Django 2.0 alpha 1 is now available. It represents the first stage in the 2.0 release cycle and is an opportunity for you to try out the changes coming in Django 2.0.

Django 2.0 has an assortment of new features which you can read about in the in-development 2.0 release notes.

This alpha milestone marks a complete feature freeze. The current release schedule calls for a beta release in about a month and a release candidate about a month from then. We'll only be able to keep this schedule if we get early and often testing from the community. Updates on the release schedule schedule are available on the django-developers mailing list.

As with all alpha and beta packages, this is not for production use. But if you'd like to take some of the new features for a spin, or to help find and fix bugs (which should be reported to the issue tracker), you can grab a copy of the alpha package from our downloads page or on PyPI.

The PGP key ID used for this release is Tim Graham: 1E8ABDC773EDE252.

22 Sep 2017 6:32pm GMT

Django 2.0 alpha 1 is now available. It represents the first stage in the 2.0 release cycle and is an opportunity for you to try out the changes coming in Django 2.0.

Django 2.0 has an assortment of new features which you can read about in the in-development 2.0 release notes.

This alpha milestone marks a complete feature freeze. The current release schedule calls for a beta release in about a month and a release candidate about a month from then. We'll only be able to keep this schedule if we get early and often testing from the community. Updates on the release schedule schedule are available on the django-developers mailing list.

As with all alpha and beta packages, this is not for production use. But if you'd like to take some of the new features for a spin, or to help find and fix bugs (which should be reported to the issue tracker), you can grab a copy of the alpha package from our downloads page or on PyPI.

The PGP key ID used for this release is Tim Graham: 1E8ABDC773EDE252.

22 Sep 2017 6:32pm GMT

Python is one of the easiest languages to learn, but that doesn't mean all the programming concepts become easy. One that's hard: testing.

Kenneth Love, well-known speaker, organizer, and teacher in Python, has years of experience bringing complicated subjects within reach. He'll be our guest for a webinar on Visual Testing With PyCharm. He'll introduce testing concepts and show how PyCharm makes it easier by putting a visual face on testing.

We interviewed Kenneth to help set the scene for the webinar.

It's a pleasure having you with us for a webinar. My son and I spent summer vacation going through your Python Basics course twice and it was a big help. Do you get that a lot?

Yeah, I've had quite a few people thank me for making that course, and others that I've made, over the years. It seems to be a course that has really caught people that weren't able to get going at Python through other means.

Give us a little background on yourself, your Python origin story and what you're doing now.

I've been using Python for 8-10 years now. I picked it up, almost on a whim, after a friend and I both kind of soured on Ruby. I made the mistake of learning Django before I really learned Python. Eventually, though, I figured out that I should know how more than just an ORM and HTTP works and learned the core parts of the language. I've been a full-time Python developer or teacher for the past six or seven years.

I just recently changed jobs and left teaching to go back into the world of development. Things are changing all the time and I felt like I needed to get back to development for awhile.

Is testing hard to teach?

I think it's a tricky area to teach. People are used to writing and running code. But writing code that runs your code and looks at what happens? It's just a bit too meta for a lot of beginners. It's also the case that a lot of beginners write code that's hard to test, through no fault of their own (they're beginners! I know I wrote messy code when I was first starting) so that can really increase the complexity of learning to test.

Do you have special tricks that seem to work on conceptually hard topics? Debugging, testing, things like that.

Like with most topics, you have to strip away the magic and the special words. We often have a problem in tech of gatekeeping with jargon. We use words that have very specific meanings, like say "fixtures", and we expect everyone to just know them. When someone is new to testing, the term "fixtures" might not have any meaning at all, or mean something completely unrelated. If you can define these terms for them, make them familiar and comfortable, then they can get onto the business of learning this new technique a lot faster.

You're also well-known for organizing. Can you give us your origin story on how you got involved?

I honestly just kind of stumbled into it! My friend Lacey Williams Henschel wanted to do a Django Girls workshop here in Portland. The company I worked for had a great space for it, so I volunteered it. She wanted a co-organizer so I volunteered for that, too. We had a great event and went on to do two more of them after that.

I got into organizing DjangoCon US by being a pain in the butt. I was a pretty big critic of the previous incarnation of DjangoCon US, before it became community-ran. So when the community took it over, I kinda got tapped to help out (no one forced me, of course, but I felt like I had to step up). Since then I've been involved in the program selection for the past two years and did a lot of speaker encouragement and talk rating before that.

Django Girls is such a wonderful story. Mind giving us some background and status on that?

Honestly, I'm not heavily involved with Django Girls any longer. Lacey and I organized the three events and then we both mostly bowed out. It's definitely an amazing organization and I'm completely astounded by the numbers they recently posted. I would love to see more DG-like organizations pop up for other languages and frameworks.

Let's close out with kind of an open-ended question. What's something you think you'll study a lot more in the next year?

I'm really interested in this recent upswing of languages that are focused more on speed and safety. I'd really like to spend some time digging into the Rust programming language. Due to work, I'll likely be learning more about React and Go as well.

22 Sep 2017 3:00pm GMT

Python is one of the easiest languages to learn, but that doesn't mean all the programming concepts become easy. One that's hard: testing.

Kenneth Love, well-known speaker, organizer, and teacher in Python, has years of experience bringing complicated subjects within reach. He'll be our guest for a webinar on Visual Testing With PyCharm. He'll introduce testing concepts and show how PyCharm makes it easier by putting a visual face on testing.

We interviewed Kenneth to help set the scene for the webinar.

It's a pleasure having you with us for a webinar. My son and I spent summer vacation going through your Python Basics course twice and it was a big help. Do you get that a lot?

Yeah, I've had quite a few people thank me for making that course, and others that I've made, over the years. It seems to be a course that has really caught people that weren't able to get going at Python through other means.

Give us a little background on yourself, your Python origin story and what you're doing now.

I've been using Python for 8-10 years now. I picked it up, almost on a whim, after a friend and I both kind of soured on Ruby. I made the mistake of learning Django before I really learned Python. Eventually, though, I figured out that I should know how more than just an ORM and HTTP works and learned the core parts of the language. I've been a full-time Python developer or teacher for the past six or seven years.

I just recently changed jobs and left teaching to go back into the world of development. Things are changing all the time and I felt like I needed to get back to development for awhile.

Is testing hard to teach?

I think it's a tricky area to teach. People are used to writing and running code. But writing code that runs your code and looks at what happens? It's just a bit too meta for a lot of beginners. It's also the case that a lot of beginners write code that's hard to test, through no fault of their own (they're beginners! I know I wrote messy code when I was first starting) so that can really increase the complexity of learning to test.

Do you have special tricks that seem to work on conceptually hard topics? Debugging, testing, things like that.

Like with most topics, you have to strip away the magic and the special words. We often have a problem in tech of gatekeeping with jargon. We use words that have very specific meanings, like say "fixtures", and we expect everyone to just know them. When someone is new to testing, the term "fixtures" might not have any meaning at all, or mean something completely unrelated. If you can define these terms for them, make them familiar and comfortable, then they can get onto the business of learning this new technique a lot faster.

You're also well-known for organizing. Can you give us your origin story on how you got involved?

I honestly just kind of stumbled into it! My friend Lacey Williams Henschel wanted to do a Django Girls workshop here in Portland. The company I worked for had a great space for it, so I volunteered it. She wanted a co-organizer so I volunteered for that, too. We had a great event and went on to do two more of them after that.

I got into organizing DjangoCon US by being a pain in the butt. I was a pretty big critic of the previous incarnation of DjangoCon US, before it became community-ran. So when the community took it over, I kinda got tapped to help out (no one forced me, of course, but I felt like I had to step up). Since then I've been involved in the program selection for the past two years and did a lot of speaker encouragement and talk rating before that.

Django Girls is such a wonderful story. Mind giving us some background and status on that?

Honestly, I'm not heavily involved with Django Girls any longer. Lacey and I organized the three events and then we both mostly bowed out. It's definitely an amazing organization and I'm completely astounded by the numbers they recently posted. I would love to see more DG-like organizations pop up for other languages and frameworks.

Let's close out with kind of an open-ended question. What's something you think you'll study a lot more in the next year?

I'm really interested in this recent upswing of languages that are focused more on speed and safety. I'd really like to spend some time digging into the Rust programming language. Due to work, I'll likely be learning more about React and Go as well.

22 Sep 2017 3:00pm GMT

NumFOCUS is pleased to welcome the Open Journals as a fiscally sponsored project. Open Journals is a collection of open source, open access journals. The team behind Open Journals believes that code review and high-quality, reusable software are a critical-but often overlooked-part of academic research. The primary goal of Open Journals is to provide venues […]

22 Sep 2017 2:00pm GMT

NumFOCUS is pleased to welcome the Open Journals as a fiscally sponsored project. Open Journals is a collection of open source, open access journals. The team behind Open Journals believes that code review and high-quality, reusable software are a critical-but often overlooked-part of academic research. The primary goal of Open Journals is to provide venues […]

22 Sep 2017 2:00pm GMT

Writing and sharing scripts is essential to productivity in development teams. But if you want the script to work everywhere, you can only depend on the tools that are everywhere. That usually means not using the newer features of a language, and not using libraries. In this article, I explain how I use Nix to write scripts that can run everywhere, regardless on what they depend on.

In my work as a software engineer, I often write small scripts to help with administrative actions or provide some nice view of what we are working on. From time to time, one of these scripts is useful enough that I want to share it with my colleagues.

But sharing the scripts comes with a challenge. I write my scripts in Python because it is a great language for that kind of things. But the best parts are outside of the standard library. As a result, to share my scripts, I also have to write a documentation explaining how to get the right version of python, create a python virtual environment, activate it, install the dependencies, and run the script. That's not worth the hassle, and my colleagues won't bother following the complex set of instructions for a script.

Recently, I've started using Nix to make self-contained scripts, and it's been working pretty well.

A script that can download its dependencies

For example, some of the things I like using in python scripts are:

• the new string formatting syntax in Python 3.6,
• the requests package for anything that touches HTTP
• docopt for argument parsing.

