Mentoria - Entregable 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "47fc327d",
   "metadata": {},
   "source": [
    "# Mentoria \n",
    "\n",
    "### Hito 1 - Práctico de análisis y visualización"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd2c8ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f43f1aba",
   "metadata": {},
   "source": [
    "Se leen los archivos `.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89eed074",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2019 = pd.read_csv(\"dataset\\DataSet2019.csv\", sep=';')\n",
    "df_2020 = pd.read_csv(\"dataset\\DataSet2020.csv\",sep=';')\n",
    "df_2021 = pd.read_csv(\"dataset\\DataSet2021.csv\",sep=';')\n",
    "df_2022 = pd.read_csv(\"dataset\\DataSet2022.csv\",sep=';')\n",
    "df_Montos = pd.read_csv(\"dataset\\DataSetMontos.csv\",sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a7383e1",
   "metadata": {},
   "source": [
    "Definimos las columnas de los dataset\n",
    "\n",
    "**DataSet20XX.csv**  \n",
    "\n",
    "`seg_id`\t\t= Id Interno del seguimiento  \n",
    "`prs_id`\t\t= Id Interno de la persona  \n",
    "`seg_codigo`\t= Id Interno de la acción realizada  \n",
    "`hora`\t\t    = Fecha y Hora de la acción  \n",
    "`seg_usuario`   = Usuario que realizo la acción  \n",
    "`obn_acreedor`\t= Id Interno del acreedor  \n",
    "`obn_interno`\t= Id Interno de la obligación  \n",
    "`obn_producto`\t= Id Interno de tipo de producto  \n",
    "`obn_cabecera`\t= Id Interno de Asignación  \n",
    "`fecha_alta`\t= Fecha de Ingreso de la obligación  \n",
    "`obn_estudio`\t= Id Interno del estudio asignado  \n",
    "`estado` \t    = Estado de la obligación  \n",
    "\n",
    "\n",
    "**DataSetMontos.csv**  \n",
    "\n",
    "`obn_id`\t\t= Id Interno de la obligación  \n",
    "`monto_deuda`\t= Importe de deuda original  \n",
    "\n",
    "Observamos los dataframes.\n",
    "\n",
    "#### DataFrame 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b58ae9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['seg_id', 'prs_id', 'seg_codigo', 'hora', 'seg_usuario', 'obn_acreedor',\n",
       "       'obn_interno', 'obn_producto', 'obn_cabecera', 'fecha_alta',\n",
       "       'obn_estudio', 'estado                                      19605115',\n",
       "       '794514', '9', '08/10/2019 10:59:00', '330', '264', '1445961', '1',\n",
       "       '9275', '24/09/2019', '940', 'B                       '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2019.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ff3848",
   "metadata": {},
   "source": [
    "Vemos que hay mas columnas de las mencionadas anteriormente. Se revisan las columnas en cuestion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3b09331",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seg_id</th>\n",
       "      <th>prs_id</th>\n",
       "      <th>seg_codigo</th>\n",
       "      <th>hora</th>\n",
       "      <th>seg_usuario</th>\n",
       "      <th>obn_acreedor</th>\n",
       "      <th>obn_interno</th>\n",
       "      <th>obn_producto</th>\n",
       "      <th>obn_cabecera</th>\n",
       "      <th>fecha_alta</th>\n",
       "      <th>...</th>\n",
       "      <th>9</th>\n",
       "      <th>08/10/2019 10:59:00</th>\n",
       "      <th>330</th>\n",
       "      <th>264</th>\n",
       "      <th>1445961</th>\n",
       "      <th>1</th>\n",
       "      <th>9275</th>\n",
       "      <th>24/09/2019</th>\n",
       "      <th>940</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19605119</td>\n",
       "      <td>787994</td>\n",
       "      <td>323</td>\n",
       "      <td>08/10/2019 11:00:00</td>\n",
       "      <td>341</td>\n",
       "      <td>269</td>\n",
       "      <td>1438679</td>\n",
       "      <td>102</td>\n",
       "      <td>9269</td>\n",
       "      <td>23/09/2019</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>1</th>\n",
       "      <td>19605121</td>\n",
       "      <td>771970</td>\n",
       "      <td>326</td>\n",
       "      <td>08/10/2019 11:00:00</td>\n",
       "      <td>313</td>\n",
       "      <td>264</td>\n",
       "      <td>1411199</td>\n",
       "      <td>1</td>\n",
       "      <td>9243</td>\n",
       "      <td>23/08/2019</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>2</th>\n",
       "      <td>19605122</td>\n",
       "      <td>771866</td>\n",
       "      <td>33</td>\n",
       "      <td>08/10/2019 11:00:00</td>\n",
       "      <td>349</td>\n",
       "      <td>264</td>\n",
       "      <td>1411092</td>\n",
       "      <td>1</td>\n",
       "      <td>9243</td>\n",
       "      <td>23/08/2019</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>3</th>\n",
       "      <td>19605123</td>\n",
       "      <td>772097</td>\n",
       "      <td>323</td>\n",
       "      <td>08/10/2019 11:00:00</td>\n",
       "      <td>362</td>\n",
       "      <td>264</td>\n",
       "      <td>1411333</td>\n",
       "      <td>1</td>\n",
       "      <td>9243</td>\n",
       "      <td>23/08/2019</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>4</th>\n",
       "      <td>19605130</td>\n",
       "      <td>793962</td>\n",
       "      <td>323</td>\n",
       "      <td>08/10/2019 11:00:00</td>\n",
       "      <td>407</td>\n",
       "      <td>266</td>\n",
       "      <td>1445070</td>\n",
       "      <td>2</td>\n",
       "      <td>9272</td>\n",
       "      <td>24/09/2019</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>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     seg_id  prs_id  seg_codigo                 hora  seg_usuario  \\\n",
       "0  19605119  787994         323  08/10/2019 11:00:00          341   \n",
       "1  19605121  771970         326  08/10/2019 11:00:00          313   \n",
       "2  19605122  771866          33  08/10/2019 11:00:00          349   \n",
       "3  19605123  772097         323  08/10/2019 11:00:00          362   \n",
       "4  19605130  793962         323  08/10/2019 11:00:00          407   \n",
       "\n",
       "   obn_acreedor  obn_interno  obn_producto  obn_cabecera  fecha_alta  ...   9  \\\n",
       "0           269      1438679           102          9269  23/09/2019  ... NaN   \n",
       "1           264      1411199             1          9243  23/08/2019  ... NaN   \n",
       "2           264      1411092             1          9243  23/08/2019  ... NaN   \n",
       "3           264      1411333             1          9243  23/08/2019  ... NaN   \n",
       "4           266      1445070             2          9272  24/09/2019  ... NaN   \n",
       "\n",
       "  08/10/2019 10:59:00  330  264  1445961   1  9275  24/09/2019  940  \\\n",
       "0                 NaN  NaN  NaN      NaN NaN   NaN         NaN  NaN   \n",
       "1                 NaN  NaN  NaN      NaN NaN   NaN         NaN  NaN   \n",
       "2                 NaN  NaN  NaN      NaN NaN   NaN         NaN  NaN   \n",
       "3                 NaN  NaN  NaN      NaN NaN   NaN         NaN  NaN   \n",
       "4                 NaN  NaN  NaN      NaN NaN   NaN         NaN  NaN   \n",
       "\n",
       "   B                         \n",
       "0                       NaN  \n",
       "1                       NaN  \n",
       "2                       NaN  \n",
       "3                       NaN  \n",
       "4                       NaN  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2019.dtypes\n",
    "df_2019[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3469f1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seg_id</th>\n",
       "      <th>prs_id</th>\n",
       "      <th>seg_codigo</th>\n",
       "      <th>seg_usuario</th>\n",
       "      <th>obn_acreedor</th>\n",
       "      <th>obn_interno</th>\n",
       "      <th>obn_producto</th>\n",
       "      <th>obn_cabecera</th>\n",
       "      <th>obn_estudio</th>\n",
       "      <th>794514</th>\n",
       "      <th>9</th>\n",
       "      <th>08/10/2019 10:59:00</th>\n",
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       "      <th>264</th>\n",
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       "      <th>24/09/2019</th>\n",
       "      <th>940</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1066261.00</td>\n",
       "      <td>1066261.00</td>\n",
       "      <td>1066261.00</td>\n",
       "      <td>1066261.00</td>\n",
       "      <td>1066261.00</td>\n",
       "      <td>1066261.0</td>\n",
       "      <td>1066261.00</td>\n",
       "      <td>1066261.00</td>\n",
       "      <td>1066261.00</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18439712.81</td>\n",
       "      <td>753286.30</td>\n",
       "      <td>252.94</td>\n",
       "      <td>317.43</td>\n",
       "      <td>265.05</td>\n",
       "      <td>1399858.4</td>\n",
       "      <td>9.49</td>\n",
       "      <td>9163.24</td>\n",
       "      <td>973.68</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1858286.50</td>\n",
       "      <td>61517.55</td>\n",
       "      <td>123.59</td>\n",
       "      <td>81.17</td>\n",
       "      <td>1.81</td>\n",
       "      <td>36395.3</td>\n",
       "      <td>26.31</td>\n",
       "      <td>102.98</td>\n",
       "      <td>66.21</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15120414.00</td>\n",
       "      <td>74678.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>264.00</td>\n",
       "      <td>1354293.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9002.00</td>\n",
       "      <td>4.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16803356.00</td>\n",
       "      <td>747887.00</td>\n",
       "      <td>223.00</td>\n",
       "      <td>228.00</td>\n",
       "      <td>264.00</td>\n",
       "      <td>1374773.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9079.00</td>\n",
       "      <td>940.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18301453.00</td>\n",
       "      <td>753868.00</td>\n",
       "      <td>323.00</td>\n",
       "      <td>335.00</td>\n",
       "      <td>264.00</td>\n",
       "      <td>1392037.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9174.00</td>\n",
       "      <td>940.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19975867.00</td>\n",
       "      <td>772180.00</td>\n",
       "      <td>326.00</td>\n",
       "      <td>377.00</td>\n",
       "      <td>266.00</td>\n",
       "      <td>1412884.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>9247.00</td>\n",
       "      <td>1041.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32870430.00</td>\n",
