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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "</div>\n",
+ "<img src=\"images/img0.png\">"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Contributors**\n",
+ "- Juan Carlos Basto Pineda (juan.basto.pineda@gmail.com)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Estos datos son proporcionados directamente por el gobierno de Colombia y pueden ser encontrados [aquÃ](https://www.datos.gov.co/Educaci-n/Saber-11-2020-2/rnvb-vnyh)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(\"data/Saber_11__2020-2.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "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>ESTU_TIPODOCUMENTO</th>\n",
+ " <th>ESTU_NACIONALIDAD</th>\n",
+ " <th>ESTU_GENERO</th>\n",
+ " <th>ESTU_FECHANACIMIENTO</th>\n",
+ " <th>PERIODO</th>\n",
+ " <th>ESTU_CONSECUTIVO</th>\n",
+ " <th>ESTU_ESTUDIANTE</th>\n",
+ " <th>ESTU_PAIS_RESIDE</th>\n",
+ " <th>ESTU_TIENEETNIA</th>\n",
+ " <th>ESTU_DEPTO_RESIDE</th>\n",
+ " <th>...</th>\n",
+ " <th>PUNT_INGLES</th>\n",
+ " <th>PERCENTIL_INGLES</th>\n",
+ " <th>DESEMP_INGLES</th>\n",
+ " <th>PUNT_GLOBAL</th>\n",
+ " <th>PERCENTIL_GLOBAL</th>\n",
+ " <th>ESTU_INSE_INDIVIDUAL</th>\n",
+ " <th>ESTU_NSE_INDIVIDUAL</th>\n",
+ " <th>ESTU_NSE_ESTABLECIMIENTO</th>\n",
+ " <th>ESTU_ESTADOINVESTIGACION</th>\n",
+ " <th>ESTU_GENERACION-E</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>TI</td>\n",
+ " <td>SUIZA</td>\n",
+ " <td>F</td>\n",
+ " <td>03/03/2003 12:00:00 AM</td>\n",
+ " <td>20204</td>\n",
+ " <td>SB11202040211436</td>\n",
+ " <td>ESTUDIANTE</td>\n",
+ " <td>SUIZA</td>\n",
+ " <td>No</td>\n",
+ " <td>CUNDINAMARCA</td>\n",
+ " <td>...</td>\n",
+ " <td>55.0</td>\n",
+ " <td>81</td>\n",
+ " <td>A1</td>\n",
+ " <td>244</td>\n",
+ " <td>49</td>\n",
+ " <td>54.882365</td>\n",
+ " <td>3.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>PUBLICAR</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>PEP</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>M</td>\n",
+ " <td>05/10/2002 12:00:00 AM</td>\n",
+ " <td>20204</td>\n",
+ " <td>SB11202040433216</td>\n",
+ " <td>ESTUDIANTE</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>No</td>\n",
+ " <td>CUNDINAMARCA</td>\n",
+ " <td>...</td>\n",
+ " <td>33.0</td>\n",
+ " <td>6</td>\n",
+ " <td>A-</td>\n",
+ " <td>238</td>\n",
+ " <td>44</td>\n",
+ " <td>49.252311</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>PUBLICAR</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>TI</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>F</td>\n",
+ " <td>12/14/2003 12:00:00 AM</td>\n",
+ " <td>20204</td>\n",
+ " <td>SB11202040244180</td>\n",
+ " <td>ESTUDIANTE</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>No</td>\n",
+ " <td>CUNDINAMARCA</td>\n",
+ " <td>...</td>\n",
+ " <td>59.0</td>\n",
+ " <td>87</td>\n",
+ " <td>A2</td>\n",
+ " <td>325</td>\n",
+ " <td>94</td>\n",
+ " <td>40.733672</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>PUBLICAR</td>\n",
+ " <td>GENERACION E - GRATUIDAD</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>CE</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>M</td>\n",
+ " <td>04/12/2003 12:00:00 AM</td>\n",
+ " <td>20204</td>\n",
+ " <td>SB11202040210971</td>\n",
+ " <td>ESTUDIANTE</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>No</td>\n",
+ " <td>CUNDINAMARCA</td>\n",
+ " <td>...</td>\n",
+ " <td>47.0</td>\n",
+ " <td>58</td>\n",
+ " <td>A-</td>\n",
+ " <td>238</td>\n",
+ " <td>45</td>\n",
+ " <td>48.217953</td>\n",
+ " <td>2.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>PUBLICAR</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>TI</td>\n",
+ " <td>COLOMBIA</td>\n",
+ " <td>F</td>\n",
+ " <td>03/03/2004 12:00:00 AM</td>\n",
+ " <td>20204</td>\n",
+ " <td>SB11202040235382</td>\n",
+ " <td>ESTUDIANTE</td>\n",
+ " <td>COLOMBIA</td>\n",
+ " <td>No</td>\n",
+ " <td>CUNDINAMARCA</td>\n",
+ " <td>...</td>\n",
+ " <td>43.0</td>\n",
+ " <td>40</td>\n",
+ " <td>A-</td>\n",
+ " <td>202</td>\n",
+ " <td>19</td>\n",
+ " <td>60.912192</td>\n",
+ " <td>3.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>PUBLICAR</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 81 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ESTU_TIPODOCUMENTO ESTU_NACIONALIDAD ESTU_GENERO ESTU_FECHANACIMIENTO \\\n",
+ "0 TI SUIZA F 03/03/2003 12:00:00 AM \n",
+ "1 PEP VENEZUELA M 05/10/2002 12:00:00 AM \n",
+ "2 TI VENEZUELA F 12/14/2003 12:00:00 AM \n",
+ "3 CE VENEZUELA M 04/12/2003 12:00:00 AM \n",
