207 lines
11 KiB
Markdown
207 lines
11 KiB
Markdown
**Important**: The content is being updated and amended throughout the
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spring semester of 2020!
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# An Introduction to Python and Programming
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The purpose of this repository is to serve as an interactive "book" for a
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thorough introductory course on programming in the
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**[Python](https://www.python.org/)** language.
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The course's **main goal** is to **prepare** the student for **further
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studies** in the "field" of **data science**.
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The "chapters" are written in [Jupyter notebooks](https://jupyter-notebook.readthedocs.io/en/stable/)
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which are a de-facto standard for exchanging code and results among data
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science professionals and researchers.
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They can be viewed in a plain web browser with the help of
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[nbviewer](https://nbviewer.jupyter.org/):
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- *Introduction*: Start up
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([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb)
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| [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_01_review.ipynb)
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| [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_02_exercises.ipynb))
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- **Part A: Expressing Logic**
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- *Chapter 1*: Elements of a Program
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([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb)
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| [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_01_review.ipynb)
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| [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_02_exercises.ipynb))
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- *Chapter 2*: Functions & Modularization
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([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb)
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| [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_01_review.ipynb)
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| [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_02_exercises.ipynb))
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- *Chapter 3*: Conditionals & Exceptions
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([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb)
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| [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_01_review.ipynb)
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| [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb))
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- *Chapter 4*: Recursion & Looping
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([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb)
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| [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_01_review.ipynb)
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| [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_02_exercises.ipynb))
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- **Part B: Managing Data and Memory**
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- *Chapter 5*: Bits & Numbers
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([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)
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| [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_01_review.ipynb)
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| [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_02_exercises.ipynb))
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- *Chapter 6*: Bytes & Text
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([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)
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| [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_01_review.ipynb))
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- *Chapter 7*: Sequential Data
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([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)
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| [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_01_review.ipynb)
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| [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb))
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- *Chapter 8*: Mappings & Sets
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([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb)
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| [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_01_review.ipynb)
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| [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_02_exercises.ipynb))
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However, it is recommended that students **install Python and Jupyter
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locally** and run the code in the notebooks on their own.
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This way, the student can play with the code and learn more efficiently.
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Precise **installation instructions** are either in the [00th notebook](
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https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb)
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or further below.
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Feedback is encouraged and will be incorporated.
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Open an issue in the [issues tracker](https://github.com/webartifex/intro-to-python/issues)
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or initiate a [pull request](https://help.github.com/en/articles/about-pull-requests)
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if you are familiar with the concept.
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## Prerequisites
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To be suitable for *total beginners*, there are *no* formal prerequisites.
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It is only expected that the student has:
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- a *solid* understanding of the **English language**,
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- knowledge of **basic mathematics** from high school,
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- the ability to **think conceptually** and **reason logically**, and
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- the willingness to **invest 2-4 hours a day for a month**.
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## Installation
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To follow this course, a working installation of **Python 3.7** or higher is
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expected.
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A popular and beginner friendly way is to install the [Anaconda Distribution](
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https://www.anaconda.com/distribution/) that not only ships Python but comes
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pre-packaged with a lot of third-party libraries from the so-called
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"scientific stack".
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Just go to the [download](https://www.anaconda.com/distribution/#download-section)
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section and install the latest version (i.e., *2019-10* with Python 3.7 at the
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time of this writing) for your operating system.
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Then, among others, you will find an entry "Anaconda Navigator" in your start
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menu like below.
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Click on it.
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<img src="static/anaconda_start_menu.png" width="30%">
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A window opens showing you several applications that come with the Anaconda
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Distribution.
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Now, click on "JupyterLab."
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<img src="static/anaconda_navigator.png" width="50%">
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A new tab in your web browser opens with the website being "localhost" and some
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number (e.g., 8888).
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This is the [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/)
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application that is used to display and run the Jupyter notebooks mentioned
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above.
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On the left, you see the files and folders in your local user folder.
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This file browser works like any other.
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In the center, you have several options to launch a new notebook file.
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<img src="static/jupyter_lab.png" width="50%">
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Next, to download the course's materials as a ZIP file, click on the green
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"Clone or download" button on the top right on this website.
