{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "mP9aApBqQJed" }, "source": [ "Creando rutinas de preprocesamiento declarativas\n", "=============================================" ] }, { "cell_type": "markdown", "metadata": { "id": "2V8sUvroQJej" }, "source": [ "## Introducción\n", "\n", "Las programación declaritiva es una tendencia en ingeniería de software donde, a diferencía de la programación imperativa, permite describir las instrucciones que se desean realizar para que luego, un proceso, convierta esas instrucciones en código que puede ser ejecutado.\n", "\n", "Kedro es una framework de código abierto de Python diseñado para crear código de ciencia de datos declarativo, reproducible, mantenible y modular.\n", "\n", "Kedro aplica prácticas de ingeniería de software para ayudar a los usuarios a crear pipelines de ciencia de datos y data engineering listos para producción." ] }, { "cell_type": "markdown", "metadata": { "id": "tRmZrmz7QJel" }, "source": [ "### Instalación" ] }, { "cell_type": "markdown", "metadata": { "id": "3Yj6mHMaQJel" }, "source": [ "Necesitaremos instalar las librerias:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "IeIXxuSaQJem", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "38478847-9b68-4d6f-a324-aadc2f911010" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/198.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m198.6/198.6 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/98.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.2/98.2 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.5/236.5 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m206.5/206.5 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.2/41.2 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.6/118.6 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m739.1/739.1 kB\u001b[0m \u001b[31m31.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "%pip install kedro kedro-viz --quiet" ] }, { "cell_type": "markdown", "metadata": { "id": "tTLrI09WQJeo" }, "source": [ "### Sobre el conjunto de datos del censo UCI\n", "\n", "El conjunto de datos del censo de la UCI es un conjunto de datos en el que cada registro representa a una persona. Cada registro contiene 14 columnas que describen a una una sola persona, de la base de datos del censo de Estados Unidos de 1994. Esto incluye información como la edad, el estado civil y el nivel educativo. La tarea es determinar si una persona tiene un ingreso alto (definido como ganar más de $50 mil al año). Esta tarea, dado el tipo de datos que utiliza, se usa a menudo en el estudio de equidad, en parte debido a los atributos comprensibles del conjunto de datos, incluidos algunos que contienen tipos sensibles como la edad y el género, y en parte también porque comprende una tarea claramente del mundo real." ] }, { "cell_type": "markdown", "metadata": { "id": "vWyomYxTQJep" }, "source": [ "Descargamos el conjunto de datos" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "ovck-xIsQJeq" }, "outputs": [], "source": [ "!wget https://santiagxf.blob.core.windows.net/public/datasets/uci_census.zip \\\n", " --quiet --no-clobber\n", "!mkdir -p datasets/uci_census\n", "!unzip -qq uci_census.zip -d datasets/uci_census" ] }, { "cell_type": "markdown", "metadata": { "id": "EbVjMnWWQJeq" }, "source": [ "Lo importamos" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "K3qSdhVHQJeq" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "train = pd.read_csv('datasets/uci_census/data/adult-train.csv')\n", "test = pd.read_csv('datasets/uci_census/data/adult-test.csv')" ] }, { "cell_type": "code", "source": [ "train" ], "metadata": { "id": "6GyGTqzJzaaY", "outputId": "68de04d3-2260-48c5-8d4c-02ef6ce1cd84", "colab": { "base_uri": "https://localhost:8080/", "height": 562 } }, "execution_count": 6, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " income age workclass fnlwgt education education-num \\\n", "0 <=50K 39 State-gov 77516 Bachelors 13 \n", "1 <=50K 50 Self-emp-not-inc 83311 Bachelors 13 \n", "2 <=50K 38 Private 215646 HS-grad 9 \n", "3 <=50K 53 Private 234721 11th 7 \n", "4 <=50K 28 Private 338409 Bachelors 13 \n", "... ... ... ... ... ... ... \n", "32556 <=50K 27 Private 257302 Assoc-acdm 12 \n", "32557 >50K 40 Private 154374 HS-grad 9 \n", "32558 <=50K 58 Private 151910 HS-grad 9 \n", "32559 <=50K 22 Private 201490 HS-grad 9 \n", "32560 >50K 52 Self-emp-inc 287927 