My scripts will usually look like:

#! /usr/bin/env nix-shell
#! nix-shell -i python -p python36 python36Packages.requests2 python36Packages.docopt -I nixpkgs=https://github.com/NixOS/nixpkgs-channels/archive/nixos-17.03.tar.gz
# You'll need nix to automatically download the dependencies: `curl https://nixos.org/nix/install | sh`
"""
Some script

Usage:
  some_script.py <user> <variant> [--api=<api>]
  some_script.py -h | --help

Arguments:
  <user>        format: 42
  <variant>     format: 2

Options:
  -h --help     Show this screen.
  --api=<api>   The api instance to talk to [default: the-api.internal]
"""
import requests
from docopt import docopt

def doit(user, variant, api):
    # The actual thing I want to do
    pass

if __name__ == '__main__':
    arguments = docopt(__doc__)
    doit(arguments['<user>'], arguments['<variant>'], arguments['--api'])

Now, anybody can run the script with ./some_script.py, which will download the dependencies and run the script with them. They'll need to install nix if they don't have it yet (with curl https://nixos.org/nix/install | sh).

What actually happens there?

The first line of the script is a shebang. It tells the system to run the script with nix-shell, one of the tools provided by Nix.

The second line specifies how to invoke nix-shell:

• -i python: this argument means we want nix-shell to run the script with the python command (-i stands for interpreter).
• -p python36 python36Packages.requests2 python36Packages.docopt: this argument is the list of the nix packages that my script is going to use. Here, I want python 3.6, and the python packages requests and docopt. I usually use the Nix packages page to find the name of the dependencies I want.
• -I nixpkgs=https://github.com/NixOS/nixpkgs-channels/archive/nixos-17.03.tar.gz: the version of nix I want to use for this script. This ensures people running the script in the future will get compatible versions of the dependencies.

When I run the script, nix-shell starts by downloading https://github.com/NixOS/nixpkgs-channels/archive/nixos-17.03.tar.gz if it has not done so recently. This is the definition of all the nix packages. Then, nix-shell looks into this file to know how to download the packages I want and their dependencies. Finally, it runs my script in an environment where the dependencies are available.

Try it now

This technique allows coworkers to share scripts written in different technologies without the cost of teaching everybody how to install dependencies and run scripts in each language.

Try it yourself right now with the example script from earlier. Make sure you have Nix (or install it with curl https://nixos.org/nix/install | sh), get the script, and run it without caring about the dependencies!

22 Sep 2017 1:00pm GMT

Writing and sharing scripts is essential to productivity in development teams. But if you want the script to work everywhere, you can only depend on the tools that are everywhere. That usually means not using the newer features of a language, and not using libraries. In this article, I explain how I use Nix to write scripts that can run everywhere, regardless on what they depend on.

In my work as a software engineer, I often write small scripts to help with administrative actions or provide some nice view of what we are working on. From time to time, one of these scripts is useful enough that I want to share it with my colleagues.

But sharing the scripts comes with a challenge. I write my scripts in Python because it is a great language for that kind of things. But the best parts are outside of the standard library. As a result, to share my scripts, I also have to write a documentation explaining how to get the right version of python, create a python virtual environment, activate it, install the dependencies, and run the script. That's not worth the hassle, and my colleagues won't bother following the complex set of instructions for a script.

Recently, I've started using Nix to make self-contained scripts, and it's been working pretty well.

A script that can download its dependencies

For example, some of the things I like using in python scripts are:

• the new string formatting syntax in Python 3.6,
• the requests package for anything that touches HTTP
• docopt for argument parsing.

My scripts will usually look like:

#! /usr/bin/env nix-shell
#! nix-shell -i python -p python36 python36Packages.requests2 python36Packages.docopt -I nixpkgs=https://github.com/NixOS/nixpkgs-channels/archive/nixos-17.03.tar.gz
# You'll need nix to automatically download the dependencies: `curl https://nixos.org/nix/install | sh`
"""
Some script

Usage:
  some_script.py <user> <variant> [--api=<api>]
  some_script.py -h | --help

Arguments:
  <user>        format: 42
  <variant>     format: 2

Options:
  -h --help     Show this screen.
  --api=<api>   The api instance to talk to [default: the-api.internal]
"""
import requests
from docopt import docopt

def doit(user, variant, api):
    # The actual thing I want to do
    pass

if __name__ == '__main__':
    arguments = docopt(__doc__)
    doit(arguments['<user>'], arguments['<variant>'], arguments['--api'])

Now, anybody can run the script with ./some_script.py, which will download the dependencies and run the script with them. They'll need to install nix if they don't have it yet (with curl https://nixos.org/nix/install | sh).

What actually happens there?

The first line of the script is a shebang. It tells the system to run the script with nix-shell, one of the tools provided by Nix.

The second line specifies how to invoke nix-shell:

• -i python: this argument means we want nix-shell to run the script with the python command (-i stands for interpreter).
• -p python36 python36Packages.requests2 python36Packages.docopt: this argument is the list of the nix packages that my script is going to use. Here, I want python 3.6, and the python packages requests and docopt. I usually use the Nix packages page to find the name of the dependencies I want.
• -I nixpkgs=https://github.com/NixOS/nixpkgs-channels/archive/nixos-17.03.tar.gz: the version of nix I want to use for this script. This ensures people running the script in the future will get compatible versions of the dependencies.

When I run the script, nix-shell starts by downloading https://github.com/NixOS/nixpkgs-channels/archive/nixos-17.03.tar.gz if it has not done so recently. This is the definition of all the nix packages. Then, nix-shell looks into this file to know how to download the packages I want and their dependencies. Finally, it runs my script in an environment where the dependencies are available.

Try it now

This technique allows coworkers to share scripts written in different technologies without the cost of teaching everybody how to install dependencies and run scripts in each language.

Try it yourself right now with the example script from earlier. Make sure you have Nix (or install it with curl https://nixos.org/nix/install | sh), get the script, and run it without caring about the dependencies!

22 Sep 2017 1:00pm GMT

Worthy Read Free Continuous Delivery eBook from GoCD This free reference guide will take you back to the basics. You'll find visuals and definitions on key concepts and questions you need to answer about your teams to determine your readiness for continuous delivery. Download and share with your team. GoCD , advert How Did We Build Book Recommender Systems in an Hour Part 1 - The Fundamentals Building recommender systems today requires specialized expertise in analytics, machine learning and software engineering, and learning new skills and tools is difficult and time-consuming. In this post, we will start from scratch, covering some basic fundamental techniques and implementations in Python. In the future posts, we will cover more sophisticated methods such as content-based filtering and collaborative based filtering. machine learning Python Gem #19: Look up table > if chain What should be a really simple function has turned into a fifty-line gargantuan that's too hard to read properly because of the sheer number of lines. The culprit; a seriously long if-elsif-else chain. But not to fear; there's a better way! core-python My favorite terrible Python error message TypeError: object() takes no parameters core-python A TDD approach or building microprocessor model in Python Modern microprocessors are a very complex piece of machinery with a lot of different parts. I've learned assembler and microprocessors in my undergraduate course some years ago but, as I've been working on higher level software languages for quite some time now, I forgot many things. Lately I decided to revisit some of the topics on microprocessors and to build a very simple model of a microprocessor in Python for education purposes (I work as CS professor). You can find the final source code and some samples at https://github.com/joaoventura/simproc. simulator 4 interactive Sankey diagrams made in Python - plotly - Medium Plotly has a new member of the Plotly.js chart family: The Sankey diagram. Jupyter notebook is at above the footnote of the blogpost. charts Saasler Integrations, simplified: Integrate your SaaS app with others apps in days, not months. Get started for free! advert Mapping Traffic Accidents in Metro Manila I got my hands on a dataset on traffic accidents in Metro Manila early this year, and decided to enter the realm of data science with a simple cleaning and visualization project. The primary goal: supplement the dataset's human readable location data with geographic coordinates. data science CPython internals: Arbitrary-precision integer implementation Have you ever noticed that Python supports integers of any size? Here is a quick glance at it. cpython How Did We Build Book Recommender Systems in An Hour Part 2 - k Nearest Neighbors and Matrix… In the last post, we saw how we could use simple correlational techniques to create a measure of similarity between the books' users based on their rating records. In this post, we will explain how you can use those same sort of similarity metrics to recommend books to a book's readers. machine learning Posting messages to Slack using incoming webhooks and Python3 Requests API I wanted to make a short post on using Slack's incoming webhooks feature to post messages to Slack using the human-friendly Requests API in Python3. slack Find Top Developers We help companies like Airbnb, Pfizer, and Artsy find great developers. Let us find your next great hire. Get started today. advert Devs unknowingly use "malicious" modules snuck into official Python repository | Ars Technica Code packages available in PyPI contained modified installation scripts. pypi Jupyter, python, Image compression and svd In this short blogpost, I will explore few topics to illustrate the interactivity of the jupyter environment and the python availability of high quality libraries in the ecosystem. jupyter Building a desktop notification tool using python The term desktop notifications refer to a graphical control element that communicates certain events to the user without forcing them to react to this notification immediately. In other words, it is a simple application which produces a notification message in form of a pop-up message on desktop. desktop app Getting Started on Geospatial Analysis with Python, GeoJSON and GeoPandas Geospatial analysis applies statistical analysis to data that has geographical or geometrical components. In this tutorial, we'll use Python to learn the basics of acquiring geospatial data, handling it, and visualizing it. More specifically, we'll do some interactive visualizations of the United States! geo Automating Web Analytics through Python The current blog deals with the case how to implement web analytics in Python. I am enthusiastic about the options that are available inside Google Analytics. Google Analytics has a rich variety of metrics and dimensions available. It has a good visualization and an intuitive Graphic User Interface (GUI). However, in certain situations it makes sense to automate webanalytics and add advanced statistics and visualizations. In the current blog, I will show how to do that using Python. analytics TransitFlow - using Python and Processing to visualize regional transit frequency Transit networks with higher frequency and shorter waiting times will yield a more reliable and empowering experience for passengers than those with lower frequency and longer waiting times. geo Remote-controlling macOS with a Python Telegram bot I needed a quick way to remotely perform system operations: adjusting & muting the system volume, screen brightness, and putting the display to sleep. After exploring several options, I found none of them to be viable for me, and thus, I set out to develop my own solution. macOS How to put that GPU to good use with Python In the example below, I've demonstrated how this can be done using Python in a way that doesn't require deep knowledge of CUDA and its intricacies. For this example, I suggest using the Anaconda Python distribution, which makes managing different Python environments a breeze. Follow the download and setup instructions for Anaconda as given here for your specific operating system. GPU Return of the Consoles: Recreating Pokémon in Python Pokemon Create a Github Gist with a simple Python script How to share code snippets from command line? github , gist Don't mess with the celery while it running a task Some time ago we decided to move part of our logic to celery. Several tasks was wrote and in one of them bash script was called, and this task never applied correct: first half of script was executed and then halted. I found that script running in processes, and can't understand why it just stoped. Then I set some breakpoints in bash script and realised, that script stops at celery Using categorical data in machine learning with python: from dummy variables to Deep category… From dummy variables to Deep category embedding and Cat2vec?-?Part 1 (Basic Methods). machine learning Spatial joins in GeoPandas You can join two GeoPandas GeoDataFrames through conventional means with merge, but you can also use sjoin to capitalize on the spatial relationship between two frames. pandas The Beginner's Guide to Text Vectorization text processing Projects face-alignment - 165 Stars, 25 Fork 2D and 3D Face alignment library build using pytorch MetaDockers - 44 Stars, 8 Fork Responsible for visualization the vulhub or docker. mygf-instagram - 34 Stars, 1 Fork Like posts of my girlfriend's Instagram using web api, don't need any api key and access tokens just login and password graphscii - 3 Stars, 0 Fork Print ASCII graphs in the terminal. xlsx2csv_gui - 2 Stars, 0 Fork Convert excel xlsx file's table to csv file, A GUI application on top of python/pyqt and other opensource softwares.

