diff --git a/notebook1_MildredA.ipynb b/notebook1_MildredA.ipynb deleted file mode 100644 index aa11da049a6840e57f1fafa4a5286d26d5933ad0..0000000000000000000000000000000000000000 --- a/notebook1_MildredA.ipynb +++ /dev/null @@ -1,1799 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " <img src=\"images/img0.png\">" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Mildred Arias (mildarias181@gmail.com)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " ## Análisis de Datos de la Prueba Saber SABER 11°" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Librerias necesarias para dicho Análisis de Datos" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "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": "markdown", - "metadata": {}, - "source": [ - "### Explorando los Datos" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hay distintas maneras de resumir y accesar a los datos guardados en un DataFrame, usando los atributos y métodos que proveé el objeto DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv(\"data/Saber_11__2020-2.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "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", - 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" <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": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head() #Esta función devuelve las primeras filas del objeto según la posición." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### ¿Qué tipo de datos contiene cada columna del Dataframe? " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ESTU_TIPODOCUMENTO object\n", - "ESTU_NACIONALIDAD object\n", - "ESTU_GENERO object\n", - "ESTU_FECHANACIMIENTO object\n", - "PERIODO int64\n", - " ... \n", - "ESTU_INSE_INDIVIDUAL float64\n", - "ESTU_NSE_INDIVIDUAL float64\n", - "ESTU_NSE_ESTABLECIMIENTO float64\n", - "ESTU_ESTADOINVESTIGACION object\n", - "ESTU_GENERACION-E object\n", - "Length: 81, dtype: object" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.dtypes #con .dtypes podemos ver que tipo de datos contiene cada columna " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(504872, 81)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.shape # Devuelve una tupla que representa la dimensionalidad del DataFrame." - 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" <td>06/14/2002 12:00:00 AM</td>\n", - " <td>20204</td>\n", - " <td>SB11202040168607</td>\n", - " <td>ESTUDIANTE</td>\n", - " <td>COLOMBIA</td>\n", - " <td>No</td>\n", - " <td>BOGOTÃ</td>\n", - " <td>...</td>\n", - " <td>52.0</td>\n", - " <td>75</td>\n", - " <td>A1</td>\n", - " <td>290</td>\n", - " <td>80</td>\n", - " <td>58.316936</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>504871</th>\n", - " <td>TI</td>\n", - " <td>COLOMBIA</td>\n", - " <td>F</td>\n", - " <td>02/20/2002 12:00:00 AM</td>\n", - " <td>20204</td>\n", - " <td>SB11202040525571</td>\n", - " <td>ESTUDIANTE</td>\n", - " <td>COLOMBIA</td>\n", - " <td>No</td>\n", - " <td>BOGOTÃ</td>\n", - " <td>...