HS-grad 9 \n", "\n", " marital-status occupation relationship race \\\n", "0 Never-married Adm-clerical Not-in-family White \n", "1 Married-civ-spouse Exec-managerial Husband White \n", "2 Divorced Handlers-cleaners Not-in-family White \n", "3 Married-civ-spouse Handlers-cleaners Husband Black \n", "4 Married-civ-spouse Prof-specialty Wife Black \n", "... ... ... ... ... \n", "32556 Married-civ-spouse Tech-support Wife White \n", "32557 Married-civ-spouse Machine-op-inspct Husband White \n", "32558 Widowed Adm-clerical Unmarried White \n", "32559 Never-married Adm-clerical Own-child White \n", "32560 Married-civ-spouse Exec-managerial Wife White \n", "\n", " gender capital-gain capital-loss hours-per-week native-country \n", "0 Male 2174 0 40 United-States \n", "1 Male 0 0 13 United-States \n", "2 Male 0 0 40 United-States \n", "3 Male 0 0 40 United-States \n", "4 Female 0 0 40 Cuba \n", "... ... ... ... ... ... \n", "32556 Female 0 0 38 United-States \n", "32557 Male 0 0 40 United-States \n", "32558 Female 0 0 40 United-States \n", "32559 Male 0 0 20 United-States \n", "32560 Female 15024 0 40 United-States \n", "\n", "[32561 rows x 15 columns]" ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "train", "summary": "{\n \"name\": \"train\",\n \"rows\": 32561,\n \"fields\": [\n {\n \"column\": \"income\",\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 \"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 ],\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 \"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 ],\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 ],\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 ],\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 ],\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 ],\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 ],\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 ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gender\",\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}" } }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "source": [ "## Proyectos\n", "\n", "Kedro utiliza el concepto de proyectos para organizar los elementos necesarios en un dado problema." ], "metadata": { "id": "FXi1DGUS3Eyz" } }, { "cell_type": "code", "source": [ "!kedro new --starter=spaceflights-pandas" ], "metadata": { "id": "5iwROuDZsMoh", "outputId": "91af8933-0e9c-4dd9-d538-da2db4aa54e7", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2;36m[06/29/25 16:47:10]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Using \u001b]8;id=669892;file:///usr/local/lib/python3.11/dist-packages/kedro/framework/project/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=391138;file:///usr/local/lib/python3.11/dist-packages/kedro/framework/project/__init__.py#272\u001b\\\u001b[2m272\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m \u001b[32m'/usr/local/lib/python3.11/dist-pac\u001b[0m \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m \u001b[32mkages/kedro/framework/project/rich_\u001b[0m \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m \u001b[32mlogging.yml'\u001b[0m as logging \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m configuration. \u001b[2m \u001b[0m\n", "\n", "\u001b[36m\u001b[1mProject Name\u001b[0m\n", "\u001b[36m\u001b[1m============\u001b[0m\n", "Please enter a human readable name for your new project.\n", "Spaces, hyphens, and underscores are allowed.\n", " [Spaceflights Pandas]: spaceflights\n", "\n", "Congratulations!\n", "Your project 'spaceflights' has been created in the directory \n", "/content/spaceflights\n", "\u001b[0m\n", "\u001b[2;36m[06/29/25 16:47:22]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Kedro is sending anonymous usage data \u001b]8;id=475426;file:///usr/local/lib/python3.11/dist-packages/kedro_telemetry/plugin.py\u001b\\\u001b[2mplugin.