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Linkoteca. python


PyPI packages not in the standard library:

  • virtualenv is a very popular tool that creates isolated Python environments for Python libraries. If you’re not familiar with this tool, I highly recommend learning it, as it is a very useful tool, and I’ll be making comparisons to it for the rest of this answer.

    It works by installing a bunch of files in a directory (eg: env/), and then modifying the PATH environment variable to prefix it with a custom bin directory (eg: env/bin/). An exact copy of the python or python3 binary is placed in this directory, but Python is programmed to look for libraries relative to its path first, in the environment directory. It’s not part of Python’s standard library, but is officially blessed by the PyPA (Python Packaging Authority). Once activated, you can install packages in the virtual environment using pip.

  • pyenv is used to isolate Python versions. For example, you may want to test your code against Python 2.7, 3.6, 3.7 and 3.8, so you’ll need a way to switch between them. Once activated, it prefixes the PATH environment variable with ~/.pyenv/shims, where there are special files matching the Python commands (python, pip). These are not copies of the Python-shipped commands; they are special scripts that decide on the fly which version of Python to run based on the PYENV_VERSION environment variable, or the .python-version file, or the ~/.pyenv/version file. pyenv also makes the process of downloading and installing multiple Python versions easier, using the command pyenv install.
  • pyenv-virtualenv is a plugin for pyenv by the same author as pyenv, to allow you to use pyenv and virtualenv at the same time conveniently. However, if you’re using Python 3.3 or later, pyenv-virtualenv will try to run python -m venv if it is available, instead of virtualenv. You can use virtualenv and pyenv together without pyenv-virtualenv, if you don’t want the convenience features.
  • virtualenvwrapper is a set of extensions to virtualenv (see docs). It gives you commands like mkvirtualenv, lssitepackages, and especially workon for switching between different virtualenv directories. This tool is especially useful if you want multiple virtualenv directories.
  • pyenv-virtualenvwrapper is a plugin for pyenv by the same author as pyenv, to conveniently integrate virtualenvwrapper into pyenv.
  • pipenv aims to combine Pipfile, pip and virtualenv into one command on the command-line. The virtualenv directory typically gets placed in ~/.local/share/virtualenvs/XXX, with XXX being a hash of the path of the project directory. This is different from virtualenv, where the directory is typically in the current working directory. pipenv is meant to be used when developing Python applications (as opposed to libraries). There are alternatives to pipenv, such as poetry, which I won’t list here since this question is only about the packages that are similarly named.

Standard library:

  • pyvenv is a script shipped with Python 3 but deprecated in Python 3.6 as it had problems (not to mention the confusing name). In Python 3.6+, the exact equivalent is python3 -m venv.
  • venv is a package shipped with Python 3, which you can run using python3 -m venv (although for some reason some distros separate it out into a separate distro package, such as python3-venv on Ubuntu/Debian). It serves the same purpose as virtualenv, but only has a subset of its features (see a comparison here). virtualenv continues to be more popular than venv, especially since the former supports both Python 2 and 3.

Recommendation for beginners:

This is my personal recommendation for beginners: start by learning virtualenv and pip, tools which work with both Python 2 and 3 and in a variety of situations, and pick up other tools once you start needing them.

virtualenvwrapper is a set of extensions to Ian Bicking’s virtualenv tool. The extensions include wrappers for creating and deleting virtual environments and otherwise managing your development workflow, making it easier to work on more than one project at a time without introducing conflicts in their dependencies.
Features

Organizes all of your virtual environments in one place.
Wrappers for managing your virtual environments (create, delete, copy).
Use a single command to switch between environments.
Tab completion for commands that take a virtual environment as argument.
User-configurable hooks for all operations (see Per-User Customization).
Plugin system for creating more sharable extensions (see Extending Virtualenvwrapper).

Well, a virtual environment is just a directory with three important components:

A site-packages/ folder where third party libraries are installed.
Symlinks to Python executables installed on your system.
Scripts that ensure executed Python code uses the Python interpreter and site packages installed inside the given virtual environment.

(venv) % pip freeze
numpy==1.15.3

And write the output to a file, which we’ll call requirements.txt.

(venv) % pip freeze > requirements.txt

Duplicating Environments

Sara% cd test-project/
Sara% python3 -m venv venv/
(venv) Sara% pip install -r requirements.txtCollecting numpy==1.15.3 (from -r i (line 1))
Installing collected packages: numpy
Successfully installed numpy-1.15.3

…python -m pip executes pip using the Python interpreter you specified as python. So /usr/bin/python3.7 -m pip means you are executing pip for your interpreter located at /usr/bin/python3.7.