</td>\n", - " <td>48.0</td>\n", - " <td>63</td>\n", - " <td>A1</td>\n", - " <td>261</td>\n", - " <td>61</td>\n", - " <td>57.375730</td>\n", - " <td>3.0</td>\n", - " <td>3.0</td>\n", - " <td>PUBLICAR</td>\n", - " <td>GENERACION E - GRATUIDAD</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "<p>5 rows × 81 columns</p>\n", - "</div>" - ], - "text/plain": [ - " ESTU_TIPODOCUMENTO ESTU_NACIONALIDAD ESTU_GENERO \\\n", - "504867 TI COLOMBIA M \n", - "504868 CC COLOMBIA M \n", - "504869 TI COLOMBIA F \n", - "504870 TI COLOMBIA M \n", - "504871 TI COLOMBIA F \n", - "\n", - " ESTU_FECHANACIMIENTO PERIODO ESTU_CONSECUTIVO ESTU_ESTUDIANTE \\\n", - "504867 12/26/2003 12:00:00 AM 20204 SB11202040105446 ESTUDIANTE \n", - "504868 10/27/1996 12:00:00 AM 20204 SB11202040168415 ESTUDIANTE \n", - "504869 01/14/2005 12:00:00 AM 20204 SB11202040185843 ESTUDIANTE \n", - "504870 06/14/2002 12:00:00 AM 20204 SB11202040168607 ESTUDIANTE \n", - "504871 02/20/2002 12:00:00 AM 20204 SB11202040525571 ESTUDIANTE \n", - "\n", - " ESTU_PAIS_RESIDE ESTU_TIENEETNIA ESTU_DEPTO_RESIDE ... PUNT_INGLES \\\n", - "504867 COLOMBIA No BOGOTà ... 45.0 \n", - "504868 COLOMBIA No BOGOTà ... 54.0 \n", - "504869 COLOMBIA No BOGOTà ... 67.0 \n", - "504870 COLOMBIA No BOGOTà ... 52.0 \n", - "504871 COLOMBIA No BOGOTà ... 48.0 \n", - "\n", - " PERCENTIL_INGLES DESEMP_INGLES PUNT_GLOBAL PERCENTIL_GLOBAL \\\n", - "504867 52 A- 282 75 \n", - "504868 79 A1 257 59 \n", - "504869 93 A2 330 95 \n", - "504870 75 A1 290 80 \n", - "504871 63 A1 261 61 \n", - "\n", - " ESTU_INSE_INDIVIDUAL ESTU_NSE_INDIVIDUAL ESTU_NSE_ESTABLECIMIENTO \\\n", - "504867 45.311449 2.0 2.0 \n", - "504868 38.124729 1.0 3.0 \n", - "504869 53.586443 3.0 3.0 \n", - "504870 58.316936 3.0 3.0 \n", - "504871 57.375730 3.0 3.0 \n", - "\n", - " ESTU_ESTADOINVESTIGACION ESTU_GENERACION-E \n", - "504867 PUBLICAR NO \n", - "504868 PUBLICAR NO \n", - "504869 PUBLICAR NO \n", - "504870 PUBLICAR NO \n", - "504871 PUBLICAR GENERACION E - GRATUIDAD \n", - "\n", - "[5 rows x 81 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.tail() #Esta función devuelve las últimas filas del objeto según la posición. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- para facilitar un poco el analisis veamos las etiquetas de las columnas" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "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": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Veamos los valores unicos presentes en la columna mencionada" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 65, 43, 72, 55, 48, 45, 68, 57, 47, 58, 70, 67, 34,\n", - " 53, 56, 74, 37, 44, 60, 50, 39, 52, 27, 20, 75, 51,\n", - " 32, 31, 42, 35, 25, 38, 49, 40, 62, 59, 69, 54, 41,\n", - " 46, 76, 36, 64, 66, 71, 23, 30, 61, 63, 28, 79, 33,\n", - " 73, 29, 24, 78, 26, 80, 100, 0, 17, 77, 22, 81, 21,\n", - " 82, 19, 18, 83])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.unique(df['PUNT_MATEMATICAS']) " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "69" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['PUNT_MATEMATICAS'].nunique() #Cuenta el número de elementos distintos en el eje especificado." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculando estadÃsticas de los datos en un DataFrame de Pandas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ahora calculemos algunas estadÃsticas para entender un poco de los datos con los que estamos trabajando. \n", - "\n", - "Exploremos el desempeño de los estudiantes en las asignaturas según el puntaje de dichas asignaturas en relación al estrato socioeconómico de la vivienda según el recibo de energÃa eléctrica (“FAMI_ESTRATOVIVIENDAâ€) y el Nivel Socioeconómico del evaluado (“ESTU_NSE_INDIVIDUALâ€). Se busca una correlación para responder a las siguientes preguntas:\n", - "- ¿Es igual el desempeño de las distintas asignaturas en todos los estratos?