       "      <td>835300.00</td>\n",
       "      <td>387.00</td>\n",
       "      <td>461.00</td>\n",
       "      <td>272.00</td>\n",
       "      <td>1511833.0</td>\n",
       "      <td>102.00</td>\n",
       "      <td>9395.00</td>\n",
       "      <td>1339.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            seg_id      prs_id  seg_codigo  seg_usuario  obn_acreedor  \\\n",
       "count   1066261.00  1066261.00  1066261.00   1066261.00    1066261.00   \n",
       "mean   18439712.81   753286.30      252.94       317.43        265.05   \n",
       "std     1858286.50    61517.55      123.59        81.17          1.81   \n",
       "min    15120414.00    74678.00        1.00        88.00        264.00   \n",
       "25%    16803356.00   747887.00      223.00       228.00        264.00   \n",
       "50%    18301453.00   753868.00      323.00       335.00        264.00   \n",
       "75%    19975867.00   772180.00      326.00       377.00        266.00   \n",
       "max    32870430.00   835300.00      387.00       461.00        272.00   \n",
       "\n",
       "       obn_interno  obn_producto  obn_cabecera  obn_estudio  794514    9  \\\n",
       "count    1066261.0    1066261.00    1066261.00   1066261.00     0.0  0.0   \n",
       "mean     1399858.4          9.49       9163.24       973.68     NaN  NaN   \n",
       "std        36395.3         26.31        102.98        66.21     NaN  NaN   \n",
       "min      1354293.0          1.00       9002.00         4.00     NaN  NaN   \n",
       "25%      1374773.0          1.00       9079.00       940.00     NaN  NaN   \n",
       "50%      1392037.0          1.00       9174.00       940.00     NaN  NaN   \n",
       "75%      1412884.0          2.00       9247.00      1041.00     NaN  NaN   \n",
       "max      1511833.0        102.00       9395.00      1339.00     NaN  NaN   \n",
       "\n",
       "       08/10/2019 10:59:00  330  264  1445961    1  9275  24/09/2019  940  \\\n",
       "count                  0.0  0.0  0.0      0.0  0.0   0.0         0.0  0.0   \n",
       "mean                   NaN  NaN  NaN      NaN  NaN   NaN         NaN  NaN   \n",
       "std                    NaN  NaN  NaN      NaN  NaN   NaN         NaN  NaN   \n",
       "min                    NaN  NaN  NaN      NaN  NaN   NaN         NaN  NaN   \n",
       "25%                    NaN  NaN  NaN      NaN  NaN   NaN         NaN  NaN   \n",
       "50%                    NaN  NaN  NaN      NaN  NaN   NaN         NaN  NaN   \n",
       "75%                    NaN  NaN  NaN      NaN  NaN   NaN         NaN  NaN   \n",
       "max                    NaN  NaN  NaN      NaN  NaN   NaN         NaN  NaN   \n",
       "\n",
       "       B                         \n",
       "count                       0.0  \n",
       "mean                        NaN  \n",
       "std                         NaN  \n",
       "min                         NaN  \n",
       "25%                         NaN  \n",
       "50%                         NaN  \n",
       "75%                         NaN  \n",
       "max                         NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2019.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae7f606d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>seg_id</th>\n",
       "      <th>prs_id</th>\n",
       "      <th>seg_codigo</th>\n",
       "      <th>hora</th>\n",
       "      <th>seg_usuario</th>\n",
       "      <th>obn_acreedor</th>\n",
       "      <th>obn_interno</th>\n",
       "      <th>obn_producto</th>\n",
       "      <th>obn_cabecera</th>\n",
       "      <th>fecha_alta</th>\n",
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       "      <td>28/10/2019</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>26/12/2019 09:01:43</td>\n",
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       "      <td>270</td>\n",
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       "      <td>9323</td>\n",
       "      <td>28/10/2019</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",
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       "    <tr>\n",
       "      <th>1066258</th>\n",
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       "      <td>9323</td>\n",
       "      <td>28/10/2019</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>1066259</th>\n",
       "      <td>32870429</td>\n",
       "      <td>701629</td>\n",
       "      <td>361</td>\n",
       "      <td>29/12/2019 09:01:44</td>\n",
       "      <td>206</td>\n",
       "      <td>270</td>\n",
       "      <td>1459936</td>\n",
       "      <td>6</td>\n",
       "      <td>9323</td>\n",
       "      <td>28/10/2019</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",
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       "    <tr>\n",
       "      <th>1066260</th>\n",
       "      <td>32870430</td>\n",
       "      <td>701629</td>\n",
       "      <td>379</td>\n",
       "      <td>29/12/2019 09:01:44</td>\n",
       "      <td>206</td>\n",
       "      <td>270</td>\n",
       "      <td>1459936</td>\n",
       "      <td>6</td>\n",
       "      <td>9323</td>\n",
       "      <td>28/10/2019</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>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           seg_id  prs_id  seg_codigo                 hora  seg_usuario  \\\n",
       "1066256  32870425  701629         323  26/12/2019 09:01:43          294   \n",
       "1066257  32870426  701629         323  26/12/2019 09:01:43          294   \n",
       "1066258  32870427  701629         323  26/12/2019 09:01:44          294   \n",
       "1066259  32870429  701629         361  29/12/2019 09:01:44          206   \n",
       "1066260  32870430  701629         379  29/12/2019 09:01:44          206   \n",
       "\n",
       "         obn_acreedor  obn_interno  obn_producto  obn_cabecera  fecha_alta  \\\n",
       "1066256           270      1459936             6          9323  28/10/2019   \n",
       "1066257           270      1459936             6          9323  28/10/2019   \n",
       "1066258           270      1459936             6          9323  28/10/2019   \n",
       "1066259           270      1459936             6          9323  28/10/2019   \n",
       "1066260           270      1459936             6          9323  28/10/2019   \n",
       "\n",
       "         ...   9 08/10/2019 10:59:00  330  264  1445961   1  9275  24/09/2019  \\\n",
       "1066256  ... NaN                 NaN  NaN  NaN      NaN NaN   NaN         NaN   \n",
       "1066257  ... NaN                 NaN  NaN  NaN      NaN NaN   NaN         NaN   \n",
       "1066258  ... NaN                 NaN  NaN  NaN      NaN NaN   NaN         NaN   \n",
       "1066259  ... NaN                 NaN  NaN  NaN      NaN NaN   NaN         NaN   \n",
       "1066260  ... NaN                 NaN  NaN  NaN      NaN NaN   NaN         NaN   \n",
       "\n",
       "         940  B                         \n",
       "1066256  NaN                       NaN  \n",
       "1066257  NaN                       NaN  \n",
       "1066258  NaN                       NaN  \n",
       "1066259  NaN                       NaN  \n",
       "1066260  NaN                       NaN  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2019.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5097b075",
   "metadata": {},
   "source": [
    "Se observan que tienen valores nulos. Se definen entonces las columnas del dataset y se renombra la columna estado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bd2a83e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seg_id</th>\n",
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       "      <th>obn_estudio</th>\n",
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       "      <td>206</td>\n",
       "      <td>270</td>\n",
       "      <td>1459936</td>\n",
       "      <td>6</td>\n",
       "      <td>9323</td>\n",
       "      <td>28/10/2019</td>\n",
       "      <td>1112</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           seg_id  prs_id  seg_codigo                 hora  seg_usuario  \\\n",
       "1066256  32870425  701629         323  26/12/2019 09:01:43          294   \n",
       "1066257  32870426  701629         323  26/12/2019 09:01:43          294   \n",
       "1066258  32870427  701629         323  26/12/2019 09:01:44          294   \n",
       "1066259  32870429  701629         361  29/12/2019 09:01:44          206   \n",
       "1066260  32870430  701629         379  29/12/2019 09:01:44          206   \n",
       "\n",
       "         obn_acreedor  obn_interno  obn_producto  obn_cabecera  fecha_alta  \\\n",
       "1066256           270      1459936             6          9323  28/10/2019   \n",
       "1066257           270      1459936             6          9323  28/10/2019   \n",
       "1066258           270      1459936             6          9323  28/10/2019   \n",
       "1066259           270      1459936             6          9323  28/10/2019   \n",
       "1066260           270      1459936             6          9323  28/10/2019   \n",
       "\n",
       "         obn_estudio                 estado  \n",
       "1066256         1112  C                      \n",
       "1066257         1112  C                      \n",
       "1066258         1112  C                      \n",
       "1066259         1112  C                      \n",
       "1066260         1112                      C  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['seg_id', 'prs_id', 'seg_codigo', 'hora', 'seg_usuario', 'obn_acreedor',\n",
    "       'obn_interno', 'obn_producto', 'obn_cabecera', 'fecha_alta',\n",
    "       'obn_estudio', 'estado' ]\n",
    "df_2019.rename(columns= {'estado                                      19605115':'estado'}, inplace=True)\n",
    "df_2019 = df_2019[columns]\n",
    "df_2019.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe8a48d",
   "metadata": {},
   "source": [
    "#### DataFrame 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "931c5764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['seg_id', 'prs_id', 'seg_codigo', 'hora', 'seg_usuario', 'obn_acreedor',\n",
       "       'obn_interno', 'obn_producto', 'obn_cabecera', 'fecha_alta',\n",
       "       'obn_estudio', 'estado                                      21945454',\n",
       "       '839807', '326', '17/01/2020 15:45:51', '446', '269', '1522781', '1',\n",
       "       '9437', '16/01/2020', '1192', 'B                    '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2020.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "568c6004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seg_id                                                    int64\n",
