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.
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).
This guide discusses how to install packages using pip and a virtual environment manager: either venv for Python 3 or virtualenv for Python 2. These are the lowest-level tools for managing Python packages and are recommended if higher-level tools do not suit your needs.
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
And write the output to a file, which we’ll call requirements.txt.
…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.
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 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.
DevOps is the combination of application development and operations, which minimizes or eliminates the disconnect between software developers who build applications and systems administrators who keep infrastructure running.
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.
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.