de la vida en EEUU. Los siguientes ejemplos usan datos tomados del  [Religious Landscape Study](http://www.pewforum.org/religious-landscape-study/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cargando la data\n",
    "\n",
    "Los datos son entregados en un archivo de datos de SPSS. Esta es una especificación binaria con una sección de encabezado describiendo los datos, por ejemplo, que variables/columnas están incluidas y que instancias pueden tener los datos categóricos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargando la \"metadata\" del dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"q16\", \"reltrad\", \"income\"]\n",
    "encodings = {}\n",
    "\n",
    "# For the sake of simplicity, all data cleaning operations\n",
    "# are done within the for-loop for all columns.\n",
    "with spss.SavHeaderReader(\"data/pew.sav\") as pew:\n",
    "    for column in columns:\n",
    "        encodings[column] = {\n",
    "            int(key): (\n",
    "                re.sub(\n",
    "                    r\"\\(.*\\)\",\n",
    "                    \"\",\n",
    "                    (\n",
    "                        value.decode(\"iso-8859-1\")\n",
    "                        .replace(\"\\x92\", \"'\")\n",
    "                        .replace(\" Churches\", \"\")\n",
    "                        .replace(\"Less than $10,000\", \"<$10k\")\n",
    "                        .replace(\"10 to under $20,000\", \"$10-20k\")\n",
    "                        .replace(\"20 to under $30,000\", \"$20-30k\")\n",
    "                        .replace(\"30 to under $40,000\", \"$30-40k\")\n",
    "                        .replace(\"40 to under $50,000\", \"$40-50k\")\n",
    "                        .replace(\"50 to under $75,000\", \"$50-75k\")\n",
    "                        .replace(\"75 to under $100,000\", \"$75-100k\")\n",
    "                        .replace(\"100 to under $150,000\", \"$100-150k\")\n",
    "                        .replace(\"$150,000 or more\", \">150k\")\n",
    "                    ),\n",
    "                ).strip()\n",
    "            )\n",
    "            for (key, value) in pew.all().valueLabels[column.encode()].items()\n",
    "        }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargando la data y preparándola tal como está presentada en el *paper*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with spss.SavReader(\n",
    "    \"data/pew.sav\", selectVars=[column.encode() for column in columns]\n",
    ") as pew:\n",
    "    pew = list(pew)\n",
    "\n",
    "# Use the above encodings to map the numeric data\n",
    "# to the actual labels.\n",
    "pew = pd.DataFrame(pew, columns=columns, dtype=int)\n",
    "for column in columns:\n",
    "    pew[column] = pew[column].map(encodings[column])\n",
    "\n",
    "for value in (\"Atheist\", \"Agnostic\"):\n",
    "    pew.loc[(pew[\"q16\"] == value), \"reltrad\"] = value\n",
    "\n",
    "income_columns = [\n",
    "    \"<$10k\",\n",
    "    \"$10-20k\",\n",
    "    \"$20-30k\",\n",
    "    \"$30-40k\",\n",
    "    \"$40-50k\",\n",
    "    \"$50-75k\",\n",
    "    \"$75-100k\",\n",
    "    \"$100-150k\",\n",
    "    \">150k\",\n",
    "    \"Don't know/Refused\",\n",
    "]\n",
    "\n",
    "pew = pew.groupby([\"reltrad\", \"income\"]).size().unstack(\"income\")\n",
    "pew = pew[income_columns]\n",
    "pew.index.name = \"religion\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Messy Data\n",
    "\n",
    "La siguiente celda muestra la data \"sin procesar\" (\"cruda\", raw-data) (es decir, se supone que el preprocesamiento realizado anteriormente lo realiza otra persona y al analista de datos solo se le entrega el conjunto de datos a continuación)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pew.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>income</th>\n",
       "      <th>&lt;$10k</th>\n",
       "      <th>$10-20k</th>\n",
       "      <th>$20-30k</th>\n",
       "      <th>$30-40k</th>\n",
       "      <th>$40-50k</th>\n",
       "      <th>$50-75k</th>\n",
       "      <th>$75-100k</th>\n",
       "      <th>$100-150k</th>\n",
       "      <th>&gt;150k</th>\n",
       "      <th>Don't know/Refused</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>religion</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>Agnostic</th>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>60</td>\n",
