Planet Python

Albert Cervera has found that trytond allows to execute reports for records that user has no read access and also for reports limited to a set of group that the user is not.

Impact

CVSS v3.0 Base Score: 4.3

• Attack Vector: Network
• Attack Complexity: Low
• Privileges Required: Low
• User Interaction: None
• Scope: Unchanged
• Confidentiality: Low
• Integrity: None
• Availability: None

Workaround

There is no known workaround.

Resolution

All affected users should upgrade trytond to the latest version.

Affected versions per series:

• trytond:
  • 7.2: <= 7.2.8
  • 7.0: <= 7.0.17
  • 6.0: <= 6.0.51

Non affected versions per series:

• trytond:
  • 7.2: >= 7.2.9
  • 7.0: >= 7.0.18
  • 6.0: >= 6.0.52

Reference

• https://bugs.tryton.org/13505
• https://bugs.tryton.org/13506

Concerns?

Any security concerns should be reported on the bug-tracker at https://bugs.tryton.org/ with the confidential checkbox checked.

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The standard package manager for Python is pip. It allows you to install and manage packages that aren’t part of the Python standard library. If you’re looking for an introduction to pip, then you’ve come to the right place!

In this tutorial, you’ll learn how to:

• Set up pip in your working environment
• Fix common errors related to working with pip
• Install and uninstall packages with pip
• Manage projects’ dependencies using requirements files

You can do a lot with pip, but the Python community is very active and has created some neat alternatives to pip. You’ll learn about those later in this tutorial.

Get Your Cheat Sheet: Click here to download a free pip cheat sheet that summarizes the most important pip commands.

Getting Started With pip

So, what exactly does pip do? pip is a package manager for Python. That means it’s a tool that allows you to install and manage libraries and dependencies that aren’t distributed as part of the standard library. The name pip was introduced by Ian Bicking in 2008:

I’ve finished renaming pyinstall to its new name: pip. The name pip is [an] acronym and declaration: pip installs packages. (Source)

Package management is so important that Python’s installers have included pip since versions 3.4 and 2.7.9, for Python 3 and Python 2, respectively. Many Python projects use pip, which makes it an essential tool for every Pythonista.

The concept of a package manager might be familiar to you if you’re coming from another programming language. JavaScript uses npm for package management, Ruby uses gem, and the .NET platform uses NuGet. In Python, pip has become the standard package manager.

Finding pip on Your System

The Python installer gives you the option to install pip when installing Python on your system. In fact, the option to install pip with Python is checked by default, so pip should be ready for you to use after installing Python.

Note: On some Linux (Unix) systems like Ubuntu, pip comes in a separate package called python3-pip, which you need to install with sudo apt install python3-pip. It’s not installed by default with the interpreter.

You can verify that pip is available by looking for the pip3 executable on your system. Select your operating system below and use your platform-specific command accordingly:

• Windows
• Linux + macOS

Windows PowerShell PS> where pip3 Copied!

The where command on Windows will show you where you can find the executable of pip3. If Windows can’t find an executable named pip3, then you can also try looking for pip without the three (3) at the end.

Shell $ which pip3 Copied!

The which command on Linux systems and macOS shows you where the pip3 binary file is located.

On Windows and Unix systems, pip3 may be found in more than one location. This can happen when you have multiple Python versions installed. If you can’t find pip in any location on your system, then you may consider reinstalling pip.

Instead of running your system pip directly, you can also run it as a Python module. In the next section, you’ll learn how.

Running pip as a Module

When you run your system pip directly, the command itself doesn’t reveal which Python version pip belongs to. This unfortunately means that you could use pip to install a package into the site-packages of an old Python version without noticing. To prevent this from happening, you should run pip as a Python module:

Shell $ python -m pip Copied!

Notice that you use python -m to run pip. The -m switch tells Python to run a module as an executable of the python interpreter. This way, you can ensure that your system default Python version runs the pip command. If you want to learn more about this way of running pip, then you can read Brett Cannon’s insightful article about the advantages of using python -m pip.

Note: Depending on how you installed Python, your Python executable may have a different name than python. You’ll see python used in this tutorial, but you may have to adapt the commands to use something like py or python3 instead.

Sometimes you may want to be more explicit and limit packages to a specific project. In situations like this, you should run pip inside a virtual environment.

Using pip in a Python Virtual Environment Read the full article at https://realpython.com/what-is-pip/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

This tutorial guides you on how to create dynamic QR codes in Python. It involves a bit more than just generating the QR code itself. Before reading this, you must know how a QR code generator works. Steps to Create Dynamic QR Codes Dynamic QR codes require the ability to track and update the information […]

The post How to Create Dynamic QR Code in Python appeared first on TechBeamers.

