notebook_dataset_limpio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "En este paso filtramos el dataset de alumnos por el año 2016 y hacemos join con el dataset de escuelas.\n",
    "Luego eliminamos NULLs y grabamos el file df_final.csv. Pensando en predecir el campo nivel_desemp_matematica, \n",
    "lo transofrmamos en dos categorias, SATIFACTORIO con valor 0 (satifactorio y avanzado) y 1 BÁSICO (básico y por debajo de básico).\n",
    "Para ejecutar este paso deben tener en la carpeta /desafio3/datasets , los datasets app_alumno.csv y app_escuela.csv.\n",
    "\"\"\"\n",
    "\n",
    "import pandas as pd\n",
    "df = pd.read_csv('../desafio3/datasets/app_alumno.csv', parse_dates=True, na_values=['nc'], low_memory=False)\n",
    "df_escuelas = pd.read_csv('../desafio3/datasets/app_escuela.csv', parse_dates=True, na_values=['nc'], low_memory=False)\n",
    "df_escuelas = df_escuelas.drop('icse', axis=1)\n",
    "df_provincia = pd.read_csv('../desafio3/datasets/app_provincia.csv', parse_dates=True, na_values=['nc'], low_memory=False)\n",
    "df_departamento = pd.read_csv('../desafio3/datasets/app_departamento.csv', parse_dates=True, na_values=['nc'], low_memory=False)\n",
    "df_salario = pd.read_csv('../desafio3/datasets/app_salario.csv', parse_dates=True, na_values=['nc'], low_memory=False)\n",
    "df_gastonivel = pd.read_csv('../desafio3/datasets/app_gastonivel.csv', parse_dates=True, na_values=['nc'], low_memory=False)\n",
    "df_gastoobjeto = pd.read_csv('../desafio3/datasets/app_gastoobjeto.csv', parse_dates=True, na_values=['nc'], low_memory=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Nos quedamos con los casos sin nulos en desmpeño de matemática.\n",
    "#También nos quedamos con el año 2016 y los exámenes de primaria\n",
    "df = df[(df['nivel_desemp_matematica'].notnull()) & (df['year_id'] == 2016) & (df['nivel_id'] == 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Identificamos como avanzado y satisfactorio el desmpeño en matematica segun la nota\n",
    "df['nivel_desemp_matematica'] = df['nivel_desemp_matematica'].apply(lambda x: 0 if x in ['satisfactorio','avanzado'] else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Eliminamos columnas que no vamos a usar en el modelo y aquellas observaciones que tengan na\n",
    "df = df.drop(['id','repeticion_secundaria','nivel_desemp_ciencias_sociales','nivel_desemp_ciencias_naturales','year_id', 'nivel_id'], axis=1)\n",
    "df = df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#### SE AGREGA JOIN PARA AGREGAR AL DATAFRAME LA CANTIDAD DE ALUMNOS QUE TIENE CADA ESCUELA\n",
    "df_curso = pd.read_csv('../desafio3/datasets/app_curso.csv', parse_dates=True, na_values=['nc'], low_memory=False)\n",
    "df_curso = df_curso[df_curso['year_id'] == 2016]\n",
    "df_curso_gr = df_curso.groupby(by='escuela_id')['matricula'].sum()\n",
    "df_curso_gr = df_curso_gr.reset_index()\n",
    "df_escuelas = df_escuelas.rename(columns={'id': 'escuela_id'})\n",
