Python for decision makers and business leaders Transcripts
Chapter: Data science in Python
Lecture: Jupyter notebooks

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0:00 Most of this work that I just described
0:02 was done in something called a Jupyter Notebook.
0:05 These use to be called iPython notebooks
0:08 but now it's Jupyter
0:09 because it actually runs many other languages.
0:11 Even though it started out being just Python
0:13 now you can do things like R and Julia and
0:16 even .NET and C++.
0:18 It's kind of everything.
0:19 So it's these notebooks that have this visual look here.
0:23 You can see the Lorenz differential equations.
0:25 There's a little description.
0:26 You can write a little bit of code
0:28 and a little more description, a little bit more of code
0:30 and oh, now you get a graph that you can interact with
0:32 with these little widgets.
0:33 It's really really great for exploring data when you don't
0:37 know exactly what you need to do.
0:39 You get the data and you start looking it at it
0:40 and you slice it this way and that way.
0:41 And you ask questions, and you see it.
0:44 Very very different experience than writing
0:46 the Flask web application.
0:48 In Flask we broke it into a bunch of little files
0:51 we put them all together.
0:52 We wrote a little code here, called over to that thing
0:54 passed it off to that.
0:56 Notebooks are these one thing here that you can work with
0:59 and you just kind of explore it as you go.
1:01 You don't even necessarily know where
1:02 you're going to end up.
1:03 At least the early stages of much data science
1:06 is done this way. You probably don't productize it.
1:09 You don't take this notebook and make it a web service
1:11 that then can be consumed by an application.
1:14 You probably go and convert it over to that Flask
1:16 type of story that we were talking about.
1:18 But at the beginning, the exploration and
1:20 the explanation side, it's with these notebooks.
1:23 Oh, and this also happens to be similar to things like
1:27 Matlab or R Studio.
1:29 Unlike say Matlab or Mathematica, instead of costing
1:31 thousands of dollars, no.
1:34 This costs, well, nothing. It's free.
1:36 I's supported by NSF, National Science Foundation
1:39 in the United States and a bunch of other organizations
1:42 as well as all the scientists working on this project
1:46 as part of their research.
1:48 It's open-source, just like Python.