+ "4 TI COLOMBIA F 03/03/2004 12:00:00 AM \n",
+ "\n",
+ " PERIODO ESTU_CONSECUTIVO ESTU_ESTUDIANTE ESTU_PAIS_RESIDE ESTU_TIENEETNIA \\\n",
+ "0 20204 SB11202040211436 ESTUDIANTE SUIZA No \n",
+ "1 20204 SB11202040433216 ESTUDIANTE VENEZUELA No \n",
+ "2 20204 SB11202040244180 ESTUDIANTE VENEZUELA No \n",
+ "3 20204 SB11202040210971 ESTUDIANTE VENEZUELA No \n",
+ "4 20204 SB11202040235382 ESTUDIANTE COLOMBIA No \n",
+ "\n",
+ " ESTU_DEPTO_RESIDE ... PUNT_INGLES PERCENTIL_INGLES DESEMP_INGLES \\\n",
+ "0 CUNDINAMARCA ... 55.0 81 A1 \n",
+ "1 CUNDINAMARCA ... 33.0 6 A- \n",
+ "2 CUNDINAMARCA ... 59.0 87 A2 \n",
+ "3 CUNDINAMARCA ... 47.0 58 A- \n",
+ "4 CUNDINAMARCA ... 43.0 40 A- \n",
+ "\n",
+ " PUNT_GLOBAL PERCENTIL_GLOBAL ESTU_INSE_INDIVIDUAL ESTU_NSE_INDIVIDUAL \\\n",
+ "0 244 49 54.882365 3.0 \n",
+ "1 238 44 49.252311 2.0 \n",
+ "2 325 94 40.733672 1.0 \n",
+ "3 238 45 48.217953 2.0 \n",
+ "4 202 19 60.912192 3.0 \n",
+ "\n",
+ " ESTU_NSE_ESTABLECIMIENTO ESTU_ESTADOINVESTIGACION ESTU_GENERACION-E \n",
+ "0 3.0 PUBLICAR NO \n",
+ "1 2.0 PUBLICAR NO \n",
+ "2 3.0 PUBLICAR GENERACION E - GRATUIDAD \n",
+ "3 3.0 PUBLICAR NO \n",
+ "4 3.0 PUBLICAR NO \n",
+ "\n",
+ "[5 rows x 81 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(504872, 81)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['ESTU_TIPODOCUMENTO', 'ESTU_NACIONALIDAD', 'ESTU_GENERO',\n",
+ " 'ESTU_FECHANACIMIENTO', 'PERIODO', 'ESTU_CONSECUTIVO',\n",
+ " 'ESTU_ESTUDIANTE', 'ESTU_PAIS_RESIDE', 'ESTU_TIENEETNIA',\n",
+ " 'ESTU_DEPTO_RESIDE', 'ESTU_COD_RESIDE_DEPTO', 'ESTU_MCPIO_RESIDE',\n",
+ " 'ESTU_COD_RESIDE_MCPIO', 'FAMI_ESTRATOVIVIENDA', 'FAMI_PERSONASHOGAR',\n",
+ " 'FAMI_CUARTOSHOGAR', 'FAMI_EDUCACIONPADRE', 'FAMI_EDUCACIONMADRE',\n",
+ " 'FAMI_TRABAJOLABORPADRE', 'FAMI_TRABAJOLABORMADRE',\n",
+ " 'FAMI_TIENEINTERNET', 'FAMI_TIENESERVICIOTV', 'FAMI_TIENECOMPUTADOR',\n",
+ " 'FAMI_TIENELAVADORA', 'FAMI_TIENEHORNOMICROOGAS', 'FAMI_TIENEAUTOMOVIL',\n",
+ " 'FAMI_TIENEMOTOCICLETA', 'FAMI_TIENECONSOLAVIDEOJUEGOS',\n",
+ " 'FAMI_NUMLIBROS', 'FAMI_COMELECHEDERIVADOS',\n",
+ " 'FAMI_COMECARNEPESCADOHUEVO', 'FAMI_COMECEREALFRUTOSLEGUMBRE',\n",
+ " 'FAMI_SITUACIONECONOMICA', 'ESTU_DEDICACIONLECTURADIARIA',\n",
+ " 'ESTU_DEDICACIONINTERNET', 'ESTU_HORASSEMANATRABAJA',\n",
+ " 'ESTU_TIPOREMUNERACION', 'COLE_CODIGO_ICFES',\n",
+ " 'COLE_COD_DANE_ESTABLECIMIENTO', 'COLE_NOMBRE_ESTABLECIMIENTO',\n",
+ " 'COLE_GENERO', 'COLE_NATURALEZA', 'COLE_CALENDARIO', 'COLE_BILINGUE',\n",
+ " 'COLE_CARACTER', 'COLE_COD_DANE_SEDE', 'COLE_NOMBRE_SEDE',\n",
+ " 'COLE_SEDE_PRINCIPAL', 'COLE_AREA_UBICACION', 'COLE_JORNADA',\n",
+ " 'COLE_COD_MCPIO_UBICACION', 'COLE_MCPIO_UBICACION',\n",
+ " 'COLE_COD_DEPTO_UBICACION', 'COLE_DEPTO_UBICACION',\n",
+ " 'ESTU_PRIVADO_LIBERTAD', 'ESTU_COD_MCPIO_PRESENTACION',\n",
+ " 'ESTU_MCPIO_PRESENTACION', 'ESTU_DEPTO_PRESENTACION',\n",
+ " 'ESTU_COD_DEPTO_PRESENTACION', 'PUNT_LECTURA_CRITICA',\n",
+ " 'PERCENTIL_LECTURA_CRITICA', 'DESEMP_LECTURA_CRITICA',\n",
+ " 'PUNT_MATEMATICAS', 'PERCENTIL_MATEMATICAS', 'DESEMP_MATEMATICAS',\n",
+ " 'PUNT_C_NATURALES', 'PERCENTIL_C_NATURALES', 'DESEMP_C_NATURALES',\n",
+ " 'PUNT_SOCIALES_CIUDADANAS', 'PERCENTIL_SOCIALES_CIUDADANAS',\n",
+ " 'DESEMP_SOCIALES_CIUDADANAS', 'PUNT_INGLES', 'PERCENTIL_INGLES',\n",
+ " 'DESEMP_INGLES', 'PUNT_GLOBAL', 'PERCENTIL_GLOBAL',\n",
+ " 'ESTU_INSE_INDIVIDUAL', 'ESTU_NSE_INDIVIDUAL',\n",
+ " 'ESTU_NSE_ESTABLECIMIENTO', 'ESTU_ESTADOINVESTIGACION',\n",
+ " 'ESTU_GENERACION-E'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Como ejemplo veamos cómo se construye un histograma de una de las variables, en este caso el puntaje en matemáticas:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:ylabel='Frequency'>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pd.DataFrame(df['PUNT_MATEMATICAS'].values).plot(kind='hist')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "El tipo de gráfico se puede cambiar fácilmente mediante la opción `kind`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD4CAYAAADsKpHdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcAklEQVR4nO3dfZBc1Znf8e8PMSsG78JgkF1SS0TatayEl0RapgiJEpcXQiSwyxpT3iCSXahaqrRL4VqbuEik+I91kqKQg2152axJyUAAxwuiWCxU5s0s2oorFIYdWQqv1iIMDjNSQLYlTAUtlsSTP/o2vvTc7pnpl+n78vtUTU336e7pc6Zn7nPPOc85VxGBmZnZCYOugJmZ5YMDgpmZAQ4IZmaWcEAwMzPAAcHMzBInDroCnTrjjDNi6dKlg66GmVmh7Nq166cRsSDrscIGhKVLlzI+Pj7oapiZFYqkn7R6zENGZmYGOCCYmVnCAcHMzAAHBDMzSzggmJkZUOAsI7Oi2r57kpse3cv+w0dYNDLM9WtWMLaqNuhqmTkgmM2l7bsn2XT/sxw5ehyAycNH2HT/swAOCjZwDghmfZLVE7jp0b3vBYOGI0ePc9Ojex0QbOAcEMz6IKsncN22PbS6+sj+w0c8lGQD54Bg1gdZPYF2l6I6dXjIQ0k2cA4IZj2SPsOfzXUIBRw+cnRKuYeSbK45IJj1QPMQ0UyJ9j2H/YePdFUvs9lwQDDrQqNXMNnBgXuexPFprmm+aGS406qZzZoXppl1qNErmEkwUNP94aF50wYDUZ9LWL15J9t3T3ZeUbMZckAw61DWxHGz2sgwr27+BFsuX0ltZBglZTdedi61Nmf/6aGkxgSzg4L1m4eMzDo03fj+8NA8rl+zAqhnCmVNDjfPOwwPzeOkoRM49Pb7J5k9wWxzwT0Esw61G99v9ALaHcDHVtXe6ymkew6H356acQSeYLb+U0wzjplXo6Oj4Sum2SBlZRYND82bNhBMZ/XmnS3nJWpesGZdkrQrIkazHnMPwaxDrc7wuz1YX79mBcND8zIf83yC9ZPnEMxmqd9bTDR+Vqt0Vs8nWL+4h2A2C+lU06B/Z+xjq2o8sfHCKemqDZ5PsH5wD8FsBtotQOvnGfuikeHM9/SCNesHBwSzFtJBYFBbTFy/ZkXmxHUjndWsl6YdMpJ0u6Q3JD2XKvuSpElJe5KvS1OPbZK0T9JeSWtS5edJejZ57GZJSsrnS9qWlD8laWmP22g2a82rkKfLxevXGXu/Jq7Nssykh3AH8F+Bu5rKt0TEV9IFks4C1gNnA4uAv5L00Yg4DtwCbAB+ADwErAUeBq4GDkXERyStB74MXN5xi8x6YCarkBv6fcbealGbWa9N20OIiO8DP5/hz1sH3BMR70TEK8A+4HxJC4FTIuLJqC98uAsYS73mzuT2fcBFjd6D2aDMdAjIZ+xWJt3MIXxW0pXAOPCFiDgE1Kj3ABomkrKjye3mcpLvrwFExDFJbwKnAz9tfkNJG6j3MjjzzDO7qLpZe60mcxt6sQDNLG86TTu9BfgtYCVwAPhqUp51Zh9tytu9ZmphxNaIGI2I0QULFsyqwmazkbU4rPGH6l6BlVVHPYSIeL1xW9I3ge8mdyeAJamnLgb2J+WLM8rTr5mQdCJwKjMfojLrqfSis5GTh5h/4gm8eeSor3FsldBRQJC0MCIOJHc/DTQykHYAfyHpa9QnlZcDT0fEcUlvSboAeAq4Eviz1GuuAp4EPgPsjKJusGSF1rw30aG3jzI8NI8tl6/MTSDo9yppq7ZpA4Kku4GPA2dImgD+BPi4pJXUh3ZeBf4QICKel3Qv8AJwDLg2yTACuIZ6xtIw9eyih5Py24BvSdpHvWewvgftMpu1rMyiPG0T0RywGqukgVzUz4pv2oAQEVdkFN/W5vk3ADdklI8D52SU/x3wu9PVw6zfWmUW5WWbiLwHLCs+r1S2ymsMw7Qap8zLNhF5D1hWfN7cziptuusi52mbiFaBKS8By4rPAcEqrd2K5Lyll2alwuYpYFnxecjIKq3VcIuAJzZeOLeVmUb6OgnOMrJ+cECwSiva9tLe18j6yUNGVmkehjH7FfcQrJLKsCLZi9Ss1xwQrHKKsCJ5Ol6kZv3gISOrnHYLvIqiDG2w/HFAsMopwwKvMrTB8scBwSqnDAu8ytAGyx8HBKuM7bsnWb15J5OHj0y5CEfRMoucHWX94IBgldC8RUX6qk15W5E8E2Oratx42bnURoYRMDI8xElDJ3Ddtj2s3ryT7bsnB11FKyBnGVklZE3CBvVgkLcVyTPVWKTmjCPrFfcQrBLKPAnrjCPrFQcEq4QyT8KWOdjZ3HJAsEoo8yRsmYOdzS0HBKuE5knYIk4kt1LmYGdzy5PKVhll3SnU22JbrzggmJVAWYOdzS0PGZmZGTCDgCDpdklvSHouVXaTpB9JekbSdySNJOVLJR2RtCf5+m+p15wn6VlJ+yTdLElJ+XxJ25LypyQt7X0zraoaq5OXbXzQC7bMpjGTHsIdwNqmsseAcyLiHwJ/C2xKPfZyRKxMvv4oVX4LsAFYnnw1fubVwKGI+AiwBfjyrFthliG9Ojn41YKtsgcFB0Hr1LQBISK+D/y8qex7EXEsufsDYHG7nyFpIXBKRDwZEQHcBYwlD68D7kxu3wdc1Og9mHWjigu2qhoErTd6MYfwB8DDqfvLJO2W9D8l/fOkrAZMpJ4zkZQ1HnsNIAkybwKnZ72RpA2SxiWNHzx4sAdVtzKr4oKtKgZB652usowkfRE4Bnw7KToAnBkRP5N0HrBd0tkwZXNJqG8lwzSPvb8wYiuwFWB0dDTzOWaNS0u2+gMp84KtKgZB652OewiSrgI+CfybZBiIiHgnIn6W3N4FvAx8lHqPID2stBjYn9yeAJYkP/NE4FSahqjMZqp5V9NmZV+w5VXL1o2OAoKktcC/Bz4VEW+nyhdImpfc/k3qk8c/jogDwFuSLkjmB64EHkhetgO4Krn9GWBnI8CYzVbWkElDmVYnt+JVy9aNaYeMJN0NfBw4Q9IE8CfUs4rmA48l878/SDKKPgb8J0nHgOPAH0VE42z/GuoZS8PU5xwa8w63Ad+StI96z2B9T1pmldRqaERQ2G2uZ8Orlq0bKurJ+OjoaIyPjw+6GpYzjSuiNSvydQ/MeknSrogYzXrMK5WtVDxkYtY572VkpeIhE7POOSBY6XijN7POOCCYlVhjTYZ7SzYTDghWCj7wTdVYk9FIw21sYwFU/ndj2TypbIXn/XuyeRsLmy0HBCs8H/iyeRsLmy0HBCs8H/iyeRsLmy0HBCs8H/iyeU2GzZYDghWeD3zZxlbVuPGyc6mNDCOqsZeTdcdZRlZ4XozWmtdk2Gw4IFgp+MBn1j0HBCssrz0w6y0HBCskL7oy6z0HBCukdmsPHBCyuUdl03FAsELy2oPZcY/KZsJpp1ZIXnswO17NbTPhgGCF5LUHs+Melc2EA4IVkhddzY57VDYTnkOwwvLag5m7fs2K980hgHtUNpUDglkFeDW3zcS0AUHS7cAngTci4pyk7IPANmAp8CrwryLiUPLYJuBq4DjwxxHxaFJ+HnAHMAw8BHwuIkLSfOAu4DzgZ8DlEfFqz1poZoB7VDa9mcwh3AGsbSrbCDweEcuBx5P7SDoLWA+cnbzmG5IaM3+3ABuA5clX42deDRyKiI8AW4Avd9oYMzPr3LQBISK+D/y8qXgdcGdy+05gLFV+T0S8ExGvAPuA8yUtBE6JiCcjIqj3CMYyftZ9wEWS1FlzzMysU51mGX04Ig4AJN8/lJTXgNdSz5tIymrJ7eby970mIo4BbwKnZ72ppA2SxiWNHzx4sMOqm5lZll6nnWad2Ueb8navmVoYsTUiRiNidMGCBR1W0czMsnSaZfS6pIURcSAZDnojKZ8AlqSetxjYn5QvzihPv2ZC0onAqUwdojKzHvK+Rpal0x7CDuCq5PZVwAOp8vWS5ktaRn3y+OlkWOktSRck8wNXNr2m8bM+A+xM5hnMpti+e5LVm3eybOODrN68k+27JwddpcJp7Gs0efgIwa/2NfLv0qYNCJLuBp4EVkiakHQ1sBm4WNJLwMXJfSLieeBe4AXgEeDaiGishLkGuJX6RPPLwMNJ+W3A6ZL2Af+WJGPJrJkPZL3hfY2slWmHjCLiihYPXdTi+TcAN2SUjwPnZJT/HfC709XDzFte94b3NbJWvJeRFYYPZL3hfY2sFQcEKwwfyHrDO8VaKw4IVhg+kPWGd4q1Vry5nRWGN2jrHe9rZFkcEKxQfCAz6x8PGZmZGeCAYGZmCQ8ZmVWct7GwBgcEswprrP5uLPhrrP4GHBQqyENGZhXmbSwszT0Eyz0PafSPV39bmnsIlmve0K6/vPrb0hwQLNc8pNFfXv1taR4yslzzkEZ/efW3pTkgWK4tGhlmMuPg7yGN3vHqb2vwkJHlmoc0zOaOewiWax7SMJs7DgiWex7SMJsbDghm9h6v+ag2BwQzA7yNhTkgWE75THXutVvz4d99NXScZSRphaQ9qa9fSPq8pC9JmkyVX5p6zSZJ+yTtlbQmVX6epGeTx26WpG4bZsXl1cmD4TUf1nFAiIi9EbEyIlYC5wFvA99JHt7SeCwiHgKQdBawHjgbWAt8Q1Ijn/AWYAOwPPla22m9rPi8OnkwvI2F9WodwkXAyxHxkzbPWQfcExHvRMQrwD7gfEkLgVMi4smICOAuYKxH9bIC8pnqYHjNh/UqIKwH7k7d/6ykZyTdLum0pKwGvJZ6zkRSVktuN5dPIWmDpHFJ4wcPHuxR1S1vfKY6GGOratx42bnURoYRUBsZ5sbLzvX8QYV0Paks6deATwGbkqJbgP8MRPL9q8AfAFnzAtGmfGphxFZgK8Do6Gjmc6z4rl+z4n3ZLuAz1bniNR/V1osso0uAH0bE6wCN7wCSvgl8N7k7ASxJvW4xsD8pX5xRbhXl1cn54Eyv6ulFQLiC1HCRpIURcSC5+2ngueT2DuAvJH0NWER98vjpiDgu6S1JFwBPAVcCf9aDelmB+Ux1sLwmoZq6mkOQdDJwMXB/qvi/JCmkzwC/A1wHEBHPA/cCLwCPANdGRGNM4BrgVuoTzS8DD3dTLzPrjjO9qqmrHkJEvA2c3lT2