22 Sep 2017 12:14pm GMT

Worthy Read Free Continuous Delivery eBook from GoCD This free reference guide will take you back to the basics. You'll find visuals and definitions on key concepts and questions you need to answer about your teams to determine your readiness for continuous delivery. Download and share with your team. GoCD , advert How Did We Build Book Recommender Systems in an Hour Part 1 - The Fundamentals Building recommender systems today requires specialized expertise in analytics, machine learning and software engineering, and learning new skills and tools is difficult and time-consuming. In this post, we will start from scratch, covering some basic fundamental techniques and implementations in Python. In the future posts, we will cover more sophisticated methods such as content-based filtering and collaborative based filtering. machine learning Python Gem #19: Look up table > if chain What should be a really simple function has turned into a fifty-line gargantuan that's too hard to read properly because of the sheer number of lines. The culprit; a seriously long if-elsif-else chain. But not to fear; there's a better way! core-python My favorite terrible Python error message TypeError: object() takes no parameters core-python A TDD approach or building microprocessor model in Python Modern microprocessors are a very complex piece of machinery with a lot of different parts. I've learned assembler and microprocessors in my undergraduate course some years ago but, as I've been working on higher level software languages for quite some time now, I forgot many things. Lately I decided to revisit some of the topics on microprocessors and to build a very simple model of a microprocessor in Python for education purposes (I work as CS professor). You can find the final source code and some samples at https://github.com/joaoventura/simproc. simulator 4 interactive Sankey diagrams made in Python - plotly - Medium Plotly has a new member of the Plotly.js chart family: The Sankey diagram. Jupyter notebook is at above the footnote of the blogpost. charts Saasler Integrations, simplified: Integrate your SaaS app with others apps in days, not months. Get started for free! advert Mapping Traffic Accidents in Metro Manila I got my hands on a dataset on traffic accidents in Metro Manila early this year, and decided to enter the realm of data science with a simple cleaning and visualization project. The primary goal: supplement the dataset's human readable location data with geographic coordinates. data science CPython internals: Arbitrary-precision integer implementation Have you ever noticed that Python supports integers of any size? Here is a quick glance at it. cpython How Did We Build Book Recommender Systems in An Hour Part 2 - k Nearest Neighbors and Matrix… In the last post, we saw how we could use simple correlational techniques to create a measure of similarity between the books' users based on their rating records. In this post, we will explain how you can use those same sort of similarity metrics to recommend books to a book's readers. machine learning Posting messages to Slack using incoming webhooks and Python3 Requests API I wanted to make a short post on using Slack's incoming webhooks feature to post messages to Slack using the human-friendly Requests API in Python3. slack Find Top Developers We help companies like Airbnb, Pfizer, and Artsy find great developers. Let us find your next great hire. Get started today. advert Devs unknowingly use "malicious" modules snuck into official Python repository | Ars Technica Code packages available in PyPI contained modified installation scripts. pypi Jupyter, python, Image compression and svd In this short blogpost, I will explore few topics to illustrate the interactivity of the jupyter environment and the python availability of high quality libraries in the ecosystem. jupyter Building a desktop notification tool using python The term desktop notifications refer to a graphical control element that communicates certain events to the user without forcing them to react to this notification immediately. In other words, it is a simple application which produces a notification message in form of a pop-up message on desktop. desktop app Getting Started on Geospatial Analysis with Python, GeoJSON and GeoPandas Geospatial analysis applies statistical analysis to data that has geographical or geometrical components. In this tutorial, we'll use Python to learn the basics of acquiring geospatial data, handling it, and visualizing it. More specifically, we'll do some interactive visualizations of the United States! geo Automating Web Analytics through Python The current blog deals with the case how to implement web analytics in Python. I am enthusiastic about the options that are available inside Google Analytics. Google Analytics has a rich variety of metrics and dimensions available. It has a good visualization and an intuitive Graphic User Interface (GUI). However, in certain situations it makes sense to automate webanalytics and add advanced statistics and visualizations. In the current blog, I will show how to do that using Python. analytics TransitFlow - using Python and Processing to visualize regional transit frequency Transit networks with higher frequency and shorter waiting times will yield a more reliable and empowering experience for passengers than those with lower frequency and longer waiting times. geo Remote-controlling macOS with a Python Telegram bot I needed a quick way to remotely perform system operations: adjusting & muting the system volume, screen brightness, and putting the display to sleep. After exploring several options, I found none of them to be viable for me, and thus, I set out to develop my own solution. macOS How to put that GPU to good use with Python In the example below, I've demonstrated how this can be done using Python in a way that doesn't require deep knowledge of CUDA and its intricacies. For this example, I suggest using the Anaconda Python distribution, which makes managing different Python environments a breeze. Follow the download and setup instructions for Anaconda as given here for your specific operating system. GPU Return of the Consoles: Recreating Pokémon in Python Pokemon Create a Github Gist with a simple Python script How to share code snippets from command line? github , gist Don't mess with the celery while it running a task Some time ago we decided to move part of our logic to celery. Several tasks was wrote and in one of them bash script was called, and this task never applied correct: first half of script was executed and then halted. I found that script running in processes, and can't understand why it just stoped. Then I set some breakpoints in bash script and realised, that script stops at celery Using categorical data in machine learning with python: from dummy variables to Deep category… From dummy variables to Deep category embedding and Cat2vec?-?Part 1 (Basic Methods). machine learning Spatial joins in GeoPandas You can join two GeoPandas GeoDataFrames through conventional means with merge, but you can also use sjoin to capitalize on the spatial relationship between two frames. pandas The Beginner's Guide to Text Vectorization text processing Projects face-alignment - 165 Stars, 25 Fork 2D and 3D Face alignment library build using pytorch MetaDockers - 44 Stars, 8 Fork Responsible for visualization the vulhub or docker. mygf-instagram - 34 Stars, 1 Fork Like posts of my girlfriend's Instagram using web api, don't need any api key and access tokens just login and password graphscii - 3 Stars, 0 Fork Print ASCII graphs in the terminal. xlsx2csv_gui - 2 Stars, 0 Fork Convert excel xlsx file's table to csv file, A GUI application on top of python/pyqt and other opensource softwares.

22 Sep 2017 12:14pm GMT

September feels just right to start learning, or continue with even more effort and joy. And today feels just right for the next post in our series covering learning methods and tips & tricks designed to help you to learn more effectively and make you more excited about learning Python with PyCharm Edu!

Last time we posted about how to make your IDE suit your needs and preferences, and help you stay focused and avoid distraction. Today we're going to focus more on the process of writing code.

Learn faster with a smart code editor

As you learn to code, it is encouraging to have an intelligent assistant right inside your IDE. Language-specific syntax & error highlighting are there to help you avoid code mistakes. You can use code formatting capabilities to learn how to style your code. And you won't get stuck with your Python coding with code completion and quick documentation at hand.

Use code completion
Learn with documentation
Explore the code
Fix code errors
Style your code

Please note that we use the default Mac OS keyboard shortcut scheme (Mac OS X 10.5+) in this blog post. For more about keyboard shortcuts, please see the previous blog post in this series.

Use code completion

Smart autocompletion is an essential feature of a professional code editor, which helps you save time and avoid misprints. It is equally powerful for learning because you can use it to find the right variable, function, parameter, or any code construct. Start typing or simply press Control-Space to get a list of suggested options:

Code snippets, or live templates, can also be a handy tool. They not only speed up your coding, but help you learn new language syntax as well. Use Command-J to see the full list of code snippets available. Start typing to find the one you want to add, and then use Tab to fill in the blanks:

Learn with documentation

Reading documentation is a must when learning a new programming language. PyCharm Edu makes this process as seamless as possible.

Put a cursor on a class, method, variable, or function you want to read about, and then press F1 to open the quick documentation pop-up:

Use F1 once more to switch to the tool window mode:

You can click hyperlinks to find more details. For example, click int to read about the int class:

Use the Back and Forward icons to get back to the documentation pages you visited recently:

Save your time and effort with the special Auto-update from Source icon. When switched on, it lets you read the documentation in parallel with reading your code. Go through a code fragment and read about all the code constructions you put your cursor on, without any additional shortcuts or mouse clicks:

Explore the code

Programming is much more about understanding and reading existing code than about writing new code. That's why we develop so many code navigation features in our IDEs. While learning, it is also quite important to see your code as a whole and understand how it works.

When you use any variable, function, or method and need a tip on how it works, let the code speak for itself. Press Command-B to go to the declaration of an entity; use it one more time on the declaration to see the list of usages:

Another quick way to look at the declaration is to use the pop-up (with Command-Y):

Fix code errors

Mistakes are an essential part of successful learning. They can also be very annoying and drain your motivation. The IDE helps you not get stuck in code errors, by alerting you to the most common ones and suggesting solutions.

Take a look at the code below. The red squiggly line code under task means there's something wrong with this statement:

Put the cursor on the highlighted code to see what the issue is. Then, click the red bulb or press Option-Enter to see the list of suggested fixes. Choose the right one to fix your code:

It seems there is still something wrong with the file, because there's a red icon at the top right:

Click the red stripe to navigate to the line with the error and fix it:

Now everything is fixed:

Style your code

It is important not only to write the right code, but also to write your code right. Try to follow a code style so your code is easy to read and understand. You'll thank yourself later!

Invoke Option-Command-L and let the IDE reformat your code automatically:

There we have it. Let us know how you like these features! Stay tuned for the next portion of tips & tricks for more efficient learning. Share your feedback here in the comments or report your findings on YouTrack, to help us improve PyCharm Edu.

-
Your PyCharm Edu Team

22 Sep 2017 11:45am GMT

September feels just right to start learning, or continue with even more effort and joy. And today feels just right for the next post in our series covering learning methods and tips & tricks designed to help you to learn more effectively and make you more excited about learning Python with PyCharm Edu!

