Develop Django Under the Debugger

Posted on August 24, 2017 by Paul Everitt

PyCharm Professional has long had great support for developing Django applications, including a run configuration tailored to the Django server. This winds up being a wonderful workflow, with a tool window showing the server output.

Sometimes, though, you hit a problem and want to debug your code. You stop the server, run it under the debugger, and do your debugging. Which also works really well: PyCharm’s visual debugger is a key selling point and integrates nicely into Django (e.g. template debugging.) And recently, PyCharm’s debugger has undergone dramatic speedups, especially when using Python 3.6. In fact, running under the debugger is approaching the speed of normal run configurations.

But still, it’s irritating to stop you server, restart under the debugger, stop the server, and restart under the regular runner. It messes up your flow, and you’ll be tempted to blasphemy of debugging with print().

So let’s break with the past with a crazy idea: always run under the debugger when developing Django.

Note: If you’ve taken Kenneth Love’s fantastic Django Basics course at Treehouse, you might recognize the code in this blog post. Enjoyed watching their approach to teaching.

Regular Running

Let’s start with the “normal” path. PyCharm usually makes a Django run configuration automatically, but if not, here’s how to do so: Run -> Edit Configurations -> + -> Django Server:

Create Run Config

With this run configuration selected, click the green play button to start the Django server:

Run Config

When done, click the red button to stop the server:

Stop Run

You can also restart the server, use keystrokes instead of the mouse, let Django restart automatically on code changes, get linked tracebacks to jump directly to errors, etc.

Debugging

You’re likely familiar with all that. Let’s now do the same thing, but running under the debugger. Click the green button with the bug on it to start that Run/Debug configuration, but running under the debugger instead of the normal runner:

Start Debugger

Note: If you get a link saying you can compile the debugger speedups under Cython (on Windows, we ship it by default), click the link, the speedups are worth it.

Let’s compare the restart-on-edit times side-by-side:

Startup Comparison

As you can see, the debugger’s restart hit isn’t that big, certainly compared to the savings versus other parts of your development workflow (e.g. moving print statements and reload versus moving breakpoints without restarting.)

I can proceed as normal, writing new views and trying them in the browser or the built-in REST client. But when I hit a problem, I don’t reach for print() — no, I set a breakpoint!

Use Breakpoint

In fact, I can set a breakpoint in a template and then poke around at that line of template execution:

Template Breakpoint

Much better than random print statements, which require a restart each time I want to inspect something.

Testing…Under the Debugger

Always develop under the debugger? Sounds weird…the debugger is for debugging. Let’s make it weirder: always do testing under the debugger.

For example, you might run Django tests under PyCharm’s handy Django Test run configuration:

Run Tests

But you can also launch your test runner under the debugger:

Debug Tests

Test running under the debugger is an interesting case. Often with testing you are exploring, and sometimes, you are actually intending to produce bugs when writing a test case. Having the debugger close at hand can fit well with TDD:

Test Breakpoint

Downsides

No free lunch, of course. Even if we have increased performance, dramatically for Python 3.6, there is still a speed hit. Not just on startup, but on execution.

You may run into quirks in obscure usages. For example, when running pytest tests under the debugger, you can’t get to the console (known issue slated to be fixed in 2017.3.) Also, tracebacks might be longer since the code is running under pydevd.

Conclusion

At two recent conferences I mentioned this — do your Django development under the debugger — to people who visited the booth. The reactions were fascinating, as a look of horror turned to confusion then curiosity ending with interest.

For some, it might never fit your brain. For others, it might make all the sense in the world. Want to tell me I’m crazy? It’s possible. Leave me a comment below with any feedback.

Posted in Tutorial, Video | 10 Comments

Analyzing Data in Amazon Redshift with Pandas

Posted on August 16, 2017 by Ernst Haagsman

Redshift is Amazon Web Services’ data warehousing solution. They’ve extended PostgreSQL to better suit large datasets used for analysis. When you hear about this kind of technology as a Python developer, it just makes sense to then unleash Pandas on it. So let’s have a look to see how we can analyze data in Redshift using a Pandas script!

Setting up Redshift

If you haven’t used Redshift before, you should be able to get the cluster up for free for 2 months. As long as you make sure that you don’t use more than 1 instance, and you use the smallest available instance.