       "prs_id                                                    int64\n",
       "seg_codigo                                                int64\n",
       "hora                                                     object\n",
       "seg_usuario                                               int64\n",
       "obn_acreedor                                              int64\n",
       "obn_interno                                               int64\n",
       "obn_producto                                              int64\n",
       "obn_cabecera                                              int64\n",
       "fecha_alta                                               object\n",
       "obn_estudio                                               int64\n",
       "estado                                      21945454     object\n",
       "839807                                                  float64\n",
       "326                                                     float64\n",
       "17/01/2020 15:45:51                                     float64\n",
       "446                                                     float64\n",
       "269                                                     float64\n",
       "1522781                                                 float64\n",
       "1                                                       float64\n",
       "9437                                                    float64\n",
       "16/01/2020                                              float64\n",
       "1192                                                    float64\n",
       "B                                                       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2020.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7c2a2770",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>9437</th>\n",
       "      <th>16/01/2020</th>\n",
       "      <th>1192</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1669590.00</td>\n",
       "      <td>1669590.00</td>\n",
       "      <td>1669590.00</td>\n",
       "      <td>1669590.00</td>\n",
       "      <td>1669590.00</td>\n",
       "      <td>1669590.00</td>\n",
       "      <td>1669590.00</td>\n",
       "      <td>1669590.00</td>\n",
       "      <td>1669590.00</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24977580.55</td>\n",
       "      <td>825488.84</td>\n",
       "      <td>231.37</td>\n",
       "      <td>322.85</td>\n",
       "      <td>263.72</td>\n",
       "      <td>1527683.94</td>\n",
       "      <td>9.06</td>\n",
       "      <td>9465.17</td>\n",
       "      <td>1022.10</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1856643.00</td>\n",
       "      <td>92169.74</td>\n",
       "      <td>136.92</td>\n",
       "      <td>96.52</td>\n",
       "      <td>23.49</td>\n",
       "      <td>67763.39</td>\n",
       "      <td>25.09</td>\n",
       "      <td>160.48</td>\n",
       "      <td>99.12</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21594291.00</td>\n",
       "      <td>74125.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>147.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1058479.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7433.00</td>\n",
       "      <td>231.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23437216.25</td>\n",
       "      <td>794568.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>219.00</td>\n",
       "      <td>264.00</td>\n",
       "      <td>1488102.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9364.00</td>\n",
       "      <td>940.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>25055828.00</td>\n",
       "      <td>844559.00</td>\n",
       "      <td>323.00</td>\n",
       "      <td>340.00</td>\n",
       "      <td>264.00</td>\n",
       "      <td>1540734.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9487.00</td>\n",
       "      <td>1040.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26464039.75</td>\n",
       "      <td>876764.00</td>\n",
       "      <td>332.00</td>\n",
       "      <td>410.00</td>\n",
       "      <td>269.00</td>\n",
       "      <td>1579087.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>9586.00</td>\n",
       "      <td>1092.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32870853.00</td>\n",
       "      <td>921015.00</td>\n",
       "      <td>390.00</td>\n",
       "      <td>494.00</td>\n",
       "      <td>272.00</td>\n",
       "      <td>1638182.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>9785.00</td>\n",
       "      <td>1339.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            seg_id      prs_id  seg_codigo  seg_usuario  obn_acreedor  \\\n",
       "count   1669590.00  1669590.00  1669590.00   1669590.00    1669590.00   \n",
       "mean   24977580.55   825488.84      231.37       322.85        263.72   \n",
       "std     1856643.00    92169.74      136.92        96.52         23.49   \n",
       "min    21594291.00    74125.00        2.00       147.00         36.00   \n",
       "25%    23437216.25   794568.00       53.00       219.00        264.00   \n",
       "50%    25055828.00   844559.00      323.00       340.00        264.00   \n",
       "75%    26464039.75   876764.00      332.00       410.00        269.00   \n",
       "max    32870853.00   921015.00      390.00       494.00        272.00   \n",
       "\n",
       "       obn_interno  obn_producto  obn_cabecera  obn_estudio  839807  326  \\\n",
       "count   1669590.00    1669590.00    1669590.00   1669590.00     0.0  0.0   \n",
       "mean    1527683.94          9.06       9465.17      1022.10     NaN  NaN   \n",
       "std       67763.39         25.09        160.48        99.12     NaN  NaN   \n",
       "min     1058479.00          1.00       7433.00       231.00     NaN  NaN   \n",
       "25%     1488102.00          1.00       9364.00       940.00     NaN  NaN   \n",
       "50%     1540734.00          1.00       9487.00      1040.00     NaN  NaN   \n",
       "75%     1579087.00          4.00       9586.00      1092.00     NaN  NaN   \n",
       "max     1638182.00        103.00       9785.00      1339.00     NaN  NaN   \n",
       "\n",
       "       17/01/2020 15:45:51  446  269  1522781    1  9437  16/01/2020  1192  \\\n",
       "count                  0.0  0.0  0.0      0.0  0.0   0.0         0.0   0.0   \n",
       "mean                   NaN  NaN  NaN      NaN  NaN   NaN         NaN   NaN   \n",
       "std                    NaN  NaN  NaN      NaN  NaN   NaN         NaN   NaN   \n",
       "min                    NaN  NaN  NaN      NaN  NaN   NaN         NaN   NaN   \n",
       "25%                    NaN  NaN  NaN      NaN  NaN   NaN         NaN   NaN   \n",
       "50%                    NaN  NaN  NaN      NaN  NaN   NaN         NaN   NaN   \n",
       "75%                    NaN  NaN  NaN      NaN  NaN   NaN         NaN   NaN   \n",
       "max                    NaN  NaN  NaN      NaN  NaN   NaN         NaN   NaN   \n",
       "\n",
       "       B                      \n",
       "count                    0.0  \n",
       "mean                     NaN  \n",
       "std                      NaN  \n",
       "min                      NaN  \n",
       "25%                      NaN  \n",
       "50%                      NaN  \n",
       "75%                      NaN  \n",
       "max                      NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2020.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "284a6b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>seg_id</th>\n",
       "      <th>prs_id</th>\n",
       "      <th>seg_codigo</th>\n",
       "      <th>hora</th>\n",
       "      <th>seg_usuario</th>\n",
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       "      <th>1669585</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1669586</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1669587</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669588</th>\n",
       "      <td>32870815</td>\n",
       "      <td>701629</td>\n",
       "      <td>361</td>\n",
       "      <td>01/12/2020 09:03:28</td>\n",
       "      <td>206</td>\n",
       "      <td>270</td>\n",
       "      <td>1459936</td>\n",
       "      <td>6</td>\n",
       "      <td>9323</td>\n",
       "      <td>28/10/2019</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1669589</th>\n",
       "      <td>32870816</td>\n",
       "      <td>701629</td>\n",
       "      <td>379</td>\n",
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       "      <td>9323</td>\n",
       "      <td>28/10/2019</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           seg_id  prs_id  seg_codigo                 hora  seg_usuario  \\\n",
       "1669585  32870811  701629         323  30/11/2020 09:03:27          374   \n",
       "1669586  32870812  701629         323  30/11/2020 09:03:27          374   \n",
       "1669587  32870813  701629         323  30/11/2020 09:03:27          374   \n",
       "1669588  32870815  701629         361  01/12/2020 09:03:28          206   \n",
       "1669589  32870816  701629         379  01/12/2020 09:03:28          206   \n",
       "\n",
       "         obn_acreedor  obn_interno  obn_producto  obn_cabecera  fecha_alta  \\\n",
       "1669585           270      1459936             6          9323  28/10/2019   \n",
       "1669586           270      1459936             6          9323  28/10/2019   \n",
       "1669587           270      1459936             6          9323  28/10/2019   \n",
       "1669588           270      1459936             6          9323  28/10/2019   \n",
       "1669589           270      1459936             6          9323  28/10/2019   \n",
       "\n",
       "         ...  326 17/01/2020 15:45:51  446  269  1522781   1  9437  \\\n",
       "1669585  ...  NaN                 NaN  NaN  NaN      NaN NaN   NaN   \n",
       "1669586  ...  NaN                 NaN  NaN  NaN      NaN NaN   NaN   \n",
       "1669587  ...  NaN                 NaN  NaN  NaN      NaN NaN   NaN   \n",
       "1669588  ...  NaN                 NaN  NaN  NaN      NaN NaN   NaN   \n",
       "1669589  ...  NaN                 NaN  NaN  NaN      NaN NaN   NaN   \n",
       "\n",
       "         16/01/2020  1192  B                      \n",
       "1669585         NaN   NaN                    NaN  \n",
       "1669586         NaN   NaN                    NaN  \n",
       "1669587         NaN   NaN                    NaN  \n",
