tablas.ipynb

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     "text": [
      "/home/jess_oknotok/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98045039",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://cdn.produccion.gob.ar/cdn-cep/araucano/base_araucano.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2d0c2a8",
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    {
     "data": {
      "text/plain": [
       "(820335, 13)"
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "516b7837",
   "metadata": {},
   "source": [
    "**Para más información, ver:**\n",
    "\n",
    "- [https://datacarpentry.org/python-ecology-lesson-es/05-merging-data/index.html](url)\n",
    "- [https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html](url)\n",
    "- [https://datacarpentry.org/python-socialsci/11-joins/index.html](url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d16beb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rama\n",
    "df['rama_id_map'] = df['rama_id'].map({1:'Ciencias Sociales', \n",
    "                                       2:'Ciencias Aplicadas', \n",
    "                                       3:'Ciencias de la Salud', \n",
    "                                       4:'Ciencias Humanas', \n",
    "                                       5:'Ciencias Básicas', \n",
    "                                       6:'Sin Rama'\n",
    "                                       })\n",
    "# Disciplina\n",
    "df['disciplina_id_map'] = df['disciplina_id'].map({1:'Economía y Administración', \n",
    "                                                   2: 'Ingeniería',\n",
    "                                                   3: 'Sociología, Antropología y Servicio Social',\n",
    "                                                   4: 'Ciencias Agropecuarias',\n",
    "                                                   5: 'Medicina',\n",
    "                                                   6: 'Ciencias Políticas, Relaciones Internacionales y Diplomacia',\n",
    "                                                   7: 'Derecho',\n",
    "                                                   8: 'Artes',\n",
    "                                                   9: 'Educación',\n",
    "                                                   10: 'Informática',\n",
    "                                                   11: 'Psicología',\n",
    "                                                   12: 'Historia',\n",
    "                                                   13: 'Arquitectura y Diseño',\n",
    "                                                   14: 'Letras e Idiomas',\n",
    "                                                   15: 'Meteorología',\n",
    "                                                   16: 'Biología',\n",
    "                                                   17: 'Bioquímica y Farmacia',\n",
    "                                                   18: 'Paramédicas y Auxiliares de la Medicina',\n",
    "                                                   19: 'Ciencias de la Información y de la Comunicación',\n",
    "                                                   20: 'Filosofía',\n",
    "                                                   21: 'Industrias',\n",
    "                                                   22: 'Demografía y Geografía',\n",
    "                                                   23: 'Ciencias del Suelo',\n",
    "                                                   24: 'Relaciones Institucionales y Humanas',\n",
    "                                                   25: 'Salud Pública',\n",
    "                                                   26: 'Veterinaria',\n",
    "                                                   27: 'Otras Ciencias Sociales',\n",
    "                                                   28: 'Otras Ciencias Aplicadas', \n",
    "                                                   29: 'Odontología',\n",
    "                                                   30: 'Física',\n",
    "                                                   31: 'Sanidad',\n",
    "                                                   32: 'Química',\n",
    "                                                   33: 'Teología',\n",
    "                                                   34: 'Matemática',\n",
    "                                                   35: 'Estadística',\n",
    "                                                   36: 'Astronomía',\n",
    "                                                   37: 'Sin Disciplina',\n",
    "                                                   38: 'Arqueología'\n",
    "                                                   })\n",
    "# Título\n",
    "df['tipo_titulo_id_map'] = df['tipo_titulo_id'].map({1:'Pregrado y títulos intermedios',\n",
    "                                                     2:'Tecnicatura',\n",
    "                                                     3:'Grado y profesorado',\n",
    "                                                     4:'Posgrado'\n",
    "                                                     })\n",
    "# Tipo de Gestión\n",
    "df['gestion_id_map'] = df['gestion_id'].map({1:'Estatal',\n",
    "                                             2:'Privada'\n",
    "                                            })\n",
    "# Género\n",
    "df['genero_id_map'] = df['genero_id'].map({1:'Mujer', \n",
    "                                           2:'Varon'\n",
    "                                           })\n",
    "# Región\n",
    "df['region_id_map'] = df['region_id'].map({1:'CABA', \n",
    "                                           2:'BUENOS AIRES',\n",
    "                                           3:'RESTO PAMPEANA (SANTA FE, CORDOBA, ENTRE RIOS, LA PAMPA)', \n",
    "                                           4:'NOA',\n",
    "                                           5:'NEA', \n",
    "                                           6:'CUYO',\n",
    "                                           7: 'PATAGONIA'\n",
    "                                           })\n",
    "# Tamaño de empresa\n",
    "df['tamanio_id_map'] = df['tamaño_id'].map({1:'Micro: menos de 10 empleados', \n",
    "                                           2:'Pequeña: 10 a 49 empleados',\n",
    "                                           3:'Mediana: 50 a 199 empleados', \n",