To play around, let’s use Amazon’s example dataset, and to keep things very simple, let’s only load the ‘users’ table. Configuring AWS is a complex subject, and they’re a lot better at explaining how to do it than we are, so please complete the first four steps of the AWS tutorial for setting up an example Redshift environment. We’ll use PyCharm Professional Edition as the SQL client.

Connecting to Redshift

After spinning up Redshift, you can connect PyCharm Professional to it by heading over to the database tool window (View | Tool Windows | Database), then use the green ‘+’ button, and select Redshift as the data source type. Then fill in the information for your instance:

Make sure that when you click the ‘test connection’ button you get a ‘connection successful’ notification. If you don’t, make sure that you’ve correctly configured your Redshift cluster’s VPC to allow connections from 0.0.0.0/0 on port 5439.

Now that we’ve connected PyCharm to the Redshift cluster, we can create the tables for Amazon’s example data. Copy the first code listing from here, and paste it into the SQL console that was opened in PyCharm when you connected to the database. Then execute it by pressing Ctrl + Enter, when PyCharm asks which query to execute, make sure to select the full listing. Afterward, you should see all the tables in the database tool window:

To load the sample data, go back to the query window, and use the Redshift ‘load’ command to load data from an Amazon S3 bucket into the database:

The IAM role identifier should be the identifier for the IAM role you’ve created for your Redshift cluster in the second step in the Amazon tutorial. If everything goes right, you should have about 50,000 rows of data in your users table after the command completes.

Loading Redshift Data into a Pandas Dataframe

So let’s get started with the Python code! In our example we’ll use Pandas, Matplotlib, and Seaborn. The easiest way to get all of these installed is by using Anaconda, get the Python 3 version from their website. After installing, we need to choose Anaconda as our project interpreter:

If you can’t find Anaconda in the dropdown, you can click the settings “gear” button, and then select ‘Add Local’ and find your Anaconda installation on your disk. We’re using the root Anaconda environment without Conda, as we will depend on several scientific libraries which are complicated to correctly install in Conda environments.

Pandas relies on SQLAlchemy to load data from an SQL data source. So let’s use the PyCharm package manager to install sqlalchemy: use the green ‘+’ button next to the package list and find the package. To make SQLAlchemy work well with Redshift, we’ll need to install both the postgres driver, and the Redshift additions. For postgres, you can use the PyCharm package manager to install psycopg2. Then we need to install sqlalchemy-redshift to teach SQLAlchemy the specifics of working with a Redshift cluster. This package is unfortunately not available in the default Anaconda repository, so we’ll need to add a custom repository.

To add a custom repository click the ‘Manage Repositories’ button, and then use the green ‘+’ icon to add the ‘conda-forge’ channel. Afterwards, close the ‘Manage Repositories’ screen, and install sqlalchemy-redshift. Now that we’ve done that, we can start coding!

To show how it’s done, let’s analyze something simple in Amazon’s dataset, the users dataset holds fictional users, and then indicates for every user if they like certain types of entertainment. Let’s see if there’s any correlation between the types of entertainment.

As always, the full code is available on GitHub.

Let’s open a new Python file, and start our analysis. At first, we need to load our data. Redshift is accessed just like a regular PostgreSQL database, just with a slightly different connection string to use the redshift driver:

connstr = 'redshift+psycopg2://<username>:<password>@<your cluster>.redshift.amazonaws.com:5439/<database name>'

1	connstr = 'redshift+psycopg2://:@.redshift.amazonaws.com:5439/'

Also note that Redshift by default listens on port 5439, rather than Postgres’ 5432.

After we’ve connected we can use Pandas’ standard way to load data from an SQL database:

import pandas as pd
from sqlalchemy import create_engine

engine = create_engine(connstr) 

with engine.connect() as conn, conn.begin():
   df = pd.read_sql("""
           select
             likesports as sports,
             liketheatre as theater,
             likeconcerts as concerts,
             likejazz as jazz,
             likeclassical as classical,
             likeopera as opera,
             likerock as rock,
             likevegas as vegas,
             likebroadway as broadway,
             likemusicals as musicals
           from users;""", conn)

import pandas as pd

from sqlalchemy import create_engine

engine = create_engine(connstr)

with engine.connect() as conn, conn.begin():

df = pd.read_sql("""

select

likesports as sports,

liketheatre as theater,

likeconcerts as concerts,

likejazz as jazz,

likeclassical as classical,

likeopera as opera,

likerock as rock,

likevegas as vegas,

likebroadway as broadway,

likemusicals as musicals

from users;""", conn)