       "1669588         NaN   NaN                    NaN  \n",
       "1669589         NaN   NaN                    NaN  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2020.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7db5a3",
   "metadata": {},
   "source": [
    "Se observa el mismo problema que en el `df_2019`. Renombramos de igual manera y dejamos solo las columnas pertinentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "abb04dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>9395</td>\n",
       "      <td>26/12/2019</td>\n",
       "      <td>940</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21945486</td>\n",
       "      <td>821495</td>\n",
       "      <td>326</td>\n",
       "      <td>17/01/2020 15:49:03</td>\n",
       "      <td>432</td>\n",
       "      <td>264</td>\n",
       "      <td>1488333</td>\n",
       "      <td>1</td>\n",
       "      <td>9364</td>\n",
       "      <td>23/11/2019</td>\n",
       "      <td>940</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21945496</td>\n",
       "      <td>801521</td>\n",
       "      <td>326</td>\n",
       "      <td>17/01/2020 15:49:46</td>\n",
       "      <td>439</td>\n",
       "      <td>264</td>\n",
       "      <td>1456453</td>\n",
       "      <td>1</td>\n",
       "      <td>9321</td>\n",
       "      <td>25/10/2019</td>\n",
       "      <td>1040</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21945512</td>\n",
       "      <td>801615</td>\n",
       "      <td>326</td>\n",
       "      <td>17/01/2020 15:51:12</td>\n",
       "      <td>426</td>\n",
       "      <td>264</td>\n",
       "      <td>1456550</td>\n",
       "      <td>1</td>\n",
       "      <td>9321</td>\n",
       "      <td>25/10/2019</td>\n",
       "      <td>1041</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     seg_id  prs_id  seg_codigo                 hora  seg_usuario  \\\n",
       "0  21945483  821495         326  17/01/2020 15:48:52          432   \n",
       "1  21945484  835147         326  17/01/2020 15:48:58          322   \n",
       "2  21945486  821495         326  17/01/2020 15:49:03          432   \n",
       "3  21945496  801521         326  17/01/2020 15:49:46          439   \n",
       "4  21945512  801615         326  17/01/2020 15:51:12          426   \n",
       "\n",
       "   obn_acreedor  obn_interno  obn_producto  obn_cabecera  fecha_alta  \\\n",
       "0           264      1488333             1          9364  23/11/2019   \n",
       "1           264      1511676             1          9395  26/12/2019   \n",
       "2           264      1488333             1          9364  23/11/2019   \n",
       "3           264      1456453             1          9321  25/10/2019   \n",
       "4           264      1456550             1          9321  25/10/2019   \n",
       "\n",
       "   obn_estudio                  estado  \n",
       "0          940  B                       \n",
       "1          940  B                       \n",
       "2          940  B                       \n",
       "3         1040   C                      \n",
       "4         1041   B                      "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2020.rename(columns= {'estado                                      21945454':'estado'}, inplace=True)\n",
    "df_2020 = df_2020[columns]\n",
    "df_2020[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877694fb",
   "metadata": {},
   "source": [
    "#### DataFrame 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58987c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['seg_id', 'prs_id', 'seg_codigo', 'hora', 'seg_usuario', 'obn_acreedor',\n",
       "       'obn_interno', 'obn_producto', 'obn_cabecera', 'fecha_alta',\n",
       "       'obn_estudio', 'estado                                      37470164',\n",
       "       '971009', '326', '06/10/2021 16:43:14', '531', '269', '1708410', '1',\n",
       "       '10069', '06/09/2021', '1192', 'B                   '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2021.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1a84e531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seg_id                                                    int64\n",
       "prs_id                                                    int64\n",
       "seg_codigo                                                int64\n",
       "hora                                                     object\n",
       "seg_usuario                                               int64\n",
       "obn_acreedor                                              int64\n",
       "obn_interno                                               int64\n",
       "obn_producto                                              int64\n",
       "obn_cabecera                                              int64\n",
       "fecha_alta                                               object\n",
       "obn_estudio                                               int64\n",
       "estado                                      37470164     object\n",
       "971009                                                  float64\n",
       "326                                                     float64\n",
       "06/10/2021 16:43:14                                     float64\n",
       "531                                                     float64\n",
       "269                                                     float64\n",
       "1708410                                                 float64\n",
       "1                                                       float64\n",
       "10069                                                   float64\n",
       "06/09/2021                                              float64\n",
       "1192                                                    float64\n",
       "B                                                       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2021.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "54a470ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>seg_id</th>\n",
       "      <th>prs_id</th>\n",
       "      <th>seg_codigo</th>\n",
       "      <th>seg_usuario</th>\n",
       "      <th>obn_acreedor</th>\n",
       "      <th>obn_interno</th>\n",
       "      <th>obn_producto</th>\n",
       "      <th>obn_cabecera</th>\n",
       "      <th>obn_estudio</th>\n",
       "      <th>971009</th>\n",
       "      <th>326</th>\n",
       "      <th>06/10/2021 16:43:14</th>\n",
       "      <th>531</th>\n",
       "      <th>269</th>\n",
       "      <th>1708410</th>\n",
       "      <th>1</th>\n",
       "      <th>10069</th>\n",
       "      <th>06/09/2021</th>\n",
       "      <th>1192</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2660287.00</td>\n",
       "      <td>2660287.00</td>\n",
       "      <td>2660287.00</td>\n",
       "      <td>2660287.00</td>\n",
       "      <td>2660287.00</td>\n",
       "      <td>2660287.00</td>\n",
       "      <td>2660287.00</td>\n",
       "      <td>2660287.00</td>\n",
       "      <td>2660287.00</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34010977.66</td>\n",
       "      <td>853944.52</td>\n",
       "      <td>292.93</td>\n",
       "      <td>320.32</td>\n",
       "      <td>267.79</td>\n",
       "      <td>1602998.07</td>\n",
       "      <td>21.93</td>\n",
       "      <td>9719.82</td>\n",
       "      <td>1092.55</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3530458.55</td>\n",
       "      <td>141619.31</td>\n",
       "      <td>128.64</td>\n",
       "      <td>99.61</td>\n",
       "      <td>2.66</td>\n",
       "      <td>97786.52</td>\n",
       "      <td>39.12</td>\n",
       "      <td>296.87</td>\n",
       "      <td>120.26</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28326644.00</td>\n",
       "      <td>74143.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>147.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1045862.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6967.00</td>\n",
       "      <td>312.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30779961.50</td>\n",
       "      <td>805387.00</td>\n",
       "      <td>323.00</td>\n",
       "      <td>248.00</td>\n",
       "      <td>264.00</td>\n",
       "      <td>1479767.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9326.00</td>\n",
       "      <td>1037.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34102258.00</td>\n",
       "      <td>914541.00</td>\n",
       "      <td>361.00</td>\n",
       "      <td>248.00</td>\n",
       "      <td>269.00</td>\n",
       "      <td>1640631.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>9797.00</td>\n",
       "      <td>1093.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37046509.50</td>\n",
       "      <td>947927.00</td>\n",
       "      <td>379.00</td>\n",
       "      <td>408.00</td>\n",
       "      <td>270.00</td>\n",
       "      <td>1681809.50</td>\n",
       "      <td>6.00</td>\n",
       "      <td>9967.00</td>\n",
       "      <td>1112.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40896777.00</td>\n",
       "      <td>998465.00</td>\n",
       "      <td>399.00</td>\n",
       "      <td>540.00</td>\n",
       "      <td>277.00</td>\n",
       "      <td>1755580.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>10212.00</td>\n",
       "      <td>1397.00</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            seg_id      prs_id  seg_codigo  seg_usuario  obn_acreedor  \\\n",
       "count   2660287.00  2660287.00  2660287.00   2660287.00    2660287.00   \n",
       "mean   34010977.66   853944.52      292.93       320.32        267.79   \n",
       "std     3530458.55   141619.31      128.64        99.61          2.66   \n",
       "min    28326644.00    74143.00        0.00       147.00         36.00   \n",
       "25%    30779961.50   805387.00      323.00       248.00        264.00   \n",
       "50%    34102258.00   914541.00      361.00       248.00        269.00   \n",
       "75%    37046509.50   947927.00      379.00       408.00        270.00   \n",
       "max    40896777.00   998465.00      399.00       540.00        277.00   \n",
       "\n",
       "       obn_interno  obn_producto  obn_cabecera  obn_estudio  971009  326  \\\n",
       "count   2660287.00    2660287.00    2660287.00   2660287.00     0.0  0.0   \n",
       "mean    1602998.07         21.93       9719.82      1092.55     NaN  NaN   \n",
       "std       97786.52         39.12        296.87       120.26     NaN  NaN   \n",