    "df_escuelas = df_escuelas.merge(df_curso_gr, how='inner', on ='escuela_id')\n",
    "####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.merge(df_escuelas, how='inner', on ='escuela_id')\n",
    "df = df.drop('icse_cat', axis=1)\n",
    "df = df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_curso_prov = df_curso.merge(df_escuelas, how = 'inner', on ='escuela_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_curso_prov = df_curso_prov.groupby(by='provincia_id')['matricula_x'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_curso_prov = pd.DataFrame(df_curso_prov)\n",
    "df_curso_prov['id'] = df_curso_prov.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Limpieza del dataset de salario\n",
    "df_salario = df_salario[(df_salario['year_id'] == 2016)]\n",
    "df_salario = df_salario.groupby(['provincia_id', 'name'])['value'].aggregate('mean').unstack()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limpieza del dataset de gasto nivel\n",
    "df_gastonivel = df_gastonivel[(df_gastonivel['year_id'] == 2016)]\n",
    "df_gastonivel = df_gastonivel.groupby(['provincia_id', 'name'])['value'].aggregate('mean').unstack()\n",
    "df_gastonivel = df_gastonivel.merge(df_curso_prov, how='inner', left_on=['provincia_id'],right_on=['id'])\n",
    "df_gastonivel = df_gastonivel[['inicial+primaria', 'privada', 'secundaria', 'sin_discriminar', 'snu']].div(df_gastonivel.matricula_x, axis=0)\n",
    "df_gastonivel['provincia_id'] = df_gastonivel.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limpieza del dataset de gasto objeto\n",
    "df_gastoobjeto = df_gastoobjeto[(df_gastoobjeto['year_id'] == 2016)]\n",
    "df_gastoobjeto = df_gastoobjeto.groupby(['provincia_id', 'name'])['value'].aggregate('mean').unstack()\n",
    "df_gastoobjeto = df_gastoobjeto.merge(df_curso_prov, how='inner', left_on=['provincia_id'],right_on=['id'])\n",
    "df_gastoobjeto = df_gastoobjeto[['bienes_y_servicios_no_personales', 'erogacion_de_capital', 'personal', 'transferencias_educacion_privada', 'transferencias_otras']].div(df_gastoobjeto.matricula_x, axis=0)\n",
    "df_gastoobjeto['provincia_id'] = df_gastoobjeto.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Merge de los dataset limpios\n",
    "\n",
    "df = df.merge(df_salario, how = 'inner', on = 'provincia_id')\n",
    "df = df.merge(df_gastonivel, how = 'inner', on = 'provincia_id')\n",
    "df = df.merge(df_gastoobjeto, how = 'inner', on = 'provincia_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ponderador_mate = df.ponderador_matematica\n",
    "ponderador_lengua = df.ponderador_lengua\n",
    "df.drop(columns=['ponderador_lengua', 'ponderador_matematica','ponderador_ciencias_naturales', 'ponderador_ciencias_sociales'],inplace = True)\n",
    "df_ponderadores = pd.concat([ponderador_mate, ponderador_lengua], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#######SHAPE: \n",
      " (277573, 42) \n",
      " ####### \n",
      "\n",
      "#######VALUE_COUNTS: \n",
      " 0    170447\n",
      "1    107126\n",