\n", - "- ¿Cómo es el desempeño de los estudiantes en las asignaturas según el Nivel Socioeconómico del evaluado?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Podemos calcular algunas estadÃsticas básicas, ademas de construir histogramas de las variables que tienen que ver con el puntaje de las asignaturas." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "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>Promedio</th>\n", - " <th>std</th>\n", - " </tr>\n", - " <tr>\n", - " <th>Materia</th>\n", - " <th></th>\n", - " <th></th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>Matemática</th>\n", - " <td>51.019754</td>\n", - " <td>11.647646</td>\n", - " </tr>\n", - " <tr>\n", - " <th>Ingles</th>\n", - " <td>46.883507</td>\n", - " <td>11.313117</td>\n", - " </tr>\n", - " <tr>\n", - " <th>C_Naturales</th>\n", - " <td>48.197373</td>\n", - " <td>10.499600</td>\n", - " </tr>\n", - " <tr>\n", - " <th>sociales_ciudadanas</th>\n", - " <td>48.233939</td>\n", - " <td>11.970572</td>\n", - " </tr>\n", - " <tr>\n", - " <th>LC</th>\n", - " <td>52.156784</td>\n", - " <td>10.158417</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " Promedio std\n", - "Materia \n", - "Matemática 51.019754 11.647646\n", - "Ingles 46.883507 11.313117\n", - "C_Naturales 48.197373 10.499600\n", - "sociales_ciudadanas 48.233939 11.970572\n", - "LC 52.156784 10.158417" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "media_mate = pd.Series({'Materia': 'Matemática', 'Promedio': np.mean(df['PUNT_MATEMATICAS']), 'std': np.std(df['PUNT_MATEMATICAS'])})\n", - "media_ingles= pd.Series({'Materia': 'Ingles', 'Promedio': np.mean(df['PUNT_INGLES']),'std': np.std(df['PUNT_INGLES'])})\n", - "media_cnatu= pd.Series({'Materia': 'C_Naturales', 'Promedio': np.mean(df['PUNT_C_NATURALES']),'std': np.std(df['PUNT_C_NATURALES'])})\n", - "media_social= pd.Series({'Materia': 'sociales_ciudadanas', 'Promedio': np.mean(df['PUNT_SOCIALES_CIUDADANAS']),'std': np.std(df['PUNT_SOCIALES_CIUDADANAS'])})\n", - "media_LC= pd.Series({'Materia': 'LC', 'Promedio': np.mean(df['PUNT_LECTURA_CRITICA']),'std': np.std(df['PUNT_LECTURA_CRITICA'])})\n", - "\n", - "materias = pd.DataFrame([media_mate,media_ingles,media_cnatu,media_social,media_LC])\n", - "materias = materias.set_index('Materia')\n", - "materias" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "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": [ - "fig, ax = plt.subplots()\n", - "x = np.arange(5)\n", - "\n", - "# grafico de barras\n", - "bars1 = plt.bar(x, materias['Promedio'], alpha = 0.5, label = 'Materia',color=\"purple\")\n", - "\n", - "# Ejes\n", - "plt.ylabel('Puntajes')\n", - "plt.xlabel('Materias')\n", - "plt.xticks(x, ('Matemática', 'Ingles', 'CNatu', 'SCiud', 'LCriti'))\n", - "plt.title('Promedio')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1080x720 with 5 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15,10))\n", - "\n", - "for (i, materia) in enumerate(['PUNT_MATEMATICAS', \n", - " 'PUNT_C_NATURALES', \n", - " 'PUNT_SOCIALES_CIUDADANAS', \n", - " 'PUNT_INGLES',\n", - " 'PUNT_LECTURA_CRITICA']):\n", - " plt.subplot(3, 