       "min     1045862.00          1.00       6967.00       312.00     NaN  NaN   \n",
       "25%     1479767.00          1.00       9326.00      1037.00     NaN  NaN   \n",
       "50%     1640631.00          2.00       9797.00      1093.00     NaN  NaN   \n",
       "75%     1681809.50          6.00       9967.00      1112.00     NaN  NaN   \n",
       "max     1755580.00        103.00      10212.00      1397.00     NaN  NaN   \n",
       "\n",
       "       06/10/2021 16:43:14  531  269  1708410    1  10069  06/09/2021  1192  \\\n",
       "count                  0.0  0.0  0.0      0.0  0.0    0.0         0.0   0.0   \n",
       "mean                   NaN  NaN  NaN      NaN  NaN    NaN         NaN   NaN   \n",
       "std                    NaN  NaN  NaN      NaN  NaN    NaN         NaN   NaN   \n",
       "min                    NaN  NaN  NaN      NaN  NaN    NaN         NaN   NaN   \n",
       "25%                    NaN  NaN  NaN      NaN  NaN    NaN         NaN   NaN   \n",
       "50%                    NaN  NaN  NaN      NaN  NaN    NaN         NaN   NaN   \n",
       "75%                    NaN  NaN  NaN      NaN  NaN    NaN         NaN   NaN   \n",
       "max                    NaN  NaN  NaN      NaN  NaN    NaN         NaN   NaN   \n",
       "\n",
       "       B                     \n",
       "count                   0.0  \n",
       "mean                    NaN  \n",
       "std                     NaN  \n",
       "min                     NaN  \n",
       "25%                     NaN  \n",
       "50%                     NaN  \n",
       "75%                     NaN  \n",
       "max                     NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2021.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7d4e229",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seg_id</th>\n",
       "      <th>prs_id</th>\n",
       "      <th>seg_codigo</th>\n",
       "      <th>hora</th>\n",
       "      <th>seg_usuario</th>\n",
       "      <th>obn_acreedor</th>\n",
       "      <th>obn_interno</th>\n",
       "      <th>obn_producto</th>\n",
       "      <th>obn_cabecera</th>\n",
       "      <th>fecha_alta</th>\n",
       "      <th>...</th>\n",
       "      <th>326</th>\n",
       "      <th>06/10/2021 16:43:14</th>\n",
       "      <th>531</th>\n",
       "      <th>269</th>\n",
       "      <th>1708410</th>\n",
       "      <th>1</th>\n",
       "      <th>10069</th>\n",
       "      <th>06/09/2021</th>\n",
       "      <th>1192</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2660282</th>\n",
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       "      <td>06/01/2022 11:40:23</td>\n",
       "      <td>325</td>\n",
       "      <td>264</td>\n",
       "      <td>1703162</td>\n",
       "      <td>1</td>\n",
       "      <td>10052</td>\n",
       "      <td>24/08/2021</td>\n",
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       "      <th>2660284</th>\n",
       "      <td>40531534</td>\n",
       "      <td>945607</td>\n",
       "      <td>398</td>\n",
       "      <td>06/01/2022 11:39:56</td>\n",
       "      <td>325</td>\n",
       "      <td>264</td>\n",
       "      <td>1672915</td>\n",
       "      <td>1</td>\n",
       "      <td>9952</td>\n",
       "      <td>21/05/2021</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>2660285</th>\n",
       "      <td>40531535</td>\n",
       "      <td>956089</td>\n",
       "      <td>398</td>\n",
       "      <td>06/01/2022 11:39:56</td>\n",
       "      <td>325</td>\n",
       "      <td>264</td>\n",
       "      <td>1686276</td>\n",
       "      <td>1</td>\n",
       "      <td>9982</td>\n",
       "      <td>24/06/2021</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>2660286</th>\n",
       "      <td>40531609</td>\n",
       "      <td>966607</td>\n",
       "      <td>398</td>\n",
       "      <td>06/01/2022 11:40:23</td>\n",
       "      <td>325</td>\n",
       "      <td>264</td>\n",
       "      <td>1702568</td>\n",
       "      <td>1</td>\n",
       "      <td>10052</td>\n",
       "      <td>24/08/2021</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>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           seg_id  prs_id  seg_codigo                 hora  seg_usuario  \\\n",
       "2660282  40531607  960516         398  06/01/2022 11:40:23          325   \n",
       "2660283  40531608  967182         398  06/01/2022 11:40:23          325   \n",
       "2660284  40531534  945607         398  06/01/2022 11:39:56          325   \n",
       "2660285  40531535  956089         398  06/01/2022 11:39:56          325   \n",
       "2660286  40531609  966607         398  06/01/2022 11:40:23          325   \n",
       "\n",
       "         obn_acreedor  obn_interno  obn_producto  obn_cabecera  fecha_alta  \\\n",
       "2660282           264      1694005             1         10019  23/07/2021   \n",
       "2660283           264      1703162             1         10052  24/08/2021   \n",
       "2660284           264      1672915             1          9952  21/05/2021   \n",
       "2660285           264      1686276             1          9982  24/06/2021   \n",
       "2660286           264      1702568             1         10052  24/08/2021   \n",
       "\n",
       "         ...  326 06/10/2021 16:43:14  531  269  1708410   1  10069  \\\n",
       "2660282  ...  NaN                 NaN  NaN  NaN      NaN NaN    NaN   \n",
       "2660283  ...  NaN                 NaN  NaN  NaN      NaN NaN    NaN   \n",
       "2660284  ...  NaN                 NaN  NaN  NaN      NaN NaN    NaN   \n",
       "2660285  ...  NaN                 NaN  NaN  NaN      NaN NaN    NaN   \n",
       "2660286  ...  NaN                 NaN  NaN  NaN      NaN NaN    NaN   \n",
       "\n",
       "         06/09/2021  1192  B                     \n",
       "2660282         NaN   NaN                   NaN  \n",
       "2660283         NaN   NaN                   NaN  \n",
       "2660284         NaN   NaN                   NaN  \n",
       "2660285         NaN   NaN                   NaN  \n",
       "2660286         NaN   NaN                   NaN  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2021.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96375473",
   "metadata": {},
   "source": [
    "Nuevamente el mismo problema que para los dataframe `df_2019` y `df_2020`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "15c5e4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>seg_id</th>\n",
       "      <th>prs_id</th>\n",
       "      <th>seg_codigo</th>\n",
       "      <th>hora</th>\n",
       "      <th>seg_usuario</th>\n",
       "      <th>obn_acreedor</th>\n",
       "      <th>obn_interno</th>\n",
       "      <th>obn_producto</th>\n",
       "      <th>obn_cabecera</th>\n",
       "      <th>fecha_alta</th>\n",
       "      <th>obn_estudio</th>\n",
       "      <th>estado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37294976</td>\n",
       "      <td>967387</td>\n",
       "      <td>326</td>\n",
       "      <td>04/10/2021 15:22:28</td>\n",
       "      <td>493</td>\n",
       "      <td>264</td>\n",
       "      <td>1703372</td>\n",
       "      <td>1</td>\n",
       "      <td>10052</td>\n",
       "      <td>24/08/2021</td>\n",
       "      <td>940</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37295062</td>\n",
       "      <td>945690</td>\n",
       "      <td>323</td>\n",
       "      <td>04/10/2021 15:28:39</td>\n",
       "      <td>455</td>\n",
       "      <td>264</td>\n",
       "      <td>1673000</td>\n",
       "      <td>1</td>\n",
       "      <td>9952</td>\n",
       "      <td>21/05/2021</td>\n",
       "      <td>1040</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37295071</td>\n",
       "      <td>831888</td>\n",
       "      <td>326</td>\n",
       "      <td>04/10/2021 15:29:22</td>\n",
       "      <td>369</td>\n",
       "      <td>271</td>\n",
       "      <td>1504791</td>\n",
       "      <td>2</td>\n",
       "      <td>9386</td>\n",
       "      <td>11/12/2019</td>\n",
       "      <td>1092</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37295102</td>\n",
       "      <td>975954</td>\n",
       "      <td>323</td>\n",
       "      <td>04/10/2021 15:31:14</td>\n",
       "      <td>175</td>\n",
       "      <td>264</td>\n",
       "      <td>1715481</td>\n",
       "      <td>1</td>\n",
       "      <td>10084</td>\n",
       "      <td>24/09/2021</td>\n",
       "      <td>940</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37295114</td>\n",
       "      <td>967387</td>\n",
       "      <td>326</td>\n",
       "      <td>04/10/2021 15:31:59</td>\n",
       "      <td>493</td>\n",
       "      <td>264</td>\n",
       "      <td>1703372</td>\n",
       "      <td>1</td>\n",
       "      <td>10052</td>\n",
       "      <td>24/08/2021</td>\n",
       "      <td>940</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     seg_id  prs_id  seg_codigo                 hora  seg_usuario  \\\n",
       "0  37294976  967387         326  04/10/2021 15:22:28          493   \n",
       "1  37295062  945690         323  04/10/2021 15:28:39          455   \n",
       "2  37295071  831888         326  04/10/2021 15:29:22          369   \n",
       "3  37295102  975954         323  04/10/2021 15:31:14          175   \n",
       "4  37295114  967387         326  04/10/2021 15:31:59          493   \n",
       "\n",
       "   obn_acreedor  obn_interno  obn_producto  obn_cabecera  fecha_alta  \\\n",
       "0           264      1703372             1         10052  24/08/2021   \n",
       "1           264      1673000             1          9952  21/05/2021   \n",
       "2           271      1504791             2          9386  11/12/2019   \n",
       "3           264      1715481             1         10084  24/09/2021   \n",
       "4           264      1703372             1         10052  24/08/2021   \n",
       "\n",
       "   obn_estudio                 estado  \n",
       "0          940  B                      \n",
       "1         1040  B                      \n",
       "2         1092  A                      \n",
       "3          940  B                      \n",
       "4          940  B                      "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2021.rename(columns= {'estado                                      37470164':'estado'}, inplace=True)\n",
    "df_2021 = df_2021[columns]\n",
    "df_2021[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a697b68f",
   "metadata": {},
   "source": [
