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Parcial 1 Alexandra.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": " Parcial 1 Alexandra.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/PandoraRiot/7ac4ce6f456cac022522516e68780882/-parcial-1-alexandra.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Parcial #1: Seminario ciencia de los Datos" | |
| ], | |
| "metadata": { | |
| "id": "lGysk8tJfCg9" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# (50%) Base de datos enfermedades cardiacas \n", | |
| "\n", | |
| "La siguiente base de datos **heart-disease-dataset.txt** contiene información clinica de 303 pacientes, cada uno de ellos descrito por 14 variables. El principal objetivo de la base de datos es que a partir de las variables dadas por cada paciente, diagnosticar la presencia de alguna enfermedad cardiaca (valores 1,2,3,4) o por el contrario si no presenta ninguna enfermedad (valor 0) (**ver columna 14**).\n", | |
| "\n", | |
| "\n", | |
| "Variables:\n", | |
| "\n", | |
| "1.(age) \n", | |
| "2.(sex) \n", | |
| "3.(cp) Tipo de dolor en el pecho \n", | |
| "4.(trestbps) Presión arterial en reposo (en mm Hg al ingreso al\n", | |
| " hospital)\n", | |
| "5.(chol) Colestoral sérico en mg/dl\n", | |
| "6.(fbs) Azúcar en sangre en ayunas > 120 mg/dl) (1 = verdadero; 0 = falso)\n", | |
| "\n", | |
| "7.(restecg) Resultados electrocardiográficos en reposo\n", | |
| "8.(thalach) Frecuencia cardíaca máxima alcanzada\n", | |
| "\n", | |
| "9.(exang) angina inducida por el ejercicio (1 = sí; 0 = no)\n", | |
| "\n", | |
| "10.(oldpeak) Depresión del ST inducida por el ejercicio en relación con el reposo\n", | |
| "\n", | |
| "\n", | |
| "11.(slope) Depresión del ST inducida por el ejercicio en relación con el reposo pendiente: la pendiente del segmento ST del ejercicio máximo -- Valor 1: ascendente -- Valor 2: plano -- Valor 3: descendente \n", | |
| "\n", | |
| "12.(ca) número de vasos principales (0-3) coloreados por fluoroscopia\n", | |
| " \n", | |
| "13.(thal) 3 = normales; 6 = defecto fijo; 7 = defecto reversible\n", | |
| "\n", | |
| "14.(diagnosis) diagnóstico de enfermedades del corazón. El valor 0 no es presencia de cardiopatía y los valores 1,2,3,4 indican presencia de cardiopatía.\n", | |
| "\n", | |
| " \n", | |
| "Información complementaria de las variables:\n", | |
| "\n", | |
| "\n", | |
| "* age: age in years\n", | |
| "* sex: (1 = male; 0 = female)\n", | |
| "* cp: chest pain type\n", | |
| " * Value 1: typical angina\n", | |
| " * Value 2: atypical angina\n", | |
| " * Value 3: non-anginal pain\n", | |
| " * Value 4: asymptomatic\n", | |
| "\n", | |
| "* trestbps: resting blood pressure (in mm Hg on admission to the \n", | |
| " hospital)\n", | |
| "* chol: serum cholestoral in mg/dl\n", | |
| "* fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n", | |
| "* restecg: resting electrocardiographic results\n", | |
| " * Value 0: normal\n", | |
| " * Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n", | |
| " * Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\n", | |
| "\n", | |
| "* thalach: maximum heart rate achieved\n", | |
| "* exang: exercise induced angina (1 = yes; 0 = no)\n", | |
| "* oldpeak = ST depression induced by exercise relative to rest\n", | |
| "* slope: the slope of the peak exercise ST segment\n", | |
| " -- Value 1: upsloping\n", | |
| " -- Value 2: flat\n", | |
| " -- Value 3: downsloping\n", | |
| "* ca: number of major vessels (0-3) colored by flourosopy\n", | |
| "* thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n", | |
| "* diagnosis: diagnosis of heart disease. Value 0 not heart disease presence and values 1,2,3,4 indicates the presence of heart disease.\n", | |
| "\n", | |
| "Teniendo la información dada hacer lo siguiente.\n", | |
| "\n", | |
| "1. Estime cuantos pacientes pertenecen al valor 0,1,2,3,4 teniendo en cuenta la columna 14.\n", | |
| "\n", | |
| "2. Realizar un diagrama box plot por cada una de las primeras 7 variables. **Importante**: Cada diagrama debe de contener la caja para cada tipo de enfermedad cardiaca (valores 0,1,2,3,4). Por ejemplo: Supongamos que existe una variable llamadas presión arterial sistólica, y quiero graficar el boxplot de los grupos del 1 al 4. Entonces deberia de verse algo asi:  \n", | |
| "Tenga en cuenta que se esta pidiendo es el comportamiento de los grupos 0 al 4 y que lo anterior es un ejemplo de lo que se espera ver en terminos de visualización. Ademas, es super importante que en cada grafica se especifique a que corresponde cada eje. Graficas sin etiquetas no se tendran en cuenta en la nota final.\n", | |
| "\n", | |
| "3. Seleccione 3 variables que usted considere revelantes bajo algun criterio y grafique un scatterplot 3D donde por cada punto en el espacio se pueda identificar con un color a que clase pertenece. En el siguiente ejemplo se grafican 3 variables y tres clases. La idea es que en total en el espacio 3D haya 5 colores dado que son 5 grupos de pacientes que estamos analizando (ver columna 14 - diagnostico). Es importante que busquen a ensayo error esas tres variables que les de un conocimeinto sobre lo que se esta analizando, que es la presencia de enfermedades cardiacas \n", | |
| "\n", | |
| "4. ¿Que puede concluir de los analisis anteriores?\n" | |
| ], | |
| "metadata": { | |
| "id": "PGsoLhQ4XA8N" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from google.colab import drive\n", | |
| "drive.mount('/content/drive')" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "FtaM0t4Vwmmn", | |
| "outputId": "364e027c-87c5-44b7-da3f-37dea707357c" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "metadata": { | |
| "tags": null | |
| }, | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Mounted at /content/drive\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "XtlQDQFOVG23" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import seaborn as sns\n", | |
| "import numpy as np\n", | |
| "from mpl_toolkits.mplot3d import axes3d\n", | |
| "import plotly.express as px\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "5dUNJB9ZbsXF" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# **Desarrollo Punto 1**" | |
| ], | |
| "metadata": { | |
| "id": "f0-rQu08pror" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df = pd.read_csv('heart-disease-dataset.txt')\n", | |
| "df.head(10)\n", | |
| "df\n", | |
| "#A continuación renombraré los valores con sus respectivas variables\n", | |
| "df.set_axis(['age', 'sex', 'cp', 'tretbps','chol','fbs','restecg','thalach','exang','oldpeak','slope','ca','thal','diagnosis'], axis=1)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 423 | |
| }, | |
| "id": "86Aqc9FcXqnm", | |
| "outputId": "cf8a18e9-fc6b-4f37-b241-01308e532edd" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " age sex cp tretbps chol fbs restecg thalach exang oldpeak \\\n", | |
| "0 67.0 1.0 4.0 160.0 286.0 0.0 2.0 108.0 1.0 1.5 \n", | |
| "1 67.0 1.0 4.0 120.0 229.0 0.0 2.0 129.0 1.0 2.6 \n", | |
| "2 37.0 1.0 3.0 130.0 250.0 0.0 0.0 187.0 0.0 3.5 \n", | |
| "3 41.0 0.0 2.0 130.0 204.0 0.0 2.0 172.0 0.0 1.4 \n", | |
| "4 56.0 1.0 2.0 120.0 236.0 0.0 0.0 178.0 0.0 0.8 \n", | |
| ".. ... ... ... ... ... ... ... ... ... ... \n", | |