      "Name: nivel_desemp_matematica, dtype: int64 \n",
      " ####### \n",
      "\n",
      "#######NULLs: \n",
      " sexo                                0.0\n",
      "indice_socioeconomico               0.0\n",
      "nivel_desemp_matematica             0.0\n",
      "nivel_desemp_lengua                 0.0\n",
      "tiene_notebook                      0.0\n",
      "tiene_pc                            0.0\n",
      "tiene_tablet                        0.0\n",
      "tiene_celular                       0.0\n",
      "tiene_smartphone                    0.0\n",
      "tiene_consola                       0.0\n",
      "tiene_smarttv                       0.0\n",
      "tiene_cable                         0.0\n",
      "tiene_internet_x                    0.0\n",
      "repeticion_primaria                 0.0\n",
      "escuela_id                          0.0\n",
      "ambito                              0.0\n",
      "gestion                             0.0\n",
      "indice_socioeconomico_medio         0.0\n",
      "indice_socioeconomico_medio_cat     0.0\n",
      "tecnica                             0.0\n",
      "tiene_internet_y                    0.0\n",
      "subvencion                          0.0\n",
      "dependencia                         0.0\n",
      "departamento_id                     0.0\n",
      "provincia_id                        0.0\n",
      "matricula                           0.0\n",
      "director_primaria                   0.0\n",
      "director_secundaria                 0.0\n",
      "maestro_de_grado                    0.0\n",
      "maestro_inicial                     0.0\n",
      "profesor_secundaria_15hs            0.0\n",
      "profesor_snu                        0.0\n",
      "inicial+primaria                    0.0\n",
      "privada                             0.0\n",
      "secundaria                          0.0\n",
      "sin_discriminar                     0.0\n",
      "snu                                 0.0\n",
      "bienes_y_servicios_no_personales    0.0\n",
      "erogacion_de_capital                0.0\n",
      "personal                            0.0\n",
      "transferencias_educacion_privada    0.0\n",
      "transferencias_otras                0.0\n",
      "dtype: float64 \n",
      " ####### \n",
      "\n",
      "#######BALANCE: \n",
      " 0    170447\n",
      "1    107126\n",
      "Name: nivel_desemp_matematica, dtype: int64 \n",
      " ####### \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('#######SHAPE:','\\n',df.shape,'\\n','#######','\\n')\n",
    "print('#######VALUE_COUNTS:','\\n',df['nivel_desemp_matematica'].value_counts(),'\\n','#######','\\n')\n",
    "print('#######NULLs:','\\n',(df.isnull().sum()/df.shape[0])*100,'\\n','#######','\\n')\n",
    "print('#######BALANCE:','\\n',df['nivel_desemp_matematica'].value_counts(),'\\n','#######','\\n')\n",
    "df.to_csv('../desafio3/datasets/df_final.csv')\n",
