Analsisis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7178edd1-89d3-475a-a0e3-186bba92330a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a38f0bed-0c27-4705-ba13-e2c198ac803d",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>ENT</th>\n",
       "      <th>MUN</th>\n",
       "      <th>LOC50K</th>\n",
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       "      <th>TIPOHOG</th>\n",
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       "      <th>JEFE_SEXO</th>\n",
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       "      <td>6</td>\n",
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       "      <td>34</td>\n",
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       "      <th>81526</th>\n",
       "      <td>9</td>\n",
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       "      <td>7600.0</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>81527 rows × 83 columns</p>\n",
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      ],
      "text/plain": [
       "       ENT  MUN  LOC50K       ID_VIV  COBERTURA         ESTRATO  UPM  FACTOR  \\\n",
       "0        9    2       1  90020000001          2  09-002-0001-00    1      27   \n",
       "1        9    2       1  90020000002          2  09-002-0001-00    1      27   \n",
       "2        9    2       1  90020000003          2  09-002-0001-00    1      27   \n",
       "3        9    2       1  90020000004          2  09-002-0001-00    1      27   \n",
       "4        9    2       1  90020000005          2  09-002-0001-00    1      27   \n",
       "...    ...  ...     ...          ...        ...             ...  ...     ...   \n",
       "81522    9   17       1  90170005185          2  09-017-0001-00  133      26   \n",
       "81523    9   17       1  90170005186          2  09-017-0001-00  133      26   \n",
       "81524    9   17       1  90170005187          2  09-017-0001-00  133      26   \n",
       "81525    9   17       1  90170005188          2  09-017-0001-00  133      26   \n",
       "81526    9   17       1  90170005189          2  09-017-0001-00  133      26   \n",
       "\n",
       "       CLAVIVP  PAREDES  ...  ALIM_ADL1  ALIM_ADL2  ING_ALIM_ADL1  \\\n",
       "0            1      8.0  ...          2          4              2   \n",
       "1            1      8.0  ...          2          4              2   \n",
       "2            1      8.0  ...          2          4              2   \n",
       "3            1      8.0  ...          2          4              2   \n",
       "4            1      8.0  ...          2          4              2   \n",
       "...        ...      ...  ...        ...        ...            ...   \n",
       "81522        4      8.0  ...          2          4              2   \n",
       "81523        4      8.0  ...          2          4              2   \n",
       "81524        4      8.0  ...          2          4              2   \n",
       "81525        4      8.0  ...          2          4              2   \n",
       "81526        4      8.0  ...          2          4              2   \n",
       "\n",
       "       ING_ALIM_ADL2  ING_ALIM_ADL3  TIPOHOG  INGTRHOG  JEFE_SEXO  JEFE_EDAD  \\\n",
       "0                  4              6        2   58050.0          3         72   \n",
       "1                  4              6        2   80500.0          1         57   \n",
       "2                  4              6        2       NaN          3         74   \n",
       "3                  4              6        2       0.0          3         56   \n",
       "4                  4              6        2       0.0          3         80   \n",
       "...              ...            ...      ...       ...        ...        ...   \n",
       "81522              4              6        1   14000.0          1         53   \n",
       "81523              4              6        2   22300.0          3         50   \n",
       "81524              4              6        1   13000.0          3         47   \n",
       "81525              4              6        1       0.0          3         34   \n",
       "81526              4              6        5    7600.0          3         36   \n",
       "\n",
       "       TAMLOC  \n",
       "0           5  \n",
       "1           5  \n",
       "2           5  \n",
       "3           5  \n",
       "4           5  \n",
       "...       ...  \n",