Jupyter notebooks allow you to tell stories by creating and sharing data, equations, and visualizations sequentially, with a supporting narrative as you go through the notebook.

Jupyter notebooks in PyCharm Professional provide functionality above and beyond that of browser-based Jupyter notebooks, such as code completion, dynamic plots, and quick statistics, to help you explore and work with your data quickly and effectively.  

Let’s take a look at 7 ways you can use Jupyter notebooks in PyCharm to achieve your goals. They are:

• Creating or connecting to an existing notebook
• Importing your data
• Getting acquainted with your data
• Using JetBrains AI Assistant 
• Exploring your code with PyCharm
• Getting insights from your code
• Sharing your insights and charts

The Jupyter notebook that we used in this demo is available on GitHub.

1. Creating or connecting to an existing notebook

You can create and work on your Jupyter notebooks locally or connect to one remotely with PyCharm. Let’s take a look at both options so you can decide for yourself.

Creating a new Jupyter notebook

To work with a Jupyter notebook locally, you need to go to the Project tool window inside PyCharm, navigate to the location where you want to add the notebook, and invoke a new file. You can do this by using either your keyboard shortcuts ⌘N (macOS) / Alt+Ins (Windows/Linux) or by right-clicking and selecting New | Jupyter Notebook.

Give your new notebook a name, and PyCharm will open it ready for you to start work. You can also drag local Jupyter notebooks into PyCharm, and the IDE will automatically recognise them for you. 

Connecting to a remote Jupyter notebook

Alternatively, you can connect to a remote Jupyter notebook by selecting Tools | Add Jupyter Connection. You can then choose to start a local Jupyter server, connect to an existing running local Jupyter server, or connect to a Jupyter server using a URL – all of these options are supported.

Now you have your Jupyter notebook, you need some data!

2. Importing your data

Data generally comes in two formats, CSV or database. Let’s look at importing data from a CSV file first.

Importing from a CSV file

Polars and pandas are the two most commonly used libraries for importing data into Jupyter notebooks. I’ll give you code for both in this section, and you can check out the documentation for both Polars and pandas and learn how Polars is different to pandas. 

You need to ensure your CSV is somewhere in your PyCharm project, perhaps in a folder called `data`. Then, you can invoke import pandas and subsequently use it to read the code in:

import pandas as pd df = pd.read_csv("../data/airlines.csv")

In this example, airlines.csv is the file containing the data we want to manipulate. To run this and any code cell in PyCharm, use ⇧⏎ (macOS) / Shift+Enter (Windows/Linux). You can also use the green run arrows on the toolbar at the top.

If you prefer to use Polars, you can use this code:

import polars as pl df = pl.read_csv("../data/airlines.csv") Importing from a database

If your data is in a database, as is often the case for internal projects, importing it into a Jupyter notebook will require just a few more lines of code. First, you need to set up your database connection. In this example, we’re using postgreSQL. 

For pandas, you need to use this code to read the data in:

import pandas as pd engine = create_engine("postgresql://jetbrains:jetbrains@localhost/demo") df = pd.read_sql(sql=text("SELECT * FROM airlines"), con=engine.connect())

And for Polars, it’s this code:

import polars as pl engine = create_engine("postgresql://jetbrains:jetbrains@localhost/demo") connection = engine.connect() query = "SELECT * FROM airlines" df = pl.read_database(query, connection) 3. Getting acquainted with your data

Now we’ve read our data in, we can take a look at the DataFrame or `df` as we will refer to it in our code. To print out the DataFrame, you only need a single line of code, regardless of which method you used to read the data in:

df DataFrames

PyCharm displays your DataFrame as a table firstly so you can explore it. You can scroll horizontally through the DataFrame and click on any column header to order the data by that column. You can click on the Show Column Statistics icon on the right-hand side and select Compact or Detailed to get some helpful statistics on each column of data.   

Dynamic charts

You can use PyCharm to get a dynamic chart of your DataFrame by clicking on the Chart View icon on the left-hand side. We’re using pandas in this example, but Polars DataFrames also have the same option. 

Click on the Show Series Settings icon (a cog) on the right-hand side to configure your plot to meet your needs:

In this view, you can hover your mouse over your data to learn more about it and easily spot outliers:

You can do all of this with Polars, too. 

4. Using JetBrains AI Assistant

JetBrains AI Assistant has several offerings that can make you more productive when you’re working with Jupyter notebooks inside PyCharm. Let’s take a closer look at how you can use JetBrains AI Assistant to explain a DataFrame, write code, and even explain errors. 

Explaining DataFrames

If you’ve got a DataFrame but are unsure where to start, you can click the purple AI icon on the right-hand side of the DataFrame and select Explain DataFrame. JetBrains AI Assistant will use its context to give you an overview of the DataFrame:

You can use the generated explanation to aid your understanding.

