{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"AwmR9Z6cssm3"},"source":["# Objetivos\n","- [ ] Cargar diferentes tipos de datos e identificar sus atributos\n","- [ ] Identificar posibles transformaciones\n","- [ ] Analizar valores faltantes\n","- [ ] Analizar valores ruidosos\n","- [ ] Entender distribuciones\n","- [ ] Reducción del dataset\n","- [ ] Creación de diferentes atributos\n","\n","[Datasets UCI](https://archive.ics.uci.edu/ml/datasets)\n","\n","[Seaborn](https://seaborn.pydata.org/)\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"cTPsWMa2szmO"},"source":["### Carga de Datos"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZvEdq28qsuFg","outputId":"d4e4a740-cf2c-4f58-8644-e0661031ff42","executionInfo":{"status":"ok","timestamp":1752627414554,"user_tz":180,"elapsed":71,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["from platform import python_version\n","print(python_version())"],"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["3.11.13\n"]}]},{"cell_type":"code","metadata":{"id":"g_q4ZAPUl5Hw"},"source":["# Importa PyDrive y las librerías asociadas\n","from pydrive.auth import GoogleAuth\n","from pydrive.drive import GoogleDrive\n","from google.colab import auth\n","from oauth2client.client import GoogleCredentials\n","# Autentica y crea el cliente PyDrive\n","# Eso es necesario hacer una solo ver por notebook\n","auth.authenticate_user()\n","gauth = GoogleAuth()\n","gauth.credentials = GoogleCredentials.get_application_default()\n","drive = GoogleDrive(gauth)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zjuPyLCWn_ik"},"source":["# Descargar un archivo basado en su file ID\n","file_id = '15BO2uAjKMs20jcBVOeciFm3brcEtQKVG' # Chequear su file ID en google drive\n","downloaded = drive.CreateFile({'id': file_id})\n","# Guarda tu archivo en Colab memory\n","downloaded.GetContentFile('adult.csv')\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"n7xVZjrmoUN2","executionInfo":{"status":"ok","timestamp":1752627420420,"user_tz":180,"elapsed":433,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["import pandas as pd"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"csxPSGqJoWeO","executionInfo":{"status":"ok","timestamp":1752627428907,"user_tz":180,"elapsed":143,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df = pd.read_csv('adult.data', header=None)"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"283Biqa-oeVW","outputId":"fbd6e792-d876-42a4-c5a7-cccb40a790da","executionInfo":{"status":"ok","timestamp":1752627431571,"user_tz":180,"elapsed":187,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df.head()"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" 0 1 2 3 4 5 \\\n","0 39 State-gov 77516 Bachelors 13 Never-married \n","1 50 Self-emp-not-inc 83311 Bachelors 13 Married-civ-spouse \n","2 38 Private 215646 HS-grad 9 Divorced \n","3 53 Private 234721 11th 7 Married-civ-spouse \n","4 28 Private 338409 Bachelors 13 Married-civ-spouse \n","\n"," 6 7 8 9 10 11 12 \\\n","0 Adm-clerical Not-in-family White Male 2174 0 40 \n","1 Exec-managerial Husband White Male 0 0 13 \n","2 Handlers-cleaners Not-in-family White Male 0 0 40 \n","3 Handlers-cleaners Husband Black Male 0 0 40 \n","4 Prof-specialty Wife Black Female 0 0 40 \n","\n"," 13 14 \n","0 United-States <=50K \n","1 United-States <=50K \n","2 United-States <=50K \n","3 United-States <=50K \n","4 Cuba <=50K "],"text/html":["\n","
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039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States<=50K
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States<=50K
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States<=50K
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States<=50K
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50K
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df","summary":"{\n \"name\": \"df\",\n \"rows\": 32561,\n \"fields\": [\n {\n \"column\": 0,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 13,\n \"min\": 17,\n \"max\": 90,\n \"num_unique_values\": 73,\n \"samples\": [\n 28,\n 73,\n 35\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 1,\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \" Without-pay\",\n \" Self-emp-not-inc\",\n \" ?