       "81522       5  \n",
       "81523       5  \n",
       "81524       5  \n",
       "81525       5  \n",
       "81526       5  \n",
       "\n",
       "[81527 rows x 83 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Viviendas09.CSV')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f3faf097-d8ac-4832-a238-34ede722e75b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['ENT','LOC50K','COBERTURA','ESTRATO','UPM','FACTOR','LUG_COC','COMBUSTIBLE','ESTUFA','FOCOS','FOCOS_AHORRA','ABA_AGUA_NO_ENTU','BOMBA_AGUA','REGADERA','SEPARACION1','SEPARACION2','SEPARACION3','SEPARACION4','DESTINO_BAS','TENENCIA','ESCRITURAS','FORMA_ADQUI','FINANCIAMIENTO1','FINANCIAMIENTO2','FINANCIAMIENTO3','DEUDA','DUE1_NUM','DUE2_NUM','MCONMIG','INGR_PEROTROPAIS','INGR_PERDENTPAIS','INGR_AYUGOB','INGR_JUBPEN','ALIMENTACION','ALIM_ADL1','ALIM_ADL2','ING_ALIM_ADL1','ING_ALIM_ADL2','ING_ALIM_ADL3','INGTRHOG','TAMLOC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a2c27bdc-e802-4242-ad10-40cf2f68b0cd",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>MUN</th>\n",
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       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>90020000005</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81522</th>\n",
       "      <td>17</td>\n",
       "      <td>90170005185</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81523</th>\n",
       "      <td>17</td>\n",
       "      <td>90170005186</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81524</th>\n",
       "      <td>17</td>\n",
       "      <td>90170005187</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81525</th>\n",
       "      <td>17</td>\n",
       "      <td>90170005188</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81526</th>\n",
       "      <td>17</td>\n",
       "      <td>90170005189</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81527 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MUN       ID_VIV  CLAVIVP  PAREDES  TECHOS  PISOS  COCINA  CUADORM  \\\n",
       "0        2  90020000001        1      8.0    10.0    3.0     1.0      3.0   \n",
       "1        2  90020000002        1      8.0    10.0    3.0     1.0      5.0   \n",
       "2        2  90020000003        1      8.0    10.0    3.0     1.0      4.0   \n",
       "3        2  90020000004        1      8.0    10.0    2.0     1.0      3.0   \n",
       "4        2  90020000005        1      8.0    10.0    2.0     1.0      4.0   \n",
       "...    ...          ...      ...      ...     ...    ...     ...      ...   \n",
       "81522   17  90170005185        4      8.0    10.0    3.0     1.0      2.0   \n",
       "81523   17  90170005186        4      8.0    10.0    2.0     1.0      2.0   \n",
       "81524   17  90170005187        4      8.0    10.0    3.0     1.0      2.0   \n",
       "81525   17  90170005188        4      8.0    10.0    2.0     1.0      2.0   \n",
       "81526   17  90170005189        4      8.0    10.0    2.0     1.0      1.0   \n",
       "\n",
       "       TOTCUART  ELECTRICIDAD  ...  CELULAR  INTERNET  SERV_TV_PAGA  \\\n",
       "0           4.0           1.0  ...      5.0       7.0           2.0   \n",
       "1          10.0           1.0  ...      5.0       7.0           1.0   \n",
       "2           7.0           1.0  ...      5.0       7.0           1.0   \n",
       "3           4.0           1.0  ...      5.0       7.0           2.0   \n",
       "4           5.0           1.0  ...      5.0       7.0           1.0   \n",
       "...         ...           ...  ...      ...       ...           ...   \n",
       "81522       4.0           1.0  ...      5.0       7.0           1.0   \n",
       "81523       3.0           1.0  ...      5.0       7.0           2.0   \n",
       "81524       4.0           1.0  ...      5.0       7.0           2.0   \n",
       "81525       4.0           1.0  ...      5.0       8.0           2.0   \n",
       "81526       3.0           1.0  ...      5.0       7.0           1.0   \n",
       "\n",
       "       SERV_PEL_PAGA  CON_VJUEGOS  NUMPERS  MNUMPERS  TIPOHOG  JEFE_SEXO  \\\n",
       "0                4.0          6.0        8       NaN        2          3   \n",
       "1                3.0          5.0        6       NaN        2          1   \n",
       "2                3.0          6.0        4       NaN        2          3   \n",
       "3                4.0          6.0        4       NaN        2          3   \n",