       "      <td>81</td>\n",
       "      <td>76</td>\n",
       "      <td>137</td>\n",
       "      <td>122</td>\n",
       "      <td>109</td>\n",
       "      <td>84</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atheist</th>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>52</td>\n",
       "      <td>35</td>\n",
       "      <td>70</td>\n",
       "      <td>73</td>\n",
       "      <td>59</td>\n",
       "      <td>74</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buddhist</th>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>58</td>\n",
       "      <td>62</td>\n",
       "      <td>39</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catholic</th>\n",
       "      <td>418</td>\n",
       "      <td>617</td>\n",
       "      <td>732</td>\n",
       "      <td>670</td>\n",
       "      <td>638</td>\n",
       "      <td>1116</td>\n",
       "      <td>949</td>\n",
       "      <td>792</td>\n",
       "      <td>633</td>\n",
       "      <td>1489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don't know/refused</th>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evangelical Protestant</th>\n",
       "      <td>575</td>\n",
       "      <td>869</td>\n",
       "      <td>1064</td>\n",
       "      <td>982</td>\n",
       "      <td>881</td>\n",
       "      <td>1486</td>\n",
       "      <td>949</td>\n",
       "      <td>723</td>\n",
       "      <td>414</td>\n",
       "      <td>1529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hindu</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>54</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Historically Black Protestant</th>\n",
       "      <td>228</td>\n",
       "      <td>244</td>\n",
       "      <td>236</td>\n",
       "      <td>238</td>\n",
       "      <td>197</td>\n",
       "      <td>223</td>\n",
       "      <td>131</td>\n",
       "      <td>81</td>\n",
       "      <td>78</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jehovah's Witness</th>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jewish</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>95</td>\n",
       "      <td>69</td>\n",
       "      <td>87</td>\n",
       "      <td>151</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income                         <$10k  $10-20k  $20-30k  $30-40k  $40-50k  \\\n",
       "religion                                                                   \n",
       "Agnostic                          27       34       60       81       76   \n",
       "Atheist                           12       27       37       52       35   \n",
       "Buddhist                          27       21       30       34       33   \n",
       "Catholic                         418      617      732      670      638   \n",
       "Don't know/refused                15       14       15       11       10   \n",
       "Evangelical Protestant           575      869     1064      982      881   \n",
       "Hindu                              1        9        7        9       11   \n",
       "Historically Black Protestant    228      244      236      238      197   \n",
       "Jehovah's Witness                 20       27       24       24       21   \n",
       "Jewish                            19       19       25       25       30   \n",
       "\n",
       "income                         $50-75k  $75-100k  $100-150k  >150k  \\\n",
       "religion                                                             \n",
       "Agnostic                           137       122        109     84   \n",
       "Atheist                             70        73         59     74   \n",
       "Buddhist                            58        62         39     53   \n",
       "Catholic                          1116       949        792    633   \n",
       "Don't know/refused                  35        21         17     18   \n",
       "Evangelical Protestant            1486       949        723    414   \n",
       "Hindu                               34        47         48     54   \n",
       "Historically Black Protestant      223       131         81     78   \n",
       "Jehovah's Witness                   30        15         11      6   \n",