The dataset holds users’ preferences as False, None, or True. Let’s interpret this as True being a ‘like’, None being ambivalent, and False being a dislike. To make a correlation possible, we should convert this into numeric values:

# Map dataframe to have 1 for 'True', 0 for null, and -1 for False
def bool_to_numeric(x):
   if x:
       return 1
   elif x is None:
       return 0
   else:
       return -1

df = df.applymap(bool_to_numeric)

# Map dataframe to have 1 for 'True', 0 for null, and -1 for False

def bool_to_numeric(x):

if x:

return 1

elif x is None:

return 0

else:

return -1

df = df.applymap(bool_to_numeric)

And now we’re ready to calculate the correlation matrix, and present it. To present it we’ll use Seaborn’s heatmap. We’ll also create a mask to only show the bottom half of the correlation matrix (the top half mirrors the bottom).

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Calculate correlations
corr = df.corr()

mask = np.zeros_like(corr)
mask[np.triu_indices_from(mask)] = True

sns.heatmap(corr,
           mask=mask,
           xticklabels=corr.columns.values,
           yticklabels=corr.columns.values)

plt.xticks(rotation=45)
plt.yticks(rotation=45)

plt.tight_layout()
plt.show()

import seaborn as sns

import matplotlib.pyplot as plt

import numpy as np

# Calculate correlations

corr = df.corr()

mask = np.zeros_like(corr)

mask[np.triu_indices_from(mask)] = True

sns.heatmap(corr,

mask=mask,

xticklabels=corr.columns.values,

yticklabels=corr.columns.values)

plt.xticks(rotation=45)

plt.yticks(rotation=45)

plt.tight_layout()

plt.show()

After running this code, we can see that there are no correlations in the dataset:

Which is strong evidence for Amazon’s sample dataset being a sample dataset. QED.

Fortunately, PyCharm also works great with real datasets in Redshift. Let us know in the comments what data you’re interested in analyzing!

Posted in Tutorial | Tagged aws, newsletter, pandas, redshift, SQL | 1 Comment

Support a Great Partnership: PyCharm and Django Team up Again

Posted on August 15, 2017 by Dmitry Filippov

Last June (2016) JetBrains PyCharm partnered with the Django Software Foundation to generate a big boost to Django fundraising. The campaign was a huge success. Together we raised a total of $50,000 for the Django Software Foundation!

This year we hope to repeat that success. During the two-week campaign, buy a new PyCharm Professional Edition individual license with a 30% discount code, and all the money raised will go to the DSF’s general fundraising and the Django Fellowship program.

Promotion details

Up until Aug 28th, you can effectively donate to Django by purchasing a New Individual PyCharm Professional annual subscription at 30% off. It’s very simple:

1. When buying a new annual PyCharm subscription in our e-store, on the checkout page, сlick “Have a discount code?”.
2. Enter the following 30% discount promo code:
ISUPPORTDJANGO
Alternatively, just click this shortcut link to go to the e-store with the code automatically applied
3. Fill in the other required fields on the page and click the “Place order” button.

All of the income from this promotion code will go to the DSF fundraising campaign 2017 – not just the profits, but actually the entire sales amount including taxes, transaction fees – everything. The campaign will help the DSF to maintain the healthy state of the Django project and help them continue contributing to their different outreach and diversity programs.

“Django has grown to be a world-class web framework, and coupled with PyCharm’s Django support, we can give tremendous developer productivity,” says Frank Wiles, DSF President. “Last year JetBrains was a great partner for us in support of raising money for the Django Software Foundation, on behalf of the community, I would like to extend our deepest thanks for their generous help. Together we hope to make this a yearly event!”

If you have any questions, get in touch with Django at fundraising@djangoproject.com or JetBrains at sales@jetbrains.com.