Last time we posted about how to make your IDE suit your needs and preferences, and help you stay focused and avoid distraction. Today we're going to focus more on the process of writing code.

Learn faster with a smart code editor

As you learn to code, it is encouraging to have an intelligent assistant right inside your IDE. Language-specific syntax & error highlighting are there to help you avoid code mistakes. You can use code formatting capabilities to learn how to style your code. And you won't get stuck with your Python coding with code completion and quick documentation at hand.

Use code completion
Learn with documentation
Explore the code
Fix code errors
Style your code

Please note that we use the default Mac OS keyboard shortcut scheme (Mac OS X 10.5+) in this blog post. For more about keyboard shortcuts, please see the previous blog post in this series.

Use code completion

Smart autocompletion is an essential feature of a professional code editor, which helps you save time and avoid misprints. It is equally powerful for learning because you can use it to find the right variable, function, parameter, or any code construct. Start typing or simply press Control-Space to get a list of suggested options:

Code snippets, or live templates, can also be a handy tool. They not only speed up your coding, but help you learn new language syntax as well. Use Command-J to see the full list of code snippets available. Start typing to find the one you want to add, and then use Tab to fill in the blanks:

Learn with documentation

Reading documentation is a must when learning a new programming language. PyCharm Edu makes this process as seamless as possible.

Put a cursor on a class, method, variable, or function you want to read about, and then press F1 to open the quick documentation pop-up:

Use F1 once more to switch to the tool window mode:

You can click hyperlinks to find more details. For example, click int to read about the int class:

Use the Back and Forward icons to get back to the documentation pages you visited recently:

Save your time and effort with the special Auto-update from Source icon. When switched on, it lets you read the documentation in parallel with reading your code. Go through a code fragment and read about all the code constructions you put your cursor on, without any additional shortcuts or mouse clicks:

Explore the code

Programming is much more about understanding and reading existing code than about writing new code. That's why we develop so many code navigation features in our IDEs. While learning, it is also quite important to see your code as a whole and understand how it works.

When you use any variable, function, or method and need a tip on how it works, let the code speak for itself. Press Command-B to go to the declaration of an entity; use it one more time on the declaration to see the list of usages:

Another quick way to look at the declaration is to use the pop-up (with Command-Y):

Fix code errors

Mistakes are an essential part of successful learning. They can also be very annoying and drain your motivation. The IDE helps you not get stuck in code errors, by alerting you to the most common ones and suggesting solutions.

Take a look at the code below. The red squiggly line code under task means there's something wrong with this statement:

Put the cursor on the highlighted code to see what the issue is. Then, click the red bulb or press Option-Enter to see the list of suggested fixes. Choose the right one to fix your code:

It seems there is still something wrong with the file, because there's a red icon at the top right:

Click the red stripe to navigate to the line with the error and fix it:

Now everything is fixed:

Style your code

It is important not only to write the right code, but also to write your code right. Try to follow a code style so your code is easy to read and understand. You'll thank yourself later!

Invoke Option-Command-L and let the IDE reformat your code automatically:

There we have it. Let us know how you like these features! Stay tuned for the next portion of tips & tricks for more efficient learning. Share your feedback here in the comments or report your findings on YouTrack, to help us improve PyCharm Edu.

-
Your PyCharm Edu Team

22 Sep 2017 11:45am GMT

The python module for vlc is named python-vlc.
This python module let you to test libvlc API like the VLC video player.
You can install it easy with pip python tool.

C:\Python27\Scripts>pip2.7.exe install python-vlc
Collecting python-vlc
  Downloading python-vlc-1.1.2.tar.gz (201kB)
    100% |################################| 204kB 628kB/s
Installing collected packages: python-vlc
  Running setup.py install for python-vlc ... done
Successfully installed python-vlc-1.1.2

Let's see a simple example with this python module:

import os
import sys
import vlc
import pygame
 
def call_vlc(self, player):
 
    player.get_fps()
    player.get_time()
 
if len( sys.argv )< 2 or len( sys.argv )> 3:
        print 'Help: python vlc_001.py your_video.mp4'
else:
    pygame.init()
    screen = pygame.display.set_mode((800,600),pygame.RESIZABLE)
    pygame.display.get_wm_info()
    pygame.display.get_driver()

 
    # get path to movie specified as command line argument
    movie = os.path.expanduser(sys.argv[1])
    # see if movie is accessible
    if not os.access(movie, os.R_OK):
        print('Error: %s wrong read file: ' % movie)
        sys.exit(1)
 
    # make instane of VLC and create reference to movie.
    vlcInstance = vlc.Instance()
    media = vlcInstance.media_new(movie)
 
    # make new instance of vlc player
    player = vlcInstance.media_player_new()
 
    # start with a callback
    em = player.event_manager()
    em.event_attach(vlc.EventType.MediaPlayerTimeChanged, \
        call_vlc, player)
 
    # set pygame window id to vlc player
    win_id = pygame.display.get_wm_info()['window']
    if sys.platform == "win32": # for Windows
        player.set_hwnd(win_id)
 
    # load movie into vlc player instance
    player.set_media(media)
 
    # quit pygame mixer to allow vlc full access to audio device
    pygame.mixer.quit()
 
    # start movie play
    player.play()
 
    while player.get_state() != vlc.State.Ended:
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                sys.exit(2)

The base of this python script is to make a instance of vlc and put into pygame display.
Another simple example:

C:\Python27>python.exe
Python 2.7.13 (v2.7.13:a06454b1afa1, Dec 17 2016, 20:42:59) [MSC v.1500 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import vlc
>>> inst = vlc.Instance()
Warning: option --plugin-path no longer exists.
Warning: option --plugin-path no longer exists.
>>> med = inst.media_new('rain.mp4')
>>> p = med.player_new_from_media()
>>> p.play()
0
>>>

22 Sep 2017 11:13am GMT

The python module for vlc is named python-vlc.
This python module let you to test libvlc API like the VLC video player.
You can install it easy with pip python tool.

C:\Python27\Scripts>pip2.7.exe install python-vlc
Collecting python-vlc
  Downloading python-vlc-1.1.2.tar.gz (201kB)
    100% |################################| 204kB 628kB/s
Installing collected packages: python-vlc
  Running setup.py install for python-vlc ... done
Successfully installed python-vlc-1.1.2

Let's see a simple example with this python module:

import os
import sys
import vlc
import pygame
 
def call_vlc(self, player):
 
    player.get_fps()
    player.get_time()
 
if len( sys.argv )< 2 or len( sys.argv )> 3:
        print 'Help: python vlc_001.py your_video.mp4'
else:
    pygame.init()
    screen = pygame.display.set_mode((800,600),pygame.RESIZABLE)
    pygame.display.get_wm_info()
    pygame.display.get_driver()

 
    # get path to movie specified as command line argument
    movie = os.path.expanduser(sys.argv[1])
    # see if movie is accessible
    if not os.access(movie, os.R_OK):
        print('Error: %s wrong read file: ' % movie)
        sys.exit(1)
 
    # make instane of VLC and create reference to movie.
    vlcInstance = vlc.Instance()
    media = vlcInstance.media_new(movie)
 
    # make new instance of vlc player
    player = vlcInstance.media_player_new()
 
    # start with a callback
    em = player.event_manager()
    em.event_attach(vlc.EventType.MediaPlayerTimeChanged, \
        call_vlc, player)
 
    # set pygame window id to vlc player
    win_id = pygame.display.get_wm_info()['window']
    if sys.platform == "win32": # for Windows
        player.set_hwnd(win_id)
 
    # load movie into vlc player instance
    player.set_media(media)
 
    # quit pygame mixer to allow vlc full access to audio device
    pygame.mixer.quit()
 
    # start movie play
    player.play()
 
    while player.get_state() != vlc.State.Ended:
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                sys.exit(2)

The base of this python script is to make a instance of vlc and put into pygame display.
Another simple example:

C:\Python27>python.exe
Python 2.7.13 (v2.7.13:a06454b1afa1, Dec 17 2016, 20:42:59) [MSC v.1500 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import vlc
>>> inst = vlc.Instance()
Warning: option --plugin-path no longer exists.
Warning: option --plugin-path no longer exists.
>>> med = inst.media_new('rain.mp4')
>>> p = med.player_new_from_media()
>>> p.play()
0
>>>

22 Sep 2017 11:13am GMT

Narrative version

Once upon the time there was Earl Zope II. His lands where threatened by a huge disaster called "Python 2 sunset". His only chance was to emigrate to the Python 3 wonderland. After a long preparation phase for himself and his courtiers he was able to move to the new land. But the Python 3 wonderland has strict immigration authorities: They only allow "compatible" fellows to get a residency permit. The permission itself has three levels:

• alpha - To get this level the candidate has to prove that he can breath the Python 3 air and drink the water in this land without getting falling over. Earl Zope II needed a while to acclimate in the Python 3 wonderland as its climate is a bit different from what he was used to.
• beta - This level requires the the acclimation phase was successful and people could start to rely that the candidate will stay in the new land. Earl Zope II reached this level by proving that all his courtiers - he relies on - are ready for this level.
• final - To reach this level the candidate needs to prove that he is living in successful relations in Python 3 wonderland. Other inhabitants must be able to trust him and the services he offers. After Earl Zope II now has reached the beta phase he is now able to offer his services and hope that he gets the final level about fall 2018.

Back in his own country Earl Zope II had the official name "Zope2 2.13". Before the beta phase of his immigration he thought that he would have to change the name to something like "Zope2 4.0b1". This is looks ugly and some people protested against this name. With the kind and quick help of Baiju Muthukadan Earl Zope II was able to change his official name to "Zope 4.0b1". Thank you very much Baiju Muthukadan! Earl Zope is very proud of this new shorter name and is heartily thankful for this opportunity.

Technical version

The release of the first Beta version means, that no currently existing features will be removed until the final version. But There will be some new features and many bugfixes.

New Features of Zope version 4.0

• Support for Python 3.4 up to 3.6: Currently Python 3 can only be used for new projects. There is only an experimental way to convert an existing ZODB from Python 2 to 3 as it is not possible to run the same Data.fs under both versions, see zodb.py3migrate.
• Zope now by default runs as a WSGI application. The previously used ZServer is still supported but only runs under Python 2.
• Chameleon based templates are now the default.

Changes

• The name of the distribution changed from Zope2 to Zope. The previous Zope2 package will remain as a meta package which depends on Zope. This allows packages which require Zope 4.x to depend on Zope instead of Zope2.
• Removed deprecated code and BBB imports like the Globals package or the internal help system.
• Some smaller features, bug fixes and security fixes, see the Change log.