    "df_ponderadores.to_csv('../desafio3/datasets/df_ponderadores.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "C:\\Users\\gbulgach\\AppData\\Local\\Continuum\\anaconda3\\envs\\dhdsblend\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "http://github.com/matplotlib/matplotlib/blob/master/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "corr = df.corr()\n",
    "ax = sns.heatmap(\n",
    "    corr, \n",
    "    vmin=-1, vmax=1, center=0,\n",
    "    cmap=sns.diverging_palette(20, 220, n=200),\n",
    "    square=True\n",
    ")\n",
    "ax.set_xticklabels(\n",
    "    ax.get_xticklabels(),\n",
    "    rotation=45,\n",
    "    horizontalalignment='right'\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gbulgach\\AppData\\Local\\Continuum\\anaconda3\\envs\\dhdsblend\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\Users\\gbulgach\\AppData\\Local\\Continuum\\anaconda3\\envs\\dhdsblend\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\Users\\gbulgach\\AppData\\Local\\Continuum\\anaconda3\\envs\\dhdsblend\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\Users\\gbulgach\\AppData\\Local\\Continuum\\anaconda3\\envs\\dhdsblend\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\Users\\gbulgach\\AppData\\Local\\Continuum\\anaconda3\\envs\\dhdsblend\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\Users\\gbulgach\\AppData\\Local\\Continuum\\anaconda3\\envs\\dhdsblend\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "En este paso, levantamos el nuevo dataset y aplicamos transformaciones, one hot enconder para las columnas categoricas y scalar para las numericas. Al terminar las transformaciones aplicamos RFE para eliminar features, el resultado de este paso se guarda como matriz esparsa en un file.\n",
    "En el siguiente paso se debera levantar la X y Y de files como se indica a continaucion:\n",
    "\n",
    "y = pd.read_csv('../desafio3/datasets/df_final.csv', parse_dates=True, na_values=['nc'], low_memory=False)\n",
    "y = y['nivel_desemp_matematica']\n",
    "X = scipy.sparse.load_npz('../desafio3/datasets/sparse.npz')\n",
    "\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder \n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.pipeline import make_pipeline, make_union, Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFECV\n",
    "import scipy.sparse\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "\n",
    "df = pd.read_csv('../desafio3/datasets/df_final.csv', parse_dates=True, na_values=['nc'], low_memory=False)\n",
    "\n",
    "df = df.drop('Unnamed: 0', axis=1)\n",
    "for i in df.columns:\n",
    "    if (df[i].dtype == object) or i in ['nivel_id', 'escuela_id', 'provincia_id', 'departamento_id']:\n",
    "        df[i] = pd.Categorical(df[i])\n",
    "\n",
    "X = df.drop('nivel_desemp_matematica', axis=1)\n",
    "y = df['nivel_desemp_matematica']\n",
    "folds=StratifiedKFold(n_splits=5,shuffle=True, random_state=42)\n",
    "\n",
    "class ColumnSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, columns = 'n'):\n",
    "        self.columns = columns\n",