       "Jewish                              95        69         87    151   \n",
       "\n",
       "income                         Don't know/Refused  \n",
       "religion                                           \n",
       "Agnostic                                       96  \n",
       "Atheist                                        76  \n",
       "Buddhist                                       54  \n",
       "Catholic                                     1489  \n",
       "Don't know/refused                            116  \n",
       "Evangelical Protestant                       1529  \n",
       "Hindu                                          37  \n",
       "Historically Black Protestant                 339  \n",
       "Jehovah's Witness                              37  \n",
       "Jewish                                        162  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pew.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidy Data\n",
    "\n",
    "> Este dataset tiene  **tres** variables, **religion**, **income** y **frequency**. Para hacerlo *tidy*, necesitamos *\"fundirlo\"* (hacer un **melt**), o apilarlo. En otras palabras, necesitamos convertir columnas en filas.\n",
    "\n",
    "`pandas` provee un método[pd.melt()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) para *des-pivotear* el dataset.\n",
    "\n",
    "**Notas:** `.reset_index()` transforma la columna de índice de religión en una columna de datos (`pd.melt()` lo necesita). Además, la tabla resultante se ordena implícitamente por la columna `\"religión\"`. Para llegar al mismo orden que en el *paper*, la tabla *fundida* se ordena explícitamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "molten_pew = pd.melt(pew.reset_index(), id_vars=[\"religion\"], value_name=\"frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a ordered column for the income labels.\n",
    "income_dtype = pd.api.types.CategoricalDtype(income_columns, ordered=True)\n",
    "molten_pew[\"income\"] = molten_pew[\"income\"].astype(income_dtype)\n",
    "molten_pew = molten_pew.sort_values([\"religion\", \"income\"]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molten_pew.shape"
   ]
  },
  {
   "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></th>\n",
       "      <th>religion</th>\n",
       "      <th>income</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agnostic</td>\n",
       "      <td>&lt;$10k</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Agnostic</td>\n",
       "      <td>$10-20k</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agnostic</td>\n",
       "      <td>$20-30k</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agnostic</td>\n",
       "      <td>$30-40k</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agnostic</td>\n",
       "      <td>$40-50k</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Agnostic</td>\n",
       "      <td>$50-75k</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Agnostic</td>\n",
       "      <td>$75-100k</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Agnostic</td>\n",
       "      <td>$100-150k</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Agnostic</td>\n",
       "      <td>&gt;150k</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Agnostic</td>\n",
       "      <td>Don't know/Refused</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   religion              income  frequency\n",
       "0  Agnostic               <$10k         27\n",
       "1  Agnostic             $10-20k         34\n",
       "2  Agnostic             $20-30k         60\n",
       "3  Agnostic             $30-40k         81\n",
       "4  Agnostic             $40-50k         76\n",
       "5  Agnostic             $50-75k        137\n",
       "6  Agnostic            $75-100k        122\n",
       "7  Agnostic           $100-150k        109\n",
       "8  Agnostic               >150k         84\n",
       "9  Agnostic  Don't know/Refused         96"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molten_pew.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejemplo 2: Billboard\n",
    "\n",