    "#### DataFrame 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f2cdbfe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['seg_id', 'prs_id', 'seg_codigo', 'hora', 'seg_usuario', 'obn_acreedor',\n",
       "       'obn_interno', 'obn_producto', 'obn_cabecera', 'fecha_alta',\n",
       "       'obn_estudio', 'estado '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2022.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ce5076b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seg_id           int64\n",
       "prs_id           int64\n",
       "seg_codigo       int64\n",
       "hora            object\n",
       "seg_usuario      int64\n",
       "obn_acreedor     int64\n",
       "obn_interno      int64\n",
       "obn_producto     int64\n",
       "obn_cabecera     int64\n",
       "fecha_alta      object\n",
       "obn_estudio      int64\n",
       "estado          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2022.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2fe7e68e",
   "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>seg_id</th>\n",
       "      <th>prs_id</th>\n",
       "      <th>seg_codigo</th>\n",
       "      <th>seg_usuario</th>\n",
       "      <th>obn_acreedor</th>\n",
       "      <th>obn_interno</th>\n",
       "      <th>obn_producto</th>\n",
       "      <th>obn_cabecera</th>\n",
       "      <th>obn_estudio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>532746.00</td>\n",
       "      <td>532746.00</td>\n",
       "      <td>532746.00</td>\n",
       "      <td>532746.00</td>\n",
       "      <td>532746.00</td>\n",
       "      <td>532746.00</td>\n",
       "      <td>532746.00</td>\n",
       "      <td>532746.00</td>\n",
       "      <td>532746.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41881971.77</td>\n",
       "      <td>909675.00</td>\n",
       "      <td>299.12</td>\n",
       "      <td>330.12</td>\n",
       "      <td>268.00</td>\n",
       "      <td>1681141.62</td>\n",
       "      <td>23.97</td>\n",
       "      <td>9968.64</td>\n",
       "      <td>1070.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>782437.10</td>\n",
       "      <td>147266.42</td>\n",
       "      <td>113.43</td>\n",
       "      <td>107.85</td>\n",
       "      <td>3.81</td>\n",
       "      <td>134028.82</td>\n",
       "      <td>41.10</td>\n",
       "      <td>396.58</td>\n",
       "      <td>144.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40398847.00</td>\n",
       "      <td>75843.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>147.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1151375.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8268.00</td>\n",
       "      <td>312.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41154449.25</td>\n",
       "      <td>809644.00</td>\n",
       "      <td>320.00</td>\n",
       "      <td>248.00</td>\n",
       "      <td>264.00</td>\n",
       "      <td>1508087.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9386.00</td>\n",
       "      <td>940.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41923431.50</td>\n",
       "      <td>984768.00</td>\n",
       "      <td>326.00</td>\n",
       "      <td>248.00</td>\n",
       "      <td>269.00</td>\n",
       "      <td>1739352.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>10176.00</td>\n",
       "      <td>1092.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42544126.75</td>\n",
       "      <td>1004547.00</td>\n",
       "      <td>379.00</td>\n",
       "      <td>414.00</td>\n",
       "      <td>270.00</td>\n",
       "      <td>1766284.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10251.00</td>\n",
       "      <td>1112.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>43584013.00</td>\n",
       "      <td>1032616.00</td>\n",
       "      <td>399.00</td>\n",
       "      <td>577.00</td>\n",
       "      <td>277.00</td>\n",
       "      <td>1833294.00</td>\n",
       "      <td>106.00</td>\n",
       "      <td>10361.00</td>\n",
       "      <td>1397.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            seg_id      prs_id  seg_codigo  seg_usuario  obn_acreedor  \\\n",
       "count    532746.00   532746.00   532746.00    532746.00     532746.00   \n",
       "mean   41881971.77   909675.00      299.12       330.12        268.00   \n",
       "std      782437.10   147266.42      113.43       107.85          3.81   \n",
       "min    40398847.00    75843.00        2.00       147.00         36.00   \n",
       "25%    41154449.25   809644.00      320.00       248.00        264.00   \n",
       "50%    41923431.50   984768.00      326.00       248.00        269.00   \n",
       "75%    42544126.75  1004547.00      379.00       414.00        270.00   \n",
       "max    43584013.00  1032616.00      399.00       577.00        277.00   \n",
       "\n",
       "       obn_interno  obn_producto  obn_cabecera  obn_estudio  \n",
       "count    532746.00     532746.00     532746.00    532746.00  \n",
       "mean    1681141.62         23.97       9968.64      1070.51  \n",
       "std      134028.82         41.10        396.58       144.93  \n",
       "min     1151375.00          1.00       8268.00       312.00  \n",
       "25%     1508087.75          1.00       9386.00       940.00  \n",
       "50%     1739352.00          2.00      10176.00      1092.00  \n",
       "75%     1766284.00          6.00      10251.00      1112.00  \n",
       "max     1833294.00        106.00      10361.00      1397.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2022.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2c48702f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seg_id</th>\n",
       "      <th>prs_id</th>\n",
       "      <th>seg_codigo</th>\n",
       "      <th>hora</th>\n",
       "      <th>seg_usuario</th>\n",
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       "      <th>obn_producto</th>\n",
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       "      <th>obn_estudio</th>\n",
       "      <th>estado</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>532741</th>\n",
       "      <td>43222824</td>\n",
       "      <td>1031221</td>\n",
       "      <td>323</td>\n",
       "      <td>31/03/2022 12:42:21</td>\n",
       "      <td>403</td>\n",
       "      <td>264</td>\n",
       "      <td>1831107</td>\n",
       "      <td>1</td>\n",
       "      <td>10348</td>\n",
       "      <td>23/03/2022</td>\n",
       "      <td>901</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532742</th>\n",
       "      <td>43222837</td>\n",
       "      <td>1009669</td>\n",
       "      <td>323</td>\n",
       "      <td>31/03/2022 12:43:18</td>\n",
       "      <td>556</td>\n",
       "      <td>269</td>\n",
       "      <td>1784296</td>\n",
       "      <td>1</td>\n",
       "      <td>10279</td>\n",
       "      <td>09/02/2022</td>\n",
       "      <td>1192</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532743</th>\n",
       "      <td>43222838</td>\n",
       "      <td>1031221</td>\n",
       "      <td>320</td>\n",
       "      <td>31/03/2022 12:43:23</td>\n",
       "      <td>403</td>\n",
       "      <td>264</td>\n",
       "      <td>1831107</td>\n",
       "      <td>1</td>\n",
       "      <td>10348</td>\n",
       "      <td>23/03/2022</td>\n",
       "      <td>901</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532744</th>\n",
       "      <td>43222897</td>\n",
       "      <td>1004646</td>\n",
       "      <td>8</td>\n",
       "      <td>31/03/2022 12:46:40</td>\n",
       "      <td>541</td>\n",
       "      <td>264</td>\n",
       "      <td>1766080</td>\n",
       "      <td>1</td>\n",
       "      <td>10251</td>\n",
       "      <td>21/01/2022</td>\n",
       "      <td>911</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532745</th>\n",
       "      <td>43222951</td>\n",
       "      <td>1006763</td>\n",
       "      <td>323</td>\n",
       "      <td>31/03/2022 12:49:12</td>\n",
       "      <td>427</td>\n",
       "      <td>266</td>\n",
       "      <td>1770062</td>\n",
       "      <td>4</td>\n",
       "      <td>10267</td>\n",
       "      <td>03/02/2022</td>\n",
       "      <td>896</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          seg_id   prs_id  seg_codigo                 hora  seg_usuario  \\\n",
       "532741  43222824  1031221         323  31/03/2022 12:42:21          403   \n",
       "532742  43222837  1009669         323  31/03/2022 12:43:18          556   \n",
       "532743  43222838  1031221         320  31/03/2022 12:43:23          403   \n",
       "532744  43222897  1004646           8  31/03/2022 12:46:40          541   \n",
       "532745  43222951  1006763         323  31/03/2022 12:49:12          427   \n",
       "\n",
       "        obn_acreedor  obn_interno  obn_producto  obn_cabecera  fecha_alta  \\\n",
       "532741           264      1831107             1         10348  23/03/2022   \n",
       "532742           269      1784296             1         10279  09/02/2022   \n",
       "532743           264      1831107             1         10348  23/03/2022   \n",
       "532744           264      1766080             1         10251  21/01/2022   \n",
       "532745           266      1770062             4         10267  03/02/2022   \n",
       "\n",
       "        obn_estudio                  estado  \n",
       "532741          901    L                     \n",
       "532742         1192     A                    \n",
       "532743          901    L                     \n",
       "532744          911  A                       \n",
       "532745          896                       A  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2022.rename(columns= {'estado ':'estado'}, inplace=True)\n",
    "df_2022.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5fbfe4e",
   "metadata": {},
   "source": [
    "#### DataFrame Montos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e1b710b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['obn_id', 'monto_deuda'], dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Montos.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "12298aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "obn_id           int64\n",
       "monto_deuda    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Montos.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "85cd630e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_Montos['monto_deuda'] = pd.to_numeric(df_Montos['monto_deuda'], errors = 'raise')\n",