Thanks

A big "Thank you!" to all who made this release possible:

• dedicated people investing time, thoughts and money
• nice companies allowing their people to participate on Zope sprints
• the Plone Foundation sponsoring Zope sprints

We had a great Zope 4 Phoenix Sprint helping to raise Zope from the ashes! Thank you to everybody who participated in Halle or from remote.

Roadmap

Beta one of Zope is out:

• try it out - see the installation documentation
• test it with existing projects - see the what's new
• report bugs and give feedback - using the Zope issue tracker

We need the feedback to adapt Zope to the needs in the wild. The current plan is to create new beta releases once a while after implementing features resp. bug fixes or on demand. The current plan is to release a final 4.0 version in fall of 2018. This should allow software projects built upon Zope to migrate there code before the Python 2 sunset in 2020.

22 Sep 2017 8:05am GMT

Narrative version

Once upon the time there was Earl Zope II. His lands where threatened by a huge disaster called "Python 2 sunset". His only chance was to emigrate to the Python 3 wonderland. After a long preparation phase for himself and his courtiers he was able to move to the new land. But the Python 3 wonderland has strict immigration authorities: They only allow "compatible" fellows to get a residency permit. The permission itself has three levels:

• alpha - To get this level the candidate has to prove that he can breath the Python 3 air and drink the water in this land without getting falling over. Earl Zope II needed a while to acclimate in the Python 3 wonderland as its climate is a bit different from what he was used to.
• beta - This level requires the the acclimation phase was successful and people could start to rely that the candidate will stay in the new land. Earl Zope II reached this level by proving that all his courtiers - he relies on - are ready for this level.
• final - To reach this level the candidate needs to prove that he is living in successful relations in Python 3 wonderland. Other inhabitants must be able to trust him and the services he offers. After Earl Zope II now has reached the beta phase he is now able to offer his services and hope that he gets the final level about fall 2018.

Back in his own country Earl Zope II had the official name "Zope2 2.13". Before the beta phase of his immigration he thought that he would have to change the name to something like "Zope2 4.0b1". This is looks ugly and some people protested against this name. With the kind and quick help of Baiju Muthukadan Earl Zope II was able to change his official name to "Zope 4.0b1". Thank you very much Baiju Muthukadan! Earl Zope is very proud of this new shorter name and is heartily thankful for this opportunity.

Technical version

The release of the first Beta version means, that no currently existing features will be removed until the final version. But There will be some new features and many bugfixes.

New Features of Zope version 4.0

• Support for Python 3.4 up to 3.6: Currently Python 3 can only be used for new projects. There is only an experimental way to convert an existing ZODB from Python 2 to 3 as it is not possible to run the same Data.fs under both versions, see zodb.py3migrate.
• Zope now by default runs as a WSGI application. The previously used ZServer is still supported but only runs under Python 2.
• Chameleon based templates are now the default.

Changes

• The name of the distribution changed from Zope2 to Zope. The previous Zope2 package will remain as a meta package which depends on Zope. This allows packages which require Zope 4.x to depend on Zope instead of Zope2.
• Removed deprecated code and BBB imports like the Globals package or the internal help system.
• Some smaller features, bug fixes and security fixes, see the Change log.

Thanks

A big "Thank you!" to all who made this release possible:

• dedicated people investing time, thoughts and money
• nice companies allowing their people to participate on Zope sprints
• the Plone Foundation sponsoring Zope sprints

We had a great Zope 4 Phoenix Sprint helping to raise Zope from the ashes! Thank you to everybody who participated in Halle or from remote.

Roadmap

Beta one of Zope is out:

• try it out - see the installation documentation
• test it with existing projects - see the what's new
• report bugs and give feedback - using the Zope issue tracker

We need the feedback to adapt Zope to the needs in the wild. The current plan is to create new beta releases once a while after implementing features resp. bug fixes or on demand. The current plan is to release a final 4.0 version in fall of 2018. This should allow software projects built upon Zope to migrate there code before the Python 2 sunset in 2020.

22 Sep 2017 8:05am GMT

Today we migrated the documentation of zodb.py3migrate from pythonhosted.org to zodbpy3migrate.readthedocs.io.

This requires a directory - for this example I name it redir - containing a file named index.html with the following content:

<html>
<head>
 <title>zodb.py3migrate</title>
 <meta http-equiv="refresh"
       content="0; url=http://zodbpy3migrate.rtfd.io" />
</head>
<body>
  <p>
    <a href="http://zodbpy3migrate.rtfd.io">
      Redirect to zodbpy3migrate.rtfd.io
    </a>
  </p>
</body>
</html>

To upload it to pythonhosted.org I called:

py27 setup.py upload_docs --upload-dir=redir

Now pythonhosted.org/zodb.py3migrate points to read the docs.

Credits: The HTML was taken from the Trello board of the avocado-framework.

22 Sep 2017 6:28am GMT

Today we migrated the documentation of zodb.py3migrate from pythonhosted.org to zodbpy3migrate.readthedocs.io.

This requires a directory - for this example I name it redir - containing a file named index.html with the following content:

<html>
<head>
 <title>zodb.py3migrate</title>
 <meta http-equiv="refresh"
       content="0; url=http://zodbpy3migrate.rtfd.io" />
</head>
<body>
  <p>
    <a href="http://zodbpy3migrate.rtfd.io">
      Redirect to zodbpy3migrate.rtfd.io
    </a>
  </p>
</body>
</html>

To upload it to pythonhosted.org I called:

py27 setup.py upload_docs --upload-dir=redir

Now pythonhosted.org/zodb.py3migrate points to read the docs.

Credits: The HTML was taken from the Trello board of the avocado-framework.

22 Sep 2017 6:28am GMT

In this article, some more social networking concepts will be illustrated with a few problems. The problems appeared in the programming assignments in the coursera course Applied Social Network Analysis in Python. The descriptions of the problems are taken from the assignments. The analysis is done using NetworkX. The following theory is going to be used to solve … Continue reading Some more Social Network Analysis with Python: Centrality, PageRank/HITS, Random Network Generation Models, Link Prediction

22 Sep 2017 12:02am GMT

In this article, some more social networking concepts will be illustrated with a few problems. The problems appeared in the programming assignments in the coursera course Applied Social Network Analysis in Python. The descriptions of the problems are taken from the assignments. The analysis is done using NetworkX. The following theory is going to be used to solve … Continue reading Some more Social Network Analysis with Python: Centrality, PageRank/HITS, Random Network Generation Models, Link Prediction

22 Sep 2017 12:02am GMT

I got some feedback today from someone who said they were looking through the pythontesting.net/start-here page and were still a bit confused as to how to start testing. I totally understand that and want to improve it. I want to make the site more friendly to people new to testing. What info would help? (try […]

The post What info do you need to start testing? appeared first on Python Testing.

21 Sep 2017 8:42pm GMT

I got some feedback today from someone who said they were looking through the pythontesting.net/start-here page and were still a bit confused as to how to start testing. I totally understand that and want to improve it. I want to make the site more friendly to people new to testing. What info would help? (try […]

The post What info do you need to start testing? appeared first on Python Testing.

21 Sep 2017 8:42pm GMT

School is back in session and now is the time students are thinking about their future. When considering options, science-oriented students are likely thinking about what is arguably today's hottest technology: artificial intelligence (AI).

21 Sep 2017 6:40pm GMT

School is back in session and now is the time students are thinking about their future. When considering options, science-oriented students are likely thinking about what is arguably today's hottest technology: artificial intelligence (AI).

21 Sep 2017 6:40pm GMT

In this article, we will take a look at two of the most popular web frameworks in Python: Django and Flask.

Here, we will be covering how each of these frameworks compares when looking at their learning curves, how easy it is to get started. Next, we'll also be looking at how these two stands against each other with concluding by when to use one of them.

Getting Started

One of the easiest ways to compare two frameworks is by installing them and taking note how easily a user can get started with it, which is exactly what we will do next. We will try setting up Django and Flask on a Linux machine and create an app to see how easy (or difficult) the process is with each one.

Setting up Django

In this section, we will setup Django on an Linux-powered machine. Best way to get started with any Python framework is by using virtual environments. We will install it using pip.

$ sudo apt-get install python3-pip
$ pip3 install virtualenv
$ virtualenv --python=`which python3` ~/.virtualenvs/django_env

Note: If the pip3 command gives you an error, you may need to prefix it with sudo to make it work.

Once we're done setting up our virtual environment, which we've named django_env, we must activate it to start using it:

$ source ~/.virtualenvs/django_env/bin/activate

Once activated, we can finally install Django:

$ pip install Django

Suppose our project is called mysite. Make a new directory and enter it, run following commands:

$ mkdir mysite
$ cd mysite
$ django-admin startproject mysite

If you inspect the resulting project, your directory structure will be shown as:

mysite/  
  manage.py 
  mysite/
    __init__.py 
    settings.py 
    urls.py 
    wsgi.py

Let's take a look at what is significant about each of the directories and files that were created.

• The root mysite/ directory is the container directory for our project
• mysite.py is a command line tool that enables us to work with the project in different ways
• mysite/ directory is the Python package of our project code
• mysite/__init__.py is a file which informs Python that current directory should be considered a Python package
• mysite/settings.py will contain the configuration properties for current project
• mysite/urls.py is a Python file which contains the URL definitions for this project
• mysite/wsgi.py acts as an entry for a WSGI web server that forwards requests to your project

From here, we can actually run the app using the manage.py tool. The following command does some system checks, checks for database migrations, and some other things before actually running your server:

$ python manage.py runserver

Performing system checks...

System check identified no issues (0 silenced).

You have unapplied migrations; your app may not work properly until they are applied.  
Run 'python manage.py migrate' to apply them.

September 20, 2017 - 15:50:53  
Django version 1.11, using settings 'mysite.settings'  
Starting development server at http://127.0.0.1:8000/  
Quit the server with CONTROL-C.  

Note: Running your server in this way is meant for development only, and not production environments.

To check out your app, head to http://localhost:8000/, where you should see a page saying "It worked!".

But wait, you're still not done! To actually create any pages/functionality in your site, you need to create an app within your project. But why do you need an app? In Django, apps are web applications that do something, which could be a blog, a forum, or a commenting system. The project is a collection of your apps, as well as configuration for the apps and entire website.

