{"id":23085542,"url":"https://github.com/alejandrolara11/data-preprocessing","last_synced_at":"2026-05-09T00:09:28.023Z","repository":{"id":261520971,"uuid":"884562808","full_name":"AlejandroLara11/Data-Preprocessing","owner":"AlejandroLara11","description":"Data preprocessing through the use of the libraries NumPy and pandas.","archived":false,"fork":false,"pushed_at":"2024-11-22T23:10:51.000Z","size":23,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-03T15:30:26.898Z","etag":null,"topics":["data-analysis","data-cleaning","data-preprocessing","numpy","pandas","python"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AlejandroLara11.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-07T01:21:09.000Z","updated_at":"2024-11-22T23:10:54.000Z","dependencies_parsed_at":"2024-11-07T01:44:00.340Z","dependency_job_id":"69072f90-637b-4c47-9b75-8f3b7eb2cdcc","html_url":"https://github.com/AlejandroLara11/Data-Preprocessing","commit_stats":null,"previous_names":["alejandrolara11/data-preprocessing"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AlejandroLara11/Data-Preprocessing","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlejandroLara11%2FData-Preprocessing","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlejandroLara11%2FData-Preprocessing/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlejandroLara11%2FData-Preprocessing/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlejandroLara11%2FData-Preprocessing/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AlejandroLara11","download_url":"https://codeload.github.com/AlejandroLara11/Data-Preprocessing/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlejandroLara11%2FData-Preprocessing/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32802570,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-08T08:22:46.396Z","status":"ssl_error","status_checked_at":"2026-05-08T08:22:45.650Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-cleaning","data-preprocessing","numpy","pandas","python"],"created_at":"2024-12-16T17:56:43.504Z","updated_at":"2026-05-09T00:09:28.008Z","avatar_url":"https://github.com/AlejandroLara11.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Processing with Pandas and NumPy\n\nThis repository contains code and resources for data processing using the popular Python libraries **pandas** and **NumPy**. It demonstrates a variety of data wrangling techniques such as cleaning, transformation, integration, and exploratory analysis, useful for preparing data for analysis and machine learning tasks.\n\n\n## Project Overview\nThis project provides examples and exercises for handling data, focusing on:\n- **Cleaning data** by handling null values, duplicates, and inconsistent formats.\n- **Transforming data** for analysis, including normalization, encoding, and feature engineering.\n- **Integrating data** from multiple sources.\n- **Exploring data** through summary statistics and visualization.\n\nThe code in this repository is designed for beginners and intermediate users looking to strengthen their data preprocessing skills in Python.\n\n## Requirements\n- Python 3.x\n- Libraries:\n  - [pandas](https://pandas.pydata.org/)\n  - [NumPy](https://numpy.org/)\n  - [Matplotlib](https://matplotlib.org/) (optional, for visualization examples)\n\n## Installation\nTo run the code in this repository, you need to have Python installed. Install the required libraries using:\n\n```bash\npip install pandas numpy matplotlib\nUsage\nEach script in the repository focuses on a specific data preprocessing task, such as handling null values, merging data sets, and data exploration. To run a script, simply execute:\n\n\npython script_name.py\nExample\nAn example of data preprocessing in this repository includes:\n\nHandling missing values: Fill null values with statistical measures or default values.\nData aggregation and grouping: Summarize data based on specific criteria.\nMerging data sets: Combine data from multiple sources to enrich the data set.\nOutlier detection: Identify and handle outliers using statistical methods.\nFeatures\nData Cleaning: Handle missing values, duplicate data, and inconsistent formatting.\nData Transformation: Apply scaling, encoding, and feature engineering.\nData Integration: Merge and concatenate data sets to create a single unified view.\nExploratory Data Analysis (EDA): Summary statistics and basic data visualizations.\nExamples\nHere are some examples of tasks covered in the repository:\n\nFilling missing values with mean values or default text.\nMerging DataFrames to consolidate information from different sources.\nCalculating statistics like mean, median, and standard deviation.\nVisualizing data with histograms for an initial data overview.\nContributing\nContributions are welcome! If you would like to improve or expand this project, please open an issue or submit a pull request.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falejandrolara11%2Fdata-preprocessing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falejandrolara11%2Fdata-preprocessing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falejandrolara11%2Fdata-preprocessing/lists"}