    "#df_Montos.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "74cd8bf7",
   "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>obn_id</th>\n",
       "      <th>monto_deuda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>234363.00</td>\n",
       "      <td>234363.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1590987.62</td>\n",
       "      <td>44406.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>128260.65</td>\n",
       "      <td>72484.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1354293.00</td>\n",
       "      <td>-256148.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1475951.50</td>\n",
       "      <td>8281.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1583375.00</td>\n",
       "      <td>22665.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1691384.50</td>\n",
       "      <td>54265.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1839706.00</td>\n",
       "      <td>13218122.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           obn_id  monto_deuda\n",
       "count   234363.00    234363.00\n",
       "mean   1590987.62     44406.24\n",
       "std     128260.65     72484.74\n",
       "min    1354293.00   -256148.00\n",
       "25%    1475951.50      8281.17\n",
       "50%    1583375.00     22665.95\n",
       "75%    1691384.50     54265.78\n",
       "max    1839706.00  13218122.00"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Montos.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "72561482",
   "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>obn_id</th>\n",
       "      <th>monto_deuda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1389386</td>\n",
       "      <td>45022.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1389387</td>\n",
       "      <td>58317.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1389388</td>\n",
       "      <td>24582.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1389389</td>\n",
       "      <td>146845.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1389390</td>\n",
       "      <td>59499.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obn_id  monto_deuda\n",
       "0  1389386     45022.85\n",
       "1  1389387     58317.55\n",
       "2  1389388     24582.60\n",
       "3  1389389    146845.27\n",
       "4  1389390     59499.16"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Montos[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a985118",
   "metadata": {},
   "source": [
    "#### Dataframe Periodo 2019-2022\n",
    "Concatenamos los dataframe por año en uno solo para poder analizar los distintos tipos de datos a un nivel mas general"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8ae0932f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_years = [df_2019,\n",
    "            df_2020,\n",
    "            df_2021,\n",
    "            df_2022]\n",
    "\n",
    "#Uno los dataframes por año\n",
    "df_2019_2022 = pd.DataFrame()\n",
    "for df in df_years:\n",
    "    df_2019_2022 = pd.concat([df_2019_2022,df])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "25a6b1b8",
   "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>seg_id</th>\n",
       "      <th>prs_id</th>\n",
       "      <th>seg_codigo</th>\n",
       "      <th>seg_usuario</th>\n",
       "      <th>obn_acreedor</th>\n",
       "      <th>obn_interno</th>\n",
       "      <th>obn_producto</th>\n",
       "      <th>obn_cabecera</th>\n",
       "      <th>obn_estudio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5928884.00</td>\n",
       "      <td>5928884.00</td>\n",
       "      <td>5928884.00</td>\n",
       "      <td>5928884.00</td>\n",
       "      <td>5928884.00</td>\n",
       "      <td>5928884.00</td>\n",
       "      <td>5928884.00</td>\n",
       "      <td>5928884.00</td>\n",
       "      <td>5928884.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29374040.71</td>\n",
       "      <td>832836.49</td>\n",
       "      <td>268.96</td>\n",
       "      <td>321.39</td>\n",
       "      <td>266.17</td>\n",
       "      <td>1552278.08</td>\n",
       "      <td>16.25</td>\n",
       "      <td>9570.37</td>\n",
       "      <td>1049.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7608526.61</td>\n",
       "      <td>126112.95</td>\n",
       "      <td>131.87</td>\n",
       "      <td>96.52</td>\n",
       "      <td>12.80</td>\n",
       "      <td>120128.27</td>\n",
       "      <td>34.39</td>\n",
       "      <td>346.46</td>\n",
       "      <td>118.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15120414.00</td>\n",
       "      <td>74125.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1045862.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6967.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23430840.75</td>\n",
       "      <td>769311.00</td>\n",
       "      <td>224.00</td>\n",
       "      <td>248.00</td>\n",
       "      <td>264.00</td>\n",
       "      <td>1458237.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9323.00</td>\n",
       "      <td>940.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29230942.50</td>\n",
       "      <td>840028.00</td>\n",
       "      <td>326.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>266.00</td>\n",
       "      <td>1548684.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9497.00</td>\n",
       "      <td>1041.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35508132.25</td>\n",
       "      <td>923317.00</td>\n",
       "      <td>361.00</td>\n",
       "      <td>403.00</td>\n",
       "      <td>269.00</td>\n",
       "      <td>1653616.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>9863.00</td>\n",
       "      <td>1112.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>43584013.00</td>\n",
       "      <td>1032616.00</td>\n",
       "      <td>399.00</td>\n",
       "      <td>577.00</td>\n",
       "      <td>277.00</td>\n",
       "      <td>1833294.00</td>\n",
       "      <td>106.00</td>\n",
       "      <td>10361.00</td>\n",
       "      <td>1397.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            seg_id      prs_id  seg_codigo  seg_usuario  obn_acreedor  \\\n",
       "count   5928884.00  5928884.00  5928884.00   5928884.00    5928884.00   \n",
       "mean   29374040.71   832836.49      268.96       321.39        266.17   \n",
       "std     7608526.61   126112.95      131.87        96.52         12.80   \n",
       "min    15120414.00    74125.00        0.00        88.00         36.00   \n",
       "25%    23430840.75   769311.00      224.00       248.00        264.00   \n",
       "50%    29230942.50   840028.00      326.00       300.00        266.00   \n",
       "75%    35508132.25   923317.00      361.00       403.00        269.00   \n",
       "max    43584013.00  1032616.00      399.00       577.00        277.00   \n",
       "\n",
       "       obn_interno  obn_producto  obn_cabecera  obn_estudio  \n",
       "count   5928884.00    5928884.00    5928884.00   5928884.00  \n",
       "mean    1552278.08         16.25       9570.37      1049.35  \n",
       "std      120128.27         34.39        346.46       118.53  \n",
       "min     1045862.00          1.00       6967.00         4.00  \n",
       "25%     1458237.00          1.00       9323.00       940.00  \n",
       "50%     1548684.00          1.00       9497.00      1041.00  \n",
       "75%     1653616.00          6.00       9863.00      1112.00  \n",
       "max     1833294.00        106.00      10361.00      1397.00  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2019_2022.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0721b22",
   "metadata": {},
   "source": [
    "Se observa por los descriptivos de los dataframes que la mayoria de variables son numericas. Esto no es cierto ya que en realidad son identificadores, aunque sean numeros operan como variables categoricas.\n",
    "\n",
    "Con esto en mente procedemos a hacer un conteo de los distintos identificadores a los fines de distinguirlos y poder establecer su relevancia."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aecdc853",
   "metadata": {},
   "source": [
    "#### Cantidad de personas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2de49f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184410"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "personas = list(set(df_2019_2022.prs_id.to_list())) # Se puede hacer lo mismo con la funcion unique() --> df.unique()\n",
    "len(personas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b642aa04",
   "metadata": {},
   "source": [
    "#### Cantidad de usuarios que realizaron una accion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3bb01ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ususarios = list(set(df_2019_2022.seg_usuario.to_list()))\n",
    "len(ususarios)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc11935",
   "metadata": {},
   "source": [
    "#### Cantidad de acciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1b45670f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "277"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acciones = list(set(df_2019_2022.seg_codigo.to_list()))\n",
    "len(acciones)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "459a12a9",
   "metadata": {},
   "source": [
    "#### Cantidad de acreedores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "72e46511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acreedores = list(set(df_2019_2022.obn_acreedor.to_list()))\n",
    "len(acreedores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "aa0da7ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Obligaciones totales por acreedor')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(num = 1, figsize = (12,6))\n",
    "df_2019_2022['obn_acreedor'].value_counts().plot(kind=\"bar\")\n",
    "plt.xticks(rotation = 0)\n",
    "plt.xlabel(\"Acreedores\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"Obligaciones totales por acreedor\",fontsize = 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de63dbd8",
   "metadata": {},
   "source": [
    "#### Cantidad de productos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dc90f92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "productos = list(set(df_2019_2022.obn_producto.to_list()))\n",
    "len(productos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "25c1eeeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Producto de las obligaciones')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(num = 1, figsize = (12,6))\n",
    "df_2019_2022['obn_producto'].value_counts().plot(kind=\"bar\")\n",