+22efwNwQ0b5OHBON3Uxs95xplc1eftrM5vCmV7V5IBgZlN4TUI1eS8jyw1nteSHM72qyQHBcsFZLfnjTK/q8ZCR5YKzWswGzz0EywVnteSbh/OqwT0EywVnteSXtyOvDgcEywVnteSXh/Oqw0NGlgvOaskvD+dVhwOC5YazWvJp0cgwkxkHfw/nlY+HjMysLQ/nVYd7CGbWlofzqsMBwQbK6YzF4OG8anBAsIHx6mSzfPEcgg2M0xnN8sUBwQbG6Yxm+eKAYAPj1clm+eKAYAPjdMZi2r57ktWbd7Js44Os3rzTW1iUiCeVbWCczlg8TgQoNwcEGyinMxZLu0QAf47F5yEjM5sxJwKUW1cBQdKrkp6VtEfSeFL2QUmPSXop+X5a6vmbJO2TtFfSmlT5ecnP2SfpZknqpl5m1h9OBCi3XvQQficiVkbEaHJ/I/B4RCwHHk/uI+ksYD1wNrAW+IakxoziLcAGYHnytbYH9bKc8qRkcTkRoNz6MWS0DrgzuX0nMJYqvyci3omIV4B9wPmSFgKnRMSTERHAXanXWMn4YivFNraqxo2XnUttZBgBI8NDnDR0Atdt2+PgXgLdBoQAvidpl6QNSdmHI+IAQPL9Q0l5DXgt9dqJpKyW3G4un0LSBknjksYPHjzYZdVtELw6ufjGVtV4YuOFbLl8Je8ce5dDbx91cC+JbgPC6oj4beAS4FpJH2vz3Kx5gWhTPrUwYmtEjEbE6IIFC2ZfWxs4T0qWh4N7+XQVECJif/L9DeA7wPnA68kwEMn3N5KnTwBLUi9fDOxPyhdnlFsJeVKyPBzcy6fjgCDpA5J+o3Eb+JfAc8AO4KrkaVcBDyS3dwDrJc2XtIz65PHTybDSW5IuSLKLrky9xkrGk5Ll4eBePt30ED4M/C9J/xt4GngwIh4BNgMXS3oJuDi5T0Q8D9wLvAA8AlwbEY3+5jXArdQnml8GHu6iXpZjzZOStZFhbrzsXC9qKiAH9/JRPbGneEZHR2N8fHzQ1TCrNF/gqHgk7UotE3gfb11hZh3z1iPl4oBgc8JnkuXnz7j4HBCs77xDZvn5My4Hb25nfed89fLzZ1wODgjWd85XLz9/xuXggGB953z18vNnXA4OCNZ3zlcvP3/G5eBJZes7Xyqz/PwZl4MXppmZVYgXppnZnPKahGJyQLC+8UGhmrwmobg8qWx94SujVZfXJBSXA4L1hQ8K1eU1CcXlgGB94YNCdXlNQnE5IFhf+KBQXV6TUFwOCNYXPihUV/NFkEaGhzhp6ASu27aH1Zt3eh4px5xlZH3hhUrV1rhOgjOOisUBwXrKqaaW1i65wH8X+eOAYD3js0Fr5uSCYvEcgvWMU02tmZMLisUBwXrGZ4PWzMkFxdJxQJC0RNJfS3pR0vOSPpeUf0nSpKQ9ydelqddskrRP0l5Ja1Ll50l6NnnsZknqrlk2CD4btGbNGUe1kWFuvOxcDyHmVDdzCMeAL0TEDyX9BrBL0mPJY1si4ivpJ0s6C1gPnA0sAv5K0kcj4jhwC7AB+AHwELAWeLiLutkAXL9mxfvmEMBng/arjKOG7bsnWb15pxMPcqjjgBARB4ADye23JL0ItPtU1wH3RMQ7wCuS9gHnS3oVOCUingSQdBcwhgNCYaQzi0ZOHmL+iSfw5pGj/me3KZx4kG89mUOQtBRYBTyVFH1W0jOSbpd0WlJWA15LvWwiKaslt5vLs95ng6RxSeMHDx7sRdWtS82b2B16+yjvHHuXLZev5ImNF/qf3N7HiQf51nVAkPTrwF8Cn4+IX1Af/vktYCX1HsRXG0/NeHm0KZ9aGLE1IkYjYnTBggXdVt16wP/gNhtOPMi3rgKCpCHqweDbEXE/QES8HhHHI+Jd4JvA+cnTJ4AlqZcvBvYn5Yszyq0A/A9us+HEg3zrJstIwG3AixHxtVT5wtTTPg08l9zeAayXNF/SMmA58HQyF/GWpAuSn3kl8ECn9bL+a0wKLtv4ICe0SAjzP7hlcRpqvnWTZbQa+H3gWUl7krL/AFwhaSX1YZ9XgT8EiIjnJd0LvEA9Q+naJMMI4BrgDmCY+mSyJ5RzqnlS8HjGNbn9D26tNO9xderwEBJct20PNz2610kIA6bI+IcugtHR0RgfHx90NSpn9eadTGYMB82TeDfCmUU2Y80nF1A/mfA6hf6StCsiRrMe815GNiut5gbejeCVzZ+Y49pYkXnju/zx1hU2K54UtF5pdXIxefiIr5swIA4INiueFLReaXcS0Viw5qAwtxwQbEYamUXXbdvDSUMnMDI85L1prCtZJxdpXs8y9zyHYNNqnvw79PZRhofmseXylQ4E1rF0xlFWogJ4Pctccw/BpuXVyNYvY6tqPLHxQmotho8CPJ+Qkl4D1I/fiwOCTcurka3f2g0feT6hrnnfsH78XhwQrKXG2UirlSrOLLJeSV83IYt7pHPTU3dAsEzps5EsziyyXmsMH7W6OlbV01HnoqfugGCZss5GGpxZZP3kdNRsc7EGyAHB3qcxTNSqZyDwdQ6sr2aSjvr5bXsq11uYizVATju192TtLdPM8wbWbzNJR4XqXW2teWPAfuwb5s3t7D3tegbgjcds7k33Nwn1IcwnNl44RzUqvnab23nIyKYdJgLPG9hgTDd8BE5/7iUPGVXU9t2T73XJRYtrliZ8BmaDMpPhIw9j9o57CBXUnFLaLhg4vdQGrZGO+vXLV07pLQino/aSewgV1C6lNK3mi91YjjT3FtI926pNMPeLJ5UrJD1MNB0PE1metZvz8olMe75iWoXNZq6gwcNElnftJpLdW+hcpQJC4+DYrxzevGgVBNoFg8bzfHZlRbBoZLhtT7exeO2mR/f673kWKhMQmhddle0sopMg0OAgYEVz/ZoV0y6ihPL9n/dbbuYQJK0F/hSYB9waEZvbPX+2cwitxhyLPFbeyXBQsyK336ptNnNi4BOfhtwvTJM0D/hz4BLgLOAKSWf18j3Ktqf/bFJHW/FcgRVZu3TULFXeGG+mchEQgPOBfRHx44j4JXAPsK6XbzAXOwXOpZmmjjZrbC3slcdWFtNdSyHN11VoLy9zCDXgtdT9CeAfNz9J0gZgA8CZZ545qzfIGnMs8hnybHo2njC2shtbVWNsVW1GGzQWdVRgLuQlIGRdE2PKKEhEbAW2Qn0OYTZvMBc7Bc6l6bIsHASsirzVRXfyEhAmgCWp+4uB/b1+k8ZZRBlk9XgcBMza9xaKPCowF/ISEP4GWC5pGTAJrAf+9WCrlG9l6/GY9Zr/R2YvT2mnlwJfp552entE3NDu+d66wsxs9gqxdUVEPAQ8NOh6mJlVVV7STs3MbMAcEMzMDHBAMDOzhAOCmZkBOcoymi1JB4GfdPjyM4Cf9rA6ReA2V4PbXA3dtPnvRcSCrAcKGxC6IWm8VdpVWbnN1eA2V0O/2uwhIzMzAxwQzMwsUdWAsHXQFRgAt7ka3OZq6EubKzmHYGZmU1W1h2BmZk0cEMzMDKhgQJC0VtJeSfskbRx0ffpB0hJJfy3pRUnPS/pcUv5BSY9Jein5ftqg69pLkuZJ2i3pu8n9srd3RNJ9kn6UfNb/pAJtvi75m35O0t2STipbmyXdLukNSc+lylq2UdKm5Hi2V9Kabt67UgFB0jzgz4FLgLOAKySdNdha9cUx4AsR8Q+AC4Brk3ZuBB6PiOXA48n9Mvkc8GLqftnb+6fAIxHx94F/RL3tpW2zpBrwx8BoRJxDfav89ZSvzXcAa5vKMtuY/F+vB85OXvON5DjXkUoFBOB8YF9E/DgifgncA6wbcJ16LiIORMQPk9tvUT9Q1Ki39c7kaXcCYwOpYB9IWgx8Arg1VVzm9p4CfAy4DSAifhkRhylxmxMnAsOSTgROpn5lxVK1OSK+D/y8qbhVG9cB90TEOxHxCrCP+nGuI1ULCDXgtdT9iaSstCQtBVYBTwEfjogDUA8awIcGWLVe+zrw74B3U2Vlbu9vAgeB/54Mk90q6QOUuM0RMQl8Bfg/wAHgzYj4HiVuc0qrNvb0mFa1gKCMstLm3Ur6deAvgc9HxC8GXZ9+kfRJ4I2I2DXousyhE4HfBm6JiFXA/6P4QyVtJePm64BlwCLgA5J+b7C1GrieHtOqFhAmgCWp+4updzlLR9IQ9WDw7Yi4Pyl+XdLC5PGFwBuDql+PrQY+JelV6sOAF0r6H5S3vVD/W56IiKeS+/dRDxBlbvO/AF6JiIMRcRS4H/inlLvNDa3a2NNjWtUCwt8AyyUtk/Rr1Cdjdgy4Tj0nSdTHll+MiK+lHtoBXJXcvgp4YK7r1g8RsSkiFkfEUuqf6c6I+D1K2l6AiPi/wGuSViRFFwEvUOI2Ux8qukDSycnf+EXU58fK3OaGVm3cAayXNF/SMmA58HTH7xIRlfoCLgX+FngZ+OKg69OnNv4z6t3GZ4A9ydelwOnUMxReSr5/cNB17UPbPw58N7ld6vYCK4Hx5HPeDpxWgTb/R+BHwHPAt4D5ZWszcDf1OZKj1HsAV7drI/DF5Hi2F7ikm/f21hVmZgZUb8jIzMxacEAwMzPAAcHMzBIOCGZmBjggmJlZwgHBzMwABwQzM0v8f1CyY1MDBUI9AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df['PUNT_MATEMATICAS'].value_counts().plot(kind='line',style=\"o\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ahora un ejemplo de cómo se pueden computar los cuantiles rápidamente para tener una idea de la distribución de valores. Lo mismo se puede hacer para todo el dataframe o un subconjunto de columnas como en el ejemplo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "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>PUNT_MATEMATICAS</th>\n",