Writing Code 

You can also get JetBrains AI Assistant to help you write code. Perhaps you know what kind of plot you want, but you’re not 100% sure what the code should look like. Well, now you can use JetBrains AI Assistant to help you. Let’s say you want to use ‘matplotlib’ to create a chart that finds the relationship between ‘TimeMonthName’ and ‘MinutesDelayedWeather’. By specifying the column names, we’re giving more context to the request which improves the reliability of the generated code. Try it with the following prompt:

Give me code using matplotlib to create a chart which finds the relationship between ‘TimeMonthName’ and ‘MinutesDelayedWeather’ for my dataframe df

If you like the resulting code, you can use the Insert Snippet at Caret button to insert the code and then run it:

import matplotlib.pyplot as plt # Assuming your data is in a DataFrame named 'df' # Replace 'df' with the actual name of your DataFrame if different # Plotting plt.figure(figsize=(10, 6)) plt.bar(df['TimeMonthName'], df['MinutesDelayedWeather'], color='skyblue') plt.xlabel('Month') plt.ylabel('Minutes Delayed due to Weather') plt.title('Relationship between TimeMonthName and MinutesDelayedWeather') plt.xticks(rotation=45) plt.grid(axis='y', linestyle='--', alpha=0.7) plt.tight_layout() plt.show()

If you don’t want to open the AI Assistant tool window, you can use the AI cell prompt to ask your questions. For example, we can ask the same question here and get the code we need:

Explaining errors

You can also get JetBrains AI Assistant to explain errors for you. When you get an error, click Explain with AI: 

You can use the resulting output to further your understanding of the problem and perhaps even get some code to fix it!

5. Exploring your code

PyCharm can help you get an overview of your Jupyter notebook, complete parts of your code to save your fingers, refactor it as required, debug it, and even add integrations to help you take it to the next level.

Tips for navigating and optimizing your code

Our Jupyter notebooks can grow large quite quickly, but thankfully you can use PyCharm’s Structure view to see all your notebook’s headings by clicking ⌘7 (macOS) / Alt+7 (Windows/Linux).

Code completion

Another helpful feature that you can take advantage of when using Jupyter notebooks inside PyCharm is code completion. You get both basic and type-based code completion out of the box with PyCharm, but you can also enable Full Line Code Completion in PyCharm Professional, which uses a local AI model to provide suggestions. Lastly, JetBrains AI Assistant can also help you write code and discover new libraries and frameworks. 

Refactoring

Sometimes you need to refactor your code, and in that case, you only need to know one keyboard shortcut ⌃T (macOS) / Shift+Ctrl+Alt+T (Windows/Linux) then you can choose the refactoring you want to invoke. Pick from popular options such as Rename, Change Signature, and Introduce Variable, or lesser-known options such as Extract Method, to change your code without changing the semantics: 

As your Jupyter notebook grows, it’s likely that your import statements will also grow. Sometimes you might import a package such as polars and numpy, but forget that numpy is a transitive dependency of the Polars library and as such, we don’t need to import it separately.  

To catch these cases and keep your code tidy, you can invoke Optimize Imports ⌃⌥O (macOS) / Ctrl+Alt+O (Windows/Linux) and PyCharm will remove the ones you don’t need. 

Debugging your code

You might not have used the debugger in PyCharm yet, and that’s okay. Just know that it’s there and ready to support you when you need to better understand some behavior in your Jupyter notebook. 

Place a breakpoint on the line you’re interested in by clicking in the gutter or by using ⌘F8 (macOS) / Ctrl+F8 (Windows/Linux), and then run your code with the debugger attached with the debug icon on the top toolbar:

You can also invoke PyCharm’s debugger in your Jupyter notebook with ⌥⇧⏎ (macOS) / Shift+Alt+Enter (Windows/Linux). There are some restrictions when it comes to debugging your code in a Jupyter notebook, but please try this out for yourself and share your feedback with us. 

Adding integrations into PyCharm 

IDEs wouldn’t be complete without the integrations you need. PyCharm Professional 2024.2 brings two new integrations to your workflow: DataBricks and HuggingFace.

You can enable the integrations with both Databricks and HuggingFace by going to your Settings <kbd>⌘</kbd> (macOS) / <kbd>Ctrl+Alt+S</kbd> (Windows/Linux), selecting Plugins and searching for the plugin with the corresponding name on the Marketplace tab.

6. Getting insights from your code

When analyzing your data, there’s a difference between categorical and continuous variables. Categorical data has a finite number of discrete groups or categories, whereas continuous data is one continuous measurement. Let’s look at how we can extract different insights from both the categorical and continuous variables in our airlines dataset.