Posted in Sales and Promotions | Tagged django, newsletter | 6 Comments

PyCharm 2017.2.1 out now

Posted on August 9, 2017 by Ernst Haagsman

We’re happy to announce that PyCharm 2017.2.1 is now available from our website

Improvements:

Docker: Python console volume issue fix, and various other fixes
Debugging: concurrency visualization for Python 3.6, and console fixes
Inspections: variable type comparison bugs
Namespace package resolution bug on remote interpreters
JavaScript: Go to Declaration bug fix, and more
See details for all fixes in the release notes

To update PyCharm get the new version from our website. You can also update by choosing Help | Check for Updates in PyCharm, or use JetBrains Toolbox to keep all your JetBrains tools up to date.

To keep up with the latest news on PyCharm, please subscribe to our blog posts (leave your email address in the box on the right), or follow us on Twitter.

-PyCharm Team
The Drive to Develop

Posted in Release Announcements | 2 Comments

SSH Agent: Simplify SSH keys

Posted on August 9, 2017 by Ernst Haagsman

Security is important, and that’s why we all use key pairs when authenticating to our servers. And as is best practice, we also protect our private key with a passphrase, don’t we? Ideally the passphrase is long and complicated, and therefore a massive pain to type.

So how can we be lazy while still maintaining security? SSH Agent is the answer! Rather than typing the passphrase to your key individually for every connection, we can type it only once when we turn the computer on, and then SSH agent will keep the decrypted key safe in memory.

Let’s have a look how to do this on Windows, Mac, and Linux. Feel free to skip ahead to the section that’s relevant for your OS. At the very end I’ll show how to setup PyCharm to connect using the key in SSH Agent, which is the same on all platforms.

On Windows

The default tool for SSH connections on Windows is PuTTY. Let’s walk through generating a key and then using it with PuTTY.

When you download PuTTY, you don’t just get PuTTY, you get various other tools as well. We’ll use one of those to generate a key to use with SSH later. Open PuTTYgen, and click the generate button to start creating a key:

After simulating an earthquake with your mouse, you should have a shiny new SSH key.

Give it a secure passphrase, and then save the private key somewhere safe. At this point you should copy the public key to the ~/.ssh/authorized_keys file on the computer you’d like to connect to. If you’d like a computer to try this with, you can create a VM using the Vagrantfile here. If you’re using the VM, choose ‘Save public key’ to overwrite key.pub in the files subfolder, and then run vagrant up to create the virtual machine.

Now that we have a Linux machine with the public key installed, let’s connect to it. To manually use the key, we can configure PuTTY to connect to our box. We will connect to localhost, on port 2222 (the port that Vagrant exposes SSH to by default). And then in the menu on the left select SSH, and then Auth. In the settings you can configure a key file to use:

When you click open now, you should be prompted for the username you’d like to log in as. If you’re using the sample box, the username is ‘example’. At this point you’ll need to enter the passphrase for the private key:

If you right-click the title bar, and choose Duplicate Session, you’ll need to re-enter the passphrase. Let’s see if we can make this a little more efficient.

A tool that comes with PuTTY is Pageant. What this does is hold your private key, and as long as it’s running, it can provide the unlocked private key to PuTTY (and other tools like PyCharm). After you start Pageant, you may notice that nothing seems to happen. Pageant lives in the Windows task bar on the bottom right of your screen. Right-click Pageant’s icon, and you can choose ‘Add Key’ to add your key to Pageant:

After finding your key on the filesystem, you’ll need to enter the key’s passphrase. However, after this is done, you won’t need to enter it again until Pageant is stopped (for example, when you shutdown your computer).

So when we duplicate our session, we can see that we don’t need to enter a passphrase anymore:

Although it’s possible to configure Pageant to start with Windows, it might be more interesting to use another tool: KeeAgent. If you’re already using KeePass to store your passwords, you can use the KeeAgent plugin to make KeePass work just like Pageant. And then when you unlock your KeePass workspace, your private key will automatically be unlocked as well. Read how to set it up here.

On macOS

To create a key on macOS, open a Terminal window, and run:

ssh-keygen -t rsa -f <name of file>

1	ssh-keygen -t rsa -f <name of file>

Make sure to add a passphrase for your key.

After this finishes you’ll have both the private key file, and the public key file (with a .pub extension). Let’s get a Linux computer to try our new key with. Clone this repository, copy the .pub file over key.pub in the files subdirectory, and then run vagrant up to create a VM (you’ll need Vagrant for this).