| "297 45.0 1.0 1.0 110.0 264.0 0.0 0.0 132.0 0.0 1.2 \n", | |
| "298 68.0 1.0 4.0 144.0 193.0 1.0 0.0 141.0 0.0 3.4 \n", | |
| "299 57.0 1.0 4.0 130.0 131.0 0.0 0.0 115.0 1.0 1.2 \n", | |
| "300 57.0 0.0 2.0 130.0 236.0 0.0 2.0 174.0 0.0 0.0 \n", | |
| "301 38.0 1.0 3.0 138.0 175.0 0.0 0.0 173.0 0.0 0.0 \n", | |
| "\n", | |
| " slope ca thal diagnosis \n", | |
| "0 2.0 3.0 3.0 2 \n", | |
| "1 2.0 2.0 7.0 1 \n", | |
| "2 3.0 0.0 3.0 0 \n", | |
| "3 1.0 0.0 3.0 0 \n", | |
| "4 1.0 0.0 3.0 0 \n", | |
| ".. ... ... ... ... \n", | |
| "297 2.0 0.0 7.0 1 \n", | |
| "298 2.0 2.0 7.0 2 \n", | |
| "299 2.0 1.0 7.0 3 \n", | |
| "300 2.0 1.0 3.0 1 \n", | |
| "301 1.0 ? 3.0 0 \n", | |
| "\n", | |
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| " <th></th>\n", | |
| " <th>age</th>\n", | |
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| " <th>0</th>\n", | |
| " <td>67.0</td>\n", | |
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| " .colab-df-container {\n", | |
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| " border-radius: 50%;\n", | |
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| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
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| " background-color: #3B4455;\n", | |
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| " buttonEl.style.display =\n", | |
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| "\n", | |
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| " const element = document.querySelector('#df-f2a3fabb-da5f-4b03-bd5b-07638d67f45f');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
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| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
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| " " | |
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| "metadata": {}, | |
| "execution_count": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.columns = ['age', 'sex', 'cp', 'tretbps','chol','fbs','restecg','thalach','exang','oldpeak','slope','ca','thal','diagnosis']\n", | |
| "df.columns\n", | |
| "#Aquí estoy cambiando el nombre de las columnnas " | |
| ], | |
| "metadata": { | |
| "id": "_9PrVIljbxnF", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "91e5a679-59f1-457f-fa80-5a9581d63755" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Index(['age', 'sex', 'cp', 'tretbps', 'chol', 'fbs', 'restecg', 'thalach',\n", | |
| " 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'diagnosis'],\n", | |
| " dtype='object')" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.describe()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 364 | |
| }, | |
| "id": "IzBEiJTEz24s", | |
| "outputId": "f1153a5c-f25c-4eaf-c748-947d5ad0fb74" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " age sex cp tretbps chol fbs \\\n", | |
| "count 302.000000 302.000000 302.000000 302.000000 302.000000 302.000000 \n", | |
| "mean 54.410596 0.678808 3.165563 131.645695 246.738411 0.145695 \n", | |
| "std 9.040163 0.467709 0.953612 17.612202 51.856829 0.353386 \n", | |
| "min 29.000000 0.000000 1.000000 94.000000 126.000000 0.000000 \n", | |
| "25% 48.000000 0.000000 3.000000 120.000000 211.000000 0.000000 \n", | |
| "50% 55.500000 1.000000 3.000000 130.000000 241.500000 0.000000 \n", | |
| "75% 61.000000 1.000000 4.000000 140.000000 275.000000 0.000000 \n", | |
| "max 77.000000 1.000000 4.000000 200.000000 564.000000 1.000000 \n", | |
| "\n", | |
| " restecg thalach exang oldpeak slope diagnosis \n", | |
| "count 302.000000 302.000000 302.000000 302.000000 302.000000 302.000000 \n", | |
| "mean 0.986755 149.605960 0.327815 1.035430 1.596026 0.940397 \n", | |
| "std 0.994916 22.912959 0.470196 1.160723 0.611939 1.229384 \n", | |
| "min 0.000000 71.000000 0.000000 0.000000 1.000000 0.000000 \n", | |
| "25% 0.000000 133.250000 0.000000 0.000000 1.000000 0.000000 \n", | |
| "50% 0.500000 153.000000 0.000000 0.800000 2.000000 0.000000 \n", | |
| "75% 2.000000 166.000000 1.000000 1.600000 2.000000 2.000000 \n", | |
| "max 2.000000 202.000000 1.000000 6.200000 3.000000 4.000000 " | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-6a1aad2f-e2d8-4307-a9d3-81762785e4f9\">\n", | |
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| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>age</th>\n", | |
| " <th>sex</th>\n", | |
| " <th>cp</th>\n", | |
| " <th>tretbps</th>\n", | |
| " <th>chol</th>\n", | |
| " <th>fbs</th>\n", | |
| " <th>restecg</th>\n", | |
| " <th>thalach</th>\n", | |
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| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " <td>302.000000</td>\n", | |
| " </tr>\n", | |
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| " <th>mean</th>\n", | |
| " <td>54.410596</td>\n", | |
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| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>9.040163</td>\n", | |
| " <td>0.467709</td>\n", | |
| " <td>0.953612</td>\n", | |
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| " <td>0.611939</td>\n", | |
| " <td>1.229384</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>29.000000</td>\n", | |
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| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>48.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>120.000000</td>\n", | |
| " <td>211.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>133.250000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
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| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>55.500000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>130.000000</td>\n", | |
| " <td>241.500000</td>\n", | |
| " <td>0.000000</td>\n", | |
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| " <td>153.000000</td>\n", | |
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| " <td>0.800000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
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| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>61.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>140.000000</td>\n", | |
| " <td>275.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>166.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.600000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>77.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>200.000000</td>\n", | |
| " <td>564.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>202.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>6.200000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
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| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
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| " + ' to learn more about interactive tables.';\n", | |
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| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 107 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df['age'].mean" | |
| ], | |
| "metadata": { | |
| "id": "BV3t7JBJbx4F", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "29d4d3bf-e766-49ee-8248-f7b410552e2e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<bound method NDFrame._add_numeric_operations.<locals>.mean of 0 67.0\n", | |