Continuous variables

We can get a sense of how continuous data is distributed by looking at measures of the average value in that data and the spread of the data around the average. In normally distributed data, we can use the mean to measure the average and the standard deviation to measure the spread. However, when data is not distributed normally, we can get more accurate information using the median and the interquartile range (this is the difference between the seventy-fifth and twenty-fifth percentiles). Let’s look at one of our continuous variables to understand the difference between these measurements.

In our dataset, we have lots of continuous variables, but we’ll work with `NumDelaysLateAircraft` to see what we can learn. Let’s use the following code to get some summary statistics for just that column:

df['NumDelaysLateAircraft'].describe()

Looking at this data, we can see that there is a big difference between the `mean` of ~789 and the ‘median’ (our fiftieth percentile, indicated by “50%” in the table below) of ~618.

This indicates a skew in our variable’s distribution, so let’s use PyCharm to explore it further. Click on the Chart View icon at the top left. Once the chart has been rendered, we’ll change the series settings represented by the cog on the right-hand side of the screen. Change your x-axis to `NumDelaysLateAircraft` and your y-axis to `NumDelaysLateAircraft`. 

Now drop down the y-axis using the little arrow and select `count`. The final step is to change the chart type to Histogram using the icons in the top-right corner:

Now that we can see the skew laid out visually, we can see that most of the time, the delays are not too excessive. However, we have a number of more extreme delays – one aircraft is an outlier on the right and it was delayed by 4,509 minutes, which is just over three days!

In statistics, the mean is very sensitive to outliers because it’s a geometric average, unlike the median, which, if you ordered all observations in your variable, would sit exactly in the middle of these values. When the mean is higher than the median, it’s because you have outliers on the right-hand side of the data, the higher side, as we had here. In such cases, the median is a better indicator of the true average delay, as you can see if you look at the histogram.

Categorical variables

Let’s take a look at how we can use code to get some insights from our categorical variables. In order to get something that’s a little more interesting than just `AirportCode`, we’ll analyze how many aircraft were delayed by weather, `NumDelaysWeather`, in the different months of the year, `TimeMonthName`.

Use this code to group `NumDelaysWeather` with `TimeMonthName`:

result = df[['TimeMonthName', 'NumDelaysWeather']].groupby('TimeMonthName').sum() result

This gives us the DataFrame again in table format, but click the Chart View icon on the left-hand side of the  PyCharm UI to see what we can learn:

This is okay, but it would be helpful to have the months ordered according to the Gregorian calendar. Let’s first create a variable for the months that we expect:

month_order = [ "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December" ]

Now we can ask PyCharm to use the order that we’ve just defined in `month_order`:

# Convert the 'TimeMonthName' column to a categorical type with the specified order df["TimeMonthName"] = pd.Categorical(df["TimeMonthName"], categories=month_order, ordered=True) # Now you can group by 'TimeMonthName' and perform sum operation, specifying observed=False result = df[['TimeMonthName', 'NumDelaysWeather']].groupby('TimeMonthName', observed=False).sum() result

We then click on the Chart View icon once more, but something’s wrong!

Are we really saying that there were no flights delayed in February? That can’t be right. Let’s check our assumption with some more code:

df['TimeMonthName'].value_counts()

Aha! Now we can see that `Febuary` has been misspelt in our data set, so the correct spelling in our variable name does not match. Let’s update the spelling in our dataset with this code:

df["TimeMonthName"] = df["TimeMonthName"].replace("Febuary", "February") df['TimeMonthName'].value_counts()

Great, that looks right. Now we should be able to re-run our earlier code and get a chart view that we can interpret:

From this view, we can see that there is a higher number of delays during the months of December, January, and February, and then again in June, July, and August. However, we have not standardized this data against the total number of flights, so there may just be more flights in those months, which would cause these results along with an increased number of delays in those summer and winter months.

7. Sharing your insights and charts

When your masterpiece is complete, you’ll probably want to export data, and you can do that in various ways with Jupyter notebooks in PyCharm. 

Exporting a DataFrame

You can export a DataFrame by clicking on the down arrow on the right-hand side:

You have lots of helpful formats to choose from, including SQL, CSV, and JSON:

Exporting charts

If you prefer to export the interactive plot, you can do that too by clicking on the Export to PNG icon on the right-hand side:

Viewing your notebook as a browser

You can view your whole Jupyter notebook at any time in a browser by clicking the icon in the top-right corner of your notebook:

Finally, if you want to export your Jupyter notebook to a Python file, 2024.2 lets you do that too! Right-click on your Jupyter notebook in the Project tool window and select Convert to Python File. Follow the instructions, and you’re done!

