# IPython IPython is a rich toolkit for using Python interactively, including an interactive Python shell and a Jupyter kernel for working with Python code in notebooks and other interactive frontends. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Reloading the REPL in Python DevFeed: [Reloading the REPL in Python]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-03-07T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Python](), [Rails](), [Code](), [import](), [IPython]() Tags: [code](), [import](), [python](), [rails]() ### AI overview A tutorial on reloading changed Python modules in an interactive REPL using importlib.reload(), with a comparison to Rails' reload! command and an example of automating the process through an IPython configuration. ### Source excerpt When you know something well you always look to learn by analogy. Today's short topic is reloading the REPL (read-evaluate-print-loop) interactive console in Python and then contrast between Python and Rails. And, yes, I get that Python is a language and Rails is a framework but where you tend to work directly in the Python console, you also tend to work directly in the Rails console (as opposed to the IRB Ruby console on which the Rails console is based but the Rails console improves it a lot). The reason this is important is that if you use a REPL a lot, well, you are constantly switching between editor and REPL and until this damn blog post, I had to quit my Python console every damn time. Just imagine me cursing up a blue streak and you will get a small fraction of my frustration when you see the difference below between Python and Rails. Python - Launching the Console python3 Rails - Launching the Console rails c Note: You may have to type this as bundle exec rails c (also knowing as the "bundle exec" tax). Python - Reloading the Console When You Change Your Code import importlib importlib.reload(your_module_name_here) Example: import expert_classification # try some code; find bug; fix bug import importlib importlib.reload(expert_classification) # try code again Rails - Reloading the Console When You Change Your Code reload! Note: If you change your Rails gem stack then you have to exit the console entirely. I'll give you that. Why This Matters, Why Rails is Better Here And a Rant It is hard to know when you're changing stuff with more than a single module exactly what you changed. The Rails method of typing reload! and having your whole environment reloaded solved this entirely. Note: The reload method on importlib is new in Python 3.4. Insert emoji of Munsch scream here How the hell is this possible that reloading came only on March 17, 2014 when Python started shipping in December 1989? I can't remember ever not having reload! in Rails. Sidebar: Making this ## Profiling Django for CPU bound apps DevFeed: [Profiling Django for CPU bound apps]() Original publisher: [Read original article]() Author: Graham King Published: 2011-09-15T19:24:17Z Content type: tutorial Language: en Sources: [Graham King]() Topics: [Django](), [debug](), [IPython](), [Extension]() Tags: [app](), [browser](), [database](), [debug](), [django](), [profiling](), [run](), [software]() ### AI overview A tutorial on profiling CPU-bound Django applications with django-extensions. It explains how to run the profiling server, inspect generated hotshot files with IPython and pstats, and identify functions consuming cumulative time. ### Source excerpt "Unleash the power of Django profiling with this easy guide." ## Python in the Scientific World DevFeed: [Python in the Scientific World]() Original publisher: [Read original article]() Author: Guido van Rossum (noreply@blogger.com) Published: 2009-11-05T18:12:00Z Content type: article Language: en Sources: [Guido van Rossum]() Topics: [Python](), [Computing](), [NumPy](), [data](), [IPython]() Tags: [computing](), [data](), [library](), [python](), [real-time](), [time-series]() ### AI overview The article reports on a Py4Science meeting at UC Berkeley featuring lightning talks about projects using Python for scientific work. It describes Python and extensions such as NumPy being used across fields including neuroimaging, astronomy, genomics, and symbolic mathematics. ### Source excerpt Yesterday I attended a biweekly meeting of an informal a UC Berkeley group devoted to Python in science (Py4Science), organized by Fernando Perez. The format (in honor of my visit) was a series of 4-minute lightning talks about various projects using Python in the scientific world (at Berkeley and elsewhere) followed by an hourlong Q&A session. This meant I didn't have to do a presentation and still got to interact with the audience for an hour -- my ideal format. I was blown away by the wide variety of Python use for scientific work. It looks like Python (with extensions like numpy) is becoming a standard tool for many sciences that need to process large amounts of data, from neuroimaging to astronomy. Here is a list of the topics presented (though not in the order presented). All these describing Python software; I've added names and affiliations insofar I managed to get them. (Thanks to Jarrod Millman for providing me with a complete list.) Most projects are easily found by Googling for them, so I have not included hyperlinks except in some cases where the slides emphasized them. (See also the blog comments.) Fernando gave an overview of the core Python software used throughout scientific computing: NumPy, Matplotlib, IPython (by Fernando), Mayavi, Sympy (about which more later), Cython, and lots more. On behalf of Andrew Straw (Caltech), Fernando showed a video of an experimental setup where a firefly is tracked in real time by 8 camaras spewing 100 images per second, using Python software. Nitimes, a time-series analysis tool for neuroimaging, by Ariel Rokern (UCB). A comparative genomics tool by Brent Pedersen of the Freeling Lab / Plant Biology (UCB). Copperhead: Data-Parallel Python, by Bryan Catanzaro (working with Armando Fox) and others. Nipype: Neuroimaging analysis pipeline and interfaces in Python, by Chris Burns (http://nipy.sourceforge.net/nipype/). SymPy -- a library for symbolic mathematics in Pure Python, by Ondrej Certik (runs on Google App Engin