3, i+1)\n", - " df[materia].plot(kind='hist', title=materia)\n", - " plt.xlabel(materia)\n", - " \n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### RELACION ENTRE EL ESTRATO Y LAS ASIGNATURAS\n", - "- veamos nuestra variable categorica \"FAMI_ESTRATOVIVIENDA\"" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df_1 = df[df['FAMI_ESTRATOVIVIENDA'] == 'Estrato 1']\n", - "df_2 = df[df['FAMI_ESTRATOVIVIENDA'] == 'Estrato 2']\n", - "df_3 = df[df['FAMI_ESTRATOVIVIENDA'] == 'Estrato 3']\n", - "df_4 = df[df['FAMI_ESTRATOVIVIENDA'] == 'Estrato 4']\n", - "df_5 = df[df['FAMI_ESTRATOVIVIENDA'] == 'Estrato 5']\n", - "df_6 = df[df['FAMI_ESTRATOVIVIENDA'] == 'Estrato 6']\n", - "df_7 = df[df['FAMI_ESTRATOVIVIENDA'] == 'Estrato 7']" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Estrato1\n", - "matematicas_1 = pd.Series({'Asignatura': 'Matemática','Puntaje promedio Estrato1': np.mean(df_1['PUNT_MATEMATICAS'])})\n", - "ingles_1 = pd.Series({'Asignatura': 'Inglés', 'Puntaje promedio Estrato1': np.mean(df_1['PUNT_INGLES'])})\n", - "lec_critica_1 = pd.Series({'Asignatura': 'Lectura crÃtica','Puntaje promedio Estrato1': np.mean(df_1['PUNT_LECTURA_CRITICA'])})\n", - "c_naturales_1 = pd.Series({'Asignatura': 'Ciencias naturales','Puntaje promedio Estrato1': np.mean(df_1['PUNT_C_NATURALES'])})\n", - "s_ciudadanas_1 = pd.Series({'Asignatura': 'Sociales y ciudadanas','Puntaje promedio Estrato1':np.mean(df_1['PUNT_SOCIALES_CIUDADANAS'])})\n", - "\n", - "# Estrato2\n", - "matematicas_2 = pd.Series({'Asignatura': 'Matemática','Puntaje promedio Estrato2': np.mean(df_2['PUNT_MATEMATICAS'])})\n", - "ingles_2 = pd.Series({'Asignatura': 'Inglés', 'Puntaje promedio Estrato2': np.mean(df_2['PUNT_INGLES'])})\n", - "lec_critica_2 = pd.Series({'Asignatura': 'Lectura crÃtica','Puntaje promedio Estrato2': np.mean(df_2['PUNT_LECTURA_CRITICA'])})\n", - "c_naturales_2 = pd.Series({'Asignatura': 'Ciencias naturales','Puntaje promedio Estrato2': np.mean(df_2['PUNT_C_NATURALES'])})\n", - "s_ciudadanas_2 = pd.Series({'Asignatura': 'Sociales y ciudadanas','Puntaje promedio Estrato2':np.mean(df_2['PUNT_SOCIALES_CIUDADANAS'])})\n", - "\n", - "#Estrato3\n", - "matematicas_3 = pd.Series({'Asignatura': 'Matemática','Puntaje promedio Estrato3': np.mean(df_3['PUNT_MATEMATICAS'])})\n", - "ingles_3 = pd.Series({'Asignatura': 'Inglés', 'Puntaje promedio Estrato3': np.mean(df_2['PUNT_INGLES'])})\n", - "lec_critica_3 = pd.Series({'Asignatura': 'Lectura crÃtica','Puntaje promedio Estrato3': np.mean(df_3['PUNT_LECTURA_CRITICA'])})\n", - "c_naturales_3 = pd.Series({'Asignatura': 'Ciencias naturales','Puntaje promedio Estrato3': np.mean(df_3['PUNT_C_NATURALES'])})\n", - "s_ciudadanas_3 = pd.Series({'Asignatura': 'Sociales y ciudadanas','Puntaje promedio Estrato3':np.mean(df_3['PUNT_SOCIALES_CIUDADANAS'])})\n", - "\n", - "#Estrato4\n", - "matematicas_4 = pd.Series({'Asignatura': 'Matemática','Puntaje promedio Estrato4': np.mean(df_4['PUNT_MATEMATICAS'])})\n", - "ingles_4 = pd.Series({'Asignatura': 'Inglés', 'Puntaje promedio Estrato4': np.mean(df_4['PUNT_INGLES'])})\n", - "lec_critica_4 = pd.Series({'Asignatura': 'Lectura crÃtica','Puntaje promedio Estrato4': np.mean(df_4['PUNT_LECTURA_CRITICA'])})\n", - "c_naturales_4 = pd.Series({'Asignatura': 'Ciencias naturales','Puntaje promedio Estrato4': np.mean(df_4['PUNT_C_NATURALES'])})\n", - "s_ciudadanas_4 = pd.Series({'Asignatura': 'Sociales y ciudadanas','Puntaje