When the VM has started, we can use ssh to connect to it:

ssh -i <your private key file> -p 2222 -o NoHostAuthenticationForLocalhost=yes example@localhost

1	ssh -i <your private key file> -p 2222 -o NoHostAuthenticationForLocalhost=yes example@localhost

We need to disable host key checking, as otherwise SSH will see a mismatch between your computer’s key and the VM’s key. When you run this command, you’ll be prompted for the key’s passphrase. So let’s see if we can get rid of this prompt.

macOS comes with a Keychain application which stores your passwords in the background. To see the stored passwords, use the Keychain Access application. We can store the passphrase there by using:

ssh-add -K <private key file>

1	ssh-add -K <private key file>

On version of macOS before Sierra, you’re done now. On macOS Sierra and later, you’ll need to do some additional steps: see this answer on StackOverflow.

After adding it to your Keychain, you should see that whenever you run the SSH command again, it’ll take the passphrase from your Keychain.

On Linux

On Linux you can use the ssh-add command, after running it once and entering your passphrase, you won’t have to re-enter your passphrase until you log out:

ssh-add

ssh-add

Without any parameters this will add the key in ~/.ssh/id_rsa. If you get an error ‘Could not open a connection to your authentication agent’, start ssh-agent by running: eval $(ssh-agent -s). To make this easier, you could add it to your ~/.bashrc, or see if your distribution or desktop environment has a tool that support SSH Agent. For example GNOME Keyring supports ssh-agent.

Using Your Key in PyCharm Professional Edition

After storing your key in SSH Agent (or a compatible tool), you can connect to a Python interpreter on a remote machine using that key for authentication. Please keep in mind that remote interpreters are only available in PyCharm Professional Edition. So let’s open up PyCharm, and go to the interpreter settings (File | Settings | Project Interpreter). Then use the gear icon, and choose ‘Add Remote’.

Then choose “SSH Credentials”, enter the hostname, port, and username. By setting authentication method to ‘Authentication agent’ you’re instructing PyCharm to take the credentials from SSH Agent or a compatible tool.

Finally, just click OK to connect, and you’ll be using the Python interpreter on the machine or VM you’re connecting to.

Posted in Tutorial | Tagged newsletter, remote development, remote interpreters, ssh, ssh-agent | 1 Comment

Webinar Recording: Teaching Python 3.6 with Games

Posted on August 3, 2017 by Paul Everitt

Yesterday Paul Craven, computer science professor and creator of the Arcade library for Python 2d games, gave a webinar about teaching Python to university students using game writing. The recording is now available:

During the webinar, Paul discussed his techniques for teaching introductory programming, an approach which he has refined over the years based on seeing what works and what doesn’t work. He covered:

The iterative process of producing teaching materials
Having a good toolchain (PyCharm for writing Sphinx docs, GitHub, ReadTheDocs, etc.)
How PyCharm’s IDE features impacted the quality of what students did (PEP 8, function completion, spell checking, etc.)
The effect Python 3.5/3.6 type hinting had on the learning/teaching experience
How games as a topic made learning fun, and how he adjusted the game library to make it more teachable
Teaching self-sufficiency by learning to browse APIs
The importance of already-working game examples

Paul then covered a bit of Arcade and the kinds of games it makes easy to write.

The webinar lasted a bit over an hour and had one of our highest number of questions posed during the session. We’ll try to get Paul back again in the future and go more in depth on game writing.

If you have any questions or comments about the webinar, feel free to leave them in the comments below, or you can reach us on Twitter. Paul Craven is on Twitter as well, his Twitter handle is @professorcraven.

-PyCharm Team
The Drive to Develop

Posted in Webinar | 6 Comments

Using Docker Compose on Windows in PyCharm

Posted on August 3, 2017 by Ernst Haagsman

By popular demand, PyCharm 2017.2 Professional Edition expands its Docker Compose support to those of you who run Windows. Let’s take a look and see how this works!

In our last Docker Compose post, we created a guest book in Flask. This time we’ll take a simple todo app in Django, and dockerize it. The starting point today will be a Django todo app which works locally, see the code on GitHub.

Setting Up Docker on Windows

If you don’t have Docker installed yet, you’ll need to make a decision about which version to install:

Are you using anything other than Windows 10 Pro or Enterprise, or do you have Virtualbox, VMware, or anything other than Hyper-V installed: get Docker Toolbox and Virtualbox.
If you’re on Windows 10 Pro or Enterprise, and you have either Hyper-V or no virtualization software installed: get Docker for Windows.