But when you use python -m pip with python being the specific interpreter you want to use, all of the above ambiguity is gone. If I say python3.8 -m pip then I know pip will be using and installing for my Python 3.8 interpreter (same goes for if I had said python3.7).

While we’re on the subject of how to avoid messing up your Python installation, I want to make the point that you should never install stuff into your global Python interpreter when you. develop locally (containers are a different matter)! If it’s your system install of Python then you may actually break your system if you install an incompatible version of a library that your OS relies on.

To create a «polls» app in the «apps» sub-directory, do the following first to make the directory (assuming you are at the root of your django project):

mkdir apps/polls

Next, run startapp to create «polls» in under the «app» project directory.

startapp polls apps/polls

Install the app;

INSTALLED_APPS = [
‘apps.polls’
]

migrate:

makemigrations polls
migrate

optional:
main urls.py

urlpatterns = [
path(‘polls/’,include(‘apps.polls.urls’),
]

Este tutorial no te convertirá en programadora de la noche a la mañana. Si quieres ser buena en esto, necesitarás meses o incluso años de aprendizaje y práctica. Sin embargo queremos enseñarte que programar o crear sitios web no es tan complicado como parece. Intentaremos explicar las cosas lo mejor que podamos, para perderle el miedo a la tecnología.

Django basic architecture

Django is an extremely popular and fully featured server-side web framework, written in Python. This module shows you why Django is one of the most popular web server frameworks, how to set up a development environment, and how to start using it to create your own web applications.

Django web applications typically group the code that handles each of these steps into separate files:

URLs: While it is possible to process requests from every single URL via a single function, it is much more maintainable to write a separate view function to handle each resource. A URL mapper is used to redirect HTTP requests to the appropriate view based on the request URL. The URL mapper can also match particular patterns of strings or digits that appear in a URL and pass these to a view function as data.
View: A view is a request handler function, which receives HTTP requests and returns HTTP responses. Views access the data needed to satisfy requests via models, and delegate the formatting of the response to templates.
Models: Models are Python objects that define the structure of an application’s data, and provide mechanisms to manage (add, modify, delete) and query records in the database.
Templates: A template is a text file defining the structure or layout of a file (such as an HTML page), with placeholders used to represent actual content. A view can dynamically create an HTML page using an HTML template, populating it with data from a model. A template can be used to define the structure of any type of file; it doesn’t have to be HTML!

Python has been around since the nineties. That doesn’t only mean that it has had plenty of time to grow. It has also acquired a large and supportive community.

Since Python has been around for so long, developers have made a package for every purpose. These days, you can find a package for almost everything.

Want to crunch numbers, vectors and matrices? NumPy is your guy.
Want to do calculations for tech and engineering? Use SciPy.
Want to go big in data manipulation and analysis? Give Pandas a go.
Want to start out with Artificial Intelligence? Why not use Scikit-Learn.

Python is a high level open source scripting language. Python’s built-in «re» module provides excellent support for regular expressions, with a modern and complete regex flavor. The only significant features missing from Python’s regex syntax are atomic grouping, possessive quantifiers, and Unicode properties.

The first thing to do is to import the regexp module into your script with import re.

The urllib2 module can be used to download data from the web (network resource access). This data can be a file, a website or whatever you want Python to download. The module supports HTTP, HTTPS, FTP and several other protocols.

Python geopandas choropleth map

There are different ways of creating choropleth maps in Python. In a previous notebook, I showed how you can use the Basemap library to accomplish this. More than 2 years have passed since publication and the available tools have evolved a lot. In this notebook I use the GeoPandas library to create a choropleth map. As you’ll see the code is more concise and easier to follow along.

NumPy (short for Numerical Python) is one of the top libraries equipped with useful resources to help data scientists turn Python into a powerful scientific analysis and modelling tool. The popular open source library is available under the BSD license. It is the foundational Python library for performing tasks in scientific computing. NumPy is part of a bigger Python-based ecosystem of open source tools called SciPy.

Pandas is another great library that can enhance your Python skills for data science. Just like NumPy, it belongs to the family of SciPy open source software and is available under the BSD free software license.

Matplotlib is also part of the SciPy core packages and offered under the BSD license. It is a popular Python scientific library used for producing simple and powerful visualizations. You can use the Python framework for data science for generating creative graphs, charts, histograms, and other shapes and figures—without worrying about writing many lines of code. For example, let’s see how the Matplotlib library can be used to create a simple bar chart.

This script should be run from the Python consol inside QGIS.

It adds online sources to the QGIS Browser.
Each source should contain a list with the folowing items (string type):
[sourcetype, title, authconfig, password, referer, url, username, zmax, zmin]

You can add or remove sources from the sources section of the code.

Script by Klas Karlsson
Sources from https://qms.nextgis.com/

Licence GPL-3