promedio Estrato4':np.mean(df_4['PUNT_SOCIALES_CIUDADANAS'])})\n", - "\n", - "# Estrato5\n", - "matematicas_5 = pd.Series({'Asignatura': 'Matemática','Puntaje promedio Estrato5': np.mean(df_5['PUNT_MATEMATICAS'])})\n", - "ingles_5 = pd.Series({'Asignatura': 'Inglés', 'Puntaje promedio Estrato5': np.mean(df_5['PUNT_INGLES'])})\n", - "lec_critica_5 = pd.Series({'Asignatura': 'Lectura crÃtica','Puntaje promedio Estrato5': np.mean(df_5['PUNT_LECTURA_CRITICA'])})\n", - "c_naturales_5 = pd.Series({'Asignatura': 'Ciencias naturales','Puntaje promedio Estrato5': np.mean(df_5['PUNT_C_NATURALES'])})\n", - "s_ciudadanas_5 = pd.Series({'Asignatura': 'Sociales y ciudadanas','Puntaje promedio Estrato5':np.mean(df_5['PUNT_SOCIALES_CIUDADANAS'])})\n", - "\n", - "# Estrato6\n", - "matematicas_6 = pd.Series({'Asignatura': 'Matemática','Puntaje promedio Estrato6': np.mean(df_6['PUNT_MATEMATICAS'])})\n", - "ingles_6 = pd.Series({'Asignatura': 'Inglés', 'Puntaje promedio Estrato6': np.mean(df_6['PUNT_INGLES'])})\n", - "lec_critica_6 = pd.Series({'Asignatura': 'Lectura crÃtica','Puntaje promedio Estrato6': np.mean(df_6['PUNT_LECTURA_CRITICA'])})\n", - "c_naturales_6 = pd.Series({'Asignatura': 'Ciencias naturales','Puntaje promedio Estrato6': np.mean(df_6['PUNT_C_NATURALES'])})\n", - "s_ciudadanas_6 = pd.Series({'Asignatura': 'Sociales y ciudadanas','Puntaje promedio Estrato6':np.mean(df_6['PUNT_SOCIALES_CIUDADANAS'])})\n", - "\n", - "# Estrato7\n", - "matematicas_7 = pd.Series({'Asignatura': 'Matemática','Puntaje promedio Estrato7': np.mean(df_7['PUNT_MATEMATICAS'])})\n", - "ingles_7 = pd.Series({'Asignatura': 'Inglés', 'Puntaje promedio Estrato7': np.mean(df_7['PUNT_INGLES'])})\n", - "lec_critica_7 = pd.Series({'Asignatura': 'Lectura crÃtica','Puntaje promedio Estrato7': np.mean(df_7['PUNT_LECTURA_CRITICA'])})\n", - "c_naturales_7 = pd.Series({'Asignatura': 'Ciencias naturales','Puntaje promedio Estrato7': np.mean(df_7['PUNT_C_NATURALES'])})\n", - "s_ciudadanas_7 = pd.Series({'Asignatura': 'Sociales y ciudadanas','Puntaje promedio Estrato7':np.mean(df_7['PUNT_SOCIALES_CIUDADANAS'])})" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "materias_1 = pd.DataFrame([matematicas_1,ingles_1,lec_critica_1,c_naturales_1,s_ciudadanas_1])\n", - "materias_2 = pd.DataFrame([matematicas_2,ingles_2,lec_critica_2,c_naturales_2,s_ciudadanas_2])\n", - "materias_3 = pd.DataFrame([matematicas_3,ingles_3,lec_critica_3,c_naturales_3,s_ciudadanas_3])\n", - "materias_4 = pd.DataFrame([matematicas_4,ingles_4,lec_critica_4,c_naturales_4,s_ciudadanas_4])\n", - "materias_5 = pd.DataFrame([matematicas_5,ingles_5,lec_critica_5,c_naturales_5,s_ciudadanas_5])\n", - "materias_6 = pd.DataFrame([matematicas_6,ingles_6,lec_critica_6,c_naturales_6,s_ciudadanas_6])\n", - "materias_7 = pd.DataFrame([matematicas_7,ingles_7,lec_critica_7,c_naturales_7,s_ciudadanas_7])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "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>Asignatura</th>\n", - " <th>Puntaje promedio Estrato1</th>\n", - " <th>Puntaje promedio Estrato2</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Matemática</td>\n", - " <td>48.747233</td>\n", - " <td>51.502880</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>Inglés</td>\n", - " <td>43.823423</td>\n", - " <td>46.824819</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>Lectura