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 2,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 105549,\n \"min\": 12285,\n \"max\": 1484705,\n \"num_unique_values\": 21648,\n \"samples\": [\n 128485,\n 469907,\n 235951\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 3,\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 16,\n \"samples\": [\n \" Bachelors\",\n \" HS-grad\",\n \" Some-college\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 4,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 1,\n \"max\": 16,\n \"num_unique_values\": 16,\n \"samples\": [\n 13,\n 9,\n 10\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 5,\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \" Never-married\",\n \" Married-civ-spouse\",\n \" Married-AF-spouse\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 6,\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 15,\n \"samples\": [\n \" Machine-op-inspct\",\n \" ?\",\n \" Adm-clerical\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 7,\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 6,\n \"samples\": [\n \" Not-in-family\",\n \" Husband\",\n \" Other-relative\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 8,\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \" Black\",\n \" Other\",\n \" Asian-Pac-Islander\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 9,\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \" Female\",\n \" Male\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 10,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7385,\n \"min\": 0,\n \"max\": 99999,\n \"num_unique_values\": 119,\n \"samples\": [\n 3781,\n 15831\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 11,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 402,\n \"min\": 0,\n \"max\": 4356,\n \"num_unique_values\": 92,\n \"samples\": [\n 419,\n 2051\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 12,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12,\n \"min\": 1,\n \"max\": 99,\n \"num_unique_values\": 94,\n \"samples\": [\n 6,\n 22\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 13,\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 42,\n \"samples\": [\n \" El-Salvador\",\n \" Philippines\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 14,\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \" >50K\",\n \" <=50K\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":4}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"C9m11v6opUmE","outputId":"0d46142d-820c-4422-869d-4ed13f017067","executionInfo":{"status":"ok","timestamp":1752627439710,"user_tz":180,"elapsed":86,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df.shape"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(32561, 15)"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"-KQYdeFCoh9P","executionInfo":{"status":"ok","timestamp":1752627467108,"user_tz":180,"elapsed":72,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df.rename(\n"," columns={\n"," 0:'age'\n"," ,1:'workclass'\n"," ,2:'fnlwgt'\n"," ,3:'education'\n"," ,4:'education-num'\n"," ,5:'marital-status'\n"," ,6:'occupation'\n"," ,7:'relationship'\n"," ,8:'race'\n"," ,9:'sex'\n"," ,10:'capital-gain'\n"," ,11:'capital-loss'\n"," ,12:'hours-per-week'\n"," ,13:'native-country'\n"," ,14: 'class'\n"," }, inplace=True\n",")"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"R3Q4sDmL5VyF","colab":{"base_uri":"https://localhost:8080/","height":293},"outputId":"3a678a1d-eda9-49e8-d284-c5f59bbfb8e9","executionInfo":{"status":"ok","timestamp":1752627468982,"user_tz":180,"elapsed":259,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df.head()"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" age workclass fnlwgt education education-num \\\n","0 39 State-gov 77516 Bachelors 13 \n","1 50 Self-emp-not-inc 83311 Bachelors 13 \n","2 38 Private 215646 HS-grad 9 \n","3 53 Private 234721 11th 7 \n","4 28 Private 338409 Bachelors 13 \n","\n"," marital-status occupation relationship race sex \\\n","0 Never-married Adm-clerical Not-in-family White Male \n","1 Married-civ-spouse Exec-managerial Husband White Male \n","2 Divorced Handlers-cleaners Not-in-family White Male \n","3 Married-civ-spouse Handlers-cleaners Husband Black Male \n","4 Married-civ-spouse Prof-specialty Wife Black Female \n","\n"," capital-gain capital-loss hours-per-week native-country class \n","0 2174 0 40 United-States <=50K \n","1 0 0 13 United-States <=50K \n","2 0 0 40 United-States <=50K \n","3 0 0 40 United-States <=50K \n","4 0 0 40 Cuba <=50K "],"text/html":["\n","
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countryclass
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States<=50K
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States<=50K
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States<=50K
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States<=50K
