CLI tool for data-science: 891 downloads in 7dy.

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.

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: python import pandas as pd import matplotlib.pyplot as plt import seaborn as sns df = pd.read csv 'data.csv' df.head df.info df.describe df.isnull .sum ... 30 more lines of boilerplate For 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. I did this 47 times. I lost my mind. So I built something that does it in one command . As a data science student and ML intern, I realized the friction wasn't in the analysis —it was in the setup . Every project demanded: This isn't "work." It's tax . A tax you pay before you can do anything interesting. The 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. I was in the second group. So I optimized. I built kaggle-prep – a CLI tool that automates the entire EDA workflow from dataset download to production-ready notebook. pip install kaggle-prep Full EDA pipeline kaggle-prep uciml/iris --all Quick profile only kaggle-prep uciml/iris --profile Generate starter notebook kaggle-prep uciml/iris --notebook Here's the actual output from running kaggle-prep uciml/iris --all : PS D:\Projects\kaggle kaggle-prep uciml/iris --all No local data found in 'data'. Initiating download... Downloading 'uciml/iris' via kagglehub Zero-Config mode ... Download complete Files saved to: data Loaded: Iris.csv 150 rows, 6 columns =========================================================== DATA PROFILE SUMMARY =========================================================== Dataset: uciml/iris Shape: 150 rows x 6 columns Memory: 0.01 MB Duplicates: 0 Missing: 0 0.00% Numeric: 5 | Categorical: 1 Within seconds, you have a complete data profile. The tool generates: 9 EDA Visualizations automatically generated : Here's the actual correlation matrix generated: Key insights from the data: A complete, production-ready preprocessing script Includes scaling, encoding, and split logic I quietly released this on PyPI without any marketing push. Here's the organic growth: | Metric | Value | |---|---| Monthly Downloads | 891 | Last 7 Days | 282 | Yesterday | 216 | Python Versions | 3.10, 3.11, 3.12, 3.13, 3.14 | Platforms | Windows, macOS, Linux | Platform Distribution: | OS | Usage | |---|---| | Windows | ~45% | | Linux | ~35% | | macOS | ~20% | Python Version Distribution: | Version | Usage | |---|---| | Python 3.11 | ~40% | | Python 3.12 | ~30% | | Python 3.10 | ~15% | | Python 3.13+ | ~10% | User Base Demographics: Dataset URL → Download via KaggleHub → Load & Validate → → Profile Generation → → Statistical Analysis → → Visualization Generation → → Preprocessing Script Generation → → Notebook Generation → → All Outputs Saved kagglehub to handle authentication automaticallyI added a --feedback command that: kaggle-prep --feedback I'm actively building the Pro version based on user feedback: | Feature | Status | Expected | |---|---|---| | Automated PDF Reports | In Development | October 2026 | | Auto-ML Baseline | In Development | November 2026 | | Competition Optimization | Planned | December 2026 | | Custom Visualization Config | Planned | January 2027 | pip install kaggle-prep kaggle-prep uciml/iris --all GitHub: Link to your repo PyPI: Link to your package Issues/Feature Requests: Link to your issues page I built this to solve my own frustration. But the response tells me the frustration is universal . The last month validated three things: This is my first open-source project that actually serves a real user base. And I'm just getting started. I'm building in public. If you want to: pip install kaggle-prep Built by a student, for the data science community. 891 monthly downloads and counting. Downloads last month: 891 | Stars: Your count | Contributors: Your count Command Output If this tool saved you time, please star the repo. It helps more than you know.