| "1 67.0\n", | |
| "2 37.0\n", | |
| "3 41.0\n", | |
| "4 56.0\n", | |
| " ... \n", | |
| "297 45.0\n", | |
| "298 68.0\n", | |
| "299 57.0\n", | |
| "300 57.0\n", | |
| "301 38.0\n", | |
| "Name: age, Length: 302, dtype: float64>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 105 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df['sex'].count()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "FsXY9qarxfup", | |
| "outputId": "a8eefacc-9b7f-4460-aa41-4d99d2b1b59f" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "302" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 96 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "subjects = ['healthy','typical angina', 'atypical angina', 'non-anginal pain','asymptomatic']\n", | |
| "pd.Series(subjects)" | |
| ], | |
| "metadata": { | |
| "id": "JeQEZLT2Qgmn", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "20491057-6af4-494a-9c73-afb509ed4bcf" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0 healthy\n", | |
| "1 typical angina\n", | |
| "2 atypical angina\n", | |
| "3 non-anginal pain\n", | |
| "4 asymptomatic\n", | |
| "dtype: object" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "diagnosis = {'healthy':0,'typical angina':1,'atypical angina':2, 'non-anginal pain':3,' asymptomatic':4}\n", | |
| "d = pd.Series(subjects)\n", | |
| "d\n", | |
| "#No sé cómo sacar el punto 1 por aquí, así que lo haré por DATAFRAME " | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "SW0l69dETLAh", | |
| "outputId": "a2e03b51-af32-4c30-992d-e0c1466d51e0" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0 healthy\n", | |
| "1 typical angina\n", | |
| "2 atypical angina\n", | |
| "3 non-anginal pain\n", | |
| "4 asymptomatic\n", | |
| "dtype: object" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 10 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df['diagnosis'].unique()\n", | |
| "#Aquí me permito saber los valores que hay dentro de la columna diagnosis" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "kMl9FxRmoyqs", | |
| "outputId": "ae736f1a-8bce-451d-a273-e141ab2be9f9" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([2, 1, 0, 3, 4])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 11 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df['diagnosis'].value_counts()\n", | |
| "#Aquí resuelvo el punto 1. Dónde me piden saber cuántos pacientes hay por cada diagnóstico\n", | |
| "#0 = healthy, 1 = typical angina, 2 = atypical angina, 3 = non anginal pain, 4 = asymptomatic" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "XuuF_EfwpKYq", | |
| "outputId": "242ce050-eb9c-4e55-eb35-8aae6d677273" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0 163\n", | |
| "1 55\n", | |
| "2 36\n", | |
| "3 35\n", | |
| "4 13\n", | |
| "Name: diagnosis, dtype: int64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 12 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# **Desarrollo Punto 2**\n", | |
| "BOX PLOT" | |
| ], | |
| "metadata": { | |
| "id": "djCmgJr4plAD" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install pydataset" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "wLRJxcmurHFF", | |
| "outputId": "42e1407c-450d-4cca-b98b-9469508a03b5" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting pydataset\n", | |
| " Downloading pydataset-0.2.0.tar.gz (15.9 MB)\n", | |
| "\u001b[K |████████████████████████████████| 15.9 MB 13.1 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pydataset) (1.3.5)\n", | |
| "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pydataset) (2.8.2)\n", | |
| "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pydataset) (1.21.5)\n", | |
| "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pydataset) (2018.9)\n", | |
| "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->pydataset) (1.15.0)\n", | |
| "Building wheels for collected packages: pydataset\n", | |
| " Building wheel for pydataset (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for pydataset: filename=pydataset-0.2.0-py3-none-any.whl size=15939432 sha256=2efb5804a3c04a2bfab37ccf8676086a2164740d0dbeeef42f2df3aba3bc0a32\n", | |
| " Stored in directory: /root/.cache/pip/wheels/32/26/30/d71562a19eed948eaada9a61b4d722fa358657a3bfb5d151e2\n", | |
| "Successfully built pydataset\n", | |
| "Installing collected packages: pydataset\n", | |
| "Successfully installed pydataset-0.2.0\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "from pydataset import data \n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "1_FEYYtkqO7D", | |
| "outputId": "d18b3b42-29b9-4969-d49f-6e0c22638abc" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "initiated datasets repo at: /root/.pydataset/\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "%matplotlib inline\n", | |
| "import numpy as np" | |
| ], | |
| "metadata": { | |
| "id": "dwhxSamEqO4s" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df['age'].plot.box()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 282 | |
| }, | |
| "id": "1aMYFScOqO0T", | |
| "outputId": "a6f575e5-096e-4e63-c879-db2d87395c5a" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7fae7a59be50>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 16 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": "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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import seaborn as sns " | |
| ], | |
| "metadata": { | |
| "id": "8eTh-uLrVQBM" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['diagnosis'], \n", | |
| " y = df['age'], \n", | |
| " hue = df['sex']);\n", | |
| " #Combinación Edad y sexo con diagnostico" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "OooVecZMqOr3", | |
| "outputId": "004925f5-ec72-4f1e-ec37-cff18300aa71" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['diagnosis'], \n", | |
| " y = df['age'], \n", | |
| " );\n", | |
| " #Diagnostico y edad" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "llPzIoX9qOpc", | |
| "outputId": "a2fe705c-1c23-4e9a-8d27-4e655ff783da" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['slope'], \n", | |
| " y = df['diagnosis'], \n", | |
| " hue = df['sex']);\n", | |
| "#Diagnóstico por sexo y por Depresión del ST inducida por el ejercicio en relación \n", | |
| "#con el reposo pendiente: la pendiente del segmento ST del ejercicio máximo \n", | |
| "#-- Valor 1: ascendente -- Valor 2: plano -- Valor 3: descendente" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "K_YaVojuV-7j", | |
| "outputId": "0394ebd1-5116-412a-a859-7e93365a6f5e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['diagnosis'], \n", | |
| " y = df['ca'], \n", | |
| " );\n", | |
| " #Diagnostico y número de vasos principales (0-3) coloreados por fluoroscopia\n", | |
| " \n", | |
| " " | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "9o4s17RIWzrz", | |
| "outputId": "f716dc3e-1278-4836-dea8-7889b3bc3679" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['diagnosis'], \n", | |
| " y = df['age'], \n", | |
| " hue = df['sex']);\n", | |
| " \n", | |
| "#Diagnostico por edad y por sexo" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "5uNDIhNGX73l", | |
| "outputId": "f3460722-cee3-4ae2-e57e-7451a8a4e92e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['diagnosis'], \n", | |