Summary

Using Jupyter notebooks inside PyCharm Professional provides extensive functionality, enabling you to create code faster, explore data easily, and export your projects in the formats that matter to you. 

Download PyCharm Professional to try it out for yourself! Get an extended trial today and experience the difference PyCharm Professional can make in your data science endeavors.

Use the promo code “PyCharmNotebooks” at checkout to activate your free 60-day subscription to PyCharm Professional. The free subscription is available for individual users only.

Activate your 60-day trial

Smart IoT integrations with Akenza and Python 2024-09-16, by Dariusz Suchojad Overview

The Akenza IoT platform, on its own, excels in collecting and managing data from a myriad of IoT devices. However, it is integrations with other systems, such as enterprise resource planning (ERP), customer relationship management (CRM) platforms, workflow management or environmental monitoring tools that enable a complete view of the entire organizational landscape.

Complementing Akenza's capabilities, and enabling the smooth integrations, is the versatility of Python programming. Given how flexible Python is, the language is a natural choice when looking for a bridge between Akenza and the unique requirements of an organization looking to connect its intelligent infrastructure.

This article is about combining the two, Akenza and Python. At the end of it, you will have:

• A bi-directional connection to Akenza using Python and WebSockets
• A Python service subscribed to and receiving events from IoT devices through Akenza
• A Python service that will be sending data to IoT devices through Akenza

Since WebSocket connections are persistent, their usage enhances the responsiveness of IoT applications which in turn helps to exchange occurs in real-time, thus fostering a dynamic and agile integrated ecosystem.

Python and Akenza WebSocket connections

First, let's have a look at full Python code - to be discussed later.

# -*- coding: utf-8 -*- # Zato from zato.server.service import WSXAdapter # ############################################################################################### # ############################################################################################### if 0: from zato.server.generic.api.outconn.wsx.common import OnClosed, \ OnConnected, OnMessageReceived # ############################################################################################### # ############################################################################################### class DemoAkenza(WSXAdapter): # Our name name = 'demo.akenza' def on_connected(self, ctx:'OnConnected') -> 'None': self.logger.info('Akenza OnConnected -> %s', ctx) # ############################################################################################### def on_message_received(self, ctx:'OnMessageReceived') -> 'None': # Confirm what we received self.logger.info('Akenza OnMessageReceived -> %s', ctx.data) # This is an indication that we are connected .. if ctx.data['type'] == 'connected': # .. for testing purposes, use a fixed asset ID .. asset_id:'str' = 'abc123' # .. build our subscription message .. data = {'type': 'subscribe', 'subscriptions': [{'assetId': asset_id, 'topic': '*'}]} ctx.conn.send(data) else: # .. if we are here, it means that we received a message other than type "connected". self.logger.info('Akenza message (other than "connected") -> %s', ctx.data) # ############################################################################################## def on_closed(self, ctx:'OnClosed') -> 'None': self.logger.info('Akenza OnClosed -> %s', ctx) # ############################################################################################## # ##############################################################################################

Now, deploy the code to Zato and create a new outgoing WebSocket connection. Replace the API key with your own and make sure to set the data format to JSON.

Receiving messages from WebSockets

The WebSocket Python services that you author have three methods of interest, each reacting to specific events:

• on_connected - Invoked as soon as a WebSocket connection has been opened. Note that this is a low-level event and, in the case of Akenza, it does not mean yet that you are able to send or receive messages from it.

• on_message_received - The main method that you will be spending most time with. Invoked each time a remote WebSocket sends, or pushes, an event to your service. With Akenza, this method will be invoked each time Akenza has something to inform you about, e.g. that you subscribed to messages, that

• on_closed - Invoked when a WebSocket has been closed. It is no longer possible to use a WebSocket once it has been closed.

Let's focus on on_message_received, which is where the majority of action takes place. It receives a single parameter of type OnMessageReceived which describes the context of the received message. That is, it is in the "ctx" that you will both the current request as well as a handle to the WebSocket connection through which you can reply to the message.

The two important attributes of the context object are:

• ctx.data - A dictionary of data that Akenza sent to you

• ctx.conn - The underlying WebSocket connection through which the data was sent and through you can send a response

Now, the logic from lines 30-40 is clear:

• First, we check if Akenza confirmed that we are connected (type=='connected'). You need to check the type of a message each time Akenza sends something to you and react to it accordingly.

• Next, because we know that we are already connected (e.g. our API key was valid) we can subscribe to events from a given IoT asset. For testing purposes, the asset ID is given directly in the source code but, in practice, this information would be read from a configuration file or database.

• Finally, for messages of any other type we simply log their details. Naturally, a full integration would handle them per what is required in given circumstances, e.g. by transforming and pushing them to other applications or management systems.

