Aleatorización_DR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6d9f4bac",
   "metadata": {},
   "source": [
    "# Aleatorización Colegio Domingo Robledo\n",
    "\n",
    "## Parámetros para los bloques: edad, sexo y curso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6fabb0f7",
   "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 de la institución</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Zona</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Año</th>\n",
       "      <th>Sección</th>\n",
       "      <th>Turno</th>\n",
       "      <th>Número de varones matriculados</th>\n",
       "      <th>Número de mujeres matriculadas</th>\n",
       "      <th>Promedio de edad</th>\n",
       "      <th>Incluido en la aleatorización</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COLEGIO NACIONAL DOMINGO ROBLEDO</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Primero</td>\n",
       "      <td>A</td>\n",
       "      <td>Tarde</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.70</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COLEGIO NACIONAL DOMINGO ROBLEDO</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Primero</td>\n",
       "      <td>B</td>\n",
       "      <td>Tarde</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.77</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COLEGIO NACIONAL DOMINGO ROBLEDO</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Segundo</td>\n",
       "      <td>A</td>\n",
       "      <td>Tarde</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.53</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COLEGIO NACIONAL DOMINGO ROBLEDO</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Segundo</td>\n",
       "      <td>B</td>\n",
       "      <td>Mañana</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.48</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COLEGIO NACIONAL DOMINGO ROBLEDO</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Tercero</td>\n",
       "      <td>A</td>\n",
       "      <td>Tarde</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.87</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>COLEGIO NACIONAL DOMINGO ROBLEDO</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Tercero</td>\n",
       "      <td>B</td>\n",
       "      <td>Tarde</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.31</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>COLEGIO NACIONAL PADRE RICARDO MUSCH</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Técnico</td>\n",
       "      <td>Primero</td>\n",
       "      <td>Informática</td>\n",
       "      <td>Mañana-Tarde</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.11</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>COLEGIO NACIONAL PADRE RICARDO MUSCH</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Técnico</td>\n",
       "      <td>Primero</td>\n",
       "      <td>Contabilidad</td>\n",
       "      <td>Mañana-Tarde</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COLEGIO NACIONAL PADRE RICARDO MUSCH</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Técnico</td>\n",
       "      <td>Segundo</td>\n",
       "      <td>Informática</td>\n",
       "      <td>Mañana</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.21</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>COLEGIO NACIONAL PADRE RICARDO MUSCH</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Técnico</td>\n",
       "      <td>Segundo</td>\n",
       "      <td>Contabilidad</td>\n",
       "      <td>Mañana-Tarde</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.21</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>COLEGIO NACIONAL PADRE RICARDO MUSCH</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Técnico</td>\n",
       "      <td>Tercero</td>\n",
       "      <td>Informática</td>\n",
       "      <td>Mañana</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.41</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>COLEGIO NACIONAL PADRE RICARDO MUSCH</td>\n",
       "      <td>Público</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>Técnico</td>\n",
       "      <td>Tercero</td>\n",
       "      <td>Contabilidad</td>\n",
       "      <td>Mañana-Tarde</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.66</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>COLEGIO NACIONAL PUERTO TRIUNFO</td>\n",
       "      <td>Público</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Primero</td>\n",
       "      <td>A</td>\n",
       "      <td>Mañana</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.43</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>COLEGIO NACIONAL PUERTO TRIUNFO</td>\n",
       "      <td>Público</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Segundo</td>\n",
       "      <td>A</td>\n",
       "      <td>Mañana</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.27</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>COLEGIO NACIONAL PUERTO TRIUNFO</td>\n",
       "      <td>Público</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Tercero</td>\n",
       "      <td>A</td>\n",
       "      <td>Mañana</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.68</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>COLEGIO NACIONAL NATALIO KM. 23</td>\n",
       "      <td>Público</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Primero</td>\n",