+ " <th>PUNT_C_NATURALES</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0.90</th>\n",
+ " <td>66.0</td>\n",
+ " <td>62.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0.95</th>\n",
+ " <td>70.0</td>\n",
+ " <td>66.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0.99</th>\n",
+ " <td>77.0</td>\n",
+ " <td>73.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " PUNT_MATEMATICAS PUNT_C_NATURALES\n",
+ "0.90 66.0 62.0\n",
+ "0.95 70.0 66.0\n",
+ "0.99 77.0 73.0"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[['PUNT_MATEMATICAS','PUNT_C_NATURALES']].quantile([0.9,0.95,0.99])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ahora un ejemplo de boxplot utilizando seaborn:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Boxplot of Price vs. bedrooms')"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.boxplot(x = \"ESTU_GENERO\", y='PUNT_MATEMATICAS', data = df)\n",
+ "plt.title(\"Boxplot of Price vs. bedrooms\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Y un gráfico de cajas más, para probar una hipótesis que no deja de llamar la atención \"En los hogares con mayores de seguridad alimentar, donde se consume por ejemplo huevo, carne y pescado con mayor frecuencia, los puntajes en matemáticas son superiores\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0, 1, 2, 3]),\n",
+ " [Text(0, 0, '1 o 2 veces por semana'),\n",
+ " Text(1, 0, 'Todos o casi todos los dÃas'),\n",
+ " Text(2, 0, '3 a 5 veces por semana'),\n",
+ " Text(3, 0, 'Nunca o rara vez comemos eso')])"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.boxplot(x = \"FAMI_COMECARNEPESCADOHUEVO\", y='PUNT_MATEMATICAS', data = df)\n",
+ "plt.xticks(rotation=45)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A las variables que no toman valores numéricos contÃnuos, sino que tienen asignado un valor de entre un conjunto finito de valores, algo asà como una etiqueta, se les llama variables categóricas, pues dividen a los datos en categorÃas. El \"Estrato\" de la vivienda familiar es un ejemplo. En este caso es una variable categórica que puede ser ordenada, pues es un indicador aproximado del nivel de ingresos económicos, por lo tanto serÃa útil transformar esta variable a números para poder analizar correlaciones con otras variables, ya que las correlaciones solo funcionan entre variables numéricas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Estrato 2\n",
+ "1 Estrato 3\n",
+ "2 Estrato 1\n",
+ "3 Sin Estrato\n",
+ "4 Estrato 5\n",
+ " ... \n",
+ "504867 Estrato 2\n",
+ "504868 Estrato 2\n",
+ "504869 Estrato 2\n",
+ "504870 Estrato 2\n",
+ "504871 Estrato 3\n",
+ "Name: FAMI_ESTRATOVIVIENDA, Length: 504872, dtype: object"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[\"FAMI_ESTRATOVIVIENDA\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Algunos otros comandos que probablemente sean útiles, y quieran reutilizar en distintas formas:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.ESTU_GENERO.unique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.ESTU_GENERO.hasnans"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.ESTU_GENERO.isnull().sum()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 8d9a79dba832dd798f589a3ac61461969c4f07ff..49b69fd4ccc89540858943e1739710ada67e5929 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,86 @@
# pandas-bash-tarea-01
+**Creado por:** Juan Carlos Basto Pineda (@juan-pineda en mattermost y GitLab)
+
+### Fecha de entrega:
+Viernes 22 de Julio, 2021. Media noche COL/PER/ECU (+1h VEN)
+
+### Modo de entrega:
+Clonar este repositorio, cambiar la configuración de privacidad a *privado*
+y agregarme como colaborador. La entrega debe comprender un archivo en pdf
+llamado `Reporte_mi_nombre.pdf `, el cual debe ser autocontenido, de manera
+que en primera aproximación yo pueda revisarlo y tener una idea clara de lo que
+hicieron, cómo lo hicieron, sus reflexiones, hallazgos, conclusiones, etc.