    "    \n",
    "    def transform(self, X, *_):\n",
    "        if isinstance(X, pd.DataFrame):\n",
    "            #c para tomar columnas categoricas, cualquier otro valor para tomar continuas.\n",
    "            if self.columns == 'c':\n",
    "                categorical_columns = [col for col in X.columns if pd.api.types.is_categorical_dtype(df[col])]\n",
    "                return pd.DataFrame(X[categorical_columns])\n",
    "            else:\n",
    "                scal_columns = [col for col in X.columns if (pd.api.types.is_categorical_dtype(df[col]) == False)]\n",
    "                return pd.DataFrame(X[scal_columns])\n",
    "        else:\n",
    "            raise TypeError(\"Este Transformador solo funciona en DF de Pandas\")\n",
    "    \n",
    "    def fit(self, X, *_):\n",
    "        return self\n",
    "cat_pipe = make_pipeline(ColumnSelector('c'),OneHotEncoder(handle_unknown='error',drop = 'first'))\n",
    "num_pipe = make_pipeline(ColumnSelector('n'),RobustScaler())\n",
    "union = make_union(num_pipe,\n",
    "                   cat_pipe)\n",
    "            \n",
    "lr = LogisticRegression()\n",
    "rfe = RFECV(lr,step=2000, cv=folds, n_jobs=5) \n",
    "df_union = rfe.fit_transform(union.fit_transform(X), y)\n",
    "scipy.sparse.save_npz('../desafio3/datasets/sparse.npz',df_union)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SHAPE MATRIZ ESPARZA:  (277573, 7014)\n"
     ]
    }
   ],
   "source": [
    "X = scipy.sparse.load_npz('../desafio3/datasets/sparse.npz')\n",
    "print(\"SHAPE MATRIZ ESPARZA: \",X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analisis descriptivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x23a85af6d88>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Diferencias de resultados en examenes de matematica y lengua por clase de escuela\n",
    "\n",
    "df['nivel_desemp_lengua'] = df['nivel_desemp_lengua'].apply(lambda x: 0 if x in ['satisfactorio','avanzado'] else 1)\n",
    "\n",
    "tabla_escuelas_matematica = df.groupby(['escuela_id'])['nivel_desemp_matematica'].mean()\n",
    "tabla_escuelas_lengua = df.groupby(['escuela_id'])['nivel_desemp_lengua'].mean()\n",
    "\n",
    "tabla_escuelas = pd.concat([tabla_escuelas_lengua, tabla_escuelas_matematica], axis = 1)\n",
    "\n",
    "tabla_escuelas = tabla_escuelas.merge(df_escuelas, how = 'inner', on = 'escuela_id')\n",
    "tabla_escuelas_privada = tabla_escuelas[(tabla_escuelas['gestion'] == 'privada')]\n",
    "tabla_escuelas_estatal = tabla_escuelas[(tabla_escuelas['gestion'] == 'estatal')]\n",
    "\n",
    "\n",
    "\n",
    "sns.jointplot('nivel_desemp_matematica', 'nivel_desemp_lengua', data=tabla_escuelas_privada, kind=\"kde\", xlim = [-0.2, 1.2], ylim = [-0.2, 1.2], space=0, color=\"b\")\n",
    "sns.jointplot('nivel_desemp_matematica', 'nivel_desemp_lengua', data=tabla_escuelas_estatal, kind=\"kde\", xlim = [-0.2, 1.2], ylim = [-0.2, 1.2], space=0, color=\"g\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x23a816f2488>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Diferencias de resultados en examenes de matematica y lengua por ambito de escuela\n",
