{"slug": "cli-tool-for-data-science-891-downloads-in-7dy", "title": "CLI tool for data-science: 891 downloads in 7dy.", "summary": "A developer has released kaggle-prep, a CLI tool that automates exploratory data analysis workflows, reducing setup time from hours to seconds. The tool, which has gained 891 downloads in its first week on PyPI, generates data profiles, visualizations, and preprocessing scripts with a single command.", "body_md": "Every data science project starts the same way. You download a dataset, open a Jupyter notebook, and write the same 50+ lines of code you've written hundreds of times before:\n\n``` python\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndf = pd.read_csv('data.csv')\ndf.head()\ndf.info()\ndf.describe()\ndf.isnull().sum()\n# ... 30 more lines of boilerplate\n```\n\nFor a professional data scientist, this ritual takes **45-60 minutes**. For a student or junior analyst, it's easily **2-3 hours** of wrestling with syntax, debugging import errors, and googling \"how to plot correlation matrix in seaborn\" for the 47th time.\n\n**I did this 47 times. I lost my mind.**\n\nSo I built something that does it in **one command**.\n\nAs a data science student and ML intern, I realized the friction wasn't in the *analysis*—it was in the **setup**. Every project demanded:\n\nThis isn't \"work.\" It's **tax**. A tax you pay before you can do anything interesting.\n\nThe professional paradox: senior data scientists spend *less* time on EDA because they have their own scripts, templates, and muscle memory. Juniors spend *more* time—just when they need to focus on learning the actual data science.\n\n**I was in the second group. So I optimized.**\n\nI built `kaggle-prep`\n\n– a CLI tool that automates the entire EDA workflow from dataset download to production-ready notebook.\n\n```\npip install kaggle-prep\n# Full EDA pipeline\nkaggle-prep uciml/iris --all\n\n# Quick profile only\nkaggle-prep uciml/iris --profile\n\n# Generate starter notebook\nkaggle-prep uciml/iris --notebook\n```\n\nHere's the actual output from running `kaggle-prep uciml/iris --all`\n\n:\n\n```\nPS D:\\Projects\\kaggle> kaggle-prep uciml/iris --all\nNo local data found in 'data'. Initiating download...\nDownloading 'uciml/iris' via kagglehub (Zero-Config mode)...\nDownload complete! Files saved to: data\nLoaded: Iris.csv (150 rows, 6 columns)\n\n===========================================================\nDATA PROFILE SUMMARY\n===========================================================\n\nDataset: uciml/iris\nShape: 150 rows x 6 columns\nMemory: 0.01 MB\nDuplicates: 0\nMissing: 0 (0.00%)\nNumeric: 5 | Categorical: 1\n```\n\nWithin seconds, you have a complete data profile.\n\nThe tool generates:\n\n**9 EDA Visualizations** (automatically generated):\n\n**Here's the actual correlation matrix generated:**\n\n**Key insights from the data:**\n\n```\n   # A complete, production-ready preprocessing script\n   # Includes scaling, encoding, and split logic\n```\n\nI quietly released this on PyPI without any marketing push. Here's the organic growth:\n\n| Metric | Value |\n|---|---|\nMonthly Downloads |\n891 |\nLast 7 Days |\n282 |\nYesterday |\n216 |\nPython Versions |\n3.10, 3.11, 3.12, 3.13, 3.14 |\nPlatforms |\nWindows, macOS, Linux |\n\n**Platform Distribution:**\n\n| OS | Usage |\n|---|---|\n| Windows | ~45% |\n| Linux | ~35% |\n| macOS | ~20% |\n\n**Python Version Distribution:**\n\n| Version | Usage |\n|---|---|\n| Python 3.11 | ~40% |\n| Python 3.12 | ~30% |\n| Python 3.10 | ~15% |\n| Python 3.13+ | ~10% |\n\n**User Base Demographics:**\n\n```\nDataset URL → Download via KaggleHub → Load & Validate → \n  → Profile Generation →\n    → Statistical Analysis →\n      → Visualization Generation →\n        → Preprocessing Script Generation →\n          → Notebook Generation →\n            → All Outputs Saved\n```\n\n`kagglehub`\n\nto handle authentication automaticallyI added a `--feedback`\n\ncommand that:\n\n```\nkaggle-prep --feedback\n```\n\nI'm actively building the Pro version based on user feedback:\n\n| Feature | Status | Expected |\n|---|---|---|\n| Automated PDF Reports | In Development | October 2026 |\n| Auto-ML Baseline | In Development | November 2026 |\n| Competition Optimization | Planned | December 2026 |\n| Custom Visualization Config | Planned | January 2027 |\n\n```\npip install kaggle-prep\nkaggle-prep uciml/iris --all\n```\n\n**GitHub:** [Link to your repo]\n\n**PyPI:** [Link to your package]\n\n**Issues/Feature Requests:** [Link to your issues page]\n\nI built this to solve my own frustration. But the response tells me the frustration is **universal**.\n\nThe last month validated three things:\n\n**This is my first open-source project that actually serves a real user base.** And I'm just getting started.\n\nI'm building in public. If you want to:\n\n`pip install kaggle-prep`\n\n*Built by a student, for the data science community.*\n\n*891 monthly downloads and counting.*\n\n**Downloads last month: 891** | **Stars: [Your count]** | **Contributors: [Your count]**\n\n## Command Output\n\n*If this tool saved you time, please star the repo. It helps more than you know.*", "url": "https://wpnews.pro/news/cli-tool-for-data-science-891-downloads-in-7dy", "canonical_source": "https://dev.to/sumit_gavali_947a37c8a795/cli-tool-for-data-science-891-downloads-in-7dy-1lif", "published_at": "2026-09-02 04:53:58+00:00", "updated_at": "2026-09-02 05:22:44.122321+00:00", "lang": "en", "topics": ["developer-tools", "ai-tools", "machine-learning"], "entities": ["kaggle-prep", "PyPI", "KaggleHub"], "alternates": {"html": "https://wpnews.pro/news/cli-tool-for-data-science-891-downloads-in-7dy", "markdown": "https://wpnews.pro/news/cli-tool-for-data-science-891-downloads-in-7dy.md", "text": "https://wpnews.pro/news/cli-tool-for-data-science-891-downloads-in-7dy.txt", "jsonld": "https://wpnews.pro/news/cli-tool-for-data-science-891-downloads-in-7dy.jsonld"}}