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=492610;file:///usr/local/lib/python3.11/dist-packages/kedro_telemetry/plugin.py#233\u001b\\\u001b[2m233\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m with the sole purpose of improving \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m the product. No personal data or IP \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m addresses are stored on our side. If \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m you want to opt out, set the \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m `KEDRO_DISABLE_TELEMETRY` or \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m `DO_NOT_TRACK` environment variables, \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m or create a `.telemetry` file in the \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m current working directory with the \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m contents `consent: false`. Read more \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m at \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m \u001b[4;94mhttps://docs.kedro.org/en/stable/conf\u001b[0m \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m \u001b[4;94miguration/telemetry.html\u001b[0m \u001b[2m \u001b[0m\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Instalar dependencias del proyecto\n", "\n", "Los proyectos de Kedro tienen un archivo `requirements.txt` para especificar sus dependencias y habilitar proyectos compartibles al garantizar la coherencia en los paquetes y versiones de Python." ], "metadata": { "id": "56H-qmbfmTD5" } }, { "cell_type": "code", "source": [ "!cat spaceflights/requirements.txt" ], "metadata": { "id": "6l1pqfF9miGl", "outputId": "a3411b2c-cb6f-4fc3-fe3a-11426e2ce67a", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "ipython>=8.10\n", "jupyterlab>=3.0\n", "notebook\n", "kedro[jupyter]~=0.19.14\n", "kedro-datasets[pandas-csvdataset, pandas-exceldataset, pandas-parquetdataset, plotly-plotlydataset, plotly-jsondataset, matplotlib-matplotlibwriter]>=3.0\n", "kedro-viz>=6.7.0\n", "scikit-learn~=1.5.1\n", "seaborn~=0.12.1\n" ] } ] }, { "cell_type": "markdown", "source": [ "Para instalar todas las dependencias específicas del proyecto, ejecute lo siguiente desde el directorio raíz del proyecto:" ], "metadata": { "id": "iiKWt-TgmhYW" } }, { "cell_type": "code", "source": [ "%pip install -r spaceflights/requirements.txt" ], "metadata": { "id": "gska5uNtkoyO", "outputId": "f276559c-9ea0-4d19-e062-95f0464fdb15", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting ipython>=8.10 (from -r spaceflights/requirements.txt (line 1))\n", " Downloading ipython-9.3.0-py3-none-any.whl.metadata (4.4 kB)\n", "Collecting jupyterlab>=3.0 (from -r 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click-default-group, async-lru, asttokens, watchfiles, strawberry-graphql, stack_data, scikit-learn, jupyter-server-terminals, seaborn, jupyter-client, isoduration, ipython, jupyter-events, jupyter-server, jupyterlab-server, jupyter-lsp, jupyterlab, notebook, ipylab, kedro-datasets, kedro-viz\n", " Attempting uninstall: traitlets\n", " Found existing installation: traitlets 5.7.1\n", " Uninstalling traitlets-5.7.1:\n", " Successfully uninstalled traitlets-5.7.1\n", " Attempting uninstall: scikit-learn\n", " Found existing installation: scikit-learn 1.6.1\n", " Uninstalling scikit-learn-1.6.1:\n", " Successfully uninstalled scikit-learn-1.6.1\n", " Attempting uninstall: seaborn\n", " Found existing installation: seaborn 0.13.2\n", " Uninstalling seaborn-0.13.2:\n", " Successfully uninstalled seaborn-0.13.2\n", " Attempting uninstall: jupyter-client\n", " Found existing installation: jupyter-client 6.1.12\n", " Uninstalling jupyter-client-6.1.12:\n", " Successfully uninstalled jupyter-client-6.1.12\n", " Attempting uninstall: ipython\n", " Found existing installation: ipython 7.34.0\n", " Uninstalling ipython-7.34.0:\n", " Successfully uninstalled ipython-7.34.0\n", " Attempting uninstall: jupyter-server\n", " Found existing installation: jupyter-server 1.16.0\n", " Uninstalling jupyter-server-1.16.0:\n", " Successfully uninstalled jupyter-server-1.16.0\n", " Attempting uninstall: notebook\n", " Found existing installation: notebook 6.5.7\n", " Uninstalling notebook-6.5.7:\n", " Successfully uninstalled notebook-6.5.7\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "google-colab 1.0.0 requires ipython==7.34.0, but you have ipython 8.37.0 which is incompatible.\n", "google-colab 1.0.0 requires notebook==6.5.7, but you have notebook 7.4.3 which is incompatible.\n", "jupyter-kernel-gateway 2.5.2 requires jupyter-client<8.0,>=5.2.0, but you have jupyter-client 8.6.3 which is incompatible.