    "\n",
    "tabla_escuelas_rural = tabla_escuelas[(tabla_escuelas['ambito'] == 'rural')]\n",
    "tabla_escuelas_urbano = tabla_escuelas[(tabla_escuelas['ambito'] == 'urbano')]\n",
    "\n",
    "sns.jointplot('nivel_desemp_matematica', 'nivel_desemp_lengua', data=tabla_escuelas_rural, kind=\"kde\", xlim = [-0.2, 1.2], ylim = [-0.2, 1.2], space=0, color=\"b\")\n",
    "sns.jointplot('nivel_desemp_matematica', 'nivel_desemp_lengua', data=tabla_escuelas_urbano, kind=\"kde\", xlim = [-0.2, 1.2], ylim = [-0.2, 1.2], space=0, color=\"g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 431.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 433.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Resultados por escuela por ambito y por gestion\n",
    "tabla_escuelas_2 = tabla_escuelas[(tabla_escuelas['gestion'] != 'social/cooperativa')]\n",
    "\n",
    "sns.catplot(x=\"gestion\", y=\"nivel_desemp_lengua\",\n",
    "            hue=\"ambito\",\n",
    "            data=tabla_escuelas_2, kind=\"violin\", split=True,\n",
    "            height=5, aspect=1);\n",
    "\n",
    "sns.catplot(x=\"ambito\", y=\"nivel_desemp_matematica\",\n",
    "            hue=\"gestion\",\n",
    "            data=tabla_escuelas_2, kind=\"violin\", split=True,\n",
    "            height=5, aspect=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 403.2x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 403.2x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Resultados por alumno en cada materia\n",
    "\n",
    "df.groupby(['indice_socioeconomico'])['nivel_desemp_matematica'].mean()\n",
    "\n",
    "df.indice_socioeconomico.value_counts()\n",
    "\n",
    "sns.catplot(x=\"indice_socioeconomico\", y=\"nivel_desemp_matematica\",\n",
    "            col=\"tiene_internet_x\",\n",
    "            data=df, kind=\"bar\",\n",
    "            height=4, aspect=.7);\n",
    "\n",
    "sns.catplot(x=\"indice_socioeconomico\", y=\"nivel_desemp_lengua\",\n",
    "            col=\"tiene_internet_x\",\n",
    "            data=df, kind=\"bar\",\n",
    "            height=4, aspect=.7);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Que provincia tiene mayor ponderacion de aprobados\n",
    "sns.catplot(x=\"provincia_id\", y=\"nivel_desemp_matematica\",\n",
    "            data=df, kind=\"bar\",\n",
    "            height=7, aspect=1.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Cantidad de Alumnos'), Text(0.5,0,'Desempeño