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Top 23 Python Data Science Projects
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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Ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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pytorch-lightning
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
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ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
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d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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ipython
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
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nni
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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ydata-profiling
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
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As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.
Project mention: AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite | news.ycombinator.com | 2024-04-09Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:
- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...
- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.
There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
Project mention: Building in Public: Leveraging Tublian's AI Copilot for My Open Source Contributions | dev.to | 2024-02-12Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.
Project mention: Creating a Sales Analysis Application with Streamlit: A Practical Approach to Business Intelligence | dev.to | 2024-04-192.-Go to https://streamlit.io, log in, and create a new app from your GitHub repository.
22. Ray | Github | tutorial
Project mention: Show HN: Dropbase – Build internal web apps with just Python | news.ycombinator.com | 2023-12-05There's also that library all the AI models started using that gives you a public URL to share. After researching it: https://www.gradio.app/ is the link.
It's used specifically for making simple UIs for machine learning apps. But I guess technically you could use it for anything.
Project mention: Step by step guide to create customized chatbot by using spaCy (Python NLP library) | dev.to | 2024-03-23Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):
Project mention: Lightning AI Studios – A persistent GPU cloud environment | news.ycombinator.com | 2023-12-14
Project mention: How and where is matplotlib package making use of PySide? | /r/learnpython | 2023-12-07
Project mention: My kernel dies when I fit my LightFm model from Microsoft Recommenders | /r/Jupyter | 2023-06-16
If you’re already using ipython, this isn’t a problem because you’ll already need to download most of these dependencies anyway. But if you’re not using ipython… you’ll still need to download those dependencies.
Project mention: Prefect: A workflow orchestration tool for data pipelines | news.ycombinator.com | 2024-03-13
Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.
If you are doing data analysis I don't think any of the 3 pieces of software you mentioned are going to be that helpful.
I see these products as tools for data visualization and reporting i.e. presenting prepared datasets to users in a visually appealing way. They aren't as well suited for serious analytics.
I can't comment on Superset or Tableau but I am familiar with Power BI (it has been rolled out across my org), the type of statistics you can do with it are fairly rudimentary. If you need to do any thing beyond summarizing (counts, averages, min, max etc). It is not particularly easy.
For data analysis I use SAS or R. This software allows you do things like multivariate regression, timeseries forecasting, PCA, Cluster analysis etc. There is also plotting capability.
Both these products are kind of old school, I've been using them since early 2000's, the "new school" seems to be Python. Pretty much all the recent data science people in my organization use Python. Particularly Pandas and libraries like Seaborn (https://seaborn.pydata.org/).
The "power" users of Power BI in my organization tend to be finance/HR people for use cases like drill down into cost figures or Interactively presenting KPI's and other headline figures to management things like that.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python Data Science related posts
- My Favorite DevTools to Build AI/ML Applications!
- Release: Keras 3.3.0
- Runhouse
- Hierarchical Clustering
- Creating a Sales Analysis Application with Streamlit: A Practical Approach to Business Intelligence
- Orange Data Mining
- 🦙 Llama-2-GGML-CSV-Chatbot 🤖
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Index
What are some of the best open-source Data Science projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Keras | 60,937 |
| 2 | scikit-learn | 58,046 |
| 3 | Pandas | 41,923 |
| 4 | Airflow | 34,485 |
| 5 | streamlit | 31,506 |
| 6 | Ray | 30,988 |
| 7 | gradio | 28,730 |
| 8 | spaCy | 28,704 |
| 9 | pytorch-lightning | 26,883 |
| 10 | data-science-ipython-notebooks | 26,459 |
| 11 | ML-From-Scratch | 23,164 |
| 12 | d2l-en | 21,628 |
| 13 | dash | 20,472 |
| 14 | matplotlib | 19,223 |
| 15 | recommenders | 17,942 |
| 16 | ipython | 16,134 |
| 17 | best-of-ml-python | 15,302 |
| 18 | gensim | 15,236 |
| 19 | Prefect | 14,586 |
| 20 | nni | 13,726 |
| 21 | dvc | 13,116 |
| 22 | ydata-profiling | 12,022 |
| 23 | seaborn | 11,946 |
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