A sample message from Akenza will look like this:

INFO - WebSocketClient - Akenza message (other than "connected") -> {'type': 'subscribed', 'replyTo': None, 'timeStamp': '2023-11-20T13:32:50.028Z', 'subscriptions': [{'assetId': 'abc123', 'topic': '*', 'tagId': None, 'valid': True}], 'message': None} How to send messages to WebSockets

An aspect not to be overlooked is communication in the other direction, that is, sending of messages to WebSockets. For instance, you may have services invoked through REST APIs, or perhaps from a scheduler, and their job will be to transform such calls into configuration commands for IoT devices.

Here is the core part of such a service, reusing the same Akenza WebSocket connection:

# -*- coding: utf-8 -*- # Zato from zato.server.service import Service # ############################################################################################## # ############################################################################################## class DemoAkenzaSend(Service): # Our name name = 'demo.akenza.send' def handle(self) -> 'None': # The connection to use conn_name = 'Akenza' # Get a connection .. with self.out.wsx[conn_name].conn.client() as client: # .. and send data through it. client.send('Hello') # ############################################################################################## # ##############################################################################################

Note that responses to the messages sent to Akenza will be received using your first service's on_message_received method - WebSockets-based messaging is inherently asynchronous and the channels are independent.

Now, we have a complete picture of real-time, IoT connectivity with Akenza and WebSockets. We are able to establish persistent, responsive connections to assets, we can subscribe to and send messages to devices, and that lets us build intelligent automation and integration architectures that make use of powerful, emerging technologies.

More resources

➤ Python API integration tutorial
➤ What is an integration platform?
➤ Python Integration platform as a Service (iPaaS)
➤ What is an Enterprise Service Bus (ESB)? What is SOA?

More blog posts➤

Hello Everyone 👋 It is that time of year again when we recognize someone from our community in memory of our friend Malcolm.

Malcolm was an early core contributor to Django and had both a huge influence and impact on Django as we know it today. Besides being knowledgeable he was also especially friendly to new users and contributors. He exemplified what it means to be an amazing Open Source contributor. We still miss him to this day.

The prize

The Django Software Foundation Prizes page summarizes it nicely:

The Malcolm Tredinnick Memorial Prize is a monetary prize, awarded annually, to the person who best exemplifies the spirit of Malcolm’s work - someone who welcomes, supports, and nurtures newcomers; freely gives feedback and assistance to others, and helps to grow the community. The hope is that the recipient of the award will use the award stipend as a contribution to travel to a community event -- a DjangoCon, a PyCon, a sprint -- and continue in Malcolm’s footsteps.

Please make your nominations using our form: 2024 Malcolm Tredinnick Memorial Prize.

We will take nominations until Monday, September 30th, 2024, Anywhere on Earth, and will announce the winner(s) soon after the next DSF Board meeting in October. If you have any questions please reach out to the DSF Board at foundation@djangoproject.com.

Submit a nomination

If your Python code isn’t fast enough, you have many options for compiled languages to write a faster extension. In this article we’ll focus on Rust, which benefits from:

• Modern tooling, including a package repository called crates.io, and built-in build tool (cargo).
• Excellent Python integration and tooling. The Rust package (they’re known as “crates”) for Python support is PyO3. For packaging you can use setuptools-rust, for integration with existing setuptools projects, or for standalone extensions you can use Maturin.
• Memory- and thread-safe, so it’s much less prone to crashes or memory corruption compared to C and C++.

In particular, we’ll:

• Implement a small algorithm in Python.
• Re-implement it as a Rust extension.
• Optimize the Rust version so it runs faster.

Read more...

Python's Boolean operators are used for combining Boolean expressions and negating Boolean expressions.

Table of contents

1. Combining two if statements using and
2. Combining expressions with Boolean operators
3. Using or instead of and
4. Negating expressions
5. Embrace and, or, and not in your Boolean expressions

Combining two if statements using and

Here we have a program called word_count.py:

words_written_today = int(input("How many words did you write today? ")) if words_written_today < 50_000/30: print("Yay! But you need to write more still.") else: print("Congratulations!")

This program has an if statement that checks whether we've written enough words each day, with the assumption that we need to write 50,000 words every 30 days.

If our word count is under 1,666 words (50,000 / 30) it will say we need to write more:

$ python3 word_count.py How many words did you write today? 500 Yay! But you need to write more still.

We'd like to modify our if condition to also make sure that we only require this if today's date is in the month of November.

We could do that using Python's datetime module:

>>> from datetime import date >>> is_november = date.today().month == 11

That is_november variable will be True if it's November and False otherwise:

>>> is_november False

If we combine this with the code we had before, we could use two if statements:

from datetime import date words_written_today = int(input("How many words did you write today? ")) is_november = date.today().month == 11 if words_written_today < 50_000/30: if is_november: print("Yay! But you need to write more still.") else: print("Congratulations!") else: print("Congratulations!")