So, to create your app, move in to your project directory and run the following command:

$ cd mysite
$ python manage.py startapp myapp

This will create another directory structure where you can actually manage your models, views, etc.

manage.py  
myapp/  
  __init__.py
  admin.py
  apps.py
  migrations/
  models.py
  tests.py
  views.py
mysite/  
  __init__.py 
  settings.py 
  urls.py 
  wsgi.py

From here, you need to set up your views in views.py and URL routing in urls.py, which we'll save for another tutorial.

But you get the point, right? It takes a few commands and quite a few files to get your Django project up and running.

Setting up Flask

Just like Django, we will use a virtual environment with Flask as well. So the commands for activating a virtual environment will remain the same as before. After that, instead of installing Django, we'll install Flask instead.

$ pip install Flask

Once the installation completes, we can start creating our Flask application. Now, unlike Django, Flask doesn't have a complicated directory structure. The structure of your Flask project is entirely up to you.

Borrowing an example from the Flask homepage, you can create a runnable Flask app from just a single file:

from flask import Flask  
app = Flask(__name__)

@app.route("/")
def hello():  
    return "Hello World!"

And running the app is about as easy as setting it up:

$ FLASK_APP=hello.py flask run
 * Running on http://localhost:5000/

Visiting the URL http://localhost:5000/ should display the text "Hello World!" in your browser.

I'd encourage you to look for some sample apps on the Flask homepage to learn more. Learning by example is one of the best ways to get up and running quickly.

The framework that "wins" this area is really up to your needs and experience. Django may be more favorable for beginners since it makes decisions for you (i.e. how to structure your app), whereas in Flask you need to handle this yourself.

On the other hand, Flask is simpler to get running since it requires very little to get going. An entire Flask app can be composed from a single file. The trade-offs really depend no what you need most.

Learning Curve

Regarding learning curve, as we saw in the last section with Flask, it was very easy to get started. The app doesn't require a complicated directory structure where you needed to remember which directory/file does what. Instead, you can add files and directories as you go according to your usage. This is what Flask is about, as a micro-framework for web development.

Django, on the other hand, has a bit higher learning curve since it is more "picky" about how things are set up and work. Because of this, you need to take more time learning how to compose modules and work within the confines of the framework.

This isn't all bad though, since this allows you to easily plug in 3rd party components in to your app without having to do any work integrating them.

Employability

Which of these frameworks will help you land a job? For many developers, this is one of the more important question regarding certain libraries and frameworks: which will help me get hired?

Django has quite a few large companies on its resume, which is because many companies that use Python for web development tend to use (or at least started off with) Django to power their site. Django, being a full-fledged framework, is often used early on in development because you get much more resources and power with it out of the box.

Here are just a few companies that use (or have used) Django for their sites:

• Pinterest
• Instagram
• Disqus
• NASA

Flask is a bit harder to gauge here, mostly because of the way it is used. Flask tends to be used more for microservices, which makes it harder to tell which companies are using it. Plus, companies with a microservice architecture are less likely to say their service is "powered by Flask" since they likely have many services potentially using many different frameworks.

There are, however, hints out there of who uses Flask based on job postings, tech talks, blog posts, etc. From those, we know that the following companies have used Flask somewhere in their backend infrastructure:

• Twilio
• Linkedin
• Pinterest
• Uber
• Mailgun

While Django may be more popular among companies, Flask is arguably more common among the more tech-focused companies as they are more likely to use microservices, and therefore micro-frameworks like Flask.

Project Size and Scope

Our comparison of each framework can become very subjective thanks to many different factors, like project scope, developer experience, type of project, etc. If the project is fairly small and it doesn't need all of the overhead that Django comes with, then Flask is the ideal choice to get started and get something done very quickly.

However, if the project is larger in duration and scope, then Django is likely the way to go as it already includes much of what you need. This basically means that many common components of a web-service/website either already comes with Django, or it is already available through 3rd party open source software. In some cases you can just create a Django project, plug in a bunch of components, create your views/templates, and you're done.

While we do praise Django for its extensibility, we can't ignore that Flask does have some extensions of its own. While they aren't quite as big in scope as Django (and many of these extensions come standard in Django), it's a step in the right direction.

Django's add-on components can be as big as a blog add-on, to as small as small middleware input validation. Most of Flask's extensions are small middleware components, which is still better than nothing and very helpful, considering the average size of Flask projects.

Limitations

Every piece of tech has its problems, and these frameworks are no different. So before you choose which to use, you might want to know what disadvantages each has, which we'll be talking about in this section.

Django

So, what are the aspects of Django that work against it to be selected as your framework of choice?

Django is a very large project. Once a developer, especially beginners, starts learning Django, it's easy for them to get lost in the source code, the built-in features, and components it provides, without even using them in an app.

Django is a fairly large framework to deploy for simple use-cases, as it hides much of the control from you. If you want to use something that isn't "standard" in Django, then you have to put in some extra work to do so.

Understanding components in Django can be a little difficult and tricky at times and can lead to tough decisions, like deciding if an existing component will work for your use-case, or if it'll end up causing you more work than it is worth.

Flask

Now that we've seen some of the problems with Django, let's not forget about Flask. Since the Flask framework is so small, there isn't a lot to complain about. Well, except for that fact right there: It's so small.

Flask is a micro-framework, which means it only provides the bare-bones functionality to get you started. This doesn't mean it can't be powerful and can't scale, it just means that you'll have to create much of the functionality of your service yourself. This means you'll need to handle integrating your database, data validation, file serving, etc.

While this could be considered an advantage to those who want control over everything, it also means it'll take you longer to get set up with a fully-functional website.

Choosing Flask or Django

While it's easy to talk about what each framework does and doesn't do, let's try and make a more direct comparison of each, which we'll do in this section.

When simplicity is a factor, Flask is the way to go. It allows much more control over your app and lets you decide how you want to implement things in a project. In contrast to this, Django provides a more inclusive experience, such as providing a default admin panel for your data, an ORM on top of your database, and protection against things like SQL injection, cross-site scripting, CSRF, etc.

If you put a lot of emphasis on community support, then Django is probably better in this regard given its history. It has been around since 2005, whereas Flask was created in 2010. At the time of writing this article, Django has about 3.5x more questions/answers on Stack Overflow than Flask (about 2600 Django questions to Flask's 750).

The Flask framework is relatively lightweight. In fact, it's almost 2.5x smaller than Django in terms of amount of code. That's a big difference, especially if you need to understand the inner-workings of your web framework. In this aspect, Flask will be much easier to read and understand for most developers.

Flask should be selected for development if you need complete control over your app, which ORM you want to use, which database you need to integrate with excellent opportunities to learn more about web services. Django, on the other hand, is better when there is a more clear path to creating what you want, or you're creating something that has been done before. For example, a blog would be a good use-case for Django.

Learn More

Want to learn more about either of these frameworks? There are quite a few resources out there. Here are a few courses that I've found to be pretty helpful, and will get you up to speed much quicker:

Python and Django Full Stack Web Developer Bootcamp

REST APIs with Flask and Python

Otherwise you can also get a great start by visiting each framework's respective websites:

• Django Website
• Flask Website

Either way, the most important thing is you actually try them out, work through some examples, and decide on your own which is best for you.

Conclusion

In this article, we compared the two web frameworks, Django and Flask, by looking at their different properties and setting up a simple "Hello World!" app with each one.

You may find that if you're new to web development and decide to learn Django, it may take you a bit longer to truly understand what all of the underlying components do, and how to change them to actually do what you want. But there are many positives as well, and once you become proficient with Django, it'll end up saving you a lot of time in the end, given its huge list of components and vast community support.

A more advanced comparison for any frameworks can be done only with advanced use cases and scenarios. Just know that you can't really go wrong with either one, and learning either will set you up well for finding a job.

If you need a recommendation, then I'd personally go with Flask. By learning a framework that doesn't hide so much stuff from you, you can learn much, much more. Once you have a better understanding of the core concepts of web development and HTTP, you can start to use add-ons that abstract this away from you. But having that solid foundation of understanding is more important early on, in my opinion.

Which framework do you use, and why? Let us know in the comments!

21 Sep 2017 3:52pm GMT

In this article, we will take a look at two of the most popular web frameworks in Python: Django and Flask.

Here, we will be covering how each of these frameworks compares when looking at their learning curves, how easy it is to get started. Next, we'll also be looking at how these two stands against each other with concluding by when to use one of them.

Getting Started

One of the easiest ways to compare two frameworks is by installing them and taking note how easily a user can get started with it, which is exactly what we will do next. We will try setting up Django and Flask on a Linux machine and create an app to see how easy (or difficult) the process is with each one.

Setting up Django

In this section, we will setup Django on an Linux-powered machine. Best way to get started with any Python framework is by using virtual environments. We will install it using pip.

$ sudo apt-get install python3-pip
$ pip3 install virtualenv
$ virtualenv --python=`which python3` ~/.virtualenvs/django_env

Note: If the pip3 command gives you an error, you may need to prefix it with sudo to make it work.

Once we're done setting up our virtual environment, which we've named django_env, we must activate it to start using it:

$ source ~/.virtualenvs/django_env/bin/activate

Once activated, we can finally install Django:

$ pip install Django

Suppose our project is called mysite. Make a new directory and enter it, run following commands:

$ mkdir mysite
$ cd mysite
$ django-admin startproject mysite

If you inspect the resulting project, your directory structure will be shown as:

mysite/  
  manage.py 
  mysite/
    __init__.py 
    settings.py 
    urls.py 
    wsgi.py

Let's take a look at what is significant about each of the directories and files that were created.

• The root mysite/ directory is the container directory for our project
• mysite.py is a command line tool that enables us to work with the project in different ways
• mysite/ directory is the Python package of our project code
• mysite/__init__.py is a file which informs Python that current directory should be considered a Python package
• mysite/settings.py will contain the configuration properties for current project
• mysite/urls.py is a Python file which contains the URL definitions for this project
• mysite/wsgi.py acts as an entry for a WSGI web server that forwards requests to your project

From here, we can actually run the app using the manage.py tool. The following command does some system checks, checks for database migrations, and some other things before actually running your server:

$ python manage.py runserver

Performing system checks...

System check identified no issues (0 silenced).

You have unapplied migrations; your app may not work properly until they are applied.  
Run 'python manage.py migrate' to apply them.

September 20, 2017 - 15:50:53  
Django version 1.11, using settings 'mysite.settings'  
Starting development server at http://127.0.0.1:8000/  
Quit the server with CONTROL-C.  

Note: Running your server in this way is meant for development only, and not production environments.

To check out your app, head to http://localhost:8000/, where you should see a page saying "It worked!".

But wait, you're still not done! To actually create any pages/functionality in your site, you need to create an app within your project. But why do you need an app? In Django, apps are web applications that do something, which could be a blog, a forum, or a commenting system. The project is a collection of your apps, as well as configuration for the apps and entire website.

