elsonidoq/evolucion.ipynb

## evolucion.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f7f7f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0dacc97e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Evolución del Empleo registrado total y cada 100 habitantes - Asalariados registrados (Trabajo).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "13ab4bb5",
   "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>Período</th>\n",
       "      <th>Asalariados privados</th>\n",
       "      <th>Asalariado privados cada 100 hab</th>\n",
       "      <th>Asalariados públicos</th>\n",
       "      <th>Asal públicos cada 100 hab</th>\n",
       "      <th>Total asalariados</th>\n",
       "      <th>Total asalariados cada 100 hab</th>\n",
       "      <th>Casas particulares</th>\n",
       "      <th>Casas particulares cada 100 hab</th>\n",
       "      <th>Independientes Autónomos</th>\n",
       "      <th>Independientes Autónomos cada 100 hab</th>\n",
       "      <th>Independientes Monotributo</th>\n",
       "      <th>Independientes monotributistas cada 100 hab</th>\n",
       "      <th>Independientes Monot Social</th>\n",
       "      <th>Independientes Monot Social cada 100 hab</th>\n",
       "      <th>Total</th>\n",
       "      <th>Total cada 100 hab</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ene-12</td>\n",
       "      <td>6.068</td>\n",
       "      <td>14,54</td>\n",
       "      <td>2.549</td>\n",
       "      <td>6,11</td>\n",
       "      <td>8.617</td>\n",
       "      <td>20,65</td>\n",
       "      <td>390.0</td>\n",
       "      <td>0,93</td>\n",
       "      <td>408.0</td>\n",
       "      <td>0,98</td>\n",
       "      <td>1.315,00</td>\n",
       "      <td>3,15</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0,40</td>\n",
       "      <td>10.897</td>\n",
       "      <td>26,11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>feb-12</td>\n",
       "      <td>6.062</td>\n",
       "      <td>14,53</td>\n",
       "      <td>2.607</td>\n",
       "      <td>6,25</td>\n",
       "      <td>8.669</td>\n",
       "      <td>20,77</td>\n",
       "      <td>390.0</td>\n",
       "      <td>0,93</td>\n",
       "      <td>408.0</td>\n",
       "      <td>0,98</td>\n",
       "      <td>1.315,00</td>\n",
       "      <td>3,15</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0,40</td>\n",
       "      <td>10.951</td>\n",
       "      <td>26,24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mar-12</td>\n",
       "      <td>6.072</td>\n",
       "      <td>14,55</td>\n",
       "      <td>2.620</td>\n",
       "      <td>6,28</td>\n",
       "      <td>8.692</td>\n",
       "      <td>20,83</td>\n",
       "      <td>390.0</td>\n",
       "      <td>0,93</td>\n",
       "      <td>408.0</td>\n",
       "      <td>0,98</td>\n",
       "      <td>1.315,00</td>\n",
       "      <td>3,15</td>\n",
       "      <td>170.0</td>\n",
       "      <td>0,41</td>\n",
       "      <td>10.975</td>\n",
       "      <td>26,30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abr-12</td>\n",
       "      <td>6.057</td>\n",
       "      <td>14,51</td>\n",
       "      <td>2.624</td>\n",
       "      <td>6,29</td>\n",
       "      <td>8.681</td>\n",
       "      <td>20,80</td>\n",
       "      <td>391.0</td>\n",
       "      <td>0,94</td>\n",
       "      <td>408.0</td>\n",
       "      <td>0,98</td>\n",
       "      <td>1.318,00</td>\n",
       "      <td>3,16</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0,41</td>\n",
       "      <td>10.971</td>\n",
       "      <td>26,29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>may-12</td>\n",
       "      <td>6.066</td>\n",
       "      <td>14,54</td>\n",
       "      <td>2.597</td>\n",
       "      <td>6,22</td>\n",
       "      <td>8.663</td>\n",
       "      <td>20,76</td>\n",
       "      <td>393.0</td>\n",
       "      <td>0,94</td>\n",
       "      <td>408.0</td>\n",
       "      <td>0,98</td>\n",
       "      <td>1.322,00</td>\n",
       "      <td>3,17</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0,41</td>\n",
       "      <td>10.958</td>\n",
       "      <td>26,26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Período Asalariados privados Asalariado privados cada 100 hab  \\\n",
       "0  ene-12                6.068                            14,54   \n",
       "1  feb-12                6.062                            14,53   \n",
       "2  mar-12                6.072                            14,55   \n",
       "3  abr-12                6.057                            14,51   \n",
       "4  may-12                6.066                            14,54   \n",
       "\n",
       "   Asalariados públicos Asal públicos cada 100 hab  Total asalariados  \\\n",
       "0                 2.549                       6,11              8.617   \n",
       "1                 2.607                       6,25              8.669   \n",
       "2                 2.620                       6,28              8.692   \n",
       "3                 2.624                       6,29              8.681   \n",
       "4                 2.597                       6,22              8.663   \n",
       "\n",
       "  Total asalariados cada 100 hab  Casas particulares  \\\n",
       "0                          20,65               390.0   \n",
       "1                          20,77               390.0   \n",
       "2                          20,83               390.0   \n",
       "3                          20,80               391.0   \n",
       "4                          20,76               393.0   \n",
       "\n",
       "  Casas particulares cada 100 hab  Independientes Autónomos  \\\n",
       "0                            0,93                     408.0   \n",
       "1                            0,93                     408.0   \n",
       "2                            0,93                     408.0   \n",
       "3                            0,94                     408.0   \n",
       "4                            0,94                     408.0   \n",
       "\n",
       "  Independientes Autónomos cada 100 hab Independientes Monotributo  \\\n",
       "0                                  0,98                   1.315,00   \n",
       "1                                  0,98                   1.315,00   \n",
       "2                                  0,98                   1.315,00   \n",
       "3                                  0,98                   1.318,00   \n",
       "4                                  0,98                   1.322,00   \n",
       "\n",
       "  Independientes monotributistas cada 100 hab  Independientes Monot Social  \\\n",
       "0                                        3,15                        168.0   \n",
       "1                                        3,15                        169.0   \n",
       "2                                        3,15                        170.0   \n",
       "3                                        3,16                        173.0   \n",
       "4                                        3,17                        173.0   \n",
       "\n",
       "  Independientes Monot Social cada 100 hab   Total Total cada 100 hab  \n",
       "0                                     0,40  10.897              26,11  \n",
       "1                                     0,40  10.951              26,24  \n",
       "2                                     0,41  10.975              26,30  \n",
       "3                                     0,41  10.971              26,29  \n",
       "4                                     0,41  10.958              26,26  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d1b6cbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df[:df['Período'].tolist().index('jul-23')+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9baf2946",
   "metadata": {},
   "outputs": [],
   "source": [
    "month2int = {\n",
    "    'ene': 1, 'feb': 2, 'mar': 3, 'abr': 4, 'may': 5, 'jun': 6, 'jul':7, 'ago': 8, \n",
    "    'sep':9, 'oct': 10, 'nov': 11, 'dic': 12, \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "99eabcba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "def parse(date_str):\n",
    "    month, year = date_str.split('-')\n",
    "    month = month2int[month]\n",
    "    return datetime(2000 + int(year), month, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c48e799c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = df['Período'].apply(parse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "300316e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2975cedf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Asalariados privados'].apply(float).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "17f1b8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Asalariado privados cada 100 hab'].apply(lambda x: x.replace(',', '.')).apply(float).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3439feba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Asalariados públicos'].apply(float).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "407714fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Asal públicos cada 100 hab'].apply(lambda x: x.replace(',','.')).apply(float).plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}