       "      <td>A</td>\n",
       "      <td>Mañana</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.90</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>COLEGIO NACIONAL NATALIO KM. 23</td>\n",
       "      <td>Público</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Segundo</td>\n",
       "      <td>A</td>\n",
       "      <td>Mañana</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.66</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>COLEGIO NACIONAL NATALIO KM. 23</td>\n",
       "      <td>Público</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Científico</td>\n",
       "      <td>Tercero</td>\n",
       "      <td>A</td>\n",
       "      <td>Mañana</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.54</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\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>Total:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Nombre de la institución   Sector    Zona        Tipo  \\\n",
       "0       COLEGIO NACIONAL DOMINGO ROBLEDO  Público  Urbana  Científico   \n",
       "1       COLEGIO NACIONAL DOMINGO ROBLEDO  Público  Urbana  Científico   \n",
       "2       COLEGIO NACIONAL DOMINGO ROBLEDO  Público  Urbana  Científico   \n",
       "3       COLEGIO NACIONAL DOMINGO ROBLEDO  Público  Urbana  Científico   \n",
       "4       COLEGIO NACIONAL DOMINGO ROBLEDO  Público  Urbana  Científico   \n",
       "5       COLEGIO NACIONAL DOMINGO ROBLEDO  Público  Urbana  Científico   \n",
       "6   COLEGIO NACIONAL PADRE RICARDO MUSCH  Público  Urbana     Técnico   \n",
       "7   COLEGIO NACIONAL PADRE RICARDO MUSCH  Público  Urbana     Técnico   \n",
       "8   COLEGIO NACIONAL PADRE RICARDO MUSCH  Público  Urbana     Técnico   \n",
       "9   COLEGIO NACIONAL PADRE RICARDO MUSCH  Público  Urbana     Técnico   \n",
       "10  COLEGIO NACIONAL PADRE RICARDO MUSCH  Público  Urbana     Técnico   \n",
       "11  COLEGIO NACIONAL PADRE RICARDO MUSCH  Público  Urbana     Técnico   \n",
       "12       COLEGIO NACIONAL PUERTO TRIUNFO  Público   Rural  Científico   \n",
       "13       COLEGIO NACIONAL PUERTO TRIUNFO  Público   Rural  Científico   \n",
       "14       COLEGIO NACIONAL PUERTO TRIUNFO  Público   Rural  Científico   \n",
       "15       COLEGIO NACIONAL NATALIO KM. 23  Público   Rural  Científico   \n",
       "16       COLEGIO NACIONAL NATALIO KM. 23  Público   Rural  Científico   \n",
       "17       COLEGIO NACIONAL NATALIO KM. 23  Público   Rural  Científico   \n",
       "18                                   NaN      NaN     NaN         NaN   \n",
       "19                                   NaN      NaN     NaN         NaN   \n",
       "\n",
       "        Año       Sección         Turno  Número de varones matriculados  \\\n",
       "0   Primero             A         Tarde                             8.0   \n",
       "1   Primero             B         Tarde                            11.0   \n",
       "2   Segundo             A         Tarde                             7.0   \n",
       "3   Segundo             B        Mañana                            10.0   \n",
       "4   Tercero             A         Tarde                             5.0   \n",
       "5   Tercero             B         Tarde                            14.0   \n",
       "6   Primero   Informática  Mañana-Tarde                            13.0   \n",
       "7   Primero  Contabilidad  Mañana-Tarde                            11.0   \n",
       "8   Segundo   Informática        Mañana                            13.0   \n",
       "9   Segundo  Contabilidad  Mañana-Tarde                             6.0   \n",
       "10  Tercero   Informática        Mañana                             7.0   \n",
       "11  Tercero  Contabilidad  Mañana-Tarde                             1.0   \n",
       "12  Primero             A        Mañana                             8.0   \n",
       "13  Segundo             A        Mañana                            15.0   \n",
       "14  Tercero             A        Mañana                             8.0   \n",
       "15  Primero             A        Mañana                            14.0   \n",
       "16  Segundo             A        Mañana                            15.0   \n",
       "17  Tercero             A        Mañana                            20.0   \n",
       "18      NaN           NaN           NaN                             NaN   \n",
       "19      NaN           NaN        Total:                             NaN   \n",
       "\n",
       "    Número de mujeres matriculadas  Promedio de edad  \\\n",
       "0                             12.0             15.70   \n",
       "1                             15.0             15.77   \n",
       "2                              8.0             16.53   \n",
       "3                             11.0             16.48   \n",
       "4                             10.0             17.87   \n",
       "5                             12.0             17.31   \n",
       "6                             15.0             15.11   \n",
       "7                             15.0             16.00   \n",
       "8                             11.0             16.21   \n",
       "9                             13.0             16.21   \n",
       "10                            10.0             17.41   \n",