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50K
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df","summary":"{\n \"name\": \"df\",\n \"rows\": 32561,\n \"fields\": [\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 13,\n \"min\": 17,\n \"max\": 90,\n \"num_unique_values\": 73,\n \"samples\": [\n 28,\n 73,\n 35\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"workclass\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \" Without-pay\",\n \" Self-emp-not-inc\",\n \" ?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fnlwgt\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 105549,\n \"min\": 12285,\n \"max\": 1484705,\n \"num_unique_values\": 21648,\n \"samples\": [\n 128485,\n 469907,\n 235951\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"education\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 16,\n \"samples\": [\n \" Bachelors\",\n \" HS-grad\",\n \" Some-college\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"education-num\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 1,\n \"max\": 16,\n \"num_unique_values\": 16,\n \"samples\": [\n 13,\n 9,\n 10\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"marital-status\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \" Never-married\",\n \" Married-civ-spouse\",\n \" Married-AF-spouse\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"occupation\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 15,\n \"samples\": [\n \" Machine-op-inspct\",\n \" ?\",\n \" Adm-clerical\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"relationship\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 6,\n \"samples\": [\n \" Not-in-family\",\n \" Husband\",\n \" Other-relative\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"race\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \" Black\",\n \" Other\",\n \" Asian-Pac-Islander\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sex\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \" Female\",\n \" Male\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"capital-gain\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7385,\n \"min\": 0,\n \"max\": 99999,\n \"num_unique_values\": 119,\n \"samples\": [\n 3781,\n 15831\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"capital-loss\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 402,\n \"min\": 0,\n \"max\": 4356,\n \"num_unique_values\": 92,\n \"samples\": [\n 419,\n 2051\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hours-per-week\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12,\n \"min\": 1,\n \"max\": 99,\n \"num_unique_values\": 94,\n \"samples\": [\n 6,\n 22\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"native-country\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 42,\n \"samples\": [\n \" El-Salvador\",\n \" Philippines\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"class\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \" >50K\",\n \" <=50K\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"d8WckLMw0Df8"},"source":["# Descripción del dataset\n","***Encuesta para conocer los ingresos anuales de una persona en USA***\n","\n","**Attribute Information:**\n","\n","Listing of attributes:\n","\n",">50K, <=50K.\n","\n","**age:** continuous.\n","\n","**workclass:** Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n","\n","**fnlwgt:** continuous. (final weight)\n","\n","**education:** Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n","\n","**education-num:** continuous.\n","\n","**marital-status:** Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n","occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n","relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n","\n","**race:** White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n","\n","**sex:** Female, Male.\n","\n","**capital-gain:** continuous.\n","\n","**capital-loss:** continuous.\n","\n","**hours-per-week:** continuous.