    "> Otro uso común de este formato de datos es registrar observaciones espaciadas regularmente a lo largo del tiempo. Por ejemplo, el conjunto de datos de Billboard que se muestra en la Tabla 7, registra la fecha en que una canción ingresó por primera vez al Billboard Top 100. Tiene variables para **artist**, **track**, **date.entered**, **rank* * y **week**. El rango en cada semana después de que ingresa al top 100 se registra en 75 columnas,  `wk1` a `wk75`. Si una canción está en el Top 100 por menos de 75 semanas, las columnas restantes se llenan con valores faltantes. Esta forma de almacenamiento no es ordenada, pero es útil para la entrada de datos. Reduce la duplicación ya que, de lo contrario, cada canción de cada semana necesitaría su propia fila, y los metadatos de la canción, como el título y el artista, tendrían que repetirse.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cargando la data\n",
    "\n",
    "Los datos vienen en un archivo CSV con columnas con nombre de número de semana de una manera engorrosa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usage of \"1st\", \"2nd\", \"3rd\" should be forbidden by law :)\n",
    "usecols = [\"artist.inverted\", \"track\", \"time\", \"date.entered\"] + (\n",
    "    [f\"x{i}st.week\" for i in range(1, 76, 10) if i != 11]\n",
    "    + [f\"x{i}nd.week\" for i in range(2, 76, 10) if i != 12]\n",
    "    + [f\"x{i}rd.week\" for i in range(3, 76, 10) if i != 13]\n",
    "    + [f\"x{i}th.week\" for i in range(1, 76) if (i % 10) not in (1, 2, 3)]\n",
    "    + [f\"x11th.week\", f\"x12th.week\", f\"x13th.week\"]\n",
    ")\n",
    "billboard = pd.read_csv(\n",
    "    \"data/billboard.csv\",\n",
    "    encoding=\"iso-8859-1\",\n",
    "    parse_dates=[\"date.entered\"],\n",
    "    usecols=usecols,\n",
    ")\n",
    "\n",
    "billboard = billboard.assign(year=lambda x: x[\"date.entered\"].dt.year)\n",
    "\n",
    "# Rename the week columns.\n",
    "week_columns = {\n",
    "    column: (\"wk\" + re.sub(r\"[^\\d]+\", \"\", column))\n",
    "    for column in billboard.columns\n",
    "    if column.endswith(\".week\")\n",
    "}\n",
    "billboard = billboard.rename(columns={\"artist.inverted\": \"artist\", **week_columns})\n",
    "\n",
    "# Ensure the columns' order is the same as in the paper.\n",
    "columns = [\"year\", \"artist\", \"track\", \"time\", \"date.entered\"] + [\n",
    "    f\"wk{i}\" for i in range(1, 76)\n",
    "]\n",
    "billboard = billboard[columns]\n",
    "\n",
    "# Ensure the rows' order is similar as in the paper.\n",
    "# For unknown reasons the exact ordering as in the paper cannot be reconstructed.\n",
    "billboard = billboard[billboard[\"year\"] == 2000]\n",
    "billboard = billboard.sort_values([\"artist\", \"track\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Messy Data\n",
    "\n",
    "De nuevo, la siguiente celda muestra los datos como si fueran realmente entregados como datos \"crudos\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(267, 80)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "billboard.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>year</th>\n",
       "      <th>artist</th>\n",
       "      <th>track</th>\n",
       "      <th>time</th>\n",
       "      <th>date.entered</th>\n",
       "      <th>wk1</th>\n",
       "      <th>wk2</th>\n",
       "      <th>wk3</th>\n",
       "      <th>wk4</th>\n",
       "      <th>wk5</th>\n",
       "      <th>...</th>\n",
       "      <th>wk66</th>\n",
       "      <th>wk67</th>\n",
       "      <th>wk68</th>\n",
       "      <th>wk69</th>\n",
       "      <th>wk70</th>\n",
       "      <th>wk71</th>\n",
       "      <th>wk72</th>\n",
       "      <th>wk73</th>\n",
       "      <th>wk74</th>\n",
       "      <th>wk75</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2000</td>\n",
       "      <td>2 Pac</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
       "      <td>4:22</td>\n",
       "      <td>2000-02-26</td>\n",
       "      <td>87</td>\n",
       "      <td>82.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2000</td>\n",
       "      <td>2Ge+her</td>\n",
       "      <td>The Hardest Part Of Breaking Up (Is Getting Ba...</td>\n",
       "      <td>3:15</td>\n",
       "      <td>2000-09-02</td>\n",
       "      <td>91</td>\n",
       "      <td>87.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000</td>\n",
