2_multiples_variables_almacenadas_en_una_columna.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Múltiples variables almacenadas en una columna\n",
    "\n",
    "Este cuaderno muestra cómo se pueden aislar varias variables almacenadas en la misma columna."
   ]
  },
  {
   "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 pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejemplo: Tuberculosis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cargando la Data\n",
    "\n",
    "Seleccionamos las mismas columnas que en el *paper* y le asignamos el nombre correspondiente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    \"iso2\",\n",
    "    \"year\",\n",
    "    \"new_sp_m014\",\n",
    "    \"new_sp_m1524\",\n",
    "    \"new_sp_m2534\",\n",
    "    \"new_sp_m3544\",\n",
    "    \"new_sp_m4554\",\n",
    "    \"new_sp_m5564\",\n",
    "    \"new_sp_m65\",\n",
    "    \"new_sp_mu\",\n",
    "    \"new_sp_f014\",\n",
    "    \"new_sp_f1524\",\n",
    "    \"new_sp_f2534\",\n",
    "    \"new_sp_f3544\",\n",
    "    \"new_sp_f4554\",\n",
    "    \"new_sp_f5564\",\n",
    "    \"new_sp_f65\",\n",
    "    \"new_sp_fu\",\n",
    "]\n",
    "tb = pd.read_csv(\"data/tb.csv\", usecols=columns)\n",
    "\n",
    "rename = {column: column[7:] for column in columns if column.startswith(\"new_sp_\")}\n",
    "rename = {\"iso2\": \"country\", **rename}\n",
    "tb = tb.rename(columns=rename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Messy Data\n",
    "Se asume que los datos se proporcionan de la siguiente manera. Excepto por las columnas `\"country\"` y `\"year\"`, las columnas restantes son en realidad realizaciones conjuntas de dos variables `\"sex\"` y `\"age\"`.\n",
    "\n",
    "The data are assumed to be provided as below. Except for the `\"country\"` and `\"year\"` columns, the remaining columns are actually joint realizations of two variables `\"sex\"` and `\"age\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>m014</th>\n",
       "      <th>m1524</th>\n",
       "      <th>m2534</th>\n",
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       "      <th>f014</th>\n",
       "      <th>f1524</th>\n",
       "      <th>f2534</th>\n",
       "      <th>f3544</th>\n",
       "      <th>f4554</th>\n",
       "      <th>f5564</th>\n",
       "      <th>f65</th>\n",
       "      <th>fu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <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>36</th>\n",
       "      <td>AE</td>\n",
       "      <td>2000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>AF</td>\n",
       "      <td>2000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>AG</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>AL</td>\n",
       "      <td>2000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>AM</td>\n",
       "      <td>2000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>AN</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>AO</td>\n",
       "      <td>2000</td>\n",
       "      <td>186.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247.0</td>\n",
       "      <td>1142.0</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>844.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>AR</td>\n",
       "      <td>2000</td>\n",
       "      <td>97.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>AS</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  year   m014  m1524   m2534  m3544  m4554  m5564    m65  mu  \\\n",
       "10       AD  2000    0.0    0.0     1.0    0.0    0.0    0.0    0.0 NaN   \n",
       "36       AE  2000    2.0    4.0     4.0    6.0    5.0   12.0   10.0 NaN   \n",
       "60       AF  2000   52.0  228.0   183.0  149.0  129.0   94.0   80.0 NaN   \n",
       "87       AG  2000    0.0    0.0     0.0    0.0    0.0    0.0    1.0 NaN   \n",
       "136      AL  2000    2.0   19.0    21.0   14.0   24.0   19.0   16.0 NaN   \n",
       "165      AM  2000    2.0  152.0   130.0  131.0   63.0   26.0   21.0 NaN   \n",
       "178      AN  2000    0.0    0.0     1.0    2.0    0.0    0.0    0.0 NaN   \n",
       "207      AO  2000  186.0  999.0  1003.0  912.0  482.0  312.0  194.0 NaN   \n",
       "236      AR  2000   97.0  278.0   594.0  402.0  419.0  368.0  330.0 NaN   \n",
       "265      AS  2000    NaN    NaN     NaN    NaN    1.0    1.0    NaN NaN   \n",
       "\n",
       "      f014   f1524   f2534  f3544  f4554  f5564    f65  fu  \n",
       "10     NaN     NaN     NaN    NaN    NaN    NaN    NaN NaN  \n",
       "36     3.0    16.0     1.0    3.0    0.0    0.0    4.0 NaN  \n",
       "60    93.0   414.0   565.0  339.0  205.0   99.0   36.0 NaN  \n",
       "87     1.0     1.0     1.0    0.0    0.0    0.0    0.0 NaN  \n",
       "136    3.0    11.0    10.0    8.0    8.0    5.0   11.0 NaN  \n",
       "165    1.0    24.0    27.0   24.0    8.0    8.0    4.0 NaN  \n",
       "178    0.0     0.0     1.0    0.0    0.0    1.0    0.0 NaN  \n",
       "207  247.0  1142.0  1091.0  844.0  417.0  200.0  120.0 NaN  \n",
       "236  121.0   544.0   479.0  262.0  230.0  179.0  216.0 NaN  \n",