\n","\n","**native-country:** United-States, Cambodia, England, etc\n","\n","[Fuente](https://archive.ics.uci.edu/ml/datasets)"]},{"cell_type":"code","metadata":{"id":"nHO1PxGfsBmW","colab":{"base_uri":"https://localhost:8080/","height":293},"outputId":"dccd9dab-537c-459d-f003-c4623bdc3b1e","executionInfo":{"status":"ok","timestamp":1752627483549,"user_tz":180,"elapsed":218,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df.head()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" age workclass fnlwgt education education-num \\\n","0 39 State-gov 77516 Bachelors 13 \n","1 50 Self-emp-not-inc 83311 Bachelors 13 \n","2 38 Private 215646 HS-grad 9 \n","3 53 Private 234721 11th 7 \n","4 28 Private 338409 Bachelors 13 \n","\n"," marital-status occupation relationship race sex \\\n","0 Never-married Adm-clerical Not-in-family White Male \n","1 Married-civ-spouse Exec-managerial Husband White Male \n","2 Divorced Handlers-cleaners Not-in-family White Male \n","3 Married-civ-spouse Handlers-cleaners Husband Black Male \n","4 Married-civ-spouse Prof-specialty Wife Black Female \n","\n"," capital-gain capital-loss hours-per-week native-country class \n","0 2174 0 40 United-States <=50K \n","1 0 0 13 United-States <=50K \n","2 0 0 40 United-States <=50K \n","3 0 0 40 United-States <=50K \n","4 0 0 40 Cuba <=50K "],"text/html":["\n","
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countryclass
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States<=50K
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States<=50K
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States<=50K
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States<=50K
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50K
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\"category\",\n \"num_unique_values\": 16,\n \"samples\": [\n \" Bachelors\",\n \" HS-grad\",\n \" Some-college\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"education-num\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 1,\n \"max\": 16,\n \"num_unique_values\": 16,\n \"samples\": [\n 13,\n 9,\n 10\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"marital-status\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \" Never-married\",\n \" Married-civ-spouse\",\n \" Married-AF-spouse\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"occupation\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 15,\n \"samples\": [\n \" Machine-op-inspct\",\n \" ?\",\n \" Adm-clerical\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"relationship\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 6,\n \"samples\": [\n \" Not-in-family\",\n \" Husband\",\n \" Other-relative\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"race\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \" Black\",\n \" Other\",\n \" Asian-Pac-Islander\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sex\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \" Female\",\n \" Male\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"capital-gain\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7385,\n \"min\": 0,\n \"max\": 99999,\n \"num_unique_values\": 119,\n \"samples\": [\n 3781,\n 15831\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"capital-loss\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 402,\n \"min\": 0,\n \"max\": 4356,\n \"num_unique_values\": 92,\n \"samples\": [\n 419,\n 2051\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hours-per-week\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12,\n \"min\": 1,\n \"max\": 99,\n \"num_unique_values\": 94,\n \"samples\": [\n 6,\n 22\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"native-country\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 42,\n \"samples\": [\n \" El-Salvador\",\n \" Philippines\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"class\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \" >50K\",\n \" <=50K\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n 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Adm-clerical Own-child White Male \n","32560 Married-civ-spouse Exec-managerial Wife White Female \n","\n"," capital-gain capital-loss hours-per-week native-country class \n","32556 0 0 38 United-States <=50K \n","32557 0 0 40 United-States >50K \n","32558 0 0 40 United-States <=50K \n","32559 0 0 20 United-States <=50K \n","32560 15024 0 40 United-States >50K "],"text/html":["\n","
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3255858Private151910HS-grad9WidowedAdm-clericalUnmarriedWhiteFemale0040United-States<=50K
3255922Private201490HS-grad9Never-marriedAdm-clericalOwn-childWhiteMale0020United-States<=50K
3256052Self-emp-inc287927HS-grad9Married-civ-spouseExec-managerialWifeWhiteFemale15024040United-States>50K