Matemática')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZZQa82Eh6DfA54O0RcTTwJNmtumuA5Sm2HLgy7X8y2WudjwFOAxZIGpwOtwSYkdo0kb0VFLK3gW5N8fnAggG4NDMzq6IePZshwDBgeFrfRjbJ51TghhRrBs5My1OBZRHRERGPA63ASam30h4RrVXaNANExK3AFEkq75LMzKyWWjMIfLBWw4j4Xm9OGBG/lvQVYKOkG8m+HHouMD8ink77PJO7jTYOeDB3iDZgDNkXTp/sJt7ZJr9tJzAK2N6bnM3MrG9qDRA4O/18D7AG+ENafz0wGOhVsZE0gmwyz7cDx5LdLnsPaVbpnPxrDPZU2VYt3tO2zlxmAjMBxo8f30PmZmbWW7VmELgIQNI9EfHhzrikQ4Cb+nDO04CNEbGRrHezC5gF7JA0PCJ2pYLUOffaNrIpczo1pVi1eL7NE2l9JFnPp+s1LgIWAVQqla7FzszM+kmRZzaHSBqeW28HXtuHcz4CvDt3m6xCdpvsDl78/s50YGVaXgmcI+kASWOBycDaiNgCjJA0qUqb6QCSzgBaI2J3H3I2M7M+KPKlzi8CayQtJ5tB4L3Azb09YUTcI+lfgJ9L6gDuJbuV9SpgiaS5wFbSa6gjYrWkVcADQAcwKyJ2pcPNAJolDQNuA65P8auBxZI2kz2nOb+3+ZqZWd8V+VLn/5X0U7IRXgcBl0fEj/ty0ohYCCzsEn4WOL3K/vPJhjB3ja8Fju8m3k426MDMzPYBRXo2kPU0fgAIQNL4iPhVWUmZmVlj6bHYSPrfwFzgUGAz2YP4PwFHlJuamZk1iiIDBD4GHAk8EhHHAW8D7ik1KzMzayhFik1ExHPAU5LGRcRjwJtKzsvMzBpIkWc2f5Q0GlgG3CDpbmBHuWmZmVkjKTIa7a1pcaGkp8imfTmr1KzMzKyh1Job7bBuwr3+fo2Zme2/avVs1pHNVzYaeJrsC5UBjACeJ5tA08zMrEe15kabCCDp58A7I6IjrR9J9g19MzOzQoqMRjuss9AARMQjZC8yMzMzK6TIaLTbJX0X+HeyudHOBn5dalZmZtZQihSbS4G/BS4hmxuthRdnZzYzM+tRkaHPe8je+bKo/HTMzKwR1Rr6fHtEnCbpUfZ+iyYRcWSpmZmZWcOo1bP5aPpZGYhEzMyscVUdjRYRv02L346I3+U/wHf6clJJgyVdK+lhSb+W9GpJoyStkPRQ+nlYbv95kjZJ2iBpWi5ekbQ+tVkoaVCKHyxpaYqvkTSxL/mamVnfFBn6/JLbZWmetLdW2beorwPPAW8AxpN9afQaYHlEHA0sB65M5zsZmEY23Po0YIGkwek4S4AZqU0T8IEUnwNsTfH5wII+5mtmZn1QtdhI+kdJzwJvlLRT0rNpfSPwL709oaQxwDuAKyOH7E2gN6TdmoEz0/JUYFlEdETE40ArcFLqrbRHRGuVNs0AEXErMEWSepuzmZn1Ta3baF+IiEOAL0bEoRFxSPo0RcQ/9+Gcx5INOLgj3RpbImkYMCoink7nfgbovI02Dngy174NGFMj3l2bnWQTiJqZWR0UGfp8uaSTyP6AKxf/Xi/PeTjwEPBhsjnWrgE+x94j3obklvdU2VYt3tM2ACTNBGYCjB8/vqe8zcysl4q8Fnop8Hqy22ed09YE0NtiswN4LiL+mI7/X2TPWHZIGh4RuySNAJ5K+28jmwy0U1OKVYvn2zyR1keS9XxeIiJe+P5QpVLZa3i3mZn1jyIDBCoRcWJEXBARF6XPxX0458+AkyVNSOvTgLuBO3hxZoLpwMq0vBI4R9IBksYCk4G1EbEFGCFpUpU20wEknQG0RsTuPuRsZmZ9UGS6ms2SxkTEtp537VlE7JT0N8D306iyu8imwjkUWCJpLrAV+Ejaf7WkVcADZD2rWRGxKx1uBtCcnvncBlyf4lcDiyVtBrYD5/dH7mZm1jtFis04YKWkLflgRLy/tyeNiB+x9/DpNuD0KvvPJxvC3DW+Fji+m3g7cG5v8zMzs/5VpNh8qvQszMysoRUZjXbnQCRiZmaNq8cBApI+LOm/JT2fvtz5B0m7empnZmbWqchotM8CJwKbIuLQtHxjqVmZmVlDKVJsOifl3CXp1RFxPzCl3LTMzKyRFBkgIEmHAjcDiyTdSfbNfzMzs0KK9GzeBbQDnwd+STYL9AfLTMrMzBpLrVmfp0iakd5h83yadfnzwC/wpJZmZvYy1OrZzAce6yb+GPBP5aRjZmaNqFaxeVNE/KRrMCLWkL30zMzMrJBaxaajxrb2/k7EzMwaV61i81NJF3UNSvo42aSYZmZmhdQa+vwPwA8lTQfWpljnS9TeW3ZiZmbWOKoWm4h4CniHpFOBNwNDga8Ct0ZErVtsZmZmL1FkIs5VwKoByMXMzBpUoelqzMzM+qKuxUbSHEkb0vIoSSskPZR+Hpbbb56kTZI2SJqWi1ckrU9tFkoalOIHS1qa4mskTRz4qzMzs051KzaS3gn8dS50DbA8Io4GlgNXpv1OBqYBxwCnAQvS66QBlgAzUpsm4AMpPgfYmuLzgQXlXo2ZmdVSl2IjaTTwFeDjufBU4Ia03AycmYsvS9PlPA60Aiel3kp7RLRWadMMEBG3AlMkqazrMTOz2ga82KQ/+t8GLgOeyG0aFRFPA0TEM0DnbbRxwJO5/dqAMTXi3bXZSTfzuUmaKalFUktbW1uvr8nMzGqrR8/mH4A1EbG6Szy6rA/JLe+psq1avKdt2QkjFkVEJSIqTU1N1TM2M7M+KfI+m/42EThd0vnAYOB1kn4C7JA0PCJ2SRoBPJX23waMzrVvSrFq8Xybzp7TSLKej5mZ1cGA92wi4tKIeGNETCJ7trI5It4N3AGcl3abDqxMyyuBcyQdIGksMBlYGxFbgBGSJlVpMx1A0hlAa0TsLvvazMyse/Xo2VQzB1giaS6wFfgIQESslrSKbD62DmBWROxKbWYAzZKGAbcB16f41cBiSZuB7cD5A3YVZma2l7oWm4jYChybltuA06vsN59sCHPX+Frg+G7i7cC5/ZmrmZn1nmcQMDOz0rnYmJlZ6VxszMysdC42ZmZWOhcbMzMrnYuNmZmVzsXGzMxK52JjZmalc7ExM7PSudiYmVnpXGzMzKx0LjZmZlY6FxszMyudi42ZmZXOxcbMzEo34MVG0qsk/UjSFkkPSbo8xUdJWpFiKyQdlmszT9ImSRskTcvFK5LWpzYLJQ1K8YMlLU3xNZImDvR1mpnZi+rVs/liRBwFvBU4T9LxwDXA8og4GlgOXAkg6WRgGnAMcBqwQNLgdJwlwIzUpgn4QIrPAbam+HxgwYBclZmZdWvAi01E/CEibk/LvwceBl4DTAVuSLs1A2em5