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These libraries simplify complex data","archived":false,"fork":false,"pushed_at":"2024-10-17T16:18:21.000Z","size":5231,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-15T20:04:47.478Z","etag":null,"topics":["beautifulsoup","data","data-science","data-science-libraries","machine-learning","matplotlib","numpy","pandas","requests","scikit-learn","scikitlearn-machine-learning","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/udityamerit.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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,"zenodo":null}},"created_at":"2024-09-07T06:41:12.000Z","updated_at":"2024-11-16T18:06:02.000Z","dependencies_parsed_at":null,"dependency_job_id":"31da3b61-c4e2-41de-9a0a-d943736ba7b7","html_url":"https://github.com/udityamerit/Python-Librearies-for-Data-Science","commit_stats":null,"previous_names":["udityamerit/python-librearies-for-data-science"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/udityamerit/Python-Librearies-for-Data-Science","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/udityamerit%2FPython-Librearies-for-Data-Science","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/udityamerit%2FPython-Librearies-for-Data-Science/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/udityamerit%2FPython-Librearies-for-Data-Science/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/udityamerit%2FPython-Librearies-for-Data-Science/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/udityamerit","download_url":"https://codeload.github.com/udityamerit/Python-Librearies-for-Data-Science/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/udityamerit%2FPython-Librearies-for-Data-Science/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29145826,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-06T01:13:33.096Z","status":"online","status_checked_at":"2026-02-06T02:00:08.092Z","response_time":59,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["beautifulsoup","data","data-science","data-science-libraries","machine-learning","matplotlib","numpy","pandas","requests","scikit-learn","scikitlearn-machine-learning","tensorflow"],"created_at":"2024-11-08T12:09:41.546Z","updated_at":"2026-02-06T02:31:15.128Z","avatar_url":"https://github.com/udityamerit.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Python Libraries for Data Science\n\nThis repository provides an introduction to key Python libraries used in data science, including their installation and basic usage. It covers libraries such as `NumPy`, `pandas`, `Matplotlib`, `Scikit-learn`, `TensorFlow`, and tools for web scraping. Each section includes installation commands, basic commands, and a simple example to help you get started.\n\n## Table of Contents\n- [Installation](#installation)\n- [Libraries Overview](#libraries-overview)\n  - [NumPy](#numpy)\n  - [pandas](#pandas)\n  - [Matplotlib](#matplotlib)\n  - [Web Scraping Tools](#web-scraping-tools)\n  - [Scikit-learn](#scikit-learn)\n  - [TensorFlow](#tensorflow)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Installation\n\nYou can install the required libraries using `pip`. Run the following command:\n\n```bash\npip install numpy pandas matplotlib scikit-learn tensorflow beautifulsoup4 requests\n```\n\nThis will install:\n- NumPy for numerical computing\n- pandas for data manipulation\n- Matplotlib for data visualization\n- scikit-learn for machine learning\n- TensorFlow for deep learning\n- BeautifulSoup and requests for web scraping\n\n## Libraries Overview\n\n### NumPy\nNumPy is the fundamental package for numerical computing in Python. It provides support for arrays, matrices, and many mathematical functions.\n\n#### Installation\n```bash\npip install numpy\n```\n\n#### Basic Usage\n```python\nimport numpy as np\n\n# Create an array\narr = np.array([1, 2, 3])\nprint(arr)\n\n# Perform operations\nprint(np.mean(arr))\n```\n\n### pandas\npandas is an open-source library that provides high-performance data manipulation and analysis tools, particularly DataFrames.\n\n#### Installation\n```bash\npip install pandas\n```\n\n#### Basic Usage\n```python\nimport pandas as pd\n\n# Create a DataFrame\ndata = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}\ndf = pd.DataFrame(data)\n\n# Display the DataFrame\nprint(df)\n\n# Perform basic operations\nprint(df.describe())\n```\n\n### Matplotlib\nMatplotlib is a plotting library used for creating static, interactive, and animated visualizations in Python.\n\n#### Installation\n```bash\npip install matplotlib\n```\n\n#### Basic Usage\n```python\nimport matplotlib.pyplot as plt\n\n# Create a simple plot\nplt.plot([1, 2, 3], [4, 5, 6])\nplt.xlabel('X-axis')\nplt.ylabel('Y-axis')\nplt.title('Simple Plot')\nplt.show()\n```\n\n### Web Scraping Tools\nBeautifulSoup and requests are essential libraries for web scraping, allowing you to extract data from websites.\n\n#### Installation\n```bash\npip install beautifulsoup4 requests\n```\n\n#### Basic Usage\n```python\nimport requests\nfrom bs4 import BeautifulSoup\n\n# Fetch content from a webpage\nurl = 'https://example.com'\nresponse = requests.get(url)\n\n# Parse HTML content\nsoup = BeautifulSoup(response.text, 'html.parser')\nprint(soup.title.text)\n```\n\n### Scikit-learn\nScikit-learn is a library for machine learning, offering simple and efficient tools for data analysis and modeling.\n\n#### Installation\n```bash\npip install scikit-learn\n```\n\n#### Basic Usage\n```python\nfrom sklearn.linear_model import LinearRegression\nimport numpy as np\n\n# Sample data\nX = np.array([[1], [2], [3]])\ny = np.array([2, 4, 6])\n\n# Create a model and fit it\nmodel = LinearRegression()\nmodel.fit(X, y)\n\n# Make a prediction\nprint(model.predict([[4]]))\n```\n\n### TensorFlow\nTensorFlow is an open-source platform for machine learning and deep learning, commonly used for building neural networks.\n\n#### Installation\n```bash\npip install tensorflow\n```\n\n#### Basic Usage\n```python\nimport tensorflow as tf\n\n# Create a constant tensor\nhello = tf.constant('Hello, TensorFlow!')\nprint(hello.numpy())\n```\n\n## Contributing\nIf you'd like to contribute, feel free to fork the repository and submit a pull request. For major changes, please open an issue to discuss what you would like to change.\n\n## License\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fudityamerit%2Fpython-librearies-for-data-science","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fudityamerit%2Fpython-librearies-for-data-science","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fudityamerit%2Fpython-librearies-for-data-science/lists"}