crÃtica</td>\n", - " <td>50.081641</td>\n", - " <td>52.640378</td>\n", - " </tr>\n", - " <tr>\n", - " <th>3</th>\n", - " <td>Ciencias naturales</td>\n", - " <td>46.191237</td>\n", - " <td>48.603164</td>\n", - " </tr>\n", - " <tr>\n", - " <th>4</th>\n", - " <td>Sociales y ciudadanas</td>\n", - " <td>45.736045</td>\n", - " <td>48.637641</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " Asignatura Puntaje promedio Estrato1 Puntaje promedio Estrato2\n", - "0 Matemática 48.747233 51.502880\n", - "1 Inglés 43.823423 46.824819\n", - "2 Lectura crÃtica 50.081641 52.640378\n", - "3 Ciencias naturales 46.191237 48.603164\n", - "4 Sociales y ciudadanas 45.736045 48.637641" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "asignaturas_estratos12 = pd.merge(materias_1, materias_2, on = 'Asignatura')\n", - "asignaturas_estratos12" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "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>Asignatura</th>\n", - " <th>Puntaje promedio Estrato3</th>\n", - " <th>Puntaje promedio Estrato4</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Matemática</td>\n", - " <td>53.785794</td>\n", - " <td>55.794442</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>Inglés</td>\n", - " <td>46.824819</td>\n", - " <td>55.449565</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>Lectura crÃtica</td>\n", - " <td>54.740763</td>\n", - " <td>56.053233</td>\n", - " </tr>\n", - " <tr>\n", - " <th>3</th>\n", - " <td>Ciencias naturales</td>\n", - " <td>50.772429</td>\n", - " <td>52.764054</td>\n", - " </tr>\n", - " <tr>\n", - " <th>4</th>\n", - " <td>Sociales y ciudadanas</td>\n", - " <td>51.273761</td>\n", - " <td>53.483442</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " Asignatura Puntaje promedio Estrato3 Puntaje promedio Estrato4\n", - "0 Matemática 53.785794 55.794442\n", - "1 Inglés 46.824819 55.449565\n", - "2 Lectura crÃtica 54.740763 56.053233\n", - "3 Ciencias naturales 50.772429 52.764054\n", - "4 Sociales y ciudadanas 51.273761 53.483442" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "asignaturas_estratos34 = pd.merge(materias_3, materias_4, on = 'Asignatura')\n", - "asignaturas_estratos34" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "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>Asignatura</th>\n", - " <th>Puntaje promedio Estrato5</th>\n", - " <th>Puntaje promedio Estrato6</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Matemática</td>\n", - " <td>54.822909</td>\n", - " <td>51.546221</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>Inglés</td>\n", - " <td>55.915051</td>\n", - " <td>53.556132</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>Lectura crÃtica</td>\n", - " <td>55.105713</td>\n", - " <td>52.193967</td>\n", - " </tr>\n", - " <tr>\n", - " <th>3</th>\n", - " <td>Ciencias naturales</td>\n", - " <td>51.738282</td>\n", - " <td>48.647746</td>\n", - " </tr>\n", - " <tr>\n", - " <th>4</th>\n", - " <td>Sociales y ciudadanas</td>\n", - " <td>52.715771</td>\n", - " <td>49.589361</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " Asignatura Puntaje promedio Estrato5 Puntaje promedio Estrato6\n", - "0 Matemática 54.822909 51.546221\n", - "1 Inglés 55.915051 53.556132\n", - "2 Lectura crÃtica 55.105713 52.193967\n", - "3 Ciencias naturales 51.738282 48.647746\n", - "4 Sociales y ciudadanas 52.715771 