| " y = df['thal'], \n", | |
| " hue = df['sex']);\n", | |
| " #Diagnóstico por Sexo y por resultado exámenes\n", | |
| " #VALORES: #3 = normales; 6 = defecto fijo; 7 = defecto reversible\n", | |
| " " | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "_KnqpW4wYepk", | |
| "outputId": "e2b0ad96-39b7-41ec-9ed6-fffec21573b8" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['diagnosis'], \n", | |
| " y = df['thal'], \n", | |
| " );\n", | |
| " #Diagnóstico por valores 3,6,7" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "7C1ZGOkSZH1g", | |
| "outputId": "a2336086-8342-4523-96cb-03bec3b41e87" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['diagnosis'], \n", | |
| " y = df['chol'], \n", | |
| " hue = df['sex']);\n", | |
| " #Diagnostico colesterol por sexo y niveles:\n", | |
| " #Colestoral sérico en mg/dl 6." | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "ITDoVO5fZbTF", | |
| "outputId": "3ea1e283-c363-4c17-8b09-69b52a0e82f2" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['diagnosis'], \n", | |
| " y = df['age'], \n", | |
| " hue = df['fbs']);\n", | |
| " #Diagnóstico por edad y por azúcar en la sangre\n", | |
| " #Valores: Azúcar en sangre en ayunas > 120 mg/dl) (1 = verdadero; 0 = falso)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "IyadfzNuZ4sE", | |
| "outputId": "3544bf52-6a71-46e2-9fc9-b76c3f40ca65" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['diagnosis'], \n", | |
| " y = df['thalach'], \n", | |
| " hue = df['sex']);\n", | |
| " #Diagnóstico frecuencia cardíaca máxima alcanzada por sexo\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "0V_cXVfEaQTU", | |
| "outputId": "0f97a67d-7099-4d3e-a44a-ede39dff12fd" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sns.boxplot(x = df['diagnosis'], \n", | |
| " y = df['thalach'], \n", | |
| " );" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 279 | |
| }, | |
| "id": "8cV1XK2qat-t", | |
| "outputId": "685f912c-7203-447f-a496-2c9225c3fed9" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### **Desarrollo Punto 3**\n", | |
| "**`scatterplot 3D`**" | |
| ], | |
| "metadata": { | |
| "id": "MDYeN9ujbDLF" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def randrange(n, vmin, vmax):\n", | |
| "return (vmax - vmin)*np.random.rand(n) + vmin\n", | |
| "fig = plt.figure()\n", | |
| "ax = fig.add_subplot(projection='3d')\n", | |
| "n = 100\n" | |
| ], | |
| "metadata": { | |
| "id": "jjvNm6thc0AM" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import plotly.express as px\n", | |
| "import plotly.io as pio" | |
| ], | |
| "metadata": { | |
| "id": "OsyW7LyTnPNQ" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "fig = px.scatter_3d(\n", | |
| " data_frame=df,\n", | |
| " x='age',\n", | |
| " y='diagnosis', \n", | |
| " #Tipo de dolor en el pecho\n", | |
| " #0 healthy \n", | |
| " #1 typical angina, \n", | |
| " #2 atypical angina\n", | |
| " #3 non anginal pain\n", | |
| " # 4 asymptomatic\n", | |
| "\n", | |
| " z='thalach', #Frecuencia cardíaca máxima alcanzada\n", | |
| " color='diagnosis',\n", | |
| " color_discrete_sequence=['sex','age'],\n", | |
| " log_x=True,\n", | |
| " template='ggplot2',\n", | |
| " title='ScatterPlot enfermedades cardíacas',\n", | |
| " labels={'Diagnostico por edad':'Diagnóstico por Frecuencia cardíaca máxima alcanzada'},\n", | |
| " hover_name='diagnosis',\n", | |
| " height=500,\n", | |
| "\n", | |
| ")\n", | |
| "pio.show(fig)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 517 | |
| }, | |
| "id": "Xs094ej0cz95", | |
| "outputId": "c0e2e497-afd8-48f0-d3a2-a79211539175" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
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enfermedades card\\u00edacas\"},\"height\":500}, {\"responsive\": true} ).then(function(){\n", | |
| " \n", | |
| "var gd = document.getElementById('ed9a7e63-11de-4281-ace9-2b266d72734b');\n", | |
| "var x = new MutationObserver(function (mutations, observer) {{\n", | |
| " var display = window.getComputedStyle(gd).display;\n", | |
| " if (!display || display === 'none') {{\n", | |
| " console.log([gd, 'removed!']);\n", | |
| " Plotly.purge(gd);\n", | |
| " observer.disconnect();\n", | |
| " }}\n", | |
| "}});\n", | |
| "\n", | |
| "// Listen for the removal of the full notebook cells\n", | |
| "var notebookContainer = gd.closest('#notebook-container');\n", | |
| "if (notebookContainer) {{\n", | |
| " x.observe(notebookContainer, {childList: true});\n", | |
| "}}\n", | |
| "\n", | |
| "// Listen for the clearing of the current output cell\n", | |
| "var outputEl = gd.closest('.output');\n", | |
| "if (outputEl) {{\n", | |
| " x.observe(outputEl, {childList: true});\n", | |
| "}}\n", | |
| "\n", | |
| " }) }; </script> </div>\n", | |
| "</body>\n", | |
| "</html>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### **Desarrollo Punto 4**\n", | |
| "\n", | |
| "**Análisis**\n", | |
| "El total de pacientes es de 302. \n", | |
| "\n", | |
| "\n", | |
| "A continuación encontraremos el número de pacientes por caso de diagnístico. \n", | |
| "\n", | |
| "0 -163 pacientes (Saludable) \n", | |
| "1 -55 pacientes(Angina Típica)\n", | |
| "2 -36 pacientes(Angina Atípica\n", | |
| "3 -35 pacientes(Sin dolor de Angina)\n", | |
| "4 -13 pacientes (Asintomático) \n", | |
| "\n", | |
| "_______________________________________________\n", | |
| "Además se encuentra que la población con problemas cardíacos mayormente son hombres.\n", | |
| "______________________________________________\n", | |
| "Se encuentra que la edad promedio en la qué se presentan estas afecciones cardíacas es a los 54 años, la edad mínima es de 29 años y la máxima es de 77 años. \n", | |
| "_________________________________________________" | |
| ], | |
| "metadata": { | |
| "id": "LA8QmXMfu61i" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Nueva sección" | |
| ], | |
| "metadata": { | |
| "id": "ot_hSGCGNgNg" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "(30%) De acuerdo a la base de datos **grades.csv**, estimar: \n", | |
| "\n", | |
| "1. ¿Cual fue la hora promedio de envio de los examenes por cada estudiante?\n", | |
| "2. ¿Cual fue el top 5 de estudiantes que enviaron en promedio el examen mas temprano.Entendiendo temprano lo mas cercano a las 12:00 am.\n", | |
| "3. ¿Cual fue en total la hora promedio de envio de los examenes y la desviación estandar?.\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "iw925grpnqEH" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas as pd\n", | |
| "df = pd.read_csv('grades.csv')\n", | |
| "df.head()\n", | |
| "\n", | |
| "\n", | |
| "\"\"\"\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "DESARROLLE AQUI \n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\"\"\"" | |
| ], | |
| "metadata": { | |
| "id": "KEx5iPn1pVw_", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "outputId": "4c2bc04a-0255-4e50-8744-d67b562d53e6" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'\\n\\n\\n\\nDESARROLLE AQUI \\n\\n\\n\\n'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 109 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.columns" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "6MKEaz1j211l", | |
