00-first_steps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instala las librerías contenidas en el archivo requirements. La bandera -q es para que no muestre los mensajes de instalación.\n",
    "%pip install -r ./requirements.txt -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📖 Lectura de Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./datasets/visit01.tsv\n",
      "./datasets/visit02.tsv\n",
      "./datasets/visit03.tsv\n",
      "./datasets/visit04.tsv\n",
      "./datasets/visit05.tsv\n",
      "./datasets/visit06.tsv\n",
      "./datasets/visit07.tsv\n",
      "./datasets/visit08.tsv\n",
      "./datasets/visit09.tsv\n",
      "./datasets/visit10.tsv\n"
     ]
    }
   ],
   "source": [
    "# Encuentra los archivos en esa carpeta.\n",
    "carpeta_base = './datasets/'\n",
    "prefijo = 'visit'\n",
    "sufijo = '.tsv'\n",
    "\n",
    "# Busca en la carpeta base los archivos que empiecen con el prefijo y terminen con el sufijo.\n",
    "\n",
    "archivos = [f'{carpeta_base}{archivo}' for archivo in os.listdir(carpeta_base) if archivo.startswith(prefijo) and archivo.endswith(sufijo)]\n",
    "print('\\n'.join(archivos))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_19924\\3873712065.py:3: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  datos_visitas = [pd.read_csv(archivo, delimiter = '\\t') for archivo in archivos]\n",
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_19924\\3873712065.py:3: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  datos_visitas = [pd.read_csv(archivo, delimiter = '\\t') for archivo in archivos]\n",
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_19924\\3873712065.py:3: DtypeWarning: Columns (3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,51,52,53,54,57,58,59,60,61,63,64,65,66,67,68,69,70,71,72,73,74,75,85,87,88,90,91,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,477,478,482,483,484,485,486,490,491,495,504,506,507,515,522,530,536,537,538,539,577) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  datos_visitas = [pd.read_csv(archivo, delimiter = '\\t') for archivo in archivos]\n",
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_19924\\3873712065.py:3: DtypeWarning: Columns (3,4,5,51,57,63,67,71,504,522,1277) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  datos_visitas = [pd.read_csv(archivo, delimiter = '\\t') for archivo in archivos]\n",
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_19924\\3873712065.py:3: DtypeWarning: Columns (2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,46,47,48,49,50,51,52,53,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,84,86,87,90,91,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,477,478,482,483,484,485,486,490,491,492,495,504,506,507,515,522,523,525,530,535,536,537,538,563,567) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  datos_visitas = [pd.read_csv(archivo, delimiter = '\\t') for archivo in archivos]\n"
     ]
    }
   ],
   "source": [
    "# Almacena los n archivos en variables, tomando una carpeta base:\n",
    "\n",
    "datos_visitas = [pd.read_csv(archivo, delimiter = '\\t') for archivo in archivos]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El tipo de dato de la columna HPBMDT1 es object\n"
     ]
    }
   ],
   "source": [
    "# Método para interpretar columnas de valores decimales formateados incorrectamente\n",
    "\n",
    "nombre_col = 'HPBMDT1'\n",
    "print(f'El tipo de dato de la columna {nombre_col} es {datos_visitas[0][nombre_col].dtype}')\n",
    "\n",
    "aux = datos_visitas[0][nombre_col].copy()\n",
    "\n",
    "datos_visitas[0][nombre_col] = (\n",
    "    datos_visitas[0][nombre_col]\n",
    "    .astype(str)\n",
    "    .map(lambda valor: '0' + valor if valor.startswith('.') else valor)\n",
    "    .replace('nan|\\s', '0', regex=True)\n",
    "    .astype(float)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SWANID</th>\n",
       "      <th>VISIT</th>\n",
       "      <th>INTDAY1</th>\n",
       "      <th>AGE1</th>\n",
       "      <th>LANGINT1</th>\n",
       "      <th>RACE</th>\n",
       "      <th>PREGNAN1</th>\n",
       "      <th>PREVBLO1</th>\n",
       "      <th>EATDRIN1</th>\n",
       "      <th>STRTPER1</th>\n",
       "      <th>...</th>\n",
       "      <th>SPSCDAY1</th>\n",
       "      <th>SPSCTIM1</th>\n",
       "      <th>SPSCMOD1</th>\n",
       "      <th>HPSCDAY1</th>\n",
       "      <th>HPSCTIM1</th>\n",
       "      <th>HPSCMOD1</th>\n",
       "      <th>SPBMDT1</th>\n",
       "      <th>HPBMDT1</th>\n",
       "      <th>BMDFLG1</th>\n",
       "      <th>STATUS1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>...</td>\n",
       "      <td>413</td>\n",
       "      <td>0:10:08</td>\n",
       "      <td>5</td>\n",
       "      <td>413</td>\n",
       "      <td>0:09:52</td>\n",
       "      <td>5</td>\n",
       "      <td>1.1277</td>\n",
       "      <td>0.9619</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>441</td>\n",
       "      <td>0:12:55</td>\n",
       "      <td>11</td>\n",
       "      <td>441</td>\n",
       "      <td>0:13:03</td>\n",
       "      <td>11</td>\n",
       "      <td>.914</td>\n",
       "      <td>0.8499</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10092</td>\n",
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       "      <td>364</td>\n",
       "      <td>0:18:01</td>\n",
       "      <td>5</td>\n",
       "      <td>364</td>\n",
       "      <td>0:17:39</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0377</td>\n",
       "      <td>0.8412</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10126</td>\n",
       "      <td>1</td>\n",
       "      <td>442</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>.</td>\n",
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       "      <td></td>\n",
       "      <td>.</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>4</td>\n",
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       "      <td>402</td>\n",
       "      <td>0:10:47</td>\n",
       "      <td>11</td>\n",
       "      <td>402</td>\n",
       "      <td>0:10:40</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0673</td>\n",
       "      <td>1.0313</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 576 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SWANID  VISIT INTDAY1 AGE1 LANGINT1  RACE PREGNAN1 PREVBLO1 EATDRIN1  \\\n",
       "0   10046      1     413   53        1     2        1        1        1   \n",
       "1   10056      1     357   52        1     4        1                 1   \n",
       "2   10092      1     364   46        1     4        1                 1   \n",
       "3   10126      1     442   50        1     1        1                 1   \n",
       "4   10153      1     374   52        1     3        1                 1   \n",
       "\n",
       "  STRTPER1  ... SPSCDAY1      SPSCTIM1 SPSCMOD1 HPSCDAY1      HPSCTIM1  \\\n",
       "0        2  ...      413       0:10:08        5      413       0:09:52   \n",
       "1        2  ...      441       0:12:55       11      441       0:13:03   \n",
       "2        1  ...      364       0:18:01        5      364       0:17:39   \n",
       "3        2  ...                      .                               .   \n",
       "4        2  ...      402       0:10:47       11      402       0:10:40   \n",
       "\n",
       "  HPSCMOD1 SPBMDT1 HPBMDT1 BMDFLG1 STATUS1  \n",
       "0        5  1.1277  0.9619       0       7  \n",
       "1       11    .914  0.8499       0       4  \n",
       "2        5  1.0377  0.8412       0       4  \n",
       "3                   0.0000               4  \n",
       "4       11  1.0673  1.0313       0       4  \n",
       "\n",
       "[5 rows x 576 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_visitas[0].head()"
   ]
  }
 ],
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