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\"number\",\n \"std\": 8,\n \"min\": 20,\n \"max\": 40,\n \"num_unique_values\": 3,\n \"samples\": [\n 38\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"native-country\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \" United-States\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"class\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \" >50K\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"6Ealcv0NsIxn","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9b46a8fc-7ac2-4c32-8ee8-f828205a1f3c","executionInfo":{"status":"ok","timestamp":1752627500091,"user_tz":180,"elapsed":74,"user":{"displayName":"Eduardo 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15)"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"SeuMScRfsQgA","colab":{"base_uri":"https://localhost:8080/","height":554},"outputId":"47ddfdb7-da7c-4cf4-d5a8-9e0172c99164","executionInfo":{"status":"ok","timestamp":1752627513496,"user_tz":180,"elapsed":81,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df.dtypes"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["age int64\n","workclass object\n","fnlwgt int64\n","education object\n","education-num int64\n","marital-status object\n","occupation object\n","relationship object\n","race object\n","sex object\n","capital-gain int64\n","capital-loss int64\n","hours-per-week int64\n","native-country object\n","class object\n","dtype: object"],"text/html":["
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"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"irEjV3EKsvJB"},"source":["### Analisis de valores faltantes"]},{"cell_type":"code","metadata":{"id":"IXgTG67esvse","colab":{"base_uri":"https://localhost:8080/","height":554},"outputId":"e25787d3-4ab6-42c8-bb3f-ce94d6b4e4c5","executionInfo":{"status":"ok","timestamp":1752627546449,"user_tz":180,"elapsed":90,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df.isna().sum()"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["age 0\n","workclass 0\n","fnlwgt 0\n","education 0\n","education-num 0\n","marital-status 0\n","occupation 0\n","relationship 0\n","race 0\n","sex 0\n","capital-gain 0\n","capital-loss 0\n","hours-per-week 0\n","native-country 0\n","class 0\n","dtype: int64"],"text/html":["
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"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"pYGZK_tXs7k3","colab":{"base_uri":"https://localhost:8080/","height":89},"outputId":"f8aaf583-7eb3-455e-bc2a-ea0cf38431ae","executionInfo":{"status":"ok","timestamp":1752627567380,"user_tz":180,"elapsed":138,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["null_data = df[df.isnull().any(axis=1)]\n","null_data"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Empty DataFrame\n","Columns: [age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, native-country, class]\n","Index: []"],"text/html":["\n","
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countryclass
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"null_data","repr_error":"Out of range float values are not JSON compliant: nan"}},"metadata":{},"execution_count":17}]},{"cell_type":"markdown","metadata":{"id":"352az7kvtCvK"},"source":["### Analisis de valores ruidosos"]},{"cell_type":"code","metadata":{"id":"HnGg3Y1v3uzA","executionInfo":{"status":"ok","timestamp":1752627587670,"user_tz":180,"elapsed":1726,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["import seaborn as sns\n","sns.set(rc={'figure.figsize':(5,5)})"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"id":"8_13RiYatCZW","colab":{"base_uri":"https://localhost:8080/","height":490},"outputId":"c521200d-5d3e-4189-fd83-f4664c6e37dc","executionInfo":{"status":"ok","timestamp":1752627589072,"user_tz":180,"elapsed":343,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["sns.boxplot(x='class', y='age', data=df)"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":19},{"output_type":"display_data","data":{"text/plain":["
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ageint64
workclassobject
fnlwgtint64
educationobject
education-numint64
marital-statusobject
occupationobject
relationshipobject
raceobject
sexobject
capital-gainint64
capital-lossint64
hours-per-weekint64
native-countryobject
classobject
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agehours-per-week
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95%63.00000060.000000
99%74.00000080.000000
max90.00000099.000000