| "outputId": "4a1c2f65-917b-4398-ff47-91cc8ce43827" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Index(['student_id', 'assignment1_grade', 'assignment1_submission',\n", | |
| " 'assignment2_grade', 'assignment2_submission', 'assignment3_grade',\n", | |
| " 'assignment3_submission', 'assignment4_grade', 'assignment4_submission',\n", | |
| " 'assignment5_grade', 'assignment5_submission', 'assignment6_grade',\n", | |
| " 'assignment6_submission'],\n", | |
| " dtype='object')" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 111 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df['assignment1_submission'].count() #Numero alumnos" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "E4nZEiyn9IoE", | |
| "outputId": "d44b9b0d-a39f-451d-9c10-1abe7921bd55" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "2315" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 129 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "7NIIm6HV9LNe" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df2 = df.set_index('student_id')\n", | |
| "df2.head()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 562 | |
| }, | |
| "id": "FF03T2303glu", | |
| "outputId": "c8e71378-a6e2-46b3-e686-9714e131ba79" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " assignment1_grade \\\n", | |
| "student_id \n", | |
| "B73F2C11-70F0-E37D-8B10-1D20AFED50B1 92.733946 \n", | |
| "98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1 86.790821 \n", | |
| "D0F62040-CEB0-904C-F563-2F8620916C4E 85.512541 \n", | |
| "FFDF2B2C-F514-EF7F-6538-A6A53518E9DC 86.030665 \n", | |
| "5ECBEEB6-F1CE-80AE-3164-E45E99473FB4 64.813800 \n", | |
| "\n", | |
| " assignment1_submission \\\n", | |
| "student_id \n", | |
| "B73F2C11-70F0-E37D-8B10-1D20AFED50B1 2015-11-02 06:55:34.282000000 \n", | |
| "98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1 2015-11-29 14:57:44.429000000 \n", | |
| "D0F62040-CEB0-904C-F563-2F8620916C4E 2016-01-09 05:36:02.389000000 \n", | |
| "FFDF2B2C-F514-EF7F-6538-A6A53518E9DC 2016-04-30 06:50:39.801000000 \n", | |
| "5ECBEEB6-F1CE-80AE-3164-E45E99473FB4 2015-12-13 17:06:10.750000000 \n", | |
| "\n", | |
| " assignment2_grade \\\n", | |
| "student_id \n", | |
| "B73F2C11-70F0-E37D-8B10-1D20AFED50B1 83.030552 \n", | |
| "98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1 86.290821 \n", | |
| "D0F62040-CEB0-904C-F563-2F8620916C4E 85.512541 \n", | |
| "FFDF2B2C-F514-EF7F-6538-A6A53518E9DC 68.824532 \n", | |
| "5ECBEEB6-F1CE-80AE-3164-E45E99473FB4 51.491040 \n", | |
| "\n", | |
| " assignment2_submission \\\n", | |
| "student_id \n", | |
| "B73F2C11-70F0-E37D-8B10-1D20AFED50B1 2015-11-09 02:22:58.938000000 \n", | |
| "98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1 2015-12-06 17:41:18.449000000 \n", | |
| "D0F62040-CEB0-904C-F563-2F8620916C4E 2016-01-09 06:39:44.416000000 \n", | |
| "FFDF2B2C-F514-EF7F-6538-A6A53518E9DC 2016-04-30 17:20:38.727000000 \n", | |
| "5ECBEEB6-F1CE-80AE-3164-E45E99473FB4 2015-12-14 12:25:12.056000000 \n", | |
| "\n", | |
| " assignment3_grade \\\n", | |
| "student_id \n", | |
| "B73F2C11-70F0-E37D-8B10-1D20AFED50B1 67.164441 \n", | |
| "98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1 69.772657 \n", | |
| "D0F62040-CEB0-904C-F563-2F8620916C4E 68.410033 \n", | |
| "FFDF2B2C-F514-EF7F-6538-A6A53518E9DC 61.942079 \n", | |
| "5ECBEEB6-F1CE-80AE-3164-E45E99473FB4 41.932832 \n", | |
| "\n", | |
| " assignment3_submission \\\n", | |
| "student_id \n", | |
| "B73F2C11-70F0-E37D-8B10-1D20AFED50B1 2015-11-12 08:58:33.998000000 \n", | |
| "98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1 2015-12-10 08:54:55.904000000 \n", | |
| "D0F62040-CEB0-904C-F563-2F8620916C4E 2016-01-15 20:22:45.882000000 \n", | |
| "FFDF2B2C-F514-EF7F-6538-A6A53518E9DC 2016-05-12 07:47:16.326000000 \n", | |
| "5ECBEEB6-F1CE-80AE-3164-E45E99473FB4 2015-12-29 14:25:22.594000000 \n", | |
| "\n", | |
| " assignment4_grade \\\n", | |
| "student_id \n", | |
| "B73F2C11-70F0-E37D-8B10-1D20AFED50B1 53.011553 \n", | |
| "98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1 55.098125 \n", | |
| "D0F62040-CEB0-904C-F563-2F8620916C4E 54.728026 \n", | |
| "FFDF2B2C-F514-EF7F-6538-A6A53518E9DC 49.553663 \n", | |
| "5ECBEEB6-F1CE-80AE-3164-E45E99473FB4 36.929549 \n", | |
| "\n", | |
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| "0 2015-11-22 18:31:15.934000000 \n", | |
| "1 2015-12-21 17:07:24.275000000 \n", | |
| "2 2016-01-17 16:24:42.765000000 \n", | |
| "3 2016-05-26 08:09:12.058000000 \n", | |
| "4 2016-01-05 01:06:59.546000000 " | |
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| " <div id=\"df-babfd023-35af-4e3c-938d-202ea4f53502\">\n", | |
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| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>student_id</th>\n", | |
| " <th>assignment1_grade</th>\n", | |
| " <th>assignment1_submission</th>\n", | |
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| " <th>assignment6_grade</th>\n", | |
| " <th>assignment6_submission</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>63.636535</td>\n", | |
| " <td>92.733946</td>\n", | |
| " <td>2015-11-02 06:55:34.282000000</td>\n", | |
| " <td>83.030552</td>\n", | |
| " <td>2015-11-09 02:22:58.938000000</td>\n", | |
| " <td>67.164441</td>\n", | |
| " <td>2015-11-12 08:58:33.998000000</td>\n", | |
| " <td>53.011553</td>\n", | |
| " <td>2015-11-16 01:21:24.663000000</td>\n", | |
| " <td>47.710398</td>\n", | |
| " <td>2015-11-20 13:24:59.692000000</td>\n", | |
| " <td>38.168318</td>\n", | |
| " <td>2015-11-22 18:31:15.934000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>65.361703</td>\n", | |
| " <td>86.790821</td>\n", | |
| " <td>2015-11-29 14:57:44.429000000</td>\n", | |
| " <td>86.290821</td>\n", | |
| " <td>2015-12-06 17:41:18.449000000</td>\n", | |
| " <td>69.772657</td>\n", | |
| " <td>2015-12-10 08:54:55.904000000</td>\n", | |
| " <td>55.098125</td>\n", | |
| " <td>2015-12-13 17:32:30.941000000</td>\n", | |
| " <td>49.588313</td>\n", | |
| " <td>2015-12-19 23:26:39.285000000</td>\n", | |
| " <td>44.629482</td>\n", | |
| " <td>2015-12-21 17:07:24.275000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>64.624678</td>\n", | |
| " <td>85.512541</td>\n", | |
| " <td>2016-01-09 05:36:02.389000000</td>\n", | |
| " <td>85.512541</td>\n", | |
| " <td>2016-01-09 06:39:44.416000000</td>\n", | |
| " <td>68.410033</td>\n", | |
| " <td>2016-01-15 20:22:45.882000000</td>\n", | |
| " <td>54.728026</td>\n", | |
| " <td>2016-01-11 12:41:50.749000000</td>\n", | |
| " <td>49.255224</td>\n", | |
| " <td>2016-01-11 17:31:12.489000000</td>\n", | |
| " <td>44.329701</td>\n", | |
| " <td>2016-01-17 16:24:42.765000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>60.083816</td>\n", | |
| " <td>86.030665</td>\n", | |
| " <td>2016-04-30 06:50:39.801000000</td>\n", | |
| " <td>68.824532</td>\n", | |
| " <td>2016-04-30 17:20:38.727000000</td>\n", | |
| " <td>61.942079</td>\n", | |
| " <td>2016-05-12 07:47:16.326000000</td>\n", | |
| " <td>49.553663</td>\n", | |
| " <td>2016-05-07 16:09:20.485000000</td>\n", | |
| " <td>49.553663</td>\n", | |
| " <td>2016-05-24 12:51:18.016000000</td>\n", | |
| " <td>44.598297</td>\n", | |
| " <td>2016-05-26 08:09:12.058000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>43.606735</td>\n", | |
| " <td>64.813800</td>\n", | |
