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Python-LecturesNote: Andreas Ernst has improvised and updated the repo to python 3, LinkIntroductionPython is a modern, robust, high level programming language. It is very easy to pick up even if you are completely new to programming.InstallationMac OS X and Linux comes pre installed with python. Windows users can download python from .To install IPython run,$ pip install ipython[all]This will install all the necessary dependencies for the notebook, qtconsole, tests etc.Installation from unofficial distributionsInstalling all the necessary libraries might prove troublesome. Anaconda and Canopy comes pre packaged with all the necessary python libraries and also IPython.AnacondaDownload Anaconda from is completely free and includes more than 300 python packages. Both python 2.7 and 3.4 options are available.CanopyDownload Canopy from has a premium version which offers 300+ python packages. But the free version works just fine. Canopy as of now supports only 2.7 but it comes with its own text editor and IPython environment.Launching IPython NotebookFrom the terminalIn Canopy and Anaconda, Open the respective terminals and execute the above.How to learn from this resource?You can download the pdf copy from here : Get Started with PythonIt is better to download all the ipython notebooks from this repository and learn it on the notebook itself rather than having to refer to a pdf.Launch ipython notebook from the folder which contains the notebooks. Open each one of them All Output > Clear">Cell > All Output > ClearThis will clear all the outputs and now you can understand each statement and learn interactively.Table of contents00 - Introduction and Installation01 - Variable, Operators and Built-in Functions02 - Print Statement, Precision and FieldWidth03 - Lists, Tuples and Sets04 - Strings and Dictionaries05 - Control Flow Statements06 - Functions07 - ClassesThese are online read-only versions.LicenseThis work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit

The IPython notebook is to date the closest thing to a mathematical "word processor", and much of the credit for this accomplishment, as well as for IPython itself, belongs to the IPython project's principal organizer, Fernando Perez. For his work on IPython, Perez received the Free Software Foundation's 2012 award for the advancement of free software. Compared to Mathematica or Maple, IPython has the advantage that it's free and has many of the most-often-used features of these expensive programs. Compared to Sage, IPython requires less storage space and setup time. For a student or a professional who requires mathematical results but who would prefer not to spend thousands of dollars on proprietary software, IPython may be the best choice of the math programs presently available. This article set covers IPython installation and use, with many practical examples of math processing in diverse fields. To navigate this article set, use the arrows and drop-down lists at the top and bottom of each page.

P. Molnarunread,May 17, 2021, 11:38:56 AM5/17/21to spyd...@googlegroups.com Thanks for the reply. I used the new installer and put Spyder in my user file tree. I just removed Spyder and reinstalled it, this time in a venv. I got ete same missing kernel message when I ran the new installation for the first time. When I checked the installation I found that the correct kernel had been installed. Of course because my venv contained python3.9 the pip that I used to install the kernel was pip3. Here is what happened: (Spyder-5.0.2) C:\Users\SPM\Apps\Spyder-5.0.2>pip install spyder-kernels==2.0.2 Collecting spyder-kernels==2.0.2 Using cached spyder_kernels-2.0.2-py2.py3-none-any.whl (65 kB) Collecting jupyter-client>=5.3.4 Using cached jupyter_client-6.1.12-py3-none-any.whl (112 kB) Collecting cloudpickle Using cached cloudpickle-1.6.0-py3-none-any.whl (23 kB) Collecting ipython>=7.6.0 Using cached ipython-7.23.1-py3-none-any.whl (785 kB) Collecting ipykernel>=5.3.0 Using cached ipykernel-5.5.4-py3-none-any.whl (120 kB) Collecting decorator Using cached decorator-4.4.2-py2.py3-none-any.whl (9.2 kB) Collecting pyzmq>=17 Using cached pyzmq-22.0.3-cp39-cp39-win_amd64.whl (1.2 MB) Collecting traitlets>=4.1.0 Using cached traitlets-5.0.5-py3-none-any.whl (100 kB) Collecting tornado>=4.2 Using cached tornado-6.1-cp39-cp39-win_amd64.whl (422 kB) Collecting backcall Using cached backcall-0.2.0-py2.py3-none-any.whl (11 kB) Collecting pygments Using cached Pygments-2.9.0-py3-none-any.whl (1.0 MB) Collecting matplotlib-inline Using cached matplotlib_inline-0.1.2-py3-none-any.whl (8.2 kB) Requirement already satisfied: setuptools>=18.5 in c:\users\spm\apps\spyder-5.0.2\lib\site-packages (from ipython>=7.6.0->spyder-kernels==2.0.2) (56.0.0) Collecting prompt-toolkit!=3.0.0,!=3.0.1,=2.0.0 Using cached prompt_toolkit-3.0.18-py3-none-any.whl (367 kB) Collecting pickleshare Using cached pickleshare-0.7.5-py2.py3-none-any.whl (6.9 kB) Collecting colorama Using cached colorama-0.4.4-py2.py3-none-any.whl (16 kB) Collecting jedi>=0.16 Using cached jedi-0.18.0-py2.py3-none-any.whl (1.4 MB) Collecting parso=0.8.0 Using cached parso-0.8.2-py2.py3-none-any.whl (94 kB) Collecting jupyter-core>=4.6.0 Using cached jupyter_core-4.7.1-py3-none-any.whl (82 kB) Collecting python-dateutil>=2.1 Using cached python_dateutil-2.8.1-py2.py3-none-any.whl (227 kB) Collecting pywin32>=1.0 Using cached pywin32-300-cp39-cp39-win_amd64.whl (9.2 MB) Collecting wcwidth Using cached wcwidth-0.2.5-py2.py3-none-any.whl (30 kB) Collecting six>=1.5 Using cached six-1.16.0-py2.py3-none-any.whl (11 kB) Collecting ipython-genutils Using cached ipython_genutils-0.2.0-py2.py3-none-any.whl (26 kB) Installing collected packages: ipython-genutils, wcwidth, traitlets, six, pywin32, parso, tornado, pyzmq, python-dateutil, pygments, prompt-toolkit, pickleshare, matplotlib-inline, jupyter-core, jedi, decorator, colorama, backcall, jupyter-client, ipython, ipykernel, cloudpickle, spyder-kernels Successfully installed backcall-0.2.0 cloudpickle-1.6.0 colorama-0.4.4 decorator-4.4.2 ipykernel-5.5.4 ipython-7.23.1 ipython-genutils-0.2.0 jedi-0.18.0 jupyter-client-6.1.12

