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Lex Fridman Podcast
#224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming
Sep 23, 2021
00:00
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Quick takeaways
- SciPy provides a powerful and accessible toolkit for scientific computing in Python.
- The creator of SciPy explores the intersection of open source software, collaboration, and economics.
- SciPy empowers researchers to tackle complex problems and visualize data.
- Making programming tools more accessible to scientists and engineers is crucial.
- Challenges in open-source projects include lack of funding and limited resources.
- Python 3 migration requires appealing features and substantial improvements to encourage adoption.
Deep dives
SciPy: Building a Robust Scientific Computing Environment
SciPy was created as a way to provide scientists and engineers with a powerful and accessible toolkit for scientific computing in Python. It started as a collection of modules that added functionality to Python's existing numeric library, providing tools for tasks such as ordinary differential equations, integration, optimization, and plotting. The goal was to create a coherent and efficient environment for R&D, allowing users to leverage the power of Python while still maintaining compatibility with the numeric array library. As the project grew, it faced challenges in consensus building and managing the increasing number of contributors. However, SciPy's impact and popularity continued to grow, with the addition of key modules like Matplotlib for advanced plotting capabilities. The release of SciPy in 2001 and the subsequent creation of the SciPy conference provided a platform for scientists and programmers to come together and discuss the evolving landscape of scientific computing in Python.
The Journey Towards Open Source Economics
The creation and development of SciPy sparked a personal journey for its creator, exploring the intersection of open source software, collaboration, and economics. This journey led to a study of economics and a realization of the importance of preserving the culture of open source, while also finding ways to sustainably support oneself and the community. It involved understanding the role of prices and market signals, the need for creating frictionless experiences for users, and the challenges of connecting the ethos of open source with the necessity of building profitable ventures. Although not fully resolved, these insights have shaped the creator's ongoing pursuit of building open source tools that both benefit users and provide avenues for sustainable economic growth.
The Impact of SciPy: Empowering Scientists and Engineers
SciPy has had a profound impact on the scientific and engineering communities, providing a powerful and accessible toolset for their work. By extending Python's capabilities with modules for numerical computing and analysis, SciPy has empowered researchers to tackle complex problems and leverage the power of programming in their work. The availability of functions for ordinary differential equations, integration, optimization, and plotting has made it easier for scientists and engineers to perform advanced calculations and visualize their data. The growth and popularity of SciPy, along with the contributions from a vibrant community, have ensured its continued evolution and impact, enabling researchers worldwide to push the boundaries of their fields.
Making programming accessible to scientists
The podcast episode emphasized the importance of making programming tools more accessible to scientists and engineers. This involves providing them with powerful building blocks and functions that are easy to use and understand. It is also important to have rich documentation that allows scientists to quickly grasp the functionality of different functions and delve into the complexities involved.
The challenges of open-source development
The podcast discussed the challenges faced in the development of open-source projects like NumPy. Lack of funding and limited resources have been major obstacles in keeping up with the evolving needs of the community. The speaker highlighted that volunteer labor and limited financial support have made it difficult to implement desired features such as interactive environments, good documentation, and GPU compatibility.
Lessons from the evolution of Python
The episode touched upon the challenges faced during the transition from Python 2 to Python 3, highlighting lessons learned. It was noted that the slow migration was partly due to the lack of appealing features in the early releases of Python 3. However, it also illustrated the difficulty of moving a large user base away from a familiar version and the importance of providing substantial improvements to encourage adoption. The evolution of Python 3 showcased the importance of considering user needs and ensuring that new versions offer compelling advantages.
Importance of Packaging and Conda
Conda was created to address the challenges of packaging and distribution in the Python ecosystem. It provides a cross-platform package manager that simplifies the installation and management of libraries and dependencies. Conda's focus is on user-friendly installations and ensuring compatibility across different operating systems, making it a valuable tool for data scientists and developers.
The Mission of QuantSight Labs
QuantSight Labs is a nonprofit organization aiming to improve and advance open source projects in the Python ecosystem. It focuses on making core libraries such as NumPy, SciPy, and scikit-learn better, faster, and more scalable. The organization works closely with the community, offering financial support and collaborating with developers to address critical areas of improvement.
The Role of Open Teams
Open Teams is a company that emerged from the efforts of QuantSight. It serves as an incubator for new ideas and products within the Python ecosystem. Open Teams aims to foster innovation and create new companies by providing the necessary resources and support. The profits generated by these ventures are reinvested into open source projects, ensuring the sustainability and growth of the community.
The Power of Open Source Communities
Open source communities have the power to drive business and innovation. By leveraging the organic formation of teams within these communities, businesses can naturally develop and thrive. The connection to open source is vital, as it allows companies to create customizable solutions that are cost-effective and adaptable. Open Teams, a concept specific to the Piedata ecosystem, serves as a marketplace for connecting enterprise software companies with open source communities, providing solutions to solve their software problems.
Hiring Great Programmers and Building Successful Teams
When it comes to hiring programmers and building successful teams, it is crucial to look for individuals who possess curiosity, a desire to continuously learn, and a commitment to open source. An effective method for finding talented programmers is to look for contributors in open source projects, as their involvement demonstrates both skill and passion. Balancing the abilities of open source contributors with those focused on product development can create a powerful team dynamic. Additionally, it is important to create an environment that fosters learning and encourages innovation to attract and retain top talent.
Travis Oliphant is a data scientist, entrepreneur, and creator of NumPy, SciPy, and Anaconda. Please support this podcast by checking out our sponsors:
– Novo: https://banknovo.com/lex
– Allform: https://allform.com/lex to get 20% off
– Onnit: https://lexfridman.com/onnit to get up to 10% off
– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil
– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium
EPISODE LINKS:
Travis’s Twitter: https://twitter.com/teoliphant
Travis’s Wiki Page: https://en.wikipedia.org/wiki/Travis_Oliphant
NumPy: https://numpy.org/
SciPy: https://scipy.org/about.html
Anaconda: https://www.anaconda.com/products/individual
Quansight: https://www.quansight.com
PODCAST INFO:
Podcast website: https://lexfridman.com/podcast
Apple Podcasts: https://apple.co/2lwqZIr
Spotify: https://spoti.fi/2nEwCF8
RSS: https://lexfridman.com/feed/podcast/
YouTube Full Episodes: https://youtube.com/lexfridman
YouTube Clips: https://youtube.com/lexclips
SUPPORT & CONNECT:
– Check out the sponsors above, it’s the best way to support this podcast
– Support on Patreon: https://www.patreon.com/lexfridman
– Twitter: https://twitter.com/lexfridman
– Instagram: https://www.instagram.com/lexfridman
– LinkedIn: https://www.linkedin.com/in/lexfridman
– Facebook: https://www.facebook.com/lexfridman
– Medium: https://medium.com/@lexfridman
OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(07:06) – Early programming
(28:47) – SciPy
(45:41) – Open source
(57:23) – NumPy
(1:34:39) – Guido van Rossum
(1:46:57) – Efficiency
(1:55:49) – Objects
(2:02:47) – Numba
(2:11:53) – Anaconda
(2:16:20) – Conda
(2:31:56) – Quansight Labs
(2:35:32) – OpenTeams
(2:43:05) – GitHub
(2:48:35) – Marketing
(2:53:13) – Great programming
(3:04:03) – Hiring
(3:08:01) – Advice for young people
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