| " <td>2015-12-13 17:06:10.750000000</td>\n", | |
| " <td>51.491040</td>\n", | |
| " <td>2015-12-14 12:25:12.056000000</td>\n", | |
| " <td>41.932832</td>\n", | |
| " <td>2015-12-29 14:25:22.594000000</td>\n", | |
| " <td>36.929549</td>\n", | |
| " <td>2015-12-28 01:29:55.901000000</td>\n", | |
| " <td>33.236594</td>\n", | |
| " <td>2015-12-29 14:46:06.628000000</td>\n", | |
| " <td>33.236594</td>\n", | |
| " <td>2016-01-05 01:06:59.546000000</td>\n", | |
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| { | |
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| "source": [ | |
| "df['student_id'].mean()" | |
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| "output_type": "execute_result", | |
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| "58.09667041068115" | |
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| "df.describe()" | |
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| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " student_id assignment1_grade assignment2_grade assignment3_grade \\\n", | |
| "count 2315.000000 2315.000000 2315.000000 2315.000000 \n", | |
| "mean 58.096670 74.535732 66.849007 60.623197 \n", | |
| "std 14.058527 16.353252 15.959210 15.492469 \n", | |
| "min 11.076457 14.423297 12.980967 12.307682 \n", | |
| "25% 48.530166 63.670100 56.127794 49.866390 \n", | |
| "50% 59.435131 77.208365 68.142124 61.307206 \n", | |
| "75% 68.150145 87.502146 78.310880 71.292632 \n", | |
| "max 97.571739 100.695583 99.936206 99.655813 \n", | |
| "\n", | |
| " assignment4_grade assignment5_grade assignment6_grade \n", | |
| "count 2315.000000 2315.000000 2315.000000 \n", | |
| "mean 54.112112 48.618522 43.841452 \n", | |
| "std 14.687431 13.927054 13.259413 \n", | |
| "min 9.126146 8.213531 7.392178 \n", | |
| "25% 43.852636 38.859619 34.828619 \n", | |
| "50% 54.442888 48.681165 43.172442 \n", | |
| "75% 63.789234 57.662236 52.086086 \n", | |
| "max 98.755813 97.571739 97.571739 " | |
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| " <th></th>\n", | |
| " <th>student_id</th>\n", | |
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| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>2315.000000</td>\n", | |
| " <td>2315.000000</td>\n", | |
| " <td>2315.000000</td>\n", | |
| " <td>2315.000000</td>\n", | |
| " <td>2315.000000</td>\n", | |
| " <td>2315.000000</td>\n", | |
| " <td>2315.000000</td>\n", | |
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| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>58.096670</td>\n", | |
| " <td>74.535732</td>\n", | |
| " <td>66.849007</td>\n", | |
| " <td>60.623197</td>\n", | |
| " <td>54.112112</td>\n", | |
| " <td>48.618522</td>\n", | |
| " <td>43.841452</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>14.058527</td>\n", | |
| " <td>16.353252</td>\n", | |
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| " <td>15.492469</td>\n", | |
| " <td>14.687431</td>\n", | |
| " <td>13.927054</td>\n", | |
| " <td>13.259413</td>\n", | |
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| " <th>min</th>\n", | |
| " <td>11.076457</td>\n", | |
| " <td>14.423297</td>\n", | |
| " <td>12.980967</td>\n", | |
| " <td>12.307682</td>\n", | |
| " <td>9.126146</td>\n", | |
| " <td>8.213531</td>\n", | |
| " <td>7.392178</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>48.530166</td>\n", | |
| " <td>63.670100</td>\n", | |
| " <td>56.127794</td>\n", | |
| " <td>49.866390</td>\n", | |
| " <td>43.852636</td>\n", | |
| " <td>38.859619</td>\n", | |
| " <td>34.828619</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>59.435131</td>\n", | |
| " <td>77.208365</td>\n", | |
| " <td>68.142124</td>\n", | |
| " <td>61.307206</td>\n", | |
| " <td>54.442888</td>\n", | |
| " <td>48.681165</td>\n", | |
| " <td>43.172442</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>68.150145</td>\n", | |
| " <td>87.502146</td>\n", | |
| " <td>78.310880</td>\n", | |
| " <td>71.292632</td>\n", | |
| " <td>63.789234</td>\n", | |
| " <td>57.662236</td>\n", | |
| " <td>52.086086</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>97.571739</td>\n", | |
| " <td>100.695583</td>\n", | |
| " <td>99.936206</td>\n", | |
| " <td>99.655813</td>\n", | |
| " <td>98.755813</td>\n", | |
| " <td>97.571739</td>\n", | |
| " <td>97.571739</td>\n", | |
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| "\n", | |
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| "early = df[df['assignment1_submission'] <= '2015-12-31']\n", | |
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| "8 C9D51293-BD58-F113-4167-A7C0BAFCB6E5 66.595568 \n", | |
| "\n", | |
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| "4 2015-12-13 17:06:10.750000000 51.491040 \n", | |
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| " <th>0</th>\n", | |
| " <td>B73F2C11-70F0-E37D-8B10-1D20AFED50B1</td>\n", | |
| " <td>92.733946</td>\n", | |
| " <td>2015-11-02 06:55:34.282000000</td>\n", | |
| " <td>83.030552</td>\n", | |
| " <td>2015-11-09 02:22:58.938000000</td>\n", | |
| " <td>67.164441</td>\n", | |
| " <td>2015-11-12 08:58:33.998000000</td>\n", | |
| " <td>53.011553</td>\n", | |
| " <td>2015-11-16 01:21:24.663000000</td>\n", | |
| " <td>47.710398</td>\n", | |
| " <td>2015-11-20 13:24:59.692000000</td>\n", | |
| " <td>38.168318</td>\n", | |
| " <td>2015-11-22 18:31:15.934000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1</td>\n", | |
| " <td>86.790821</td>\n", | |
| " <td>2015-11-29 14:57:44.429000000</td>\n", | |
| " <td>86.290821</td>\n", | |
| " <td>2015-12-06 17:41:18.449000000</td>\n", | |
| " <td>69.772657</td>\n", | |
| " <td>2015-12-10 08:54:55.904000000</td>\n", | |
| " <td>55.098125</td>\n", | |
| " <td>2015-12-13 17:32:30.941000000</td>\n", | |
| " <td>49.588313</td>\n", | |
| " <td>2015-12-19 23:26:39.285000000</td>\n", | |
| " <td>44.629482</td>\n", | |
| " <td>2015-12-21 17:07:24.275000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>5ECBEEB6-F1CE-80AE-3164-E45E99473FB4</td>\n", | |
| " <td>64.813800</td>\n", | |
| " <td>2015-12-13 17:06:10.750000000</td>\n", | |
| " <td>51.491040</td>\n", | |
| " <td>2015-12-14 12:25:12.056000000</td>\n", | |
| " <td>41.932832</td>\n", | |
| " <td>2015-12-29 14:25:22.594000000</td>\n", | |
| " <td>36.929549</td>\n", | |
| " <td>2015-12-28 01:29:55.901000000</td>\n", | |
| " <td>33.236594</td>\n", | |
| " <td>2015-12-29 14:46:06.628000000</td>\n", | |
| " <td>33.236594</td>\n", | |
| " <td>2016-01-05 01:06:59.546000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>D09000A0-827B-C0FF-3433-BF8FF286E15B</td>\n", | |
| " <td>71.647278</td>\n", | |
| " <td>2015-12-28 04:35:32.836000000</td>\n", | |
| " <td>64.052550</td>\n", | |
| " <td>2016-01-03 21:05:38.392000000</td>\n", | |
| " <td>64.752550</td>\n", | |
| " <td>2016-01-07 08:55:43.692000000</td>\n", | |
| " <td>57.467295</td>\n", | |
| " <td>2016-01-11 00:45:28.706000000</td>\n", | |
| " <td>57.467295</td>\n", | |
| " <td>2016-01-11 00:54:13.579000000</td>\n", | |
| " <td>57.467295</td>\n", | |
| " <td>2016-01-20 19:54:46.166000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>C9D51293-BD58-F113-4167-A7C0BAFCB6E5</td>\n", | |
| " <td>66.595568</td>\n", | |
| " <td>2015-12-25 02:29:28.415000000</td>\n", | |
| " <td>52.916454</td>\n", | |