anAsojoiIjHgVbgpNRbaY+I1iptmtM5bgWmSFK5V2ZmZtXU9ZmNpNcAbwPuBkZFxNMAEfEM0HkbbRzwZK5ZGzCmRry7NjuBUf2dv5mZFVO3YiPpIGAZMC8Vmeiyy5Dc8p4q26rFe9rWmcNMSS2SWtra2oolbmZmL1tdio2kIcB/ArdExOIU3iFpeNo+AngqxbcBo3PNm1KsWry7NiPJej4vERGLIqISEZWmpqY+XZOZmVVXj9FoQ4GbgJ9ExBdym+4AzkvL04GVaXklcI6kAySNBSYDayNiCzBC0qQqbaan850BtEbE7rKuyczMajuwDuecApwCHCHpohRbTjaCbImkucBW4CMAEbFa0irgAaADmBURu1K7GUCzpGHAbcD1KX41sFjSZmA7cH7ZF2VmZtUNeLGJiNXAQVU2n16lzXyyIcxd42uB47uJtwPn9j5LMzPrT55BwMzMSudiY2ZmpXOxMTOz0rnYmJlZ6VxszMysdC42ZmZWOhcbMzMrnYuNmZmVzsXGzMxK52JjZmalc7ExM7PSudiYmVnpXGzMzKx0LjZmZlY6FxszMytdQxcbSWdK2iBpk6TL652Pmdn+qmGLTXp7578Cfwm8GZgmaXJ9szIz2z81bLEhe/30PRGxLSKeB24EzqxzTmZm+6VGLjbjgCdz623AmDrlYma2Xzuw3gmUbE+X9SH5FUkzgZlpdZekTQOS1f5hNLC93knsC/Slj9Y7Bdub/312+pz6eoQjiuzUyMVmG9k/qE5NKfaCiFgELBrIpPYXkloiolLvPMy643+fA6+Rb6PdDZwo6XBJBwIfAlbWOSczs/1Sw/ZsImKXpEuBVcBg4PqIuLPOaZmZ7ZcattgARMRNwE31zmM/5duTti/zv88Bpoiodw5mZtbgGvmZjZmZ7SNcbKzfeZog25dJmizpvnrnsb9xsbF+5WmCbF8m6Vrgdvy3b8D5F279zdME2T4rImYDJ9Q7j/2Ri431N08TZGZ7cbGxMtScJsjM9j8uNtbfepwmyMz2Py421t88TZCZ7cXFxvpVROwCOqcJegC43dME2b5C0lXAD4CjJLVI+ot657S/8AwCZmZWOvdszMysdC42ZmZWOhcbMzMrnYuNmZmVzsXGzMxK52JjZn0i6UpJX653HrZvc7GxVyxJeyRtlLRF0lpJ59U7pyIkDZL0Hyn3ByR97WW2v1LSHyWNycWuk7Srh3YTJP11b/OucszBwHuAebnYhZLG5dZXSnptf57XXnka+rXQ1vDaI+JNAJKOBhZLelVEfLvOedUUEXuAvv7Rfxj4BPA5ScOBPy/QZkI673/08dwviIjdwMldwhcCG4Dfpn2m9tf57JXLPRtrCBHxEDAd+GxnTNIVku6V9JCky1Ls+PTN8YfTtjen+N9JWp9e+LYgxU6R9EtJt0ranHoPn5V0fzrmW9J+iyX9p6RfSHpU0vQecrhQ0p3p82h6x0rn/p9MPZ6Nki6pcclLgQ9JGgKcD7xQYCWNlvRg7vORtOkLwCkpdmXB/B5OPamvpl7YvZ29lvS72JJ+ZyskHSrpf5C9ZuLGdJ7R6UV6E1Kbj6b1RyT9vxT7t3SezZKWpGmOrNFEhD/+vCI/wK5uYo8Ch5HNyXYtILJZp38BvBFYDnwg7TsBOAKoAM1kPf1BwH8BpwOnABuBscBg4DHg71PbjwGL0vJisj/kAsaT/Y/+oBo5XAjcCbwaeBXw32SvZjgeuBcYlj73Asd1c41XAp8GvghcAPw47b8rbR8CjEzLrwV+m5ZPAX6YO05P+Y0ADgV+D7w/tfkCcHlabsodaxHw8bS8Gqjktm1Iv+s3A6253Kaln4fn9l0BvLfe/7b86f+P/wdhjeq9ZAXjfWl9OHAk2R/wz0p6PXBbRNwn6XzgL8j+KAIMBSYCm4AtEfE4gKTHgDVpn83AWbnzrYvsr+WvJP2GrIhVywHg/ojYkY77CFlBexfw/Yh4LsW/T1Ygqr3C+OvAXcDSiHhOUmd8D/ApSe9L56z2PqGe8nsm5dHW5bo7b9mNl7Qw/a7GkBXNWt4DfC8ingaIiFtSfLKkS3PHeU0Px7FXIBcbaxiS/gzYHRFPSRoEzImIG7rsdouk24CpwPclfYKsN/PViPhil+Od0qXtni7LontDyHoD3eYg6cIu+3dUOZaAqpMXRsRjkpYD13XZ9I/AccCZEbG9xsCBovntdd2SDgZuBf6KrBDNJitWtex1jen22rfJCt464Gvd7WevfH5mYw1B0lHAEmB+Ct0OfDI9PO98VjNW0vuB9RHxebLbZW8AfgRcLOnwzmOlAQcvx8GpbYXsP3G/qZZDjWP8GHi/pKGShgJnp1hVETErIh7pEh4F3JkKzVt48eV1O4DXKelFfnmvAp4nu/V2AFlx67SD7HYiynW3gJ8AH5Q0Im17NzCS7He1jqxHOang+e0Vxj0beyUbKulBsucpbcC1EbEMICKWpoLRIukgsls855I9vF4oqZ3sltAVEfGssu+JrE77PkP23OLlmCPpM8AfgY+mW2rVcuhWRKyXtBi4J4Wui4hfvsw8IOvpfC8NMLibrOcE2e24x9LnWxHxuZeTX5dcd0j6JtnvcDvwBNBZ9L4OfFPSPwPv6HJ91wFrUxFaHxHnSbo/tX08l6s1GL9iwKyPUoH4YUTcWO9czPZVvo1mZmalc8/GzMxK556NmZmVzsXGzMxK52JjZmalc7ExM7PSudiYmVnpXGzMzKx0/x+cs5wyZBK/kAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Porcentaje de satisfactorio\n",
    "ax = sns.countplot(x=\"nivel_desemp_matematica\", data=df)\n",
    "ax.set(xlabel='Desempeño Matemática', ylabel='Cantidad de Alumnos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}