+Adicionalmente, debe haber uno o más notebooks que permitan reproducir el paso
+a paso de su tratmiento de los datos y análisis realizados. Esos notebooks,
+como siempre, no pueden limitarse a un montón de código que solamente lo
+entiende el que lo hizo (y eso si no deja pasar mucho tiempo para volver a
+mirarlo). Por el contrario, el notebook debe ser tan claro que cualquier
+cientÃfico de datos lo puedan seguir y entender cada paso de lo que hicieron
+gracias a los comentarios y celdas de markdown con explicaciones. **El notebook
+tiene que correr en cualquier máquina**, asà que asegúrense de afinar el path
+de los archivos y demás para que yo no tenga que estar corrigiendo esas cosas
+para lograr que me funcione. Si consideran apropiado dividir el trabajo en más
+de un notebook, usen un mismo nombre de archivo para todos, agregando un número
+al final del nombre (p.ej. *notebook_1.ipynb*, *notebook_2.ipynb*, etc.)
+Refiéranse por estos mismos nombres a ellos al interior del pdf.
+
+**NOTA:** El informe no es mejor entre más gordo. Una habilidad fundamental en
+la ciencia de datos es la capacidad de comunicar de forma eficiente y sintetizada.
+Seguramente para llegar al gráfico final, con condensa una historia, habrán hecho
+cientos de gráficos más, por favor *No los incluyan TODOS dentro del informe*.
+Si hay un conjunto de gráficos que realmente vale la pena incluir y no es tan
+pequeño, usen de forma inteligente los anexos.
+
+
+## INTRUCCIONES
+
+En la carpeta `Data/` van a encontrar un archivo llamado `Saber_11__2020-2.csv`.
+Como se describió en clase, este archivo contiene información de los estudiantes
+que finalizaron sus estudios de secundaria en 2011 en Colombia, y se enfrentaron
+a la llamada prueba 'Saber', un examen de conocimientos en las distintas asignaturas
+como matemáticas, ciencias naturales, etc.
+
+El objetivo de la tarea es realizar un análisis exploratorio de los datos utilizando
+pandas, principalmente, pero sin excluir el uso de bibliotecas adicionales como
+numpy y por supuesto el uso de herramientas para graficar como `seaborn` y `matplotlib`.
+El foco de este análisis debe ser la búsqueda de relaciones entre los distintos
+indicadores, para entender qué historias se esconden entre los datos, qué nos
+cuentan sobre distintos factores de comportamiento o la influencia de factores
+geográficos, socioeconómicos, culturales, etc. en el desempoño de los estudiantes,
+y cómo estas historias pueden ser descubiertas mediante el análisis de los datos.
+
+Ese análisis exploratorio debe incluir, como mÃnimo:
+
+- Una descripción general del dataframe.
+- Un diccionario del significado de las variables como anexo
+- Una mención sobre las variables más importantes para su análisis y por qué esas
+- Análisis estadÃstico del comportamiento de algunas variables y su posible relación con otras variablas
+- Planteamiento de hipótesis para explicar las relaciones halladas
+
+Para llegar a ello, seguramente usted tendrá que hacer un procesamiento de los datos,
+por ejemplo renombrando columnas, elimnando algunas, crendo nuevas variables,
+verificando la presencia de NANs, modificando NANs si es necesario, y quizás modificando
+variables categoricas para darles una naturaleza de variable numérica. Todo eso está
+muy bien y es necesario, pero no es vital para el informe. Pueden mencionarlo brevemente,
+y sà identificar estos pasos y su justificación más claramente en los notebooks.
+
+
+## Notas adicionales:
+- La fuente de los datos es [esta](https://www.datos.gov.co/Educaci-n/Saber-11-2020-2/rnvb-vnyh)
+- [Aqui](https://www.mineducacion.gov.co/1621/w3-article-244735.html) una información mÃnima
+sobre las pruebas
+- Un dato relevante es conocer lo mÃnimo sobre la [división polÃtico-administrativa de Colombia](https://www.colombia.co/pais-colombia/estructura-del-estado-colombiano/como-es-la-organizacion-politico-administrativa-de-colombia/#:~:text=Colombia%20cuenta%20con%201123%20municipios,ind%C3%ADgenas%20y%20los%20territorios%20colectivos.)