The reason for this is that Docker for Windows is based on Microsoft’s Hyper-V virtualization technology. Hyper-V is a seriously cool bit of tech that wraps your Windows in a hypervisor, rather than installing a hypervisor within Windows. What this means is that effectively you’ll be using a VM when you’re using your computer. Hypervisors are unable to run on a VM, so when you enable Hyper-V on Windows, you can’t run any other VM software anymore.

Setting Up Docker Toolbox

If you installed Docker for Windows, you can skip this section.

Docker Toolbox works by redirecting all your Docker commands to a Docker instance running either on a local VM, or on a cloud service. Today, let’s set up a Virtualbox VM on our local computer. Run with a cmd window:

docker-machine create --driver virtualbox default

1	docker-machine create --driver virtualbox default

Let’s verify that it works by connecting our command-line Docker. To do so, we need to run this cryptic looking command in cmd:

@FOR /f "tokens=*" %i IN ('docker-machine env default --shell=cmd') DO @%i

1	@FOR /f "tokens=*" %i IN ('docker-machine env default --shell=cmd') DO @%i

To see what it does, run docker-machine env default --shell=cmd, it will output several commands to set environment variables that configure Docker and Docker Compose. The long command above simply runs these all.

At this point, if you run docker run hello-world you should see a cheerful message that confirms that everything works:

Running Django in Docker

For our Django app we’ll need to create two containers: a database container, and a container which holds our actual application. We’ll use Docker Compose to link the containers together.

Let’s get started with writing our Dockerfile:

FROM python:3.6

WORKDIR /app

# By copying over requirements first, we make sure that Docker will cache
# our installed requirements rather than reinstall them on every build
COPY requirements.txt /app/requirements.txt
RUN pip install -r requirements.txt

# Now copy in our code, and run it
COPY . /app
EXPOSE 8000
CMD ["python", "manage.py", "runserver", "0.0.0.0:8000"]

FROM python:3.6

WORKDIR /app

# By copying over requirements first, we make sure that Docker will cache

# our installed requirements rather than reinstall them on every build

COPY requirements.txt /app/requirements.txt

RUN pip install -r requirements.txt

# Now copy in our code, and run it

COPY . /app

EXPOSE 8000

CMD ["python", "manage.py", "runserver", "0.0.0.0:8000"]

This is enough configuration to start Django, however, before we can proceed we should also make sure that we configure our database. For this we should write a compose file where we add both our Django service, and our postgres service:

version: '2'
services:
 web:
   build: .
   ports:
    - "8000:8000"
   volumes:
    - .:/app
   links:
    - db

 db:
   image: "postgres:9.6"
   ports:
     - "5432:5432"
   environment:
     POSTGRES_PASSWORD: hunter2

version: '2'

services:

web:

build: .

ports:

- "8000:8000"

volumes:

- .:/app

links:

- db

db:

image: "postgres:9.6"

ports:

- "5432:5432"

environment:

POSTGRES_PASSWORD: hunter2

The postgres image is easily configured with environment variables, for details read the image’s page on Docker hub. In this case we’re only setting the password, leaving the defaults for username and database. If you want to persist data when the container is destroyed, you’d need to create a named volume for the /var/lib/postgresql/data folder.

After adding these files, let’s just head over to our Django settings.py to configure our new database credentials:

DATABASES = {
   'default': {
       'ENGINE': 'django.db.backends.postgresql',
       'NAME': 'postgres',
       'USER': 'postgres',
       'PASSWORD': 'hunter2',
       'HOST': 'db'
   }
}

DATABASES = {

'default': {

'ENGINE': 'django.db.backends.postgresql',

'NAME': 'postgres',

'USER': 'postgres',

'PASSWORD': 'hunter2',

'HOST': 'db'

}

In a Docker Compose project, you can connect to linked containers by their service name unless you’ve specified an alias in the link section of the compose file. In this case we wrote:

links:
 - db

1 2	links: - db

Therefore we should tell Django to look for the db host. Although I’m hardcoding it here for simplicity, ideally you’d get this configuration from environment variables.

This is all the configuration we need to do, and we can get started with building our images now.