| " <td>2015-12-31 01:42:30.046000000</td>\n", | |
| " <td>48.344809</td>\n", | |
| " <td>2016-01-05 23:34:02.180000000</td>\n", | |
| " <td>47.444809</td>\n", | |
| " <td>2016-01-02 07:48:42.517000000</td>\n", | |
| " <td>37.955847</td>\n", | |
| " <td>2016-01-03 21:27:04.266000000</td>\n", | |
| " <td>37.955847</td>\n", | |
| " <td>2016-01-19 15:24:31.060000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-242c517a-5267-49cd-9cbd-85990b358098')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| " \n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", | |
| " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
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| " height: 32px;\n", | |
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| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
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| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-242c517a-5267-49cd-9cbd-85990b358098 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-242c517a-5267-49cd-9cbd-85990b358098');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
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| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 114 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "len(early)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "TVQV8Kdx4tR-", | |
| "outputId": "d083ae3c-1796-4f74-d03e-071ae2e48c0e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "1256" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 115 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "len(early)/len(df)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "MFT5U9ij4xZY", | |
| "outputId": "4612d3e2-ac8b-47be-e138-71ee6d8870c3" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.542548596112311" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 116 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "early.mean()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "FY561bTa48GQ", | |
| "outputId": "bff50bb0-84b0-4d79-8e50-7628c005e6c2" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning:\n", | |
| "\n", | |
| "Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "assignment1_grade 74.972741\n", | |
| "assignment2_grade 67.252190\n", | |
| "assignment3_grade 61.129050\n", | |
| "assignment4_grade 54.157620\n", | |
| "assignment5_grade 48.634643\n", | |
| "assignment6_grade 43.838980\n", | |
| "dtype: float64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 120 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# ¿Cuanto fue la hora promedio de envio de los examenes por cada estudiante? \n", | |
| "df['assignment1_submission'] = df.mean(axis=1)\n", | |
| "df.head()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 600 | |
| }, | |
| "id": "kw6_LC8v5On8", | |
| "outputId": "851e2cd8-1525-4f1e-c1d6-a7f05c805af5" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning:\n", | |
| "\n", | |
| "Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " student_id assignment1_grade \\\n", | |
| "0 B73F2C11-70F0-E37D-8B10-1D20AFED50B1 92.733946 \n", | |
| "1 98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1 86.790821 \n", | |
| "2 D0F62040-CEB0-904C-F563-2F8620916C4E 85.512541 \n", | |
| "3 FFDF2B2C-F514-EF7F-6538-A6A53518E9DC 86.030665 \n", | |
| "4 5ECBEEB6-F1CE-80AE-3164-E45E99473FB4 64.813800 \n", | |
| "\n", | |
| " assignment1_submission assignment2_grade assignment2_submission \\\n", | |
| "0 63.636535 83.030552 2015-11-09 02:22:58.938000000 \n", | |
| "1 65.361703 86.290821 2015-12-06 17:41:18.449000000 \n", | |
| "2 64.624678 85.512541 2016-01-09 06:39:44.416000000 \n", | |
| "3 60.083816 68.824532 2016-04-30 17:20:38.727000000 \n", | |
| "4 43.606735 51.491040 2015-12-14 12:25:12.056000000 \n", | |
| "\n", | |
| " assignment3_grade assignment3_submission assignment4_grade \\\n", | |
| "0 67.164441 2015-11-12 08:58:33.998000000 53.011553 \n", | |
| "1 69.772657 2015-12-10 08:54:55.904000000 55.098125 \n", | |
| "2 68.410033 2016-01-15 20:22:45.882000000 54.728026 \n", | |
| "3 61.942079 2016-05-12 07:47:16.326000000 49.553663 \n", | |
| "4 41.932832 2015-12-29 14:25:22.594000000 36.929549 \n", | |
| "\n", | |
| " assignment4_submission assignment5_grade \\\n", | |
| "0 2015-11-16 01:21:24.663000000 47.710398 \n", | |
| "1 2015-12-13 17:32:30.941000000 49.588313 \n", | |
| "2 2016-01-11 12:41:50.749000000 49.255224 \n", | |
| "3 2016-05-07 16:09:20.485000000 49.553663 \n", | |
| "4 2015-12-28 01:29:55.901000000 33.236594 \n", | |
| "\n", | |
| " assignment5_submission assignment6_grade \\\n", | |
| "0 2015-11-20 13:24:59.692000000 38.168318 \n", | |
| "1 2015-12-19 23:26:39.285000000 44.629482 \n", | |
| "2 2016-01-11 17:31:12.489000000 44.329701 \n", | |
| "3 2016-05-24 12:51:18.016000000 44.598297 \n", | |
| "4 2015-12-29 14:46:06.628000000 33.236594 \n", | |
| "\n", | |
| " assignment6_submission \n", | |
| "0 2015-11-22 18:31:15.934000000 \n", | |
| "1 2015-12-21 17:07:24.275000000 \n", | |
| "2 2016-01-17 16:24:42.765000000 \n", | |
| "3 2016-05-26 08:09:12.058000000 \n", | |
| "4 2016-01-05 01:06:59.546000000 " | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-528d7824-34af-486f-8b61-a6b9af3bf486\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <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", | |
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| "\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>student_id</th>\n", | |
| " <th>assignment1_grade</th>\n", | |
| " <th>assignment1_submission</th>\n", | |
| " <th>assignment2_grade</th>\n", | |
| " <th>assignment2_submission</th>\n", | |
| " <th>assignment3_grade</th>\n", | |
| " <th>assignment3_submission</th>\n", | |
| " <th>assignment4_grade</th>\n", | |
| " <th>assignment4_submission</th>\n", | |
| " <th>assignment5_grade</th>\n", | |
| " <th>assignment5_submission</th>\n", | |
| " <th>assignment6_grade</th>\n", | |
| " <th>assignment6_submission</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>B73F2C11-70F0-E37D-8B10-1D20AFED50B1</td>\n", | |
| " <td>92.733946</td>\n", | |
| " <td>63.636535</td>\n", | |
| " <td>83.030552</td>\n", | |
| " <td>2015-11-09 02:22:58.938000000</td>\n", | |
| " <td>67.164441</td>\n", | |
| " <td>2015-11-12 08:58:33.998000000</td>\n", | |
| " <td>53.011553</td>\n", | |
| " <td>2015-11-16 01:21:24.663000000</td>\n", | |
| " <td>47.710398</td>\n", | |
| " <td>2015-11-20 13:24:59.692000000</td>\n", | |
| " <td>38.168318</td>\n", | |
| " <td>2015-11-22 18:31:15.934000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1</td>\n", | |
| " <td>86.790821</td>\n", | |
| " <td>65.361703</td>\n", | |
| " <td>86.290821</td>\n", | |
| " <td>2015-12-06 17:41:18.449000000</td>\n", | |
| " <td>69.772657</td>\n", | |
| " <td>2015-12-10 08:54:55.904000000</td>\n", | |
| " <td>55.098125</td>\n", | |
| " <td>2015-12-13 17:32:30.941000000</td>\n", | |
| " <td>49.588313</td>\n", | |
| " <td>2015-12-19 23:26:39.285000000</td>\n", | |
| " <td>44.629482</td>\n", | |
| " <td>2015-12-21 17:07:24.275000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>D0F62040-CEB0-904C-F563-2F8620916C4E</td>\n", | |
| " <td>85.512541</td>\n", | |
| " <td>64.624678</td>\n", | |
| " <td>85.512541</td>\n", | |
| " <td>2016-01-09 06:39:44.416000000</td>\n", | |