       "265    NaN     NaN     NaN    NaN    1.0    NaN    NaN NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb[(tb[\"year\"] == 2000)].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Molten* Data\n",
    "\n",
    "Al igual que en el notebook anterior, la función [pd.melt()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) se puede usar para despivotear las columnas. Como antes, pandas mantiene las filas de las columnas con *missing data*, los que se descartan. Luego, sin más *missing data*, el tipo de datos de la columna se convierte como `int`. Luego, el conjunto de datos *fundido* resultante es ordenado como en el paper.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "molten_tb = pd.melt(\n",
    "    tb, id_vars=[\"country\", \"year\"], var_name=\"column\", value_name=\"cases\"\n",
    ")\n",
    "molten_tb = molten_tb[molten_tb[\"cases\"].notnull()]\n",
    "molten_tb[\"cases\"] = molten_tb[\"cases\"].astype(int)\n",
    "molten_tb = molten_tb.sort_values([\"country\", \"year\", \"column\"]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>column</th>\n",
       "      <th>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m014</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m1524</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m2534</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m3544</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m4554</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m5564</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m65</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>AE</td>\n",
       "      <td>2000</td>\n",
       "      <td>f014</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>AE</td>\n",
       "      <td>2000</td>\n",
       "      <td>f1524</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>AE</td>\n",
       "      <td>2000</td>\n",
       "      <td>f2534</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  year column  cases\n",
       "49       AD  2000   m014      0\n",
       "50       AD  2000  m1524      0\n",
       "51       AD  2000  m2534      1\n",
       "52       AD  2000  m3544      0\n",
       "53       AD  2000  m4554      0\n",
       "54       AD  2000  m5564      0\n",
       "55       AD  2000    m65      0\n",
       "165      AE  2000   f014      3\n",
       "166      AE  2000  f1524     16\n",
       "167      AE  2000  f2534      1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molten_tb[(molten_tb[\"year\"] == 2000)].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidy Data\n",
    "Usando el método [pd.Series.str.extract()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.extract.html) las dos variables son aisladas/separadas (\"splitted\"). Las etiquetas de `age` se renombran como en el documento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tidy_tb = molten_tb[[\"country\", \"year\", \"cases\"]]\n",
    "tidy_tb[[\"sex\", \"age\"]] = molten_tb[\"column\"].str.extract(r\"(f|m)(.*)\")\n",
    "tidy_tb[\"age\"] = tidy_tb[\"age\"].map(\n",
    "    {\n",
    "        \"014\": \"0-14\",\n",
    "        \"1524\": \"15-24\",\n",
    "        \"2534\": \"25-34\",\n",
    "        \"3544\": \"35-44\",\n",
    "        \"4554\": \"45-54\",\n",
    "        \"5564\": \"55-64\",\n",
    "        \"65\": \"65+\",\n",
    "        \"u\": \"unknown\",\n",
    "    }\n",
    ")\n",
    "tidy_tb = tidy_tb[[\"country\", \"year\", \"sex\", \"age\", \"cases\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m</td>\n",
       "      <td>15-24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m</td>\n",
       "      <td>25-34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m</td>\n",
       "      <td>35-44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m</td>\n",
       "      <td>45-54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m</td>\n",
       "      <td>55-64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>AD</td>\n",
       "      <td>2000</td>\n",
       "      <td>m</td>\n",
       "      <td>65+</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>AE</td>\n",
       "      <td>2000</td>\n",
       "      <td>f</td>\n",
       "      <td>0-14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>AE</td>\n",
       "      <td>2000</td>\n",
       "      <td>f</td>\n",
       "      <td>15-24</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>AE</td>\n",
       "      <td>2000</td>\n",
       "      <td>f</td>\n",
       "      <td>25-34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  year sex    age  cases\n",
       "49       AD  2000   m   0-14      0\n",
       "50       AD  2000   m  15-24      0\n",
       "51       AD  2000   m  25-34      1\n",
       "52       AD  2000   m  35-44      0\n",
       "53       AD  2000   m  45-54      0\n",
       "54       AD  2000   m  55-64      0\n",
       "55       AD  2000   m    65+      0\n",
       "165      AE  2000   f   0-14      3\n",
       "166      AE  2000   f  15-24     16\n",
       "167      AE  2000   f  25-34      1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy_tb[(tidy_tb[\"year\"] == 2000)].head(10)"
   ]
  }
 ],
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