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \"df[['age', 'hours-per-week']]\",\n \"rows\": 14,\n \"fields\": [\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 8691.527544705072,\n \"min\": 13.640432553581146,\n \"max\": 32561.0,\n \"num_unique_values\": 13,\n \"samples\": [\n 74.0,\n 58.0,\n 32561.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hours-per-week\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 8691.58887419753,\n \"min\": 1.0,\n \"max\": 32561.0,\n \"num_unique_values\": 13,\n \"samples\": [\n 80.0,\n 55.0,\n 32561.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"PS-_T8QIIrqm","colab":{"base_uri":"https://localhost:8080/","height":681},"outputId":"9485d3a1-ba66-4a01-838c-b5b619028ec5","executionInfo":{"status":"ok","timestamp":1752627654365,"user_tz":180,"elapsed":449,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["sns.distplot(df['age'])"],"execution_count":22,"outputs":[{"output_type":"stream","name":"stderr","text":["/tmp/ipython-input-22-3234920688.py:1: UserWarning: \n","\n","`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n","\n","Please adapt your code to use either `displot` (a figure-level function with\n","similar flexibility) or `histplot` (an axes-level function for histograms).\n","\n","For a guide to updating your code to use the new functions, please see\n","https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n","\n"," sns.distplot(df['age'])\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":22},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"E6bOKT3TTghR"},"source":["## Reducción del dataset"]},{"cell_type":"markdown","metadata":{"id":"N8eV3kgKZRoS"},"source":["Me quedo con las columnas mpg, cylinders, displacement, horsepower y car para autos con 8 o 6 cilindros"]},{"cell_type":"code","metadata":{"id":"FsB1NBWRWC5B","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1752627664250,"user_tz":180,"elapsed":78,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"a36d5937-19bd-4ab5-85f6-4cfb3065b2ba"},"source":["df = pd.read_csv('auto-mpg.data', header=None, delim_whitespace=True)"],"execution_count":23,"outputs":[{"output_type":"stream","name":"stderr","text":["/tmp/ipython-input-23-1675612709.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n"," df = pd.read_csv('auto-mpg.data', header=None, delim_whitespace=True)\n"]}]},{"cell_type":"code","metadata":{"id":"V2vBRm2KWR8i","colab":{"base_uri":"https://localhost:8080/","height":206},"outputId":"1aebeedb-6097-41ac-954e-fbfb03f082bd","executionInfo":{"status":"ok","timestamp":1752627666722,"user_tz":180,"elapsed":155,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df.head()"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" 0 1 2 3 4 5 6 7 8\n","0 18.0 8 307.0 130.0 3504.0 12.0 70 1 chevrolet chevelle malibu\n","1 15.0 8 350.0 165.0 3693.0 11.5 70 1 buick skylark 320\n","2 18.0 8 318.0 150.0 3436.0 11.0 70 1 plymouth satellite\n","3 16.0 8 304.0 150.0 3433.0 12.0 70 1 amc rebel sst\n","4 17.0 8 302.0 140.0 3449.0 10.5 70 1 ford torino"],"text/html":["\n","
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018.08307.0130.03504.012.0701chevrolet chevelle malibu
115.08350.0165.03693.011.5701buick skylark 320
218.08318.0150.03436.011.0701plymouth satellite
316.08304.0150.03433.012.0701amc rebel sst
417.08302.0140.03449.010.5701ford torino
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df","summary":"{\n \"name\": \"df\",\n \"rows\": 398,\n \"fields\": [\n {\n \"column\": 0,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7.815984312565782,\n \"min\": 9.0,\n \"max\": 46.6,\n \"num_unique_values\": 129,\n \"samples\": [\n 17.7,\n 30.5,\n 30.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 1,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 3,\n \"max\": 8,\n \"num_unique_values\": 5,\n \"samples\": [\n 4,\n 5,\n 6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 2,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 104.26983817119581,\n \"min\": 68.0,\n \"max\": 455.0,\n \"num_unique_values\": 82,\n \"samples\": [\n 122.0,\n 307.0,\n 360.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 3,\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 94,\n \"samples\": [\n \"112.0\",\n \"?\",\n \"78.00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 4,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 846.8417741973271,\n \"min\": 1613.0,\n \"max\": 5140.0,\n \"num_unique_values\": 351,\n \"samples\": [\n 3730.0,\n 1995.0,\n 2215.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 5,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.7576889298126757,\n \"min\": 8.0,\n \"max\": 24.8,\n \"num_unique_values\": 95,\n \"samples\": [\n 14.7,\n 18.0,\n 14.