adult.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Carga y preprocesamiento de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Carga de la [base de datos](https://archive.ics.uci.edu/dataset/2/adult) obtenida._ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importamos librerías\n",
    "\n",
    "import requests\n",
    "import re\n",
    "import os\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creamos una carpeta para que contenga a nuestro dataset\n",
    "if not os.path.exists('dataset'):\n",
    "    os.makedirs('dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ahora descargamos el dataset\n",
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'\n",
    "url2 = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names'\n",
    "\n",
    "def descargar_dataset(url, filename):\n",
    "    response = requests.get(url)\n",
    "    if response.status_code == 200:\n",
    "        with open('dataset/' + filename, 'w') as f:\n",
    "            f.write(response.text)\n",
    "    else:\n",
    "        print('Error al descargar el dataset')\n",
    "        sys.exit(1)\n",
    "\n",
    "descargar_dataset(url, 'adult.data')\n",
    "descargar_dataset(url2, 'adult.names')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ahora vamos a leer el dataset\n",
    "def leer_dataset(filename):\n",
    "    with open('dataset/' + filename, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    return lines\n",
    "\n",
    "data = leer_dataset('adult.data')\n",
    "names = leer_dataset('adult.names')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# Leemos datos\n",
    "df = pd.read_csv('dataset/adult.data', header=None) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Previsualización del head de la base de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                  1       2           3   4                    5   \\\n",
       "0  39          State-gov   77516   Bachelors  13        Never-married   \n",
       "1  50   Self-emp-not-inc   83311   Bachelors  13   Married-civ-spouse   \n",
       "2  38            Private  215646     HS-grad   9             Divorced   \n",
       "3  53            Private  234721        11th   7   Married-civ-spouse   \n",
       "4  28            Private  338409   Bachelors  13   Married-civ-spouse   \n",
       "\n",
       "                   6               7       8        9     10  11  12  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male  2174   0  40   \n",
       "1     Exec-managerial         Husband   White     Male     0   0  13   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male     0   0  40   \n",
       "3   Handlers-cleaners         Husband   Black     Male     0   0  40   \n",
       "4      Prof-specialty            Wife   Black   Female     0   0  40   \n",
       "\n",
       "               13      14  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3   United-States   <=50K  \n",
       "4            Cuba   <=50K  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() #ya tenemos los datos, ahora nos faltan los nombres de las columnas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtención de columnas del df mediante expresiones regulares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['| This data was extracted from the census bureau database found at\\n',\n",
       " '| http://www.census.gov/ftp/pub/DES/www/welcome.html\\n',\n",
       " '| Donor: Ronny Kohavi and Barry Becker,\\n',\n",
       " '|        Data Mining and Visualization\\n',\n",
       " '|        Silicon Graphics.\\n',\n",
       " '|        e-mail: ronnyk@sgi.com for questions.\\n',\n",
       " '| Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random).\\n',\n",
       " '| 48842 instances, mix of continuous and discrete    (train=32561, test=16281)\\n',\n",
       " '| 45222 if instances with unknown values are removed (train=30162, test=15060)\\n',\n",
       " '| Duplicate or conflicting instances : 6\\n',\n",
       " '| Class probabilities for adult.all file\\n',\n",
       " \"| Probability for the label '>50K'  : 23.93% / 24.78% (without unknowns)\\n\",\n",
       " \"| Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)\\n\",\n",
       " '|\\n',\n",
       " '| Extraction was done by Barry Becker from the 1994 Census database.  A set of\\n',\n",
       " '|   reasonably clean records was extracted using the following conditions:\\n',\n",
       " '|   ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))\\n',\n",
       " '|\\n',\n",
       " '| Prediction task is to determine whether a person makes over 50K\\n',\n",
       " '| a year.\\n',\n",
       " '|\\n',\n",
       " '| First cited in:\\n',\n",
       " '| @inproceedings{kohavi-nbtree,\\n',\n",
       " '|    author={Ron Kohavi},\\n',\n",
       " '|    title={Scaling Up the Accuracy of Naive-Bayes Classifiers: a\\n',\n",
       " '|           Decision-Tree Hybrid},\\n',\n",
       " '|    booktitle={Proceedings of the Second International Conference on\\n',\n",
       " '|               Knowledge Discovery and Data Mining},\\n',\n",
       " '|    year = 1996,\\n',\n",
       " '|    pages={to appear}}\\n',\n",
       " '|\\n',\n",
       " '| Error Accuracy reported as follows, after removal of unknowns from\\n',\n",
       " '|    train/test sets):\\n',\n",
       " '|    C4.5       : 84.46+-0.30\\n',\n",
       " '|    Naive-Bayes: 83.88+-0.30\\n',\n",
       " '|    NBTree     : 85.90+-0.28\\n',\n",
       " '|\\n',\n",
       " '|\\n',\n",
       " '| Following algorithms were later run with the following error rates,\\n',\n",