One of our if statements checks whether we're under our word limit. The other if statement checks whether it's the month of November. If both are true then we end up printing out that we still need to write more words. Otherwise we print a success message:

$ python3 word_count.py How many words did you write today? 500 Congratulations!

This works, but there is a better way to write this code.

We could instead use Python's and operator to combine these two conditions into one:

from datetime import date words_written_today = int(input("How many words did you write today? ")) is_november = date.today().month == 11 if is_november and words_written_today < 50_000/30: print("Yay! But you need to write more still.") else: print("Congratulations!")

We're using a single if statement to asking whether it's November and whether our word count is less than we expect.

Combining expressions with Boolean operators

Python's and operator is a …

Read the full article: https://www.pythonmorsels.com/boolean-operators/

Cog is my tool for using bits of Python to generate content inside an otherwise static file. I used it in extreme ways to generate my GitHub profile page.

If you haven’t seen it before, you can customize your GitHub profile by creating a README.md in a repo named the same as your username. So my profile is rendered from nedbat/nedbat/README.md.

My profile has a bit of static text, but much of it is badges, blog posts, links to PyPI projects, and so on. The README.md is literally a Markdown file that can be displayed by GitHub, but it’s full HTML comments containing Python code that generates the content. The generation happens once a day in a GitHub action.

There are three kinds of lines in a file run through cog: static content, code that will generate content, and generated content. My README.md is lop-sided: it has 225 lines of code, 38 of static content, and 43 of generated content.

The badges are made with shields.io image URLs. To make this easier, there are Python functions for Markdown image syntax, for building shields.io badge URLs, and so on.

I can’t walk through all of the code, but I can show a few simplified versions to convey the idea. Read the file itself if you are interested in the full details.

This makes a shields.io URL:

def shields_url(
    label=None,
    message=None,
    color=None,
    label_color=None,
    logo=None,
):
    params = {"style": "flat"}
    url = "".join([
        "/badge/",
        quote(label or ""),
        "-",
        quote(message),
        "-",
        color,
        ])
    url = "https://img.shields.io" + url
    if label_color:
        params["labelColor"] = label_color
    if logo:
        params["logo"] = logo
    return url + "?" + urlencode(params)

This makes a Markdown image:

def md_image(image_url, text, link):
    return f'[]({link})'

Now we can make a Markdown badge:

def badge(text=None, link=None, **kwargs):
    return md_image(image_url=shields_url(**kwargs), text=text, link=link)

Anything print’ed will become part of the generated portions of the file. We can add a badge to the page with:

print(badge(
    logo="discord", logo_color="white", label_color="7289da",
    message="Discord", color="ffe97c",
    text="Python Discord", link="https://discord.gg/python",
))

There are other functions built on top of these to make Mastodon badges, Stack Overflow badges, a row of badges for a PyPI project, and so on.

Building the page ends up pulling data from 10 URLs, including a JSON summary of my blog for including blog posts. It’s satisfying to be able to have this update automatically instead of having to copy data around.

The result is a convenient mix of static and generated, and it was a fun exercise in light-touch automation.

How do you take advantage of Git pre-commit hooks? How do you build custom software checks and rules that run every time you commit your code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

In this episode, we started down the path of migrating School Desk off of Heroku and onto Digital Ocean. Most of the effort was on tool changes and beginning to make a Dockerfile for deploying the app to the new setup.

Premise

The 0.4.0 release of uv does everything I currently do with pip, pyenv, pipx, pip-tools, and pipdeptree. Because of that, I'm in the process of switching to uv.

This blog post covers switching from pyenv to uv.

History

• 2024-08-29: Initial writing.

• 2024-09-12: Minor updates and publishing.

Start state

I'm running Ubuntu Linux 24.04. I have pyenv installed using the the automatic installer. pyenv is located in $HOME/.pyenv/bin/.

I have the following Pythons installed with pyenv:

$ pyenv versions system 3.7.17 3.8.19 3.9.19 * 3.10.14 (set by /home/willkg/mozilla/everett/.python-version) 3.11.9 3.12.3

I'm not sure why I have 3.7 still installed. I don't think I use that for anything.

My default version is 3.10.14 for some reason. I'm not sure why I haven't updated that to 3.12, yet.

