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

Dr. Travis Oliphant, creator of NumPy and SciPy, discusses the origins and future of these Python libraries, Anaconda's start, Numba's market entry, Python's impact on scientists, commercial projects supporting open-source efforts, and the future of scientific computing and Python libraries.