\n", "jupyter-kernel-gateway 2.5.2 requires notebook<7.0,>=5.7.6, but you have notebook 7.4.3 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed asttokens-3.0.0 async-lru-2.0.5 click-default-group-1.2.4 executing-2.2.0 fqdn-1.5.1 graphql-core-3.2.6 httptools-0.6.4 ipylab-1.1.0 ipython-8.37.0 isoduration-20.11.0 jedi-0.19.2 json5-0.12.0 jupyter-client-8.6.3 jupyter-events-0.12.0 jupyter-lsp-2.2.5 jupyter-server-2.16.0 jupyter-server-terminals-0.5.3 jupyterlab-4.4.4 jupyterlab-server-2.27.3 kedro-datasets-7.0.0 kedro-viz-11.0.2 notebook-7.4.3 overrides-7.7.0 pathspec-0.12.1 pure-eval-0.2.3 python-dotenv-1.1.1 python-json-logger-3.3.0 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 scikit-learn-1.5.2 seaborn-0.12.2 secure-1.0.1 stack_data-0.6.3 strawberry-graphql-0.275.5 traitlets-5.14.3 uri-template-1.3.0 uvloop-0.21.0 watchfiles-1.1.0\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "IPython", "traitlets" ] }, "id": "8788285b7e59471d961c0208a8166ddc" } }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## Conjuntos de datos del proyecto\n", "\n", "El proyecto de ejemplo que tenemos de vuelos espaciales utiliza tres conjuntos de datos ficticios de empresas que transportan clientes a la Luna y de regreso. Los datos vienen en dos formatos diferentes: `.csv` y `.xlsx`:\n", "\n", "* `companies.csv`: Contiene datos sobre las empresas de viajes espaciales, como su ubicación, número de flotas y clasificación\n", "\n", "* `reviews.csv`: es un conjunto de reseñas de clientes para categorías como comodidad y precio\n", "\n", "* `shuttles.xlsx`: es un conjunto de atributos para las naves espaciales de toda la flota, como su tipo de motor y capacidad de pasajeros\n", "\n" ], "metadata": { "id": "dty1gC4wnheQ" } }, { "cell_type": "markdown", "source": [ "Esta información se encuentra en el directorio `data/01_raw`, que simula su origine de datos." ], "metadata": { "id": "9fjO2wz1n9_p" } }, { "cell_type": "markdown", "source": [ "### Registro de conjuntos de datos\n", "\n", "Herramientas como querdro pueden mantener un catalog de datos con información de los origines de datos disponibles para el procesamiento. La siguiente información sobre un conjunto de datos debe registrarse antes de que Kedro pueda cargarlo:\n", "\n", "* Ubicación del archivo (ruta)\n", "* Parámetros para el conjunto de datos dado\n", "* Tipo de datos\n", "* Version\n", "\n", "Kedro utiliza archivos declarativos en formato `YAML`, un formato ampliamente utilizado en la industria para estas tareas." ], "metadata": { "id": "SZdGToSooFfu" } }, { "cell_type": "markdown", "source": [ "__catalog.yml__\n", "\n", "```yaml\n", "companies:\n", " type: pandas.CSVDataset\n", " filepath: data/01_raw/companies.csv\n", "\n", "reviews:\n", " type: pandas.CSVDataset\n", " filepath: data/01_raw/reviews.csv\n", "\n", "shuttles:\n", " type: pandas.ExcelDataset\n", " filepath: data/01_raw/shuttles.xlsx\n", " load_args:\n", " engine: openpyxl\n", "```" ], "metadata": { "id": "UBvklMtMorDH" } }, { "cell_type": "markdown", "source": [ "Para mostrar como Kedro puede configurar nuestro ambiente automáticamente, utilizaremos una función para cargar el contexto del proyecto en el que estamos trabajando en Colaboratory. Esto se logra con la función `reload_kedro`.\n", "\n", "> En ambientes productivos, usted no necesitaría ejecutar el siguiente código ya está preconfigurado. En este ejemplo, haremos que Kedro cargue el contexto del projecto dentro de Colaboratory." ], "metadata": { "id": "2rR2-bYBqGqd" } }, { "cell_type": "code", "source": [ "from kedro.ipython import reload_kedro\n", "\n", "reload_kedro(path='spaceflights')" ], "metadata": { "id": "mgztWgSrpDCk", "outputId": "d9057179-7041-46da-e64f-129d5d129553", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[06/29/25 17:04:48] INFO Kedro is sending anonymous usage data plugin.py:233\n", " with the sole purpose of improving \n", " the product. No personal data or IP \n", " addresses are stored on our side. If \n", " you want to opt out, set the \n", " `KEDRO_DISABLE_TELEMETRY` or \n", " `DO_NOT_TRACK` environment variables, \n", " or create a `.telemetry` file in the \n", " current working directory with the \n", " contents `consent: false`. Read more \n", " at \n", " https://docs.kedro.org/en/stable/conf \n", " iguration/telemetry.html \n", " INFO Kedro project spaceflights __init__.py:146\n", " INFO Defined global variable 'context', __init__.py:147\n", " 'session', 'catalog' and \n", " 'pipelines' \n", "[06/29/25 17:04:49] INFO Registered line magic 'run_viz' __init__.py:153\n" ] } ] }, { "cell_type": "markdown", "source": [ "El siguiente comando crea una variable (`companies`), que es de tipo `pandas.DataFrame` y carga el conjunto de datos (también llamado `companies` según la configuración en el archivo `catalog.yml`) desde la ruta del archivo subyacente `data/01_raw/companies.csv`.