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Then, unpack the ZIP file into a folder of your choosing, ideally somewhere
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within your personal user folder so that the files show up right away in
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JupyterLab.
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### Alternative Installation (for Instructors)
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Python can also be installed in a "pure" way as obtained from its core
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development team (i.e., without any third-party packages installed).
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However, this may be too "advanced" for a beginner as it involves working
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with a [terminal emulator](https://en.wikipedia.org/wiki/Terminal_emulator),
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which looks like the one in the picture below and is used *without* a mouse by
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typing commands into it.
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<img src="static/terminal.png" width="50%" align="center">
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Assuming that you already have a working version of Python 3.7 or higher
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installed (cf., the official [download page](https://www.python.org/downloads/)),
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the following summarizes the commands to be typed into a terminal emulator to
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get the course materials up and running on a local machine without the
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Anaconda Distribution.
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You are then responsible for understanding the concepts behind them.
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First, the [git](https://git-scm.com/) command line tool is a more professional
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way of "cloning" the course materials as compared to downloading them in a ZIP
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file.
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- `git clone https://github.com/webartifex/intro-to-python.git`
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This creates a new folder *intro-to-python* with all the materials of this
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repository in it.
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Inside this folder, it is recommended to create a so-called **virtual
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environment** with Python's [venv](https://docs.python.org/3/library/venv.html)
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module.
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This must only be done the first time.
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A virtual environment is a way of *isolating* the third-party packages
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installed by different projects, which is considered a best practice.
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- `python -m venv venv`
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The second *venv* is the environment's name and by convention often chosen to
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be *venv*.
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However, it could be another name as well.
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From then on, each time you want to resume work, go back into the
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*intro-to-python* folder inside your terminal and "activate" the virtual
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environment (*venv* is the name chosen before).
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- `source venv/bin/activate`
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This may change how the terminal's [command prompt](https://en.wikipedia.org/wiki/Command-line_interface#Command_prompt)
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looks.
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[poetry](https://poetry.eustace.io/docs/) and [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)
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are popular tools to automate the described management of virtual environments.
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After activation for the first time, you must install the project's
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**dependencies** (= the third-party packages needed to run the code), most
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notably [JupyterLab](https://pypi.org/project/jupyterlab/) in this project
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(the "python -m" is often left out [but should not be](https://snarky.ca/why-you-should-use-python-m-pip/);
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if you have poetry installed, you may just type `poetry install` instead).
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- `python -m pip install -r requirements.txt`
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The *requirements.txt* file also installs the [black](https://github.com/psf/black)
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tool (incl. the [blackcellmagic](https://github.com/csurfer/blackcellmagic)
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extension) and the [RISE](https://github.com/damianavila/RISE) extension.
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With them, the instructor can easily re-format code in a class session and
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execute code in presentation mode (currently RISE only works with the
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older `jupyter notebook` command).
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With everything installed, you can now do the equivalent of clicking the
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"JupyterLab" entry in the Anaconda Navigator.
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- `jupyter lab`
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This opens a new tab in your web browser just as above.
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## About the Author
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Alexander Hess is a PhD student at the Chair of Logistics Management at the
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[WHU - Otto Beisheim School of Management](https://www.whu.edu) where he
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conducts research on urban delivery platforms and teaches an introductory
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course on Python (cf., [Fall Term 2019](https://vlv.whu.edu/campus/all/event.asp?objgguid=0xE57C2715B01B441AAFD3E79AA05CACCF&from=vvz&gguid=0x6A2B0ED5B2B949E69957A2099E7DE2F1&mode=own&tguid=0x3980A9BBC3BF4A638E977F2DC163F44B&lang=en),
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[Spring Term 2020](https://vlv.whu.edu/campus/all/event.asp?objgguid=0x3354F4C108FF4E959CDD692A325D9AFE&from=vvz&gguid=0x262E29795DD742CFBDE72B12B69CEFD6&mode=own&lang=en&tguid=0x2E4A7D1FF3C34AD08FF07685461781C9)).
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Connect him on [LinkedIn](https://www.linkedin.com/in/webartifex).
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