       "      <td>3 Doors Down</td>\n",
       "      <td>Kryptonite</td>\n",
       "      <td>3:53</td>\n",
       "      <td>2000-04-08</td>\n",
       "      <td>81</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>2000</td>\n",
       "      <td>3 Doors Down</td>\n",
       "      <td>Loser</td>\n",
       "      <td>4:24</td>\n",
       "      <td>2000-10-21</td>\n",
       "      <td>76</td>\n",
       "      <td>76.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2000</td>\n",
       "      <td>504 Boyz</td>\n",
       "      <td>Wobble Wobble</td>\n",
       "      <td>3:35</td>\n",
       "      <td>2000-04-15</td>\n",
       "      <td>57</td>\n",
       "      <td>34.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2000</td>\n",
       "      <td>98¡</td>\n",
       "      <td>Give Me Just One Night (Una Noche)</td>\n",
       "      <td>3:24</td>\n",
       "      <td>2000-08-19</td>\n",
       "      <td>51</td>\n",
       "      <td>39.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>2000</td>\n",
       "      <td>A*Teens</td>\n",
       "      <td>Dancing Queen</td>\n",
       "      <td>3:44</td>\n",
       "      <td>2000-07-08</td>\n",
       "      <td>97</td>\n",
       "      <td>97.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2000</td>\n",
       "      <td>Aaliyah</td>\n",
       "      <td>I Don't Wanna</td>\n",
       "      <td>4:15</td>\n",
       "      <td>2000-01-29</td>\n",
       "      <td>84</td>\n",
       "      <td>62.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2000</td>\n",
       "      <td>Aaliyah</td>\n",
       "      <td>Try Again</td>\n",
       "      <td>4:03</td>\n",
       "      <td>2000-03-18</td>\n",
       "      <td>59</td>\n",
       "      <td>53.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2000</td>\n",
       "      <td>Adams, Yolanda</td>\n",
       "      <td>Open My Heart</td>\n",
       "      <td>5:30</td>\n",
       "      <td>2000-08-26</td>\n",
       "      <td>76</td>\n",
       "      <td>76.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year          artist                                              track  \\\n",
       "246  2000           2 Pac                Baby Don't Cry (Keep Ya Head Up II)   \n",
       "287  2000         2Ge+her  The Hardest Part Of Breaking Up (Is Getting Ba...   \n",
       "24   2000    3 Doors Down                                         Kryptonite   \n",
       "193  2000    3 Doors Down                                              Loser   \n",
       "69   2000        504 Boyz                                      Wobble Wobble   \n",
       "22   2000             98¡                 Give Me Just One Night (Una Noche)   \n",
       "304  2000         A*Teens                                      Dancing Queen   \n",
       "135  2000         Aaliyah                                      I Don't Wanna   \n",
       "14   2000         Aaliyah                                          Try Again   \n",
       "200  2000  Adams, Yolanda                                      Open My Heart   \n",
       "\n",
       "     time date.entered  wk1   wk2   wk3   wk4    wk5  ...  wk66  wk67  wk68  \\\n",
       "246  4:22   2000-02-26   87  82.0  72.0  77.0   87.0  ...   NaN   NaN   NaN   \n",
       "287  3:15   2000-09-02   91  87.0  92.0   NaN    NaN  ...   NaN   NaN   NaN   \n",
       "24   3:53   2000-04-08   81  70.0  68.0  67.0   66.0  ...   NaN   NaN   NaN   \n",
       "193  4:24   2000-10-21   76  76.0  72.0  69.0   67.0  ...   NaN   NaN   NaN   \n",
       "69   3:35   2000-04-15   57  34.0  25.0  17.0   17.0  ...   NaN   NaN   NaN   \n",
       "22   3:24   2000-08-19   51  39.0  34.0  26.0   26.0  ...   NaN   NaN   NaN   \n",
       "304  3:44   2000-07-08   97  97.0  96.0  95.0  100.0  ...   NaN   NaN   NaN   \n",
       "135  4:15   2000-01-29   84  62.0  51.0  41.0   38.0  ...   NaN   NaN   NaN   \n",
       "14   4:03   2000-03-18   59  53.0  38.0  28.0   21.0  ...   NaN   NaN   NaN   \n",
       "200  5:30   2000-08-26   76  76.0  74.0  69.0   68.0  ...   NaN   NaN   NaN   \n",
       "\n",
       "     wk69  wk70  wk71  wk72  wk73  wk74  wk75  \n",