       "4                4.0          6.0        6       NaN        2          3   \n",
       "...              ...          ...      ...       ...      ...        ...   \n",
       "81522            4.0          6.0        3       NaN        1          1   \n",
       "81523            3.0          5.0        4       NaN        2          3   \n",
       "81524            4.0          6.0        2       NaN        1          3   \n",
       "81525            4.0          6.0        2       NaN        1          3   \n",
       "81526            4.0          6.0        1       NaN        5          3   \n",
       "\n",
       "       JEFE_EDAD  \n",
       "0             72  \n",
       "1             57  \n",
       "2             74  \n",
       "3             56  \n",
       "4             80  \n",
       "...          ...  \n",
       "81522         53  \n",
       "81523         50  \n",
       "81524         47  \n",
       "81525         34  \n",
       "81526         36  \n",
       "\n",
       "[81527 rows x 42 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(axis=0, how='all')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "19d99928-77e1-4e25-a855-93a4576d18d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'01': 'Casa única en el terreno',\n",
       " '2': 'Casa que comparte terreno con otra(s)',\n",
       " '3': 'Casa dúplex',\n",
       " '4': 'Departamento en edificio',\n",
       " '5': 'Vivienda en vecindad o cuartería',\n",
       " '6': 'Vivienda en cuarto de azotea de un edificio',\n",
       " '7': 'Local no construido para habitación',\n",
       " '8': 'Vivienda móvil',\n",
       " '9': 'Refugio',\n",
       " '99': 'No especificado de vivienda particular'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convertir = {\n",
    "    '1': 'Casa única en el terreno',\n",
    "    '2': 'Casa que comparte terreno con otra(s)',\n",
    "    '3': 'Casa dúplex',\n",
    "    '4': 'Departamento en edificio',\n",
    "    '5': 'Vivienda en vecindad o cuartería',\n",
    "    '6': 'Vivienda en cuarto de azotea de un edificio',\n",
    "    '7': 'Local no construido para habitación',\n",
    "    '8': 'Vivienda móvil',\n",
    "    '9': 'Refugio',\n",
    "    '99': 'No especificado de vivienda particular'\n",
    "}\n",
    "convertir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "892473f6-5b82-48d5-b2ee-f53dd09e1bbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "81522    4\n",
       "81523    4\n",
       "81524    4\n",
       "81525    4\n",
       "81526    4\n",
       "Name: CLAVIVP, Length: 81527, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CLAVIVP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "adeb05e8-cee2-4f04-9a1a-e6e1fa20098f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        NaN\n",
       "1        NaN\n",
       "2        NaN\n",
       "3        NaN\n",
       "4        NaN\n",
       "        ... \n",
       "81522    NaN\n",
       "81523    NaN\n",
       "81524    NaN\n",
       "81525    NaN\n",
       "81526    NaN\n",
       "Name: CLAVIVP, Length: 81527, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CLAVIVP'].map(convertir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d66e9674-754e-4afa-b4a6-882cdda4b66a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CLAVIVP\n",
       "1     25593\n",
       "2     29225\n",
       "3       696\n",
       "4     24160\n",
       "5      1405\n",
       "6       117\n",
       "7       265\n",
       "8         3\n",
       "9        27\n",
       "99       36\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('CLAVIVP').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1f75e183-e8d6-4cff-958f-204e0b1d3e3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MUN  CLAVIVP\n",
       "2    2          2062\n",
       "     4          1885\n",
       "     1          1218\n",
       "     5            97\n",
       "     3            55\n",
       "                ... \n",
       "17   5            85\n",
       "     3            33\n",
       "     7             9\n",
       "     6             6\n",
       "     99            1\n",
       "Name: CLAVIVP, Length: 128, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('MUN')['CLAVIVP'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1e704f8-c834-4780-ba39-3474f3a0eaec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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