| " <td>68.410033</td>\n", | |
| " <td>2016-01-15 20:22:45.882000000</td>\n", | |
| " <td>54.728026</td>\n", | |
| " <td>2016-01-11 12:41:50.749000000</td>\n", | |
| " <td>49.255224</td>\n", | |
| " <td>2016-01-11 17:31:12.489000000</td>\n", | |
| " <td>44.329701</td>\n", | |
| " <td>2016-01-17 16:24:42.765000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>FFDF2B2C-F514-EF7F-6538-A6A53518E9DC</td>\n", | |
| " <td>86.030665</td>\n", | |
| " <td>60.083816</td>\n", | |
| " <td>68.824532</td>\n", | |
| " <td>2016-04-30 17:20:38.727000000</td>\n", | |
| " <td>61.942079</td>\n", | |
| " <td>2016-05-12 07:47:16.326000000</td>\n", | |
| " <td>49.553663</td>\n", | |
| " <td>2016-05-07 16:09:20.485000000</td>\n", | |
| " <td>49.553663</td>\n", | |
| " <td>2016-05-24 12:51:18.016000000</td>\n", | |
| " <td>44.598297</td>\n", | |
| " <td>2016-05-26 08:09:12.058000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>5ECBEEB6-F1CE-80AE-3164-E45E99473FB4</td>\n", | |
| " <td>64.813800</td>\n", | |
| " <td>43.606735</td>\n", | |
| " <td>51.491040</td>\n", | |
| " <td>2015-12-14 12:25:12.056000000</td>\n", | |
| " <td>41.932832</td>\n", | |
| " <td>2015-12-29 14:25:22.594000000</td>\n", | |
| " <td>36.929549</td>\n", | |
| " <td>2015-12-28 01:29:55.901000000</td>\n", | |
| " <td>33.236594</td>\n", | |
| " <td>2015-12-29 14:46:06.628000000</td>\n", | |
| " <td>33.236594</td>\n", | |
| " <td>2016-01-05 01:06:59.546000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-528d7824-34af-486f-8b61-a6b9af3bf486')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| " \n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", | |
| " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-528d7824-34af-486f-8b61-a6b9af3bf486 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-528d7824-34af-486f-8b61-a6b9af3bf486');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 121 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df['assignment1_submission'].mean()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "MT4P31Qo5kSg", | |
| "outputId": "fb68980d-484a-490c-f22b-6849361b3c8b" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "58.09667041068115" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 122 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "seoecjcrrFov" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# (20%) Investigación\n", | |
| "\n", | |
| "1. Defina con sus palabras en que consiste el metodo **Term Frequency - \n", | |
| "Inverse Document Frequency (TF-IDF)** para el procesamiento de texto.\n", | |
| "\n", | |
| "TF-IDF: Permite determinar mediante un procesamiento de texto de manera estadística, un valor númerico que deja ver la importancia de una palabra clave dentro de un sitio web, dando así importancia y relevancia, entre mayor número de veces esta se repita en el sitio web. El significado de las siglas son los siguientes: \n", | |
| "Tf: Frecuencia de término \n", | |
| "IDF: Frecuencia inversa del documento\n", | |
| "\n", | |
| "Se usa para ranking de búsqueda en las páginas web, permite realizar resúmenes de textos, agrupación y clasificación de textos, autentificación de autoría de un texto y recomendación de documentos entre otros. \n", | |
| "\n", | |
| "\n", | |
| "2. Realice un ejemplo claro donde se pueda identificar la implementacion del metodo (TF-IDF) y el entendimiento del mismo.\n", | |
| "\n", | |
| "Lo visualizará a lo último. \n", | |
| "\n", | |
| "\n", | |
| "3. En que se diferencia el metodo (TF-IDF) vs CountVectorize visto en clase.\n", | |
| "\n", | |
| "En CountVectorizer solo contamos el número de veces que aparece una palabra en el documento, lo que da como resultado un sesgo a favor de las palabras más frecuentes. esto termina ignorando palabras raras que podrían haber ayudado a procesar nuestros datos de manera más eficiente. Para superar esto, usamos TfidfVectorizer.\n", | |
| "En TfidfVectorizer consideramos el peso total del documento de una palabra. Nos ayuda a lidiar con las palabras más frecuentes. Usándolo podemos penalizarlos. TfidfVectorizer pondera el número de palabras según la frecuencia con la que aparecen en los documentos." | |
| ], | |
| "metadata": { | |
| "id": "vNmc7EN7pw9A" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "BFLAD4W5gnSj" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Ejemplo punto 2. \n", | |
| "**Implementación del método TF-IDF**\n" | |
| ], | |
| "metadata": { | |
| "id": "OJvTbLHC6fcY" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# encoding=utf-8\n", | |
| "import jieba\n", | |
| "jieba.load_userdict(\"newDict.txt\") # Cargar diccionario definido por el usuario\n", | |
| "from sklearn.feature_extraction.text import TfidfVectorizer\n", | |
| "# print(dir(TfidfVectorizer))\n", | |
| "\n", | |
| "def cut(txt_name1, txt_name2):\n", | |
| " with open(txt_name1, 'r') as f1: # Abra el archivo como de solo lectura\n", | |
| " txt = f1.read()\n", | |
| " txt_encode = txt.encode('utf-8')\n", | |
| " txt_cut = jieba.cut(txt_encode) # Cortar palabras\n", | |
| " result = ' '.join(txt_cut)\n", | |
| " # print(result)\n", | |
| " with open(txt_name2, 'w') as f2: # El resultado de la segmentación de palabras se escribe en un archivo y se guarda\n", | |
| " f2.write(result)\n", | |
| " f1.close()\n", | |
| " f2.close()\n", | |
| "\n", | |
| "cut('nlp_test0.txt', 'nlp_test0_0.txt') # Separe las palabras llamando al método de corte en el archivo\n", | |
| "cut('nlp_test1.txt', 'nlp_test1_1.txt')\n", | |
| "\n", | |
| "# Lea la lista de palabras vacías del archivo y divídala en una matriz para su uso posterior\n", | |
| "stopWords_dic = open('stop_words.txt', 'r') # Leer palabras vacías del archivo\n", | |
| "stopWords_content = stopWords_dic.read()\n", | |
| "stopWords_list = stopWords_content.splitlines() # Convertir a lista en espera\n", | |
| "stopWords_dic.close()\n", | |
| "\n", | |
| "with open('nlp_test0_0.txt', 'r') as f3:\n", | |
| " res3 = f3.read()\n", | |
| "with open('nlp_test1_1.txt', 'r') as f4:\n", | |
| " res4 = f4.read()\n", | |
| "\n", | |
| "corpus = [res3, res4]\n", | |
| "# print(corpus)\n", | |
| "vector = TfidfVectorizer(stop_words=stopWords_list)\n", | |
| "tf_idf = vector.fit_transform(corpus)\n", | |
| "# print(tf_idf)\n", | |
| "\n", | |
| "word_list = vector.get_feature_names() # Obtén todas las palabras del modelo de bolsa de palabras\n", | |
| "weight_list = tf_idf.toarray()\n", | |
| "# result1 = ''.join(word_list)\n", | |
| "# result2 = ''.join(weight_list)\n", | |
| "# print(result1, result2)\n", | |
| "# with open('words_list.txt', 'w') as f3:\n", | |
| "# f3.write(result)\n", | |
| "\n", | |
| "\n", | |
| "# Imprime el peso de la palabra tf-idf de cada tipo de texto, el primero para atravesar todos los textos y el segundo para facilita el peso de las palabras bajo un cierto tipo de texto\n", | |
| "for i in range(len(weight_list)):\n", | |
| " print(\"-------No.\", i+1, \"La palabra tf-idf peso de un párrafo de texto ------\")\n", | |
| " for j in range(len(word_list)):\n", | |
| " print(word_list[j], weight_list[i][j])" | |
| ], | |
| "metadata": { | |
| "id": "WE3G5KEC6el5" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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