So, to create your app, move in to your project directory and run the following command:

$ cd mysite
$ python manage.py startapp myapp

This will create another directory structure where you can actually manage your models, views, etc.

manage.py  
myapp/  
  __init__.py
  admin.py
  apps.py
  migrations/
  models.py
  tests.py
  views.py
mysite/  
  __init__.py 
  settings.py 
  urls.py 
  wsgi.py

From here, you need to set up your views in views.py and URL routing in urls.py, which we'll save for another tutorial.

But you get the point, right? It takes a few commands and quite a few files to get your Django project up and running.

Setting up Flask

Just like Django, we will use a virtual environment with Flask as well. So the commands for activating a virtual environment will remain the same as before. After that, instead of installing Django, we'll install Flask instead.

$ pip install Flask

Once the installation completes, we can start creating our Flask application. Now, unlike Django, Flask doesn't have a complicated directory structure. The structure of your Flask project is entirely up to you.

Borrowing an example from the Flask homepage, you can create a runnable Flask app from just a single file:

from flask import Flask  
app = Flask(__name__)

@app.route("/")
def hello():  
    return "Hello World!"

And running the app is about as easy as setting it up:

$ FLASK_APP=hello.py flask run
 * Running on http://localhost:5000/

Visiting the URL http://localhost:5000/ should display the text "Hello World!" in your browser.

I'd encourage you to look for some sample apps on the Flask homepage to learn more. Learning by example is one of the best ways to get up and running quickly.

The framework that "wins" this area is really up to your needs and experience. Django may be more favorable for beginners since it makes decisions for you (i.e. how to structure your app), whereas in Flask you need to handle this yourself.

On the other hand, Flask is simpler to get running since it requires very little to get going. An entire Flask app can be composed from a single file. The trade-offs really depend no what you need most.

Learning Curve

Regarding learning curve, as we saw in the last section with Flask, it was very easy to get started. The app doesn't require a complicated directory structure where you needed to remember which directory/file does what. Instead, you can add files and directories as you go according to your usage. This is what Flask is about, as a micro-framework for web development.

Django, on the other hand, has a bit higher learning curve since it is more "picky" about how things are set up and work. Because of this, you need to take more time learning how to compose modules and work within the confines of the framework.

This isn't all bad though, since this allows you to easily plug in 3rd party components in to your app without having to do any work integrating them.

Employability

Which of these frameworks will help you land a job? For many developers, this is one of the more important question regarding certain libraries and frameworks: which will help me get hired?

Django has quite a few large companies on its resume, which is because many companies that use Python for web development tend to use (or at least started off with) Django to power their site. Django, being a full-fledged framework, is often used early on in development because you get much more resources and power with it out of the box.

Here are just a few companies that use (or have used) Django for their sites:

• Pinterest
• Instagram
• Disqus
• NASA

Flask is a bit harder to gauge here, mostly because of the way it is used. Flask tends to be used more for microservices, which makes it harder to tell which companies are using it. Plus, companies with a microservice architecture are less likely to say their service is "powered by Flask" since they likely have many services potentially using many different frameworks.

There are, however, hints out there of who uses Flask based on job postings, tech talks, blog posts, etc. From those, we know that the following companies have used Flask somewhere in their backend infrastructure:

• Twilio
• Linkedin
• Pinterest
• Uber
• Mailgun

While Django may be more popular among companies, Flask is arguably more common among the more tech-focused companies as they are more likely to use microservices, and therefore micro-frameworks like Flask.

Project Size and Scope

Our comparison of each framework can become very subjective thanks to many different factors, like project scope, developer experience, type of project, etc. If the project is fairly small and it doesn't need all of the overhead that Django comes with, then Flask is the ideal choice to get started and get something done very quickly.

However, if the project is larger in duration and scope, then Django is likely the way to go as it already includes much of what you need. This basically means that many common components of a web-service/website either already comes with Django, or it is already available through 3rd party open source software. In some cases you can just create a Django project, plug in a bunch of components, create your views/templates, and you're done.

While we do praise Django for its extensibility, we can't ignore that Flask does have some extensions of its own. While they aren't quite as big in scope as Django (and many of these extensions come standard in Django), it's a step in the right direction.

Django's add-on components can be as big as a blog add-on, to as small as small middleware input validation. Most of Flask's extensions are small middleware components, which is still better than nothing and very helpful, considering the average size of Flask projects.

Limitations

Every piece of tech has its problems, and these frameworks are no different. So before you choose which to use, you might want to know what disadvantages each has, which we'll be talking about in this section.

Django

So, what are the aspects of Django that work against it to be selected as your framework of choice?

Django is a very large project. Once a developer, especially beginners, starts learning Django, it's easy for them to get lost in the source code, the built-in features, and components it provides, without even using them in an app.

Django is a fairly large framework to deploy for simple use-cases, as it hides much of the control from you. If you want to use something that isn't "standard" in Django, then you have to put in some extra work to do so.

Understanding components in Django can be a little difficult and tricky at times and can lead to tough decisions, like deciding if an existing component will work for your use-case, or if it'll end up causing you more work than it is worth.

Flask

Now that we've seen some of the problems with Django, let's not forget about Flask. Since the Flask framework is so small, there isn't a lot to complain about. Well, except for that fact right there: It's so small.

Flask is a micro-framework, which means it only provides the bare-bones functionality to get you started. This doesn't mean it can't be powerful and can't scale, it just means that you'll have to create much of the functionality of your service yourself. This means you'll need to handle integrating your database, data validation, file serving, etc.

While this could be considered an advantage to those who want control over everything, it also means it'll take you longer to get set up with a fully-functional website.

Choosing Flask or Django

While it's easy to talk about what each framework does and doesn't do, let's try and make a more direct comparison of each, which we'll do in this section.

When simplicity is a factor, Flask is the way to go. It allows much more control over your app and lets you decide how you want to implement things in a project. In contrast to this, Django provides a more inclusive experience, such as providing a default admin panel for your data, an ORM on top of your database, and protection against things like SQL injection, cross-site scripting, CSRF, etc.

If you put a lot of emphasis on community support, then Django is probably better in this regard given its history. It has been around since 2005, whereas Flask was created in 2010. At the time of writing this article, Django has about 3.5x more questions/answers on Stack Overflow than Flask (about 2600 Django questions to Flask's 750).

The Flask framework is relatively lightweight. In fact, it's almost 2.5x smaller than Django in terms of amount of code. That's a big difference, especially if you need to understand the inner-workings of your web framework. In this aspect, Flask will be much easier to read and understand for most developers.

Flask should be selected for development if you need complete control over your app, which ORM you want to use, which database you need to integrate with excellent opportunities to learn more about web services. Django, on the other hand, is better when there is a more clear path to creating what you want, or you're creating something that has been done before. For example, a blog would be a good use-case for Django.

Learn More

Want to learn more about either of these frameworks? There are quite a few resources out there. Here are a few courses that I've found to be pretty helpful, and will get you up to speed much quicker:

Python and Django Full Stack Web Developer Bootcamp

REST APIs with Flask and Python

Otherwise you can also get a great start by visiting each framework's respective websites:

• Django Website
• Flask Website

Either way, the most important thing is you actually try them out, work through some examples, and decide on your own which is best for you.

Conclusion

In this article, we compared the two web frameworks, Django and Flask, by looking at their different properties and setting up a simple "Hello World!" app with each one.

You may find that if you're new to web development and decide to learn Django, it may take you a bit longer to truly understand what all of the underlying components do, and how to change them to actually do what you want. But there are many positives as well, and once you become proficient with Django, it'll end up saving you a lot of time in the end, given its huge list of components and vast community support.

A more advanced comparison for any frameworks can be done only with advanced use cases and scenarios. Just know that you can't really go wrong with either one, and learning either will set you up well for finding a job.

If you need a recommendation, then I'd personally go with Flask. By learning a framework that doesn't hide so much stuff from you, you can learn much, much more. Once you have a better understanding of the core concepts of web development and HTTP, you can start to use add-ons that abstract this away from you. But having that solid foundation of understanding is more important early on, in my opinion.

Which framework do you use, and why? Let us know in the comments!

21 Sep 2017 3:52pm GMT

Anaconda, the most popular Python data science platform provider, today announced that several company experts will present two sessions and one tutorial at The Strata Data Conference on September 26 and 27 at the Javits Center in New York City.

21 Sep 2017 2:11pm GMT

Anaconda, the most popular Python data science platform provider, today announced that several company experts will present two sessions and one tutorial at The Strata Data Conference on September 26 and 27 at the Javits Center in New York City.

21 Sep 2017 2:11pm GMT

21 Sep 2017 12:59pm GMT

21 Sep 2017 12:59pm GMT

After a strong start, we continue our early access program (EAP) with its second release. Download EAP 2 now!

Testing RESTful Applications

Many of us work on web applications which expose a RESTful API, or at least an API that pretends to be RESTful. To test these some of us use cURL, some browser extension, or some other piece of software. There is a REST client in PyCharm, but we've decided it can use some improvement, so we're making an all new one.

The new REST client is entirely editor based, you write your request in a file, and then run the request to get a response. Sounds easy enough, right?

To see how it works, we'll take the sample application from Flask-Restplus, which as you might expect exposes a todo API.

We'll start out by creating a new todo. This is done by POST-ing to the /todos/ endpoint. To use the new PyCharm REST client, we should start by creating a .http file. If we don't intend to save this, we can create a scratch file. Press Ctrl+Alt+Shift+Insert (Shift+Command+N on macOS) to start creating a scratch file and choose 'HTTP request' as the type. Let's type our request into the file:

### Post a todo

POST http://localhost:5000/todos/
Accept: application/json
Content-Type: application/json

{
    "task": "Create my task!"
}

Now click the green play button next to the first line, and you should see that the task was created:

You can see the response in the Run tool window, and you might also notice that PyCharm wrote a new line in our file with the name of a .json file. This file contains the response, so if we Ctrl+Click (Cmd+Click) the filename, or use Ctrl+B (Cmd+B) to go to definition we see the full response in a separate file.

Those files become really useful when we do the same request a couple times but get different results. If we use a GET request to get our todo, and then use a PUT to change it, and redo our GET, we'll now have two files there. We can then use the blue icon with the arrows to see the difference between the responses:

Now it's your turn! Get the EAP and try it yourself

Further Improvements

• Code completion for CSS now supports more notations for colors (rgb, hsl), added completion for CSS transitions, and more
• React and TypeScript code intelligence: TypeScript 2.4 mapped types, and more
• Docker improvements: you can now pass -build-arg arguments to a Docker run configuration

To read about all improvements in this version, see the release notes

As always, we really appreciate your feedback! Please let us know on YouTrack about any issues you experience or suggestions for improvement. You can also reach us on Twitter, or by leaving a comment below.