Introduction NOTE: Be sure to read my article about Jupyter, IPython's evolutionary successor. Much has been written about the difference between mathematics and other intellectual disciplines — how mathematics is less accessible, more difficult, more esoteric. But modern computers are closing the gap between mathematics and activities that have a lower threshold for productive activity, like word processing. We're seeing a rapid change in the role of computers in society and in education, driven by the fact that computer power is increasing as quickly as computer costs decline. This should allow something I call "math processing" to take its place alongside word processing in the personal toolkit of the modern computer user. When I wrote the first version of Apple Writer in 1979, my target platform (the original Apple ][) had a primitive display — 40 characters wide, uppercase only — but when people found out about my program, they couldn't get it quickly enough. The reason? It increased the efficiency with which people processed words. A few math programs enable fast, efficient math processing, but at a high cost (examples Mathematica, Maple). The high cost of these programs results from the sometimes complex programming required to produce useful mathematics, but over time open-source projects like Sage, written and maintained by volunteers, have brought costs down (my Sage tutorial). Internally, Sage relies to a large extent on the math resources of a programming language named Python. Python was chosen because it has a large, and increasing, number of math-oriented features and libraries, including the important ability to solve equations symbolically. Although Python began as a classic computer language in which source files are written and then tested, a relatively new Python project named IPython supports user interactions (IPython = interactive Python). This article introduces and explores IPython, with special emphasis

This.See below for more about installing additional moduleswhen working with Schrödinger’s Python.Running Schrödinger Scripts¶Individual Python scripts can be run via:$SCHRODINGER/run []The $SCHRODINGER/run command sets up environment variables neededfor executables and libraries shipped by Schrödinger to work properly. Itwill search a number of standard locations if the named script does not havean explicitly specified path. Along with a number of built in locations inthe SCHRODINGER directory 2, these are:The current working directory.The directory specified by the environment variableSCHRODINGER_SCRIPTS.The directory /scriptsX.Y, where is ~/.schrodinger on Linux and%LOCALAPPDATA%\Schrodinger on Windows.Your PATH.The Schrödinger script installation tools support installation intoSCHRODINGER_SCRIPTS (provided that you have write permission) andyour directory.Exploring Schrödinger Modules¶IPython and Jupyter Notebook¶An excellent way to explore Schrödinger modules is from a Python interactiveprompt. We recommend the IPython shell for this, which can be started with thecommand-line invocation:The IPython shell makes interactive exploration of code easy because itprovides tab completion and the ability to introspect code and doc stringsimmediately in the shell. There are many resources online to learn more aboutthese and other features of IPython.Note that an IPython shell is also provided from within Maestro (“Python Shell”in the Window menu).Jupyter Notebook is also available using the following command-lineinvocation:$SCHRODINGER/run jupyter notebookAccessing Your Own Modules¶This subsection can be skipped until you want to use modules that aren’tincluded in our distribution. (In addition to Schrödinger packages ourdistribution contains a number of useful third-party modules includingNumpy, SciPy, matplotlib, PyOpenGL, and BioPython.)The Schrödinger Python installation uses the PYTHONPATH environmentvariable in the same way as any other Python installation, so the easiest wayto access your own modules is by adding their directories to thePYTHONPATH. Note that these modules must be compatible with Python3.11 and compiled modules must be compatible with the Schrödinger installation(e.g. for Linux-x86 installations they must be 32-bit).If a SCHRODINGER_PYTHONPATH environment variable is present, ourPython distribution uses it in preference to the standardPYTHONPATH. This allows an incompatible local Python installationto coexist with our distribution. Because Maestro and other Schrödingerexecutables use Python, it is important to setSCHRODINGER_PYTHONPATH if your PYTHONPATH containsincompatible modules. Set it to an empty string to override thePYTHONPATH without specifying an alternate search path.Installing Additional Modules¶To install additional modules to a local directory for use withSchrödinger’s Python distribution, you can run $SCHRODINGER/runsetup.py install --prefix=$LOCAL_PY_PACKAGES on the setup.py fileprovided with the package. (For this to work, your$LOCAL_PY_PACKAGES/lib/python3.11/site-packages directory must existand be in your PYTHONPATH.) See Installing Python Modules for generalinformation on installing python packages.Per-user Virtual Environments for Installing Additional Modules¶We recommend virtual environments for users who want to experimentwith additional modules that are not shipped with Schrödinger Python. A Python“virtual environment” is an isolated, lightweight, user-local Pythoninstallation that can access the Schrödinger modules and to which users caneasily install additional Python modules. The venv Python module documentation