+- El paÃs se divide en 32 departamentos. Será interesante ver si hay alguna diferencia entre los
+resultados de los distintos de departamentos, y entre los resultados de las capitales de departamento
+y los de las ciudades más pequeñas o zonas rurales
+- En esta misma carpeta hallan un notebook llamado `Notebook_introduction.py` con notas adicionales
+
+
+
+
+
+
+
+
+
diff --git a/data/Saber_11__2020-2.csv b/data/Saber_11__2020-2.csv
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diff --git a/notebook_introduction.ipynb b/notebook_introduction.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3a39a808b438af9895e932ef8f12e06cef7e05c0
--- /dev/null
+++ b/notebook_introduction.ipynb
@@ -0,0 +1,679 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "</div>\n",
+ "<img src=\"images/img0.png\">"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Contributors**\n",
+ "- Juan Carlos Basto Pineda (juan.basto.pineda@gmail.com)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Estos datos son proporcionados directamente por el gobierno de Colombia y pueden ser encontrados [aquÃ](https://www.datos.gov.co/Educaci-n/Saber-11-2020-2/rnvb-vnyh)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(\"data/Saber_11__2020-2.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "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>ESTU_TIPODOCUMENTO</th>\n",
+ " <th>ESTU_NACIONALIDAD</th>\n",
+ " <th>ESTU_GENERO</th>\n",
+ " <th>ESTU_FECHANACIMIENTO</th>\n",
+ " <th>PERIODO</th>\n",
+ " <th>ESTU_CONSECUTIVO</th>\n",
+ " <th>ESTU_ESTUDIANTE</th>\n",
+ " <th>ESTU_PAIS_RESIDE</th>\n",
+ " <th>ESTU_TIENEETNIA</th>\n",
+ " <th>ESTU_DEPTO_RESIDE</th>\n",
+ " <th>...</th>\n",
+ " <th>PUNT_INGLES</th>\n",
+ " <th>PERCENTIL_INGLES</th>\n",
+ " <th>DESEMP_INGLES</th>\n",
+ " <th>PUNT_GLOBAL</th>\n",
+ " <th>PERCENTIL_GLOBAL</th>\n",
+ " <th>ESTU_INSE_INDIVIDUAL</th>\n",
+ " <th>ESTU_NSE_INDIVIDUAL</th>\n",
+ " <th>ESTU_NSE_ESTABLECIMIENTO</th>\n",
+ " <th>ESTU_ESTADOINVESTIGACION</th>\n",
+ " <th>ESTU_GENERACION-E</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>TI</td>\n",
+ " <td>SUIZA</td>\n",
+ " <td>F</td>\n",
+ " <td>03/03/2003 12:00:00 AM</td>\n",
+ " <td>20204</td>\n",
+ " <td>SB11202040211436</td>\n",
+ " <td>ESTUDIANTE</td>\n",
+ " <td>SUIZA</td>\n",
+ " <td>No</td>\n",
+ " <td>CUNDINAMARCA</td>\n",
+ " <td>...</td>\n",
+ " <td>55.0</td>\n",
+ " <td>81</td>\n",
+ " <td>A1</td>\n",
+ " <td>244</td>\n",
+ " <td>49</td>\n",
+ " <td>54.882365</td>\n",
+ " <td>3.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>PUBLICAR</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>PEP</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>M</td>\n",
+ " <td>05/10/2002 12:00:00 AM</td>\n",
+ " <td>20204</td>\n",
+ " <td>SB11202040433216</td>\n",
+ " <td>ESTUDIANTE</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>No</td>\n",
+ " <td>CUNDINAMARCA</td>\n",
+ " <td>...</td>\n",
+ " <td>33.0</td>\n",
+ " <td>6</td>\n",
+ " <td>A-</td>\n",
+ " <td>238</td>\n",
+ " <td>44</td>\n",
+ " <td>49.252311</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>PUBLICAR</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>TI</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>F</td>\n",
+ " <td>12/14/2003 12:00:00 AM</td>\n",
+ " <td>20204</td>\n",
+ " <td>SB11202040244180</td>\n",
+ " <td>ESTUDIANTE</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>No</td>\n",
+ " <td>CUNDINAMARCA</td>\n",
+ " <td>...</td>\n",
+ " <td>59.0</td>\n",
+ " <td>87</td>\n",
+ " <td>A2</td>\n",
+ " <td>325</td>\n",
+ " <td>94</td>\n",
+ " <td>40.733672</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>PUBLICAR</td>\n",
+ " <td>GENERACION E - GRATUIDAD</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>CE</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>M</td>\n",
+ " <td>04/12/2003 12:00:00 AM</td>\n",
+ " <td>20204</td>\n",
+ " <td>SB11202040210971</td>\n",
+ " <td>ESTUDIANTE</td>\n",
+ " <td>VENEZUELA</td>\n",
+ " <td>No</td>\n",
+ " <td>CUNDINAMARCA</td>\n",
+ " <td>...</td>\n",
+ " <td>47.0</td>\n",
+ " <td>58</td>\n",
+ " <td>A-</td>\n",
+ " <td>238</td>\n",
+ " <td>45</td>\n",
+ " <td>48.217953</td>\n",
+ " <td>2.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>PUBLICAR</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>TI</td>\n",
+ " <td>COLOMBIA</td>\n",
+ " <td>F</td>\n",
+ " <td>03/03/2004 12:00:00 AM</td>\n",
+ " <td>20204</td>\n",
+ " <td>SB11202040235382</td>\n",
+ " <td>ESTUDIANTE</td>\n",
+ " <td>COLOMBIA</td>\n",
+ " <td>No</td>\n",
+ " <td>CUNDINAMARCA</td>\n",
+ " <td>...</td>\n",
+ " <td>43.0</td>\n",
+ " <td>40</td>\n",
+ " <td>A-</td>\n",
+ " <td>202</td>\n",
+ " <td>19</td>\n",
+ " <td>60.912192</td>\n",
+ " <td>3.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>PUBLICAR</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 81 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ESTU_TIPODOCUMENTO ESTU_NACIONALIDAD ESTU_GENERO ESTU_FECHANACIMIENTO \\\n",
+ "0 TI SUIZA F 03/03/2003 12:00:00 AM \n",
+ "1 PEP VENEZUELA M 05/10/2002 12:00:00 AM \n",
+ "2 TI VENEZUELA F 12/14/2003 12:00:00 AM \n",
+ "3 CE VENEZUELA M 04/12/2003 12:00:00 AM \n",
+ "4 TI COLOMBIA F 03/03/2004 12:00:00 AM \n",
+ "\n",
+ " PERIODO ESTU_CONSECUTIVO ESTU_ESTUDIANTE ESTU_PAIS_RESIDE ESTU_TIENEETNIA \\\n",
+ "0 20204 SB11202040211436 ESTUDIANTE SUIZA No \n",
+ "1 20204 SB11202040433216 ESTUDIANTE VENEZUELA No \n",
+ "2 20204 SB11202040244180 ESTUDIANTE VENEZUELA No \n",
+ "3 20204 SB11202040210971 ESTUDIANTE VENEZUELA No \n",
+ "4 20204 SB11202040235382 ESTUDIANTE COLOMBIA No \n",
+ "\n",
+ " ESTU_DEPTO_RESIDE ... PUNT_INGLES PERCENTIL_INGLES DESEMP_INGLES \\\n",
+ "0 CUNDINAMARCA ... 55.0 81 A1 \n",
+ "1 CUNDINAMARCA ... 33.0 6 A- \n",
+ "2 CUNDINAMARCA ... 59.0 87 A2 \n",
+ "3 CUNDINAMARCA ... 47.0 58 A- \n",
+ "4 CUNDINAMARCA ... 43.0 40 A- \n",
+ "\n",
+ " PUNT_GLOBAL PERCENTIL_GLOBAL ESTU_INSE_INDIVIDUAL ESTU_NSE_INDIVIDUAL \\\n",
+ "0 244 49 54.882365 3.0 \n",
+ "1 238 44 49.252311 2.0 \n",
+ "2 325 94 40.733672 1.0 \n",
+ "3 238 45 48.217953 2.0 \n",
+ "4 202 19 60.912192 3.0 \n",
+ "\n",
+ " ESTU_NSE_ESTABLECIMIENTO ESTU_ESTADOINVESTIGACION ESTU_GENERACION-E \n",
+ "0 3.0 PUBLICAR NO \n",
+ "1 2.0 PUBLICAR NO \n",
+ "2 3.0 PUBLICAR GENERACION E - GRATUIDAD \n",
+ "3 3.0 PUBLICAR NO \n",
+ "4 3.0 PUBLICAR NO \n",
+ "\n",