Let’s first let PyCharm know where to find Docker. Go to Settings | Build, Execution, Deployment | Docker, and make sure that your Docker is configured. If you’re using Docker for Windows, and there’s no Docker listed, just click the green ‘+’ icon, and the defaults should be correct. If you’re using Docker machine, select the ‘Docker Machine’ radio button, and select the correct machine in the dropdown:

After that’s set up, we can go and add our Docker run configuration, go to the ‘Edit Run Configurations’ screen, and add a Docker Deployment run configuration.

Let’s name it Rebuild Images, and in the Deployment field, select the compose file:

Now when we run this configuration, we should see that all the layers are pulled from Docker hub, and both the database and Django are started.

Setting up the Python Remote Docker Interpreter

Now to make sure that we can debug our Django project, let’s configure PyCharm to use the Python interpreter within our Docker container. To do so go to Settings | Project Interpreter, and use the gear icon to select Add Remote:

Choose the Docker Compose interpreter type, and make sure the docker-compose.yml file is selected. The service you choose under ‘service’ is the service you want to debug with this run configuration, when you start it, all services will still be started either way. As the only Python service is ‘web’, let’s select that here:

Afterwards you should see that PyCharm detected the packages we configured in requirements.txt, and the path mappings:

Now we can add a normal Django server run configuration, just make sure to set host to ‘0.0.0.0’ to make sure that we listen to requests coming from outside the Docker container.

Now first run the migrations, by going to Tools | Run manage.py task, and then writing migrate. After this command has completed, we can use the regular run and debug icons in PyCharm to run and debug our Django project. So let’s run it!

To see our Django application in the browser, go to http://localhost:8000 if you’re using Docker for Windows. If you’re using Docker Machine, we’ll first need to check on which IP our Docker Machine is running, run docker-machine ip default on the command line. In my case this is 192.168.99.100, so I’ll go to http://192.168.99.100:8000 in the browser.

Now if you see a message “DisallowedHost at /”, go to Django’s settings.py, and find ALLOWED_HOSTS. During development we can change this to: ALLOWED_HOSTS = [‘*’] to disable this check. Please make sure you appropriately configure it when running in production however.

When everything works, we can add a breakpoint, and debug as usual:

Posted in Tutorial | Tagged Docker, docker compose, newsletter, remote interpreters | 10 Comments

PyCharm Edu 4 is Out, Enhanced for Both Learners and Educators

Posted on July 31, 2017 by Tatiana Vasilyeva

PyCharm Edu 4 is out now! This update brings a better user experience to both learners and educators, making the product’s use as simple as possible, whether it is used for learning, or for teaching.

First of all, we’ve changed the welcoming UI. Now you begin by choosing your role, Learner or Educator. Depending on your choice, you get access to the courses you can join as a learner and can practice with the help of simple and effective “fill in the missing code” exercises. Or, you can create your own code practice tasks and integrated tests as an educator.

Learner

With PyCharm Edu 4, learners can now easily choose the course to join and start learning thanks to the new Welcoming UI.

Learn more about PyCharm Edu for learners.

Educator

Educators can now easily manage and share their learning materials thanks to better integration with Stepik.

Learn more about PyCharm Edu for educators.

Please let us know what you think about PyCharm Edu! Share your feedback here in the comments, on twitter or report your findings on YouTrack.

—
Your PyCharm Edu Team

Posted in Education, Release Announcements, Screencast | Tagged learn programming, teach programming | 2 Comments

PyCharm Blog

Develop Django Under the Debugger

Regular Running

Debugging

Testing…Under the Debugger

Downsides

Conclusion

PyCharm 2017.2.1 out now

Webinar Recording: Teaching Python 3.6 with Games

PyCharm Edu 4 is Out, Enhanced for Both Learners and Educators

Learner

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PyCharm Links

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Regular Running

Debugging

Testing…Under the Debugger

Downsides

Conclusion

Setting up Redshift

Connecting to Redshift

Loading Redshift Data into a Pandas Dataframe

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On Windows

On macOS

On Linux

Using Your Key in PyCharm Professional Edition

Setting Up Docker on Windows

Setting Up Docker Toolbox

Running Django in Docker

Setting up the Python Remote Docker Interpreter

Learner

Educator

PyCharm Links

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Tags

Other JetBrains Blogs

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