       "11                            11.0             17.66   \n",
       "12                            15.0             15.43   \n",
       "13                            11.0             16.27   \n",
       "14                            17.0             17.68   \n",
       "15                             6.0             15.90   \n",
       "16                            12.0             17.66   \n",
       "17                            13.0             17.54   \n",
       "18                             NaN               NaN   \n",
       "19                             NaN               NaN   \n",
       "\n",
       "   Incluido en la aleatorización  \n",
       "0                             SI  \n",
       "1                             SI  \n",
       "2                             SI  \n",
       "3                             SI  \n",
       "4                             NO  \n",
       "5                             NO  \n",
       "6                             SI  \n",
       "7                             SI  \n",
       "8                             SI  \n",
       "9                             SI  \n",
       "10                            NO  \n",
       "11                            NO  \n",
       "12                            SI  \n",
       "13                            SI  \n",
       "14                            NO  \n",
       "15                            SI  \n",
       "16                            SI  \n",
       "17                            NO  \n",
       "18                           NaN  \n",
       "19                           NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('Colegios_Secciones.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "296516d0",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'data.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Carga del dataset \u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m# Función de aleatorización por bloques\u001b[39;00m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstratified_randomization\u001b[39m(data, strata_cols, treatment_col):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    936\u001b[0m     dialect,\n\u001b[1;32m    937\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m    947\u001b[0m )\n\u001b[1;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[1;32m   1736\u001b[0m     f,\n\u001b[1;32m   1737\u001b[0m     mode,\n\u001b[1;32m   1738\u001b[0m     encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1739\u001b[0m     compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1740\u001b[0m     memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m   1741\u001b[0m     is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[1;32m   1742\u001b[0m     errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1743\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1744\u001b[0m )\n\u001b[1;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    852\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    853\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    854\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    855\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[1;32m    857\u001b[0m             handle,\n\u001b[1;32m    858\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[1;32m    859\u001b[0m             encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[1;32m    860\u001b[0m             errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[1;32m    861\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    862\u001b[0m         )\n\u001b[1;32m    863\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    864\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    865\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data.csv'"
     ]
    }
   ],
   "source": [
    "# Importando las bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Carga del dataset \n",
    "df = pd.read_csv('Colegios_Secciones.csv')\n",
    "\n",
    "# Función de aleatorización por bloques\n",
    "def stratified_randomization(data, strata_cols, treatment_col, seed_value=10):\n",
    "\n",
    "    # Establecer la semilla para reproducibilidad\n",
    "    np.random.seed(seed_value)\n",
    "    \n",
    "    # Inicialización de la columna de tratamiento\n",
    "    data[treatment_col] = None\n",
    "    \n",
    "    # Obtener valores únicos para cada estrato\n",
    "    unique_strata = data[strata_cols].drop_duplicates()\n",
    "    \n",
    "    # Iterar sobre cada combinación única de estratos\n",
    "    for _, stratum in unique_strata.iterrows():\n",
    "        mask = (data[strata_cols] == stratum).all(axis=1)\n",
    "        subset = data[mask]\n",
    "        assignments = [\"control\" if i < len(subset) / 2 else \"treatment\" for i in range(len(subset))]\n",
    "        np.random.shuffle(assignments)\n",
    "        data.loc[mask, treatment_col] = assignments\n",
    "        \n",
    "    return data\n",
    "\n",
    "# Aplicar la aleatorización por bloques\n",
    "df_randomized = stratified_randomization(df, ['V', 'Edad', 'curso'], 'assignment')\n",
    "\n",
    "# Mostrar los primeros registros para verificar\n",
    "print(df_randomized.head())\n",
    "\n",
    "# Guardar a CSV \n",
    "# df_randomized.to_csv('randomized_data.csv', index=False)"
   ]
  }
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