       "246   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "287   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "24    NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "193   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "69    NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "22    NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "304   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "135   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "14    NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "200   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[10 rows x 80 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "billboard.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \"Tidy\" Data\n",
    "\n",
    "Como antes, el método `pd.melt()` se usa para transformar los datos desde un formato \"wide\" (\"ancho\") a uno \"largo\" (\"long\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "molten_billboard = pd.melt(\n",
    "    billboard,\n",
    "    id_vars=[\"year\", \"artist\", \"track\", \"time\", \"date.entered\"],\n",
    "    var_name=\"week\",\n",
    "    value_name=\"rank\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A diferencia de `R`, `pandas` mantiene (innecesariamente según algunos) filas para semanas donde la canción ya estaba fuera del ranking. Estas observaciones son descartadas. También, una nueva columna `\"date\"` es añadida, indicando cuando exactamente una canción en particular estuvo en un cierto ranking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pandas keeps \"wide\" variables that had missing values as rows.\n",
    "molten_billboard = molten_billboard[molten_billboard[\"rank\"].notnull()]\n",
    "\n",
    "# Cast as integer after missing values are removed.\n",
    "molten_billboard[\"week\"] = molten_billboard[\"week\"].map(lambda x: int(x[2:]))\n",
    "molten_billboard[\"rank\"] = molten_billboard[\"rank\"].map(int)\n",
    "\n",
    "# Calculate the actual week from the date of first entering the list.\n",
    "molten_billboard = molten_billboard.assign(\n",
    "    date=lambda x: x[\"date.entered\"] + (x[\"week\"] - 1) * datetime.timedelta(weeks=1)\n",
    ")\n",
    "\n",
    "# Sort rows and columns as in the paper.\n",
    "molten_billboard = molten_billboard[\n",
    "    [\"year\", \"artist\", \"time\", \"track\", \"date\", \"week\", \"rank\"]\n",
    "]\n",
    "molten_billboard = molten_billboard.sort_values([\"artist\", \"track\", \"week\"])\n",
    "molten_billboard = molten_billboard.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ten en cuenta que este conjunto de datos aún no está completamente `tidy`, como se explicará en el notebook 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>artist</th>\n",
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       "      <th>track</th>\n",
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       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>2 Pac</td>\n",
       "      <td>4:22</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
       "      <td>2000-02-26</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>2 Pac</td>\n",
       "      <td>4:22</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
       "      <td>2000-03-04</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>2 Pac</td>\n",
       "      <td>4:22</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
       "      <td>2000-03-11</td>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>2 Pac</td>\n",
       "      <td>4:22</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
       "      <td>2000-03-18</td>\n",
       "      <td>4</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>2 Pac</td>\n",
       "      <td>4:22</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
       "      <td>2000-03-25</td>\n",
       "      <td>5</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000</td>\n",
       "      <td>2 Pac</td>\n",
       "      <td>4:22</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
       "      <td>2000-04-01</td>\n",
       "      <td>6</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000</td>\n",
       "      <td>2 Pac</td>\n",
       "      <td>4:22</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
       "      <td>2000-04-08</td>\n",
       "      <td>7</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000</td>\n",
       "      <td>2Ge+her</td>\n",
       "      <td>3:15</td>\n",