\n", "\n" ], "metadata": { "id": "qZIiBuE6p6R7" } }, { "cell_type": "code", "source": [ "companies = catalog.load(\"companies\")" ], "metadata": { "id": "aGY0plb_pG2D", "outputId": "964f4be0-b0c5-4cca-cc60-4f118943b5c9", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[06/29/25 17:05:55] INFO Loading data from companies data_catalog.py:403\n", " (CSVDataset)... \n" ] } ] }, { "cell_type": "code", "source": [ "companies.head()" ], "metadata": { "id": "Pr2XeOwmpxBU", "outputId": "bebd8738-72b3-44b1-de3c-27aab75fd47c", "colab": { "base_uri": "https://localhost:8080/", "height": 206 } }, "execution_count": 10, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\n", " id company_rating company_location total_fleet_count \\\n", "0 3888 100% Isle of Man 1.0 \n", "1 46728 100% NaN 1.0 \n", "2 34618 38% Isle of Man 1.0 \n", "3 28619 100% Bosnia and Herzegovina 1.0 \n", "4 8240 NaN Chile 1.0 \n", "\n", " iata_approved \n", "0 f \n", "1 f \n", "2 f \n", "3 f \n", "4 t " ], "text/html": [ "\n", "
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idcompany_ratingcompany_locationtotal_fleet_countiata_approved
03888100%Isle of Man1.0f
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23461838%Isle of Man1.0f
328619100%Bosnia and Herzegovina1.0f
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "companies", "summary": "{\n \"name\": \"companies\",\n \"rows\": 10019,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14445,\n \"min\": 1,\n \"max\": 50098,\n \"num_unique_values\": 10019,\n \"samples\": [\n 38698,\n 33518,\n 1867\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 58,\n \"samples\": [\n \"100%\",\n \"0%\",\n \"94%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company_location\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 128,\n \"samples\": [\n \"Maldives\",\n \"Lithuania\",\n \"Russian Federation\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"total_fleet_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.446152691022986,\n \"min\": 1.0,\n \"max\": 1105.0,\n \"num_unique_values\": 48,\n \"samples\": [\n 32.0,\n 139.0,\n 31.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"iata_approved\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"t\",\n \"f\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "markdown", "source": [ "Podemos también ver todos los conjuntos que tenemos disponibles:" ], "metadata": { "id": "nvBpStPyqwHj" } }, { "cell_type": "code", "source": [ "catalog.list()" ], "metadata": { "id": "SkUFnTwLqy0m", "outputId": "60fb9729-aae3-4ca8-df19-8cda4867ba56", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\n", "[\n", " 'companies',\n", " 'reviews',\n", " 'shuttles',\n", " 'preprocessed_companies',\n", " 'preprocessed_shuttles',\n", " 'model_input_table',\n", " 'regressor',\n", " 'shuttle_passenger_capacity_plot_exp',\n", " 'shuttle_passenger_capacity_plot_go',\n", " 'dummy_confusion_matrix',\n", " 'parameters',\n", " 'params:model_options',\n", " 'params:model_options.test_size',\n", " 'params:model_options.random_state',\n", " 'params:model_options.features'\n", "]" ] }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "markdown", "source": [ "> Note que el proyecto tiene más conjuntos de datos de los que configuramos en un principio. Puede ignorar los otros conjuntos de datos." ], "metadata": { "id": "VCJPb3ourLLw" } }, { "cell_type": "markdown", "source": [ "## Crear un pipeline de procesamiento de datos\n", "\n", "El proceso de procesamiento de datos prepara los datos para la construcción del modelo combinando los conjuntos de datos para crear una tabla con los predictores del modelo.\n", "\n", "En este ejemplo, el proceso de procesamiento de datos se compone de lo siguiente:" ], "metadata": { "id": "QUI7ro9OramE" } }, { "cell_type": "code", "source": [ "##" ], "metadata": { "id": "eI7YyRG9rP8W" }, "execution_count": null, "outputs": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.12 ('sphinx')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "vscode": { "interpreter": { "hash": "c0c26a04c01997af4d3a54c44ba2029caf4208eaf3de13f3aa81bddca06af044" } }, "colab": { "provenance": [] } }, "nbformat": 4, "nbformat_minor": 0 }