21 Sep 2017 11:44am GMT

After a strong start, we continue our early access program (EAP) with its second release. Download EAP 2 now!

Testing RESTful Applications

Many of us work on web applications which expose a RESTful API, or at least an API that pretends to be RESTful. To test these some of us use cURL, some browser extension, or some other piece of software. There is a REST client in PyCharm, but we've decided it can use some improvement, so we're making an all new one.

The new REST client is entirely editor based, you write your request in a file, and then run the request to get a response. Sounds easy enough, right?

To see how it works, we'll take the sample application from Flask-Restplus, which as you might expect exposes a todo API.

We'll start out by creating a new todo. This is done by POST-ing to the /todos/ endpoint. To use the new PyCharm REST client, we should start by creating a .http file. If we don't intend to save this, we can create a scratch file. Press Ctrl+Alt+Shift+Insert (Shift+Command+N on macOS) to start creating a scratch file and choose 'HTTP request' as the type. Let's type our request into the file:

### Post a todo

POST http://localhost:5000/todos/
Accept: application/json
Content-Type: application/json

{
    "task": "Create my task!"
}

Now click the green play button next to the first line, and you should see that the task was created:

You can see the response in the Run tool window, and you might also notice that PyCharm wrote a new line in our file with the name of a .json file. This file contains the response, so if we Ctrl+Click (Cmd+Click) the filename, or use Ctrl+B (Cmd+B) to go to definition we see the full response in a separate file.

Those files become really useful when we do the same request a couple times but get different results. If we use a GET request to get our todo, and then use a PUT to change it, and redo our GET, we'll now have two files there. We can then use the blue icon with the arrows to see the difference between the responses:

Now it's your turn! Get the EAP and try it yourself

Further Improvements

• Code completion for CSS now supports more notations for colors (rgb, hsl), added completion for CSS transitions, and more
• React and TypeScript code intelligence: TypeScript 2.4 mapped types, and more
• Docker improvements: you can now pass -build-arg arguments to a Docker run configuration

To read about all improvements in this version, see the release notes

As always, we really appreciate your feedback! Please let us know on YouTrack about any issues you experience or suggestions for improvement. You can also reach us on Twitter, or by leaving a comment below.

21 Sep 2017 11:44am GMT

Gestern musste ich morgens zur Station nach KWT um unsere Rerservierten Bustickets für die Weihnachtsferien in Capetown abzuholen. Der Bahnhof selber ist seit Dezember aus kostengründen ohne Zugverbindung - aber Translux und co - die langdistanzbusse haben dort ihre Büros.


Größere Kartenansicht

© benste CC NC SA

10 Nov 2011 10:57am GMT

Niemand ist besorgt um so was - mit dem Auto fährt man einfach durch, und in der City - nahe Gnobie- "ne das ist erst gefährlich wenn die Feuerwehr da ist" - 30min später auf dem Rückweg war die Feuerwehr da.

© benste CC NC SA

09 Nov 2011 8:25pm GMT

Brai = Grillabend o.ä.

Die möchte gern Techniker beim Flicken ihrer SpeakOn / Klinke Stecker Verzweigungen...

Die Damen "Mamas" der Siedlung bei der offiziellen Eröffnungsrede

Auch wenn weniger Leute da waren als erwartet, Laute Musik und viele Leute ...

Und natürlich ein Feuer mit echtem Holz zum Grillen.

© benste CC NC SA

08 Nov 2011 2:30pm GMT

One of our missions was bringing Katja's Linux Server back to her room. While doing that we saw her new decoration.

Björn, Simphiwe carried the PC to Katja's school

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07 Nov 2011 2:00pm GMT

Today I went with Björn to Needs Camp to Visit Katja's guest family for a special Party. First of all we visited some friends of Nelisa - yeah the one I'm working with in Quigney - Katja's guest fathers sister - who did her a haircut.

African Women usually get their hair done by arranging extensions and not like Europeans just cutting some hair.

In between she looked like this...

And then she was done - looks amazing considering the amount of hair she had last week - doesn't it ?

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06 Nov 2011 7:45pm GMT

Irgendwie viel mir heute auf das ich meine Blogposts mal ein bischen umstrukturieren muss - wenn ich immer nur von neuen Plätzen berichte, dann müsste ich ja eine Rundreise machen. Hier also mal ein paar Sachen aus meinem heutigen Alltag.

Erst einmal vorweg, Samstag zählt zumindest für uns Voluntäre zu den freien Tagen.

Dieses Wochenende sind nur Rommel und ich auf der Farm - Katja und Björn sind ja mittlerweile in ihren Einsatzstellen, und meine Mitbewohner Kyle und Jonathan sind zu Hause in Grahamstown - sowie auch Sipho der in Dimbaza wohnt.
Robin, die Frau von Rommel ist in Woodie Cape - schon seit Donnerstag um da ein paar Sachen zur erledigen.
Naja wie dem auch sei heute morgen haben wir uns erstmal ein gemeinsames Weetbix/Müsli Frühstück gegönnt und haben uns dann auf den Weg nach East London gemacht. 2 Sachen waren auf der Checkliste Vodacom, Ethienne (Imobilienmakler) außerdem auf dem Rückweg die fehlenden Dinge nach NeedsCamp bringen.

Nachdem wir gerade auf der Dirtroad losgefahren sind mussten wir feststellen das wir die Sachen für Needscamp und Ethienne nicht eingepackt hatten aber die Pumpe für die Wasserversorgung im Auto hatten.

Also sind wir in EastLondon ersteinmal nach Farmerama - nein nicht das onlinespiel farmville - sondern einen Laden mit ganz vielen Sachen für eine Farm - in Berea einem nördlichen Stadteil gefahren.

In Farmerama haben wir uns dann beraten lassen für einen Schnellverschluss der uns das leben mit der Pumpe leichter machen soll und außerdem eine leichtere Pumpe zur Reperatur gebracht, damit es nicht immer so ein großer Aufwand ist, wenn mal wieder das Wasser ausgegangen ist.

Fego Caffé ist in der Hemmingways Mall, dort mussten wir und PIN und PUK einer unserer Datensimcards geben lassen, da bei der PIN Abfrage leider ein zahlendreher unterlaufen ist. Naja auf jeden Fall speichern die Shops in Südafrika so sensible Daten wie eine PUK - die im Prinzip zugang zu einem gesperrten Phone verschafft.

Im Cafe hat Rommel dann ein paar online Transaktionen mit dem 3G Modem durchgeführt, welches ja jetzt wieder funktionierte - und übrigens mittlerweile in Ubuntu meinem Linuxsystem perfekt klappt.

Nebenbei bin ich nach 8ta gegangen um dort etwas über deren neue Deals zu erfahren, da wir in einigen von Hilltops Centern Internet anbieten wollen. Das Bild zeigt die Abdeckung UMTS in NeedsCamp Katjas Ort. 8ta ist ein neuer Telefonanbieter von Telkom, nachdem Vodafone sich Telkoms anteile an Vodacom gekauft hat müssen die komplett neu aufbauen.
Wir haben uns dazu entschieden mal eine kostenlose Prepaidkarte zu testen zu organisieren, denn wer weis wie genau die Karte oben ist ... Bevor man einen noch so billigen Deal für 24 Monate signed sollte man wissen obs geht.

Danach gings nach Checkers in Vincent, gesucht wurden zwei Hotplates für WoodyCape - R 129.00 eine - also ca. 12€ für eine zweigeteilte Kochplatte.
Wie man sieht im Hintergrund gibts schon Weihnachtsdeko - Anfang November und das in Südafrika bei sonnig warmen min- 25°C

Mittagessen haben wir uns bei einem Pakistanischen Curry Imbiss gegönnt - sehr empfehlenswert !
Naja und nachdem wir dann vor ner Stunde oder so zurück gekommen sind habe ich noch den Kühlschrank geputzt den ich heute morgen zum defrosten einfach nach draußen gestellt hatte. Jetzt ist der auch mal wieder sauber und ohne 3m dicke Eisschicht...

Morgen ... ja darüber werde ich gesondert berichten ... aber vermutlich erst am Montag, denn dann bin ich nochmal wieder in Quigney(East London) und habe kostenloses Internet.

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05 Nov 2011 4:33pm GMT

Sterkspruit is one of Hilltops Computer Centres in the far north of Eastern Cape. On the trip to J'burg we've used the opportunity to take a look at the centre.

Pupils in the big classroom

The Trainer

School in Countryside

Adult Class in the Afternoon

"Town"

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31 Oct 2011 4:58pm GMT

What are you doing in an internet cafe if your ADSL and Faxline has been discontinued before months end. Well my idea was sitting outside and eating some ice cream.
At least it's sunny and not as rainy as on the weekend.

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31 Oct 2011 3:11pm GMT

For those who are traveling through Zastron - there is a very nice Restaurant which is serving delicious food at reasanable prices.
In addition they're selling home made juices jams and honey.

interior

home made specialities - the shop in the shop

the Bar

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30 Oct 2011 4:47pm GMT

Having the 10 - 12h trip from J'burg back to ELS I was able to take a lot of pcitures including these different roadsides

Plain Street

Orange River in its beginngings (near Lesotho)

Zastron Anglican Church

The Bridge in Between "Free State" and Eastern Cape next to Zastron

my new Background ;)

If you listen to GoogleMaps you'll end up traveling 50km of gravel road - as it was just renewed we didn't have that many problems and saved 1h compared to going the official way with all it's constructions sites

Freeway

getting dark

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29 Oct 2011 4:23pm GMT

Klar einiges mag anders sein, vieles aber gleich - aber ein in Deutschland täglich übliches Bild einer Straßenbaustelle - wie läuft das eigentlich in Südafrika ?

Ersteinmal vorweg - NEIN keine Ureinwohner die mit den Händen graben - auch wenn hier mehr Manpower genutzt wird - sind sie fleißig mit Technologie am arbeiten.

Eine ganz normale "Bundesstraße"

und wie sie erweitert wird

gaaaanz viele LKWs

denn hier wird eine Seite über einen langen Abschnitt komplett gesperrt, so das eine Ampelschaltung mit hier 45 Minuten Wartezeit entsteht

Aber wenigstens scheinen die ihren Spaß zu haben ;) - Wie auch wir denn gücklicher Weise mussten wir nie länger als 10 min. warten.

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28 Oct 2011 4:20pm GMT