    "plt.xticks(rotation = 0)\n",
    "plt.xlabel(\"Productos\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"Producto de las obligaciones\",fontsize = 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d158c2",
   "metadata": {},
   "source": [
    "#### Cantidad de estudios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "5b357e8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de estudios TOTALES = 144\n",
      "Cantidad de estudios con mas de 50000 obligaciones = 17\n"
     ]
    }
   ],
   "source": [
    "estudios = list(set(df_2019_2022.obn_estudio.to_list()))\n",
    "df_estudios = df_2019_2022.groupby('obn_estudio').count()\n",
    "df_filtered_estudios = df_estudios[df_estudios['seg_id']>=50000]\n",
    "filtered_estudios = df_filtered_estudios['seg_id'].to_dict()\n",
    "\n",
    "print( f\"Cantidad de estudios TOTALES = {len(estudios)}\\n\\\n",
    "Cantidad de estudios con mas de 50000 obligaciones = {len(filtered_estudios.keys())}\")\n",
    "\n",
    "#La idea de estos dataframes, lista y dict es usarlos en el crosstab y heatmap con los estados de obligaciones"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c19e56f",
   "metadata": {},
   "source": [
    "#### Estados de obligacion posibles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fa8c2461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R', 'A', 'O', 'B', 'C', 'L']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Al ser de tipo 'string' los datos, no se pueden limpiar solo con el set\n",
    "estados = list(set(df_2019_2022.estado.to_list()))\n",
    "\n",
    "#Creamos un nuevo arreglo para añadir los valores de forma correcta\n",
    "estados_posibles = []\n",
    "for estado in estados:\n",
    "    estados_posibles.append(estado.rstrip())\n",
    "\n",
    "#Filtramos ahora si con el set\n",
    "estados_posibles = list(set(estados_posibles))\n",
    "estados_posibles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "43f81ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Horrible funcion, trate hacerla mas elegante pero no me funcionaba :(\n",
    "def statusFixer(df_status):\n",
    "    if(df_status.rstrip() == 'R' ):\n",
    "        return 'R'\n",
    "    elif(df_status.rstrip() == 'A' ):\n",
    "        return 'A'\n",
    "    elif(df_status.rstrip() == 'O' ):\n",
    "        return 'O'\n",
    "    elif(df_status.rstrip() == 'B' ):\n",
    "        return 'B'\n",
    "    elif(df_status.rstrip() == 'C' ):\n",
    "        return 'C'\n",
    "    elif(df_status.rstrip() == 'L' ):\n",
    "        return 'L'\n",
    "    else:\n",
    "        return None    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a89eed19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Estado del total de obligaciones')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UwAAtSZIkNTBAS5IkSQ0M0JIkSVIDA7QkSZLUwAAtSZIkNTBAS5IkSQ0M0JIkSVIDA7QkSZLUwAAtSZIkNTBAS5IkSQ0M0JIkSVIDA7QkSZLUwAAtSZIkNTBAS5IkSQ0GC9BJtk/y7SRnJDknyStnaXOjJB9KckGSU5KsGqoeSZIkaSEMeQX6OuBhVXUv4N7AvkkeMNbmGcBVVXUn4F+A1w5YjyRJkrTZBgvQ1VnXzy7vpxprdgDw3v7xccDDk2SomiRJkqTNNWgf6CTbJTkduAw4qapOGWuyK3ARQFWtB34G3HKW7RySZE2SNWvXrh2yZEmSJGlegwboqrq+qu4N7AbsleQem7idI6tqdVWtXrFixYLWKEmSJLXYIqNwVNVPgS8B+46tugTYHSDJMuBmwBVboiZJkiRpUww5CseKJDv3j3cAHgl8d6zZ8cDT+scHAl+sqvF+0pIkSdKisWzAba8E3ptkO7qg/uGqOiHJq4A1VXU88C7gmCQXAFcCBw1YjyRJkrTZBgvQVXUmcJ9Zlr985PEvgScMVYMkSZK00PwmQkmSJKmBAVqSJElqYICWJEmSGhigJUmSpAYGaEmSJKmBAVqSJElqYICWJEmSGhigJUmSpAYGaEmSJKmBAVqSJElqYICWJEmSGhigJUmSpAYGaEmSJKmBAVqSJElqYICWJEmSGhigJUmSpAYGaEmSJKmBAVqSJElqYICWJEmSGhigJUmSpAYGaEmSJKmBAVqSJElqYICWJEmSGhigJUmSpAYGaEmSJKmBAVqSJElqYICWJEmSGhigJUmSpAYGaEmSJKmBAVqSJElqYICWJEmSGhigJUmSpAYGaEmSJKmBAVqSJElqYICWJEmSGhigJUmSpAaDBegkuyf5UpJzk5yT5HmztNknyc+SnN5PLx+qHkmSJGkhLBtw2+uBv6+q05LcBPhOkpOq6tyxdl+tqscOWIckSZK0YAa7Al1Vl1bVaf3ja4DzgF2H2p8kSZK0JWyRPtBJVgH3AU6ZZfXeSc5I8ukkd98S9UiSJEmbasguHAAk2Qn4CPD8qrp6bPVpwO2qal2S/YCPA3vOso1DgEMA9thjj2ELliRJkuYx6BXoJMvpwvP7q+qj4+ur6uqqWtc/PhFYnmSXWdodWVWrq2r1ihUrhixZkiRJmteQo3AEeBdwXlW9YY42t+nbkWSvvp4rhqpJkiRJ2lxDduF4EPCXwFlJTu+XvRjYA6Cq3g4cCDw7yXrgF8BBVVUD1iRJkiRtlsECdFV9DchG2rwFeMtQNUiSJEkLzW8ilCRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoMFqCT7J7kS0nOTXJOkufN0iZJ3pTkgiRnJrnvUPVIkiRJC2HZgNteD/x9VZ2W5CbAd5KcVFXnjrR5NLBnP90feFv/U5IkSVqUBrsCXVWXVtVp/eNrgPOAXceaHQAcXZ1vATsnWTlUTZIkSdLm2iJ9oJOsAu4DnDK2alfgopH5i9kwZJPkkCRrkqxZu3btYHVKkiRJGzN4gE6yE/AR4PlVdfWmbKOqjqyq1VW1esWKFQtboCRJktRg0ACdZDldeH5/VX10liaXALuPzO/WL5MkSZIWpSFH4QjwLuC8qnrDHM2OB57aj8bxAOBnVXXpUDVJkiRJm2vIUTgeBPwlcFaS0/tlLwb2AKiqtwMnAvsBFwDXAk8fsB5JkiRpsw0WoKvqa0A20qaA5wxVgyRJkrTQ/CZCSZIkqYEBWpIkSWowUYBO8oVJlkmSJElbu3n7QCfZHtgR2CXJzfldn+abMssXnkiSJElbu43dRPgs4PnAbYHv8LsAfTXwluHKkiRJkhaneQN0Vb0ReGOS/11Vb95CNUmSJEmL1kTD2FXVm5M8EFg1+pyqOnqguiRJkqRFaaIAneQY4I7A6cD1/eICDNCSJEnapkz6RSqrgbv1X3wiSZIkbbMmHQf6bOA2QxYiSZIkLQWTXoHeBTg3ybeB62YWVtX+g1QlSZIkLVKTBujDhyxCkiRJWiomHYXjy0MXIkmSJC0Fk47CcQ3dqBsANwSWAz+vqpsOVZgkSZK0GE16BfomM4+TBDgAeMBQRUmSJEmL1aSjcPxWdT4O/MnClyNJkiQtbpN24fizkdkb0I0L/ctBKpIkSZIWsUlH4fjTkcfrgQvpunFIkiRJ25RJ+0A/fehCJEmSpKVgoj7QSXZL8rEkl/XTR5LsNnRxkiRJ0mIz6U2E7wGOB27bT5/sl0mSJEnblEkD9Iqqek9Vre+no4AVA9YlSZIkLUqTBugrkjwlyXb99BTgiiELkyRJkhajSQP0XwF/DvwEuBQ4EDh4oJokSZKkRWvSYexeBTytqq4CSHIL4J/pgrUkSZK0zZj0CvQ9Z8IzQFVdCdxnmJIkSZKkxWvSAH2DJDefmemvQE969VqSJEnaakwagl8PfDPJv/fzTwD+aZiSJEmSpMVr0m8iPDrJGuBh/aI/q6pzhytLkiRJWpwm7obRB2ZDsyRJkrZpk/aBliRJkoQBWpIkSWpigJYkSZIaGKAlSZKkBgZoSZIkqYEBWpIkSWowWIBO8u4klyU5e471+yT5WZLT++nlQ9UiSZIkLZQhv477KOAtwNHztPlqVT12wBokSZKkBTXYFeiq+gpw5VDblyRJkqZh2n2g905yRpJPJ7n7lGuRJEmSNmrILhwbcxpwu6pal2Q/4OPAnrM1THIIcAjAHnvsscUKlCRJksZN7Qp0VV1dVev6xycCy5PsMkfbI6tqdVWtXrFixRatU5IkSRo1tQCd5DZJ0j/eq6/limnVI0mSJE1isC4cST4I7APskuRi4BXAcoCqejtwIPDsJOuBXwAHVVUNVY8kSZK0EAYL0FX1FxtZ/xa6Ye4kSZKkJWPao3BIkiRJS4oBWpIkSWpggJYkSZIaGKAlSZKkBgZoSZIkqYEBWpIkSWpggJYkSZIaGKAlSZKkBgZoSZIkqYEBWpIkSWpggJYkSZIaGKAlSZKkBsumXYA0pFWHfWraJQzqwtc8ZtolSJK0zfEKtCRJktTAAC1JkiQ1MEBLkiRJDQzQkiRJUgMDtCRJktTAAC1JkiQ1MEBLkiRJDQzQkiRJUgMDtCRJktTAAC1JkiQ1MEBLkiRJDQzQkiRJUgMDtCRJktTAAC1JkiQ1MEBLkiRJDQzQkiRJUgMDtCRJktTAAC1JkiQ1MEBLkiRJDQzQkiRJUgMDtCRJktTAAC1JkiQ1MEBLkiRJDQzQkiRJUoPBAnSSdye5LMnZc6xPkjcluSDJmUnuO1QtkiRJ0kIZ8gr0UcC+86x/NLBnPx0CvG3AWiRJkqQFsWyoDVfVV5KsmqfJAcDRVVXAt5LsnGRlVV06VE2Slo5Vh31q2iUM6sLXPGbaJUiSNtE0+0DvClw0Mn9xv0ySJElatJbETYRJDkmyJsmatWvXTrscSZIkbcOmGaAvAXYfmd+tX7aBqjqyqlZX1eoVK1ZskeIkSZKk2UwzQB8PPLUfjeMBwM/s/yxJkqTFbrCbCJN8ENgH2CXJxcArgOUAVfV24ERgP+AC4Frg6UPVIkmSJC2UIUfh+IuNrC/gOUPtX5IkSRrCkriJUJIkSVosDNCSJElSAwO0JEmS1MAALUmSJDUwQEuSJEkNDNCSJElSAwO0JEmS1MAALUmSJDUwQEuSJEkNDNCSJElSAwO0JEmS1MAALUmSJDUwQEuSJEkNDNCSJElSAwO0JEmS1MAALUmSJDUwQEuSJEkNDNCSJElSAwO0JEmS1MAALUmSJDUwQEuSJEkNDNCSJElSAwO0JEmS1MAALUmSJDUwQEuSJEkNDNCSJElSAwO0JEmS1MAALUmSJDUwQEuSJEkNDNCSJElSAwO0JEmS1MAALUmSJDUwQEuSJEkNDNCSJElSAwO0JEmS1MAALUmSJDUwQEuSJEkNBg3QSfZNcn6SC5IcNsv6g5OsTXJ6Pz1zyHokSZKkzbVsqA0n2Q44AngkcDFwapLjq+rcsaYfqqpDh6pDkiRJWkhDXoHeC7igqn5QVb8CjgUOGHB/kiRJ0uCGDNC7AheNzF/cLxv3+CRnJjkuye6zbSjJIUnWJFmzdu3aIWqVJEmSJjLtmwg/CayqqnsCJwHvna1RVR1ZVauravWKFSu2aIGSJEnSqCED9CXA6BXl3fplv1VVV1TVdf3sO4E/GrAeSZIkabMNGaBPBfZMcvskNwQOAo4fbZBk5cjs/sB5A9YjSZIkbbbBRuGoqvVJDgU+C2wHvLuqzknyKmBNVR0PPDfJ/sB64Erg4KHqkSRJkhbCYAEaoKpOBE4cW/bykccvAl40ZA2SJEnSQpr2TYSSJEnSkmKAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhosm3YBkqStz6rDPjXtEgZ14WseM+0SJE2RV6AlSZKkBgZoSZIkqYEBWpIkSWpggJYkSZIaGKAlSZKkBgZoSZIkqYEBWpIkSWpggJYkSZIaGKAlSZKkBoN+E2GSfYE3AtsB76yq14ytvxFwNPBHwBXAE6vqwiFrkiRJ89uav0nSb5HUQhjsCnSS7YAjgEcDdwP+Isndxpo9A7iqqu4E/Avw2qHqkSRJkhbCkF049gIuqKofVNWvgGOBA8baHAC8t398HPDwJBmwJkmSJGmzDNmFY1fgopH5i4H7z9WmqtYn+RlwS+DyAeuSJEnaKm3N3W9g8XTBGbQP9EJJcghwSD+7Lsn506xnYLuwBX+BiJ1mFprnb+ny3C1tnr+ly3O3tG3t5+92sy0cMkBfAuw+Mr9bv2y2NhcnWQbcjO5mwt9TVUcCRw5U56KSZE1VrZ52Hdo0nr+ly3O3tHn+li7P3dK2rZ6/IftAnwrsmeT2SW4IHAQcP9bmeOBp/eMDgS9WVQ1YkyRJkrRZBrsC3fdpPhT4LN0wdu+uqnOSvApYU1XHA+8CjklyAXAlXciWJEmSFq1B+0BX1YnAiWPLXj7y+JfAE4asYQnaJrqqbMU8f0uX525p8/wtXZ67pW2bPH+xx4QkSZI0Ob/KW5IkSWpggJYkSZIaGKAXgSRHJamR6fIkJyS5y7Rr02SS3DfJ9Um+Pu1a1CbJrZO8Mcn3k1yX5JIkn06y37Rr09zGPjfXJ/lxkrclufm0a9Nk+nN4wrTrULskuyY5MsnFSX7Vf26+I8lu065tSzFALx6fB1b206OAHYCPTbUitXgm8FbgHknuOu1iNJkkq4DTgD8BXgTcE3gE8Cng7dOrTBOa+dxcRfdv8E/p/h1KGkiS2wNrgHvQDUV8J+ApwN2BU/vP1a3ekvgmwm3EdVX1k/7xT5L8C/DJJDtU1S+mWZjml2QH4EnAg4EdgWcA/zDVojSpmbC1uqrWjSw/L8n7plGQmox+bl6c5EPAwVOsR9oWHAH8BnhEVV3bL/txkkcA3+vXL47v2x6QV6AXoSQ3AZ4InGV4XhIOBH5UVWcBxwBPTbJ8yjVpI5LcAtgXOGIsPANQVT/d4kVpkyW5A935/PW0a5G2VmOfm9eOruvn3wo8elvoSuUV6MVj3yQz/4nfGLgIsA/m0vAMuuAM8GXgWuAA4LipVaRJ3AkIcN60C9Emm/nc3A7Yvl/2d1OsR9ra7cn8n5vn9uv3BL69pYqaBq9ALx5fAe7dT3sBXwA+l2T3KdakjUhyJ+CPgQ8A9F9F/366UK3FLdMuQJtt5nNzL+DNdF/c9aZpFiRp22CAXjyuraoL+ulUuhtibgocMuW6NL9n0l39+nE/EsB64DDgUf7ys+h9DyjAmz6XrpnPzbOq6rl09yC8bNpFSVuxC+g+N+82x/q79esv2GIVTYkBevEquk76O067EM0uyTK6O5BfxO/+enBv4F7AmcDTp1SaJlBVVwKfBQ5NstP4+iQ7b/GitLleCbwwyW2nXYi0NaqqK+g+N/9Xkt/LJ/38c4BP95+vWzUD9OJxoyS36ae70v05cifgk1OuS3N7DLAL8I6qOnt0Ao4Fnp7EbgKL23PounKsSfKEJHdOcpckz6b7JUhLSFWdTNcH86VTLkWTu2mSe49Nq6ZdlOZ1KN09dJ9P8rAkuyfZBziJ7vP00CnWtsUYoBePRwCX9tMpwP2AJ/T/IWhxegbwpf438nH/Tjc27SO3aEVqUlU/AO5L98H/WrrQ/EVgf+w+tVS9HnhGkttNuxBN5MHAf4xN/zzVijSvqvo+sBo4h+4G+h/Q3Qd0HnC/qvrhFMvbYtLd8yRJkiRpEl6BliRJkhoYoCVJkqQGBmhJkiSpgQFakiRJamCAliRJkhoYoCVJkqQGBmhJWqSSXJ/k9JHpsHnaPi7JXF+vO98+1m1elZK07Vk27QIkSXP6RVXde8K2jwNOoPsmPknSgLwCLUlLTJLXJDk3yZlJ/jnJA+m+PfF1/ZXqOyb56ySnJjkjyUeS7Ng/9/ZJvpnkrCSvHtlmkrwuydn9uif2y1cm+Uq/3bOTPHg6r1qSFg+/iVCSFqkk1wNnjSz6f8DngW8Ad6mqSrJzVf00yVHACVV1XP/cW858zXwflP+7qt6c5HjguKo6OslzgNdW1U5JHg/8DbAvsAtwKnB/4EnA9lX1T0m2A3asqmu2xOuXpMXKLhyStHht0IUjyTLgl8C7kpxA121jNvfog/POwE7AZ/vlDwIe3z8+Bnht//iPgQ9W1fXAfyf5MnA/uiD97iTLgY9X1ekL8LokaUmzC4ckLSFVtR7YCzgOeCzwmTmaHgUcWlV/CLwS2H50Mw37+wrwEOAS4KgkT92EsiVpq2KAlqQlJMlOwM2q6kTgb4F79auuAW4y0vQmwKX9leMnjyz/OnBQ/3h0+VeBJybZLskKutD87SS3o+v+8Q7gncB9F/o1SdJSYxcOSVq8dkhy+sj8Z4A3Ap9Isj0Q4O/6dccC70jyXOBA4GXAKcDa/udMuH4e8IEkLwQ+MbLtjwF7A2fQXaH+x6r6SZKnAS9I8mtgHeAVaEnbPG8ilCRJkhrYhUOSJElqYICWJEmSGhigJUmSpAYGaEmSJKmBAVqSJElqYICWJEmSGhigJUmSpAYGaEmSJKnB/weL1mibS5DZmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(num = 1, figsize = (12,6))\n",
    "df_2019_2022['estado'].apply(statusFixer).value_counts().plot(kind=\"bar\")\n",
    "plt.xticks(rotation = 0, fontsize = 14)\n",
    "plt.xlabel(\"Estados\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"Estado del total de obligaciones\",fontsize = 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "747685c3",
   "metadata": {},
   "source": [
    "#### Relacion entre estudios y estados de obligaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "0b694fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Crosstab y heatmap entre estudios y estados de obligaciones"
   ]
  }
 ],
 "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}