pandas, every mention
21 scenes · ← back to pandas
tap a year for its mentions
every year anyone Wes McKinney 8Matt Turck 8Peter Wang 4Zainab (Zeneb) 2Kedro Product Manager 2Savin Goyal 1Nick Schrock 1Mike Abbott 1Matt Housley 1Julien Le Dem 1
Verbatim, from the transcripts: the passages where pandas comes up
Fundamentals of Data Engineering | Joe Reis and Matt Housley
- ▶ 14:41 Matt Housley Or, yeah, yeah, data engineers really need to know Spark, or they need to know Python, or they need to know Pandas, or Snowflake, or whatever technology it is.
Fireside Chat: Abe Gong (Founder & CEO, Superconductive) with Matt Turck (Partner, FirstMark)
Fireside Chat: Nick Schrock (Founder & CEO, Elementl) with Matt Turck (Partner, FirstMark)
- ▶ 17:00 Nick Schrock They want to use tools like pandas scikit-learn.
Fireside Chat: Savin Goyal (ML Infra team (Metaflow), Netflix) with Matt Turck (Partner, FirstMark)
- ▶ 10:39 Savin Goyal Where, let's say, you know, if you are, say, looking at, like, uh, Pandas Matrix, uh, of, like, say, you know, like, 50 gigs, like, all of a sudden, uh, your laptop will not be able to support that.
Introducing Kedro
- ▶ 11:55 Kedro Product Manager So just for your purposes, you see that we're using a panda CSV data set. 2 times in the scene
Data Observability and Pipelines: OpenLineage and Marquez
- ▶ 9:54 Julien Le Dem It was like, before you have all those projects that are interested in metadata, Amundsen, DataHub, Marques, Atlas, that have to build all those integrations with all those projects, whether they're the Sparks, the Pandas, uh, the…
Fireside Chat: Wes McKinney (Founder & CEO, Ursa Computing) with Matt Turck (Partner, FirstMark)
- ▶ 0:08 Matt Turck I'd love to start with, with pandas, uh, actually, which was like your sort of like original baby.
- ▶ 2:16 Matt Turck And to make this even more approachable to, to folks that may not, um, you know, spend that much time in the industry, the, uh, a lot of energy is spent, like, talking about, uh, machine learning and AI and all the things, and, uh, but,… 3 times in the scene
- ▶ 3:34 Matt Turck And still in an effort to make this, uh, educational, do you want to talk about, uh, data structures in the context of, of pandas, and, uh, in particular, there's a term that comes back a lot, which is data frame. 4 times in the scene
- ▶ 6:01 Wes McKinney Uh, the array computing that you have in NumPy, we needed the tabular data manipulation, data cleaning, data loading that you have in pandas. 2 times in the scene
- ▶ 9:10 Wes McKinney So, uh, you know, I was interested in this problem and I'd experienced it from the perspective of pandas and the Python ecosystem, like wanting to build bridges from Python into all of these other, all of these other systems.
- ▶ 12:40 Wes McKinney There are also row oriented interfaces, which makes them an awkward fit when you need to convert to a column oriented tool like pandas. 2 times in the scene
- ▶ 16:11 Wes McKinney Um, and part of, part of what's motivated the, the data warehouses to support arrow is because we build a really, uh, a really efficient bridge between arrow and pandas. 2 times in the scene
- ▶ 24:30 Wes McKinney So you can use spark at the single node scale as an alternative to pandas through the koalas interface, but you'll find that for many workloads, it's simply slower than pandas, which is not super impressive.
Data Science Is A Literacy, Not A Job // Peter Wang, Anaconda (FirstMark's Data Driven NYC)
- ▶ 1:11 Peter Wang So, like, we have a couple of, um, a couple of the core maintainers for Pandas are full-time developers, and they're just paid to work on Pandas, and same thing with scikit-learn.
- ▶ 3:54 Peter Wang Um, if you look at Anaconda, though, people who have to go and write Python code in a Jupyter notebook or Python scripts using Pandas and NumPy, we have, you know, we're now in the millions of unique Conda package downloaders every week.
- ▶ 15:37 Peter Wang We, we do open source in a different way, so we don't do, like, an open core model where, like, you know, 5000 rows of a data frame are free in Pandas, and then 5000 in the first row cost you 20 bucks. 2 times in the scene
Ion Stoica, Databricks // Creating Apache Spark // Data Driven NYC (FirstMark Capital)
- ▶ 22:49 Ion Stoica Um, you know, it's, ah, ah, in terms of the parser, it's a high parser, and recently we also released this, ah, DataFrames API, ah, ah, which is very similar with, you know, the Pandas, ah, which is very popular.
Michael Rubenstein and Catherine Williams, App Nexus // Data Driven #31 // Nov 2014
- ▶ 7:15 Catherine Williams In terms of tools at that time, um, maybe because our data was a little bit rigid, we used a lot of Bayesian math, Bayesian probability, um, we still had a little bit of a business intelligence focus at that time, so Excel was still in…
Mike Abbott, KPCB // Data Driven #30 // Oct 2014 (Hosted by FirstMark Capital)
- ▶ 5:16 Mike Abbott And, uh, you know, I, I actually still write software, um, and recently, because I'm meeting all these companies, I want to look at, like, you know, what, if I use Pandas, which, thank you for contributing, that's a great, great tool on…
Panel discussion // Data Driven NYC #12 // Jan 2013
- ▶ 33:34 Zainab (Zeneb) trying to convince everyone in, in the world to use, uh, Pandas for Python, it's great. 2 times in the scene