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 6,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3,\n \"min\": 70,\n \"max\": 82,\n \"num_unique_values\": 13,\n \"samples\": [\n 81,\n 79,\n 70\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 7,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 3,\n \"num_unique_values\": 3,\n \"samples\": [\n 1,\n 3,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 8,\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 305,\n \"samples\": [\n \"mazda rx-4\",\n \"ford f108\",\n \"buick century luxus (sw)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"MNtJK8Q_WtVx","executionInfo":{"status":"ok","timestamp":1752627670119,"user_tz":180,"elapsed":76,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df=df.rename(\n"," columns=(\n"," {0:'mpg',\n"," 1:'cylinders',\n"," 2:'displacement',\n"," 3:'horsepower',\n"," 4:'weight',\n"," 5:'acceleration',\n"," 6:'model',\n"," 7:'origin',\n"," 8:'car'\n"," }\n"," )\n","\n",")"],"execution_count":25,"outputs":[]},{"cell_type":"code","metadata":{"id":"XeaI58DIXk3Z","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0e7926c3-65ab-4c24-aab0-58d8e3da6707","executionInfo":{"status":"ok","timestamp":1752627672944,"user_tz":180,"elapsed":100,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df['cylinders'].unique()"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([8, 4, 6, 3, 5])"]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"wlU5pTBXZJ3D"},"source":["### Reducción por columna"]},{"cell_type":"code","metadata":{"id":"NADloEKkYzx6","colab":{"base_uri":"https://localhost:8080/","height":143},"outputId":"5c61b497-0d0a-480e-e56a-b330cbff269a","executionInfo":{"status":"ok","timestamp":1752627683670,"user_tz":180,"elapsed":81,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df1 = df[['mpg','cylinders', 'displacement', 'horsepower', 'car']]\n","df1.head(3)"],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" mpg cylinders displacement horsepower car\n","0 18.0 8 307.0 130.0 chevrolet chevelle malibu\n","1 15.0 8 350.0 165.0 buick skylark 320\n","2 18.0 8 318.0 150.0 plymouth satellite"],"text/html":["\n","
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mpgcylindersdisplacementhorsepowercar
018.08307.0130.0chevrolet chevelle malibu
115.08350.0165.0buick skylark 320
218.08318.0150.0plymouth satellite
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df1","summary":"{\n \"name\": \"df1\",\n \"rows\": 398,\n \"fields\": [\n {\n \"column\": \"mpg\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7.815984312565782,\n \"min\": 9.0,\n \"max\": 46.6,\n \"num_unique_values\": 129,\n \"samples\": [\n 17.7,\n 30.5,\n 30.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cylinders\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 3,\n \"max\": 8,\n \"num_unique_values\": 5,\n \"samples\": [\n 4,\n 5,\n 6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"displacement\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 104.26983817119581,\n \"min\": 68.0,\n \"max\": 455.0,\n \"num_unique_values\": 82,\n \"samples\": [\n 122.0,\n 307.0,\n 360.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"horsepower\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 94,\n \"samples\": [\n \"112.0\",\n \"?\",\n \"78.00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"car\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 305,\n \"samples\": [\n \"mazda rx-4\",\n \"ford f108\",\n \"buick century luxus (sw)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"ynbQcQWDb9od","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1bf1f126-c9bc-44cf-d3b0-c73aafcee650","executionInfo":{"status":"ok","timestamp":1752627691843,"user_tz":180,"elapsed":102,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df1.shape"],"execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(398, 5)"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"fEG3Zd3sZXca"},"source":["### Reducción con fila"]},{"cell_type":"code","metadata":{"id":"i-j4W-2eZdhz","colab":{"base_uri":"https://localhost:8080/"},"outputId":"33e7fa27-45e8-49aa-e4f7-46676680b429","executionInfo":{"status":"ok","timestamp":1752627702179,"user_tz":180,"elapsed":80,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df2 = df1[ (df1['cylinders']==8) | (df1['cylinders']==6) ]\n","df2['cylinders'].unique()"],"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([8, 6])"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","metadata":{"id":"-3mos31jb3Wk","colab":{"base_uri":"https://localhost:8080/"},"outputId":"95e2dbfa-c8e5-480d-b9bb-0025ea03fbb4","executionInfo":{"status":"ok","timestamp":1752627714642,"user_tz":180,"elapsed":89,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df2.shape"],"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(187, 