       " '|    all after removal of unknowns and using the original train/test split.\\n',\n",
       " '|    All these numbers are straight runs using MLC++ with default values.\\n',\n",
       " '|\\n',\n",
       " '|    Algorithm               Error\\n',\n",
       " '| -- ----------------        -----\\n',\n",
       " '| 1  C4.5                    15.54\\n',\n",
       " '| 2  C4.5-auto               14.46\\n',\n",
       " '| 3  C4.5 rules              14.94\\n',\n",
       " '| 4  Voted ID3 (0.6)         15.64\\n',\n",
       " '| 5  Voted ID3 (0.8)         16.47\\n',\n",
       " '| 6  T2                      16.84\\n',\n",
       " '| 7  1R                      19.54\\n',\n",
       " '| 8  NBTree                  14.10\\n',\n",
       " '| 9  CN2                     16.00\\n',\n",
       " '| 10 HOODG                   14.82\\n',\n",
       " '| 11 FSS Naive Bayes         14.05\\n',\n",
       " '| 12 IDTM (Decision table)   14.46\\n',\n",
       " '| 13 Naive-Bayes             16.12\\n',\n",
       " '| 14 Nearest-neighbor (1)    21.42\\n',\n",
       " '| 15 Nearest-neighbor (3)    20.35\\n',\n",
       " '| 16 OC1                     15.04\\n',\n",
       " '| 17 Pebls                   Crashed.  Unknown why (bounds WERE increased)\\n',\n",
       " '|\\n',\n",
       " '| Conversion of original data as follows:\\n',\n",
       " '| 1. Discretized agrossincome into two ranges with threshold 50,000.\\n',\n",
       " '| 2. Convert U.S. to US to avoid periods.\\n',\n",
       " '| 3. Convert Unknown to \"?\"\\n',\n",
       " '| 4. Run MLC++ GenCVFiles to generate data,test.\\n',\n",
       " '|\\n',\n",
       " '| Description of fnlwgt (final weight)\\n',\n",
       " '|\\n',\n",
       " '| The weights on the CPS files are controlled to independent estimates of the\\n',\n",
       " '| civilian noninstitutional population of the US.  These are prepared monthly\\n',\n",
       " '| for us by Population Division here at the Census Bureau.  We use 3 sets of\\n',\n",
       " '| controls.\\n',\n",
       " '|  These are:\\n',\n",
       " '|          1.  A single cell estimate of the population 16+ for each state.\\n',\n",
       " '|          2.  Controls for Hispanic Origin by age and sex.\\n',\n",
       " '|          3.  Controls by Race, age and sex.\\n',\n",
       " '|\\n',\n",
       " '| We use all three sets of controls in our weighting program and \"rake\" through\\n',\n",
       " '| them 6 times so that by the end we come back to all the controls we used.\\n',\n",
       " '|\\n',\n",
       " '| The term estimate refers to population totals derived from CPS by creating\\n',\n",
       " '| \"weighted tallies\" of any specified socio-economic characteristics of the\\n',\n",
       " '| population.\\n',\n",
       " '|\\n',\n",
       " '| People with similar demographic characteristics should have\\n',\n",
       " '| similar weights.  There is one important caveat to remember\\n',\n",
       " '| about this statement.  That is that since the CPS sample is\\n',\n",
       " '| actually a collection of 51 state samples, each with its own\\n',\n",
       " '| probability of selection, the statement only applies within\\n',\n",
       " '| state.\\n',\n",
       " '\\n',\n",
       " '\\n',\n",
       " '>50K, <=50K.\\n',\n",
       " '\\n',\n",
       " 'age: continuous.\\n',\n",
       " 'workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\\n',\n",
       " 'fnlwgt: continuous.\\n',\n",
       " 'education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\\n',\n",
       " 'education-num: continuous.\\n',\n",
       " 'marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\\n',\n",
       " 'occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\\n',\n",
       " 'relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\\n',\n",
       " 'race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\\n',\n",
       " 'sex: Female, Male.\\n',\n",
       " 'capital-gain: continuous.\\n',\n",
       " 'capital-loss: continuous.\\n',\n",
       " 'hours-per-week: continuous.\\n',\n",
       " 'native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\\n']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creacion de columnas para el dataset\n",
    "#primero leemos el archivo .names para obtener los nombres de las columnas\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age: continuous.\\n',\n",
       " 'workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\\n',\n",
       " 'fnlwgt: continuous.\\n',\n",
       " 'education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\\n',\n",
       " 'education-num: continuous.\\n',\n",
       " 'marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\\n',\n",
       " 'occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\\n',\n",
       " 'relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\\n',\n",
       " 'race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\\n',\n",
       " 'sex: Female, Male.\\n',\n",
       " 'capital-gain: continuous.\\n',\n",
       " 'capital-loss: continuous.\\n',\n",
       " 'hours-per-week: continuous.\\n',\n",
       " 'native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\\n']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#podemos comprobar que el nombre de las columnas est'a despues de '>50K, <=50K.\\n','\\n', son age, workclass, fnlwgt, ect\n",
    "#ahora vamos a extraer los nombres de las columnas usando expresiones regulares para llegar a la parte que nos interesa\n",
    "metadata = names[96:]\n",