       "      <td>The Hardest Part Of Breaking Up (Is Getting Ba...</td>\n",
       "      <td>2000-09-02</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000</td>\n",
       "      <td>2Ge+her</td>\n",
       "      <td>3:15</td>\n",
       "      <td>The Hardest Part Of Breaking Up (Is Getting Ba...</td>\n",
       "      <td>2000-09-09</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000</td>\n",
       "      <td>2Ge+her</td>\n",
       "      <td>3:15</td>\n",
       "      <td>The Hardest Part Of Breaking Up (Is Getting Ba...</td>\n",
       "      <td>2000-09-16</td>\n",
       "      <td>3</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000</td>\n",
       "      <td>3 Doors Down</td>\n",
       "      <td>3:53</td>\n",
       "      <td>Kryptonite</td>\n",
       "      <td>2000-04-08</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2000</td>\n",
       "      <td>3 Doors Down</td>\n",
       "      <td>3:53</td>\n",
       "      <td>Kryptonite</td>\n",
       "      <td>2000-04-15</td>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2000</td>\n",
       "      <td>3 Doors Down</td>\n",
       "      <td>3:53</td>\n",
       "      <td>Kryptonite</td>\n",
       "      <td>2000-04-22</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000</td>\n",
       "      <td>3 Doors Down</td>\n",
       "      <td>3:53</td>\n",
       "      <td>Kryptonite</td>\n",
       "      <td>2000-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2000</td>\n",
       "      <td>3 Doors Down</td>\n",
       "      <td>3:53</td>\n",
       "      <td>Kryptonite</td>\n",
       "      <td>2000-05-06</td>\n",
       "      <td>5</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year        artist  time  \\\n",
       "0   2000         2 Pac  4:22   \n",
       "1   2000         2 Pac  4:22   \n",
       "2   2000         2 Pac  4:22   \n",
       "3   2000         2 Pac  4:22   \n",
       "4   2000         2 Pac  4:22   \n",
       "5   2000         2 Pac  4:22   \n",
       "6   2000         2 Pac  4:22   \n",
       "7   2000       2Ge+her  3:15   \n",
       "8   2000       2Ge+her  3:15   \n",
       "9   2000       2Ge+her  3:15   \n",
       "10  2000  3 Doors Down  3:53   \n",
       "11  2000  3 Doors Down  3:53   \n",
       "12  2000  3 Doors Down  3:53   \n",
       "13  2000  3 Doors Down  3:53   \n",
       "14  2000  3 Doors Down  3:53   \n",
       "\n",
       "                                                track       date  week  rank  \n",
       "0                 Baby Don't Cry (Keep Ya Head Up II) 2000-02-26     1    87  \n",
       "1                 Baby Don't Cry (Keep Ya Head Up II) 2000-03-04     2    82  \n",
       "2                 Baby Don't Cry (Keep Ya Head Up II) 2000-03-11     3    72  \n",
       "3                 Baby Don't Cry (Keep Ya Head Up II) 2000-03-18     4    77  \n",
       "4                 Baby Don't Cry (Keep Ya Head Up II) 2000-03-25     5    87  \n",
       "5                 Baby Don't Cry (Keep Ya Head Up II) 2000-04-01     6    94  \n",
       "6                 Baby Don't Cry (Keep Ya Head Up II) 2000-04-08     7    99  \n",
       "7   The Hardest Part Of Breaking Up (Is Getting Ba... 2000-09-02     1    91  \n",
       "8   The Hardest Part Of Breaking Up (Is Getting Ba... 2000-09-09     2    87  \n",
       "9   The Hardest Part Of Breaking Up (Is Getting Ba... 2000-09-16     3    92  \n",
       "10                                         Kryptonite 2000-04-08     1    81  \n",
       "11                                         Kryptonite 2000-04-15     2    70  \n",
       "12                                         Kryptonite 2000-04-22     3    68  \n",
       "13                                         Kryptonite 2000-04-29     4    67  \n",
       "14                                         Kryptonite 2000-05-06     5    66  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molten_billboard.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guardando los datos\n",
    "El dataset de bilboard ya \"ordenado\" (\"tidy\") es guardado como input para el notebook 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "molten_billboard.to_csv(\"data/billboard_cleaned.csv\", index=False)"
   ]
  }
 ],
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