49.589361" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "asignaturas_estratos56 = pd.merge(materias_5, materias_6, on = 'Asignatura')\n", - "asignaturas_estratos56" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[ Asignatura Puntaje promedio Estrato1 Puntaje promedio Estrato2\n", - " 0 Matemática 48.747233 51.502880\n", - " 1 Inglés 43.823423 46.824819\n", - " 2 Lectura crÃtica 50.081641 52.640378\n", - " 3 Ciencias naturales 46.191237 48.603164\n", - " 4 Sociales y ciudadanas 45.736045 48.637641,\n", - " Asignatura Puntaje promedio Estrato3 Puntaje promedio Estrato4\n", - " 0 Matemática 53.785794 55.794442\n", - " 1 Inglés 46.824819 55.449565\n", - " 2 Lectura crÃtica 54.740763 56.053233\n", - " 3 Ciencias naturales 50.772429 52.764054\n", - " 4 Sociales y ciudadanas 51.273761 53.483442,\n", - " Asignatura Puntaje promedio Estrato5 Puntaje promedio Estrato6\n", - " 0 Matemática 54.822909 51.546221\n", - " 1 Inglés 55.915051 53.556132\n", - " 2 Lectura crÃtica 55.105713 52.193967\n", - " 3 Ciencias naturales 51.738282 48.647746\n", - " 4 Sociales y ciudadanas 52.715771 49.589361]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "v=[asignaturas_estratos12,asignaturas_estratos34,asignaturas_estratos56]\n", - "v" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Estrato 2', 'Estrato 3', 'Estrato 1', 'Sin Estrato', 'Estrato 5',\n", - " 'Estrato 6', 'Estrato 4', nan], dtype=object)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.unique(df[\"FAMI_ESTRATOVIVIENDA\"]) #Veamos los valores unicos presentes en la columna mencionada" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "sustituimos los nan que estan en la columna \"FAMI_ESTRATOVIVIENDA\" por cero de la siguiente forma:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "df[\"FAMI_ESTRATOVIVIENDA\"] = df[\"FAMI_ESTRATOVIVIENDA\"].fillna(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Vamos a pasar la variable categorica \"FAMI_ESTRATOVIVIENDA\" a numerica" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 2\n", - "1 3\n", - "2 1\n", - "3 7\n", - "4 5\n", - " ..\n", - "504867 2\n", - "504868 2\n", - "504869 2\n", - "504870 2\n", - "504871 3\n", - "Name: FAMI_ESTRATOVIVIENDA, Length: 504872, dtype: int64" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[\"FAMI_ESTRATOVIVIENDA\"].replace(['Estrato 1', 'Estrato 2', 'Estrato 3','Estrato 4','Estrato 5','Estrato 6','Sin Estrato'],[1, 2,3,4,5,6,7], inplace=True)\n", - "df.FAMI_ESTRATOVIVIENDA.astype(int)#despues de haber pasado mi variables categoricas a numerica, convierto esos numeros a enteros." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2, 3, 1, 7, 5, 6, 4, 0])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.unique(df[\"FAMI_ESTRATOVIVIENDA\"]) #verificamos que los nan toman valor 0" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.FAMI_ESTRATOVIVIENDA.isnull().sum() #verifico que los nan fueron sustituidos por cero" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "conteo=df.FAMI_ESTRATOVIVIENDA.value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<matplotlib.legend.Legend at 0x7f2d223d0fd0>" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.title(\"Porcentaje de poblacion estudiantil que pertenecen a los distintos Estratos\", fontsize=16, color=\"black\") \n", - "conteo.plot.pie(autopct='%1.2f%%')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<matplotlib.legend.Legend