In my 3.10.14, I have the following Python packages installed:

$ pip freeze appdirs==1.4.4 argcomplete==3.1.1 attrs==22.2.0 cffi==1.15.1 click==8.1.3 colorama==0.4.6 diskcache==5.4.0 distlib==0.3.8 distro==1.8.0 filelock==3.14.0 glean-parser==6.1.1 glean-sdk==50.1.4 Jinja2==3.1.2 jsonschema==4.17.3 MarkupSafe==2.0.1 MozPhab==1.5.1 packaging==24.0 pathspec==0.11.0 pbr==6.0.0 pipx==1.5.0 platformdirs==4.2.1 pycparser==2.21 pyrsistent==0.19.3 python-hglib==2.6.2 PyYAML==6.0 sentry-sdk==1.16.0 stevedore==5.2.0 tomli==2.0.1 userpath==1.8.0 virtualenv==20.26.2 virtualenv-clone==0.5.7 virtualenvwrapper==6.1.0 yamllint==1.29.0

That probably means I installed the following in the Python 3.10.14 Python environment:

• MozPhab

• pipx

• virtualenvwrapper

Maybe I installed some other things for some reason lost in the sands of time.

Then I had a whole bunch of things installed with pipx.

I have many open source projects all of which have a .python-version file listing the Python versions the project uses.

I think that covers the start state.

Steps

First, I made a list of things I had.

• I listed all the versions of Python I have installed so I know what I need to reinstall with uv.

  $ pyenv versions

• I listed all the packages I have installed in my 3.10.14 environment (the default one).

  $ pip freeze

• I listed all the packages I installed with pipx.

  $ pipx list

I uninstalled all the packages I installed with pipx.

$ pipx uninstall PACKAGE

Then I uninstalled pyenv and everything it uses. I followed the pyenv uninstall instructions:

$ rm -rf $(pyenv root)

Then I removed the bits in my shell that add to the PATH and set up pyenv and virtualenvwrapper.

Then I started a new shell that didn't have all the pyenv and virtualenvwrapper stuff in it.

Then I installed uv using the uv standalone installer.

Then I ran uv --version to make sure it was installed.

Then I installed the shell autocompletion.

Note

I have a dotfiles thing and separate out bashrc changes by what changes them. You can see my home-grown thing that works for me here:

https://github.com/willkg/dotfiles

These instructions are specific to my home-grown dotfiles thing.

$ echo 'eval "$(uv generate-shell-completion bash)"' >> ~/dotfiles/bash.d/20-uv.bash

Then I started a new shell to pick up those changes.

Then I installed Python versions:

$ uv python install 3.8 3.9 3.10 3.11 3.12 Searching for Python versions matching: Python 3.10 Searching for Python versions matching: Python 3.11 Searching for Python versions matching: Python 3.12 Searching for Python versions matching: Python 3.8 Searching for Python versions matching: Python 3.9 Installed 5 versions in 8.14s + cpython-3.8.19-linux-x86_64-gnu + cpython-3.9.19-linux-x86_64-gnu + cpython-3.10.14-linux-x86_64-gnu + cpython-3.11.9-linux-x86_64-gnu + cpython-3.12.5-linux-x86_64-gnu

When I type "python", I want it to be a Python managed by uv. Also, I like having "pythonX.Y" symlinks, so I created a uv-sync script which creates symlinks to uv-managed Python versions:

https://github.com/willkg/dotfiles/blob/main/dotfiles/bin/uv-sync

Then I installed all my tools using uv tool install.

$ uv tool install PACKAGE

For tox, I had to install the tox-uv package in the tox environment:

$ uv tool install --with tox-uv tox

Now I've got everything I do mostly working.

So what does that give me?

I installed uv and I can upgrade uv using uv self update.

Python interpreters are managed using uv python. I can create symlinks to interpreters using uv-sync script. Adding new interpreters and removing old ones is pretty straight-forward.

When I type python, it opens up a Python shell with the latest uv-managed Python version. I can type pythonX.Y and get specific shells.

I can use tools written in Python and manage them with uv tool including ones where I want to install them in an "editable" mode.

I can write scripts that require dependencies and it's a lot easier to run them now.

I can create and manage virtual environments with uv venv.

Next steps

Delete all the .python-version files I've got.

Update documentation for my projects and add a uv tool install PACKAGE option to installation instructions.

Probably discover some additional things to add to this doc.

This is the last of a series of posts I’m doing as a sort of Python/Django Advent calendar, offering a small tip or piece of information each day from the first Sunday of Advent through Christmas Eve. See the first post for an introduction.

Python contains multitudes

There are a lot of container types available in the Python standard library, and it can be confusing sometimes to keep track of them all. So since it’s …

Read full entry

In this quiz, you’ll test your understanding of the new features in Python 3.13.

By working through this quiz, you’ll revisit how to compile a custom Python build, disable the Global Interpreter Lock (GIL), enable the Just-In-Time (JIT) compiler, determine the availability of new features at runtime, assess the performance improvements in Python 3.13, and make a C extension module targeting Python’s new ABI.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]