+ "[5 rows x 81 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(504872, 81)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['ESTU_TIPODOCUMENTO', 'ESTU_NACIONALIDAD', 'ESTU_GENERO',\n",
+ " 'ESTU_FECHANACIMIENTO', 'PERIODO', 'ESTU_CONSECUTIVO',\n",
+ " 'ESTU_ESTUDIANTE', 'ESTU_PAIS_RESIDE', 'ESTU_TIENEETNIA',\n",
+ " 'ESTU_DEPTO_RESIDE', 'ESTU_COD_RESIDE_DEPTO', 'ESTU_MCPIO_RESIDE',\n",
+ " 'ESTU_COD_RESIDE_MCPIO', 'FAMI_ESTRATOVIVIENDA', 'FAMI_PERSONASHOGAR',\n",
+ " 'FAMI_CUARTOSHOGAR', 'FAMI_EDUCACIONPADRE', 'FAMI_EDUCACIONMADRE',\n",
+ " 'FAMI_TRABAJOLABORPADRE', 'FAMI_TRABAJOLABORMADRE',\n",
+ " 'FAMI_TIENEINTERNET', 'FAMI_TIENESERVICIOTV', 'FAMI_TIENECOMPUTADOR',\n",
+ " 'FAMI_TIENELAVADORA', 'FAMI_TIENEHORNOMICROOGAS', 'FAMI_TIENEAUTOMOVIL',\n",
+ " 'FAMI_TIENEMOTOCICLETA', 'FAMI_TIENECONSOLAVIDEOJUEGOS',\n",
+ " 'FAMI_NUMLIBROS', 'FAMI_COMELECHEDERIVADOS',\n",
+ " 'FAMI_COMECARNEPESCADOHUEVO', 'FAMI_COMECEREALFRUTOSLEGUMBRE',\n",
+ " 'FAMI_SITUACIONECONOMICA', 'ESTU_DEDICACIONLECTURADIARIA',\n",
+ " 'ESTU_DEDICACIONINTERNET', 'ESTU_HORASSEMANATRABAJA',\n",
+ " 'ESTU_TIPOREMUNERACION', 'COLE_CODIGO_ICFES',\n",
+ " 'COLE_COD_DANE_ESTABLECIMIENTO', 'COLE_NOMBRE_ESTABLECIMIENTO',\n",
+ " 'COLE_GENERO', 'COLE_NATURALEZA', 'COLE_CALENDARIO', 'COLE_BILINGUE',\n",
+ " 'COLE_CARACTER', 'COLE_COD_DANE_SEDE', 'COLE_NOMBRE_SEDE',\n",
+ " 'COLE_SEDE_PRINCIPAL', 'COLE_AREA_UBICACION', 'COLE_JORNADA',\n",
+ " 'COLE_COD_MCPIO_UBICACION', 'COLE_MCPIO_UBICACION',\n",
+ " 'COLE_COD_DEPTO_UBICACION', 'COLE_DEPTO_UBICACION',\n",
+ " 'ESTU_PRIVADO_LIBERTAD', 'ESTU_COD_MCPIO_PRESENTACION',\n",
+ " 'ESTU_MCPIO_PRESENTACION', 'ESTU_DEPTO_PRESENTACION',\n",
+ " 'ESTU_COD_DEPTO_PRESENTACION', 'PUNT_LECTURA_CRITICA',\n",
+ " 'PERCENTIL_LECTURA_CRITICA', 'DESEMP_LECTURA_CRITICA',\n",
+ " 'PUNT_MATEMATICAS', 'PERCENTIL_MATEMATICAS', 'DESEMP_MATEMATICAS',\n",
+ " 'PUNT_C_NATURALES', 'PERCENTIL_C_NATURALES', 'DESEMP_C_NATURALES',\n",
+ " 'PUNT_SOCIALES_CIUDADANAS', 'PERCENTIL_SOCIALES_CIUDADANAS',\n",
+ " 'DESEMP_SOCIALES_CIUDADANAS', 'PUNT_INGLES', 'PERCENTIL_INGLES',\n",
+ " 'DESEMP_INGLES', 'PUNT_GLOBAL', 'PERCENTIL_GLOBAL',\n",
+ " 'ESTU_INSE_INDIVIDUAL', 'ESTU_NSE_INDIVIDUAL',\n",
+ " 'ESTU_NSE_ESTABLECIMIENTO', 'ESTU_ESTADOINVESTIGACION',\n",
+ " 'ESTU_GENERACION-E'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Como ejemplo veamos cómo se construye un histograma de una de las variables, en este caso el puntaje en matemáticas:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:ylabel='Frequency'>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pd.DataFrame(df['PUNT_MATEMATICAS'].values).plot(kind='hist')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "El tipo de gráfico se puede cambiar fácilmente mediante la opción `kind`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df['PUNT_MATEMATICAS'].value_counts().plot(kind='line',style=\"o\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ahora un ejemplo de cómo se pueden computar los cuantiles rápidamente para tener una idea de la distribución de valores. Lo mismo se puede hacer para todo el dataframe o un subconjunto de columnas como en el ejemplo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "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>PUNT_MATEMATICAS</th>\n",
+ " <th>PUNT_C_NATURALES</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0.90</th>\n",
+ " <td>66.0</td>\n",
+ " <td>62.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0.95</th>\n",
+ " <td>70.0</td>\n",
+ " <td>66.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0.99</th>\n",
+ " <td>77.0</td>\n",
+ " <td>73.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " PUNT_MATEMATICAS PUNT_C_NATURALES\n",
+ "0.90 66.0 62.0\n",
+ "0.95 70.0 66.0\n",
+ "0.99 77.0 73.0"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[['PUNT_MATEMATICAS','PUNT_C_NATURALES']].quantile([0.9,0.95,0.99])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ahora un ejemplo de boxplot utilizando seaborn:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Boxplot of Price vs. bedrooms')"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.boxplot(x = \"ESTU_GENERO\", y='PUNT_MATEMATICAS', data = df)\n",
+ "plt.title(\"Boxplot of Price vs. bedrooms\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Y un gráfico de cajas más, para probar una hipótesis que no deja de llamar la atención \"En los hogares con mayores de seguridad alimentar, donde se consume por ejemplo huevo, carne y pescado con mayor frecuencia, los puntajes en matemáticas son superiores\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0, 1, 2, 3]),\n",
+ " [Text(0, 0, '1 o 2 veces por semana'),\n",
+ " Text(1, 0, 'Todos o casi todos los dÃas'),\n",
+ " Text(2, 0, '3 a 5 veces por semana'),\n",
+ " Text(3, 0, 'Nunca o rara vez comemos eso')])"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.boxplot(x = \"FAMI_COMECARNEPESCADOHUEVO\", y='PUNT_MATEMATICAS', data = df)\n",
+ "plt.xticks(rotation=45)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A las variables que no toman valores numéricos contÃnuos, sino que tienen asignado un valor de entre un conjunto finito de valores, algo asà como una etiqueta, se les llama variables categóricas, pues dividen a los datos en categorÃas. El \"Estrato\" de la vivienda familiar es un ejemplo. En este caso es una variable categórica que puede ser ordenada, pues es un indicador aproximado del nivel de ingresos económicos, por lo tanto serÃa útil transformar esta variable a números para poder analizar correlaciones con otras variables, ya que las correlaciones solo funcionan entre variables numéricas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Estrato 2\n",
+ "1 Estrato 3\n",
+ "2 Estrato 1\n",
+ "3 Sin Estrato\n",
+ "4 Estrato 5\n",
+ " ... \n",
+ "504867 Estrato 2\n",
+ "504868 Estrato 2\n",
+ "504869 Estrato 2\n",
+ "504870 Estrato 2\n",
+ "504871 Estrato 3\n",
+ "Name: FAMI_ESTRATOVIVIENDA, Length: 504872, dtype: object"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[\"FAMI_ESTRATOVIVIENDA\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Algunos otros comandos que probablemente sean útiles, y quieran reutilizar en distintas formas:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.ESTU_GENERO.unique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.ESTU_GENERO.hasnans"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.ESTU_GENERO.isnull().sum()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}