at 0x7f2d22346cc0>" - ] - }, - "execution_count": 27, - "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": [ - "plt.xlabel(\"Estratos\", fontsize = 14, color=\"black\") \n", - "plt.ylabel(\"Counts\", fontsize = 14, color=\"black\") \n", - "conteo.plot.bar(color=\"purple\")\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- como podemos notar el mayor porcentaje de la poblacion estudiantil se encuentra en el estrato 2 en comparacion con los demas estratos " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Relacion entre los Estratos y el Puntaje Matematico" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Matemáticas')" - ] - }, - "execution_count": 28, - "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_ESTRATOVIVIENDA\", y='PUNT_MATEMATICAS', data=df)\n", - "plt.title(\"Matemáticas\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Relacion entre los Estratos y el Puntaje de Ingles" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Ingles')" - ] - }, - "execution_count": 29, - "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_ESTRATOVIVIENDA\", y='PUNT_INGLES', data=df)\n", - "plt.title(\"Ingles\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Relacion entre los Estratos y el Puntaje_Lectura_Critica" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'LECTURA_CRITICA')" - ] - }, - "execution_count": 30, - "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_ESTRATOVIVIENDA\", y= 'PUNT_LECTURA_CRITICA', data=df)\n", - "plt.title('LECTURA_CRITICA')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Relacion entre los Estratos y el Puntaje de ciencias Naturales" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C_NATURALES')" - ] - }, - "execution_count": 31, - "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_ESTRATOVIVIENDA\", y= 'PUNT_C_NATURALES', data=df)\n", - "plt.title('C_NATURALES')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Relacion entre los Estratos y el Puntaje de Sociales y ciudadanas" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'SOCIALES_CIUDADANAS')" - ] - }, - "execution_count": 32, - "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_ESTRATOVIVIENDA\", y= 'PUNT_SOCIALES_CIUDADANAS', data=df)\n", - "plt.title('SOCIALES_CIUDADANAS')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Relación entre el Ãndice Socioeconómico del evaluado y los estratos" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<AxesSubplot:xlabel='FAMI_ESTRATOVIVIENDA', ylabel='ESTU_INSE_INDIVIDUAL'>" - ] - }, - "execution_count": 33, - "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_ESTRATOVIVIENDA\", y='ESTU_INSE_INDIVIDUAL', data = df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Relación entre el Puntaje Global y los estratos" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<AxesSubplot:xlabel='FAMI_ESTRATOVIVIENDA', ylabel='PUNT_GLOBAL'>" - ] - }, - "execution_count": 34, - "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_ESTRATOVIVIENDA\", y=\"PUNT_GLOBAL\", data = df)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<AxesSubplot:xlabel='FAMI_TIENEINTERNET', ylabel='FAMI_ESTRATOVIVIENDA'>" - ] - }, - "execution_count": 35, - "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_TIENEINTERNET', y=\"FAMI_ESTRATOVIVIENDA\", data = df)" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}