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'workclass',\n",
       " 'fnlwgt',\n",
       " 'education',\n",
       " 'education-num',\n",
       " 'marital-status',\n",
       " 'occupation',\n",
       " 'relationship',\n",
       " 'race',\n",
       " 'sex',\n",
       " 'capital-gain',\n",
       " 'capital-loss',\n",
       " 'hours-per-week',\n",
       " 'native-country']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ahora que estamos en esa parte, vamos a extraer los nombres de las columnas\n",
    "#desde age hasta native-country nos interesan\n",
    "nombre_columnas = []\n",
    "for i in range(14):\n",
    "    nombre_columnas.append(metadata[i].split(':')[0])\n",
    "\n",
    "nombre_columnas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length mismatch: Expected axis has 15 elements, new values have 14 elements",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[18], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#ahora solo queda asignar los nombres de las columnas al dataframe\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m \u001b[38;5;241m=\u001b[39m nombre_columnas\n\u001b[0;32m      3\u001b[0m df\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\generic.py:6313\u001b[0m, in \u001b[0;36mNDFrame.__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m   6311\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   6312\u001b[0m     \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getattribute__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)\n\u001b[1;32m-> 6313\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__setattr__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6314\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m   6315\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n",
      "File \u001b[1;32mproperties.pyx:69\u001b[0m, in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\generic.py:814\u001b[0m, in \u001b[0;36mNDFrame._set_axis\u001b[1;34m(self, axis, labels)\u001b[0m\n\u001b[0;32m    809\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    810\u001b[0m \u001b[38;5;124;03mThis is called from the cython code when we set the `index` attribute\u001b[39;00m\n\u001b[0;32m    811\u001b[0m \u001b[38;5;124;03mdirectly, e.g. `series.index = [1, 2, 3]`.\u001b[39;00m\n\u001b[0;32m    812\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    813\u001b[0m labels \u001b[38;5;241m=\u001b[39m ensure_index(labels)\n\u001b[1;32m--> 814\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    815\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clear_item_cache()\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\internals\\managers.py:238\u001b[0m, in \u001b[0;36mBaseBlockManager.set_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m    236\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_axis\u001b[39m(\u001b[38;5;28mself\u001b[39m, axis: AxisInt, new_labels: Index) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    237\u001b[0m     \u001b[38;5;66;03m# Caller is responsible for ensuring we have an Index object.\u001b[39;00m\n\u001b[1;32m--> 238\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_set_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_labels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    239\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis] \u001b[38;5;241m=\u001b[39m new_labels\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\internals\\base.py:98\u001b[0m, in \u001b[0;36mDataManager._validate_set_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m     95\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m new_len \u001b[38;5;241m!=\u001b[39m old_len:\n\u001b[1;32m---> 98\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m     99\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength mismatch: Expected axis has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mold_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m elements, new \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    100\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues have \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnew_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m elements\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    101\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Length mismatch: Expected axis has 15 elements, new values have 14 elements"
     ]
    }
   ],
   "source": [
    "#ahora solo queda asignar los nombres de las columnas al dataframe\n",
    "df.columns = nombre_columnas\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "\n",
       "        marital-status        occupation    relationship    race    sex  \\\n",
       "0        Never-married      Adm-clerical   Not-in-family   White   Male   \n",
       "1   Married-civ-spouse   Exec-managerial         Husband   White   Male   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "\n",
       "        marital-status        occupation    relationship    race    sex  \\\n",
       "0        Never-married      Adm-clerical   Not-in-family   White   Male   \n",
       "1   Married-civ-spouse   Exec-managerial         Husband   White   Male   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#vemos que nos da un error que nos falta una fila, podemos ver en nuestro df que lla ultima fila trata de la capacidad monetaria de la persona\n",
    "#por lo que vamos a cambiar el nombre de la columna a 'income'\n",
    "df.columns = nombre_columnas + ['income']\n",
    "df.head(2)"
   ]
  }
 ],
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