    "                                           4:'Grande: 200 empleados o más'\n",
    "                                           })\n",
    "# Letra\n",
    "df['letra_id_map'] = df['letra_id'].map({1:'Actividades administrativas y servicios de apoyo',\n",
    "                                         2: 'Administración pública, defensa y seguridad social obligatoria',\n",
    "                                         3: 'Agricultura, ganadería, caza, silvicultura y pesca',\n",
    "                                         4: 'Suministro de agua, cloacas, gestión de residuos y recuperación de materiales y saneamiento público',\n",
    "                                         5: 'Servicios de asociaciones y servicios personales',\n",
    "                                         6: 'Comercio al por mayor y al por menor, reparación de vehículos automotores y motocicletas',\n",
    "                                         7: 'Construcción',\n",
    "                                         8: 'Servicios artísticos, culturales, deportivos y de esparcimiento',\n",
    "                                         9: 'Suministro de electricidad, gas, vapor y aire acondicionado',\n",
    "                                         10: 'Enseñanza',\n",
    "                                         11: 'Intermediación financiera y servicios de seguros',\n",
    "                                         12: 'Servicios de alojamiento y servicios de comida',\n",
    "                                         13: 'Industria manufacturera',\n",
    "                                         14: 'Información y comunicaciones',\n",
    "                                         15: 'Explotación de minas y canteras',\n",
    "                                         16: 'Salud humana y servicios sociales',\n",
    "                                         17: 'Servicios de asociaciones y órganos extraterritoriales',\n",
    "                                         18: 'Servicios inmobiliarios',\n",
    "                                         19: 'Servicios profesionales, científicos y técnicos',\n",
    "                                         20: 'Servicio de transporte y almacenamiento'\n",
    "                                         })\n",
    "df = df.drop(['rama_id', 'disciplina_id', 'tipo_titulo_id','gestion_id', 'genero_id', 'region_id', 'tamaño_id', 'letra_id'], axis=1)"
   ]
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  {
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   "execution_count": 5,
   "id": "da6dc153",
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   "outputs": [
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       "<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>id</th>\n",
       "      <th>salario</th>\n",
       "      <th>anio</th>\n",
       "      <th>anioegreso</th>\n",
       "      <th>anionac</th>\n",
       "      <th>rama_id_map</th>\n",
       "      <th>disciplina_id_map</th>\n",
       "      <th>tipo_titulo_id_map</th>\n",
       "      <th>gestion_id_map</th>\n",
       "      <th>genero_id_map</th>\n",
       "      <th>region_id_map</th>\n",
       "      <th>tamanio_id_map</th>\n",
       "      <th>letra_id_map</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>2017</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Ciencias Sociales</td>\n",
       "      <td>Economía y Administración</td>\n",
       "      <td>Posgrado</td>\n",
       "      <td>Estatal</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>CABA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>2017</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Ciencias Sociales</td>\n",
       "      <td>Economía y Administración</td>\n",
       "      <td>Posgrado</td>\n",
       "      <td>Estatal</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>CABA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>2017</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Ciencias Sociales</td>\n",
       "      <td>Economía y Administración</td>\n",
       "      <td>Posgrado</td>\n",
       "      <td>Estatal</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>CABA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>2018</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Ciencias Sociales</td>\n",
       "      <td>Economía y Administración</td>\n",
       "      <td>Posgrado</td>\n",
       "      <td>Estatal</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>2018</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Ciencias Sociales</td>\n",
       "      <td>Economía y Administración</td>\n",
       "      <td>Posgrado</td>\n",
       "      <td>Estatal</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  salario  anio  anioegreso  anionac        rama_id_map  \\\n",
       "0   1      NaN  2019        2017   1976.0  Ciencias Sociales   \n",
       "1   1      NaN  2020        2017   1976.0  Ciencias Sociales   \n",
       "2   1      NaN  2021        2017   1976.0  Ciencias Sociales   \n",
       "3   2      NaN  2019        2018   1980.0  Ciencias Sociales   \n",
       "4   2      NaN  2020        2018   1980.0  Ciencias Sociales   \n",
       "\n",
       "           disciplina_id_map tipo_titulo_id_map gestion_id_map genero_id_map  \\\n",
       "0  Economía y Administración           Posgrado        Estatal         Mujer   \n",
       "1  Economía y Administración           Posgrado        Estatal         Mujer   \n",
       "2  Economía y Administración           Posgrado        Estatal         Mujer   \n",
       "3  Economía y Administración           Posgrado        Estatal         Mujer   \n",
       "4  Economía y Administración           Posgrado        Estatal         Mujer   \n",
       "\n",
       "  region_id_map tamanio_id_map letra_id_map  \n",
       "0          CABA            NaN          NaN  \n",
       "1          CABA            NaN          NaN  \n",
       "2          CABA            NaN          NaN  \n",
       "3  BUENOS AIRES            NaN          NaN  \n",
       "4  BUENOS AIRES            NaN          NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e33c63d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# https://www.statology.org/pandas-to-csv/\n",
    "df.to_csv(\"./python+tableau/base-full.csv\", index=False)"
   ]
  }
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