5)"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"eUQ1IIDRcD_-","colab":{"base_uri":"https://localhost:8080/","height":685},"outputId":"257dcbc5-afcc-4e35-cce5-c3dcc7610c13","executionInfo":{"status":"ok","timestamp":1752627721764,"user_tz":180,"elapsed":254,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["sns.distplot(df2['mpg'])"],"execution_count":31,"outputs":[{"output_type":"stream","name":"stderr","text":["/tmp/ipython-input-31-376063837.py:1: UserWarning: \n","\n","`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n","\n","Please adapt your code to use either `displot` (a figure-level function with\n","similar flexibility) or `histplot` (an axes-level function for histograms).\n","\n","For a guide to updating your code to use the new functions, please see\n","https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n","\n"," sns.distplot(df2['mpg'])\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":31},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"O-3GrPyxcztw"},"source":["## Nuevos Atributos"]},{"cell_type":"code","metadata":{"id":"Wb8y1wmf4_CV","executionInfo":{"status":"ok","timestamp":1752627729800,"user_tz":180,"elapsed":110,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["import numpy as np"],"execution_count":32,"outputs":[]},{"cell_type":"code","metadata":{"id":"B2c4185Fc6Dn","colab":{"base_uri":"https://localhost:8080/","height":310},"outputId":"34d250ef-d7c0-4057-fb5f-8e4ff03e275d","executionInfo":{"status":"ok","timestamp":1752627731519,"user_tz":180,"elapsed":112,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df2['mpg2']= np.where(df2['mpg']<=15 , '<=15', '>15')\n","df2.head()"],"execution_count":33,"outputs":[{"output_type":"stream","name":"stderr","text":["/tmp/ipython-input-33-2041382861.py:1: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," df2['mpg2']= np.where(df2['mpg']<=15 , '<=15', '>15')\n"]},{"output_type":"execute_result","data":{"text/plain":[" mpg cylinders displacement horsepower car mpg2\n","0 18.0 8 307.0 130.0 chevrolet chevelle malibu >15\n","1 15.0 8 350.0 165.0 buick skylark 320 <=15\n","2 18.0 8 318.0 150.0 plymouth satellite >15\n","3 16.0 8 304.0 150.0 amc rebel sst >15\n","4 17.0 8 302.0 140.0 ford torino >15"],"text/html":["\n","
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mpgcylindersdisplacementhorsepowercarmpg2
018.08307.0130.0chevrolet chevelle malibu>15
115.08350.0165.0buick skylark 320<=15
218.08318.0150.0plymouth satellite>15
316.08304.0150.0amc rebel sst>15
417.08302.0140.0ford torino>15
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df2","summary":"{\n \"name\": \"df2\",\n \"rows\": 187,\n \"fields\": [\n {\n \"column\": \"mpg\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.141872917467064,\n \"min\": 9.0,\n \"max\": 38.0,\n \"num_unique_values\": 51,\n \"samples\": [\n 23.5,\n 26.8,\n 24.2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cylinders\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 6,\n \"max\": 8,\n \"num_unique_values\": 2,\n \"samples\": [\n 6,\n 8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"displacement\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 75.29225903885971,\n \"min\": 145.0,\n \"max\": 455.0,\n \"num_unique_values\": 36,\n \"samples\": [\n 181.0,\n 198.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"horsepower\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 56,\n \"samples\": [\n \"130.0\",\n \"220.0\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"car\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 141,\n \"samples\": [\n \"plymouth valiant custom\",\n \"plymouth volare\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"mpg2\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"<=15\",\n \">15\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":33}]},{"cell_type":"markdown","metadata":{"id":"SmIm0maWt_5k"},"source":["# Actividades (del dataset de carros original)\n","\n","---\n","\n","\n","1. Generar un nuevo dataset de carros con cilindros mayores a 6\n","1. Discretizar la variable peso (weight). Creando una nueva variable llamada weight_new con valores: <2000, 2000-3000, 3001-4000, 4001-5500, >5500\n","2. ¿En cuál de los grupos resultantes se concentran los valores más altos de mpg?\n","2. ¿En cuál de los grupos resultantes se concentran los valores más bajos de mpg?\n","1. Tuvo algún problema (comunicarlo en el foro de dudas académicas)\n","\n","---\n"]},{"cell_type":"code","source":[],"metadata":{"id":"hNw7gRFgZZx3"},"execution_count":null,"outputs":[]}]}