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Last update: May 18, 2024 01:43 PM UTC

May 18, 2024

Juri Pakaste

Git history search with fzf

fzf is one of my favorite shell tools. I have a ton of scripts where I use it for selection. Here's one for searching git history. git log -Gpattern allows you to search for commits that contain pattern in the patch text. Combine it with fzf and you get a pretty decent history search tool.

I have this saved as ~/bin/git-search-log, so I can invoke it as git search-log pattern or git search-log pattern branch:


set -euo pipefail

# Ensure compatibility with fish etc by ensuring fzf uses bash for the preview command that
export SHELL=/bin/bash

git log -G$@ --oneline | fzf \
    --preview-window=bottom,80% \
    --preview "echo {} | sed 's/ .*//g' | xargs git show --color" \
    --bind 'enter:execute(commit=$(echo {} | sed "s/ .*//g") && git diff-tree --no-commit-id --name-only $commit -r | fzf --preview-window=bottom,80% --preview "git show --color $commit -- $(git rev-parse --show-toplevel)/\{}")'

When you run it you get an selection of matching commits, one per line, with a preview window showing the patch. If you hit enter on a commit, you get another fzf screen, this time allowing you to select files modified in that commit. Hit enter again and you're back in the first one.

May 18, 2024 01:40 PM UTC

Python Morsels

Assignment vs. Mutation in Python

In Python, "change" can mean two different things. Assignment changes which object a variable points to. Mutation, changes the object itself.

Table of contents

  1. Remember: variables are pointers
  2. Mutating a list
  3. Mutation
  4. Assignment
  5. Assignments versus mutations
  6. Changing variables and changing objects

Remember: variables are pointers

When talking about Python code, if I say we changed X, there are two different things that I might mean.

Let's say we have two variables that point to the same value:

>>> a = [2, 1, 3, 4]
>>> b = a

Remember that variables in Python are pointers. That means that two variables can point to the same object. That's actually what we've done here.

Let's change the object that the variable b points to.

Mutating a list

If we append a number …

Read the full article:

May 18, 2024 12:13 PM UTC

May 17, 2024

Real Python

The Real Python Podcast – Episode #205: Considering Accessibility & Assistive Tech as a Python Developer

What's it like to learn Python as a visually impaired or blind developer? How can you improve the accessibility of your Python web applications and learn current guidelines? This week on the show, Real Python community member Audrey van Breederode discusses her programming journey, web accessibility, and assistive technology.

[ 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 ]

May 17, 2024 12:00 PM UTC


How to Use Gemini in Python

In this tutorial, you will learn how to use Google's Gemini AI model in Python.

Steps to Access Gemini API

Follow the steps below to access the Gemini API and then use it in python.

  1. Visit Google AI Studio website.
  2. Sign in using your Google account.
  3. Create an API key.
  4. Install the Google AI Python library for the Gemini API using the command below :
    pip install google-generativeai
To read this article in full, please click here

May 17, 2024 11:36 AM UTC


PyCharm 2024.2 EAP Is Open!

This blog post marks the start of the Early Access Program for PyCharm 2024.2. The PyCharm 2024.2 EAP 1 build is now accessible for download, providing an early glimpse into the exciting updates on the horizon.

You can download the new version from our website, update directly from inside the IDE or via the free Toolbox App, or use snap packs for Ubuntu.

Download PyCharm 2024.2 EAP

If you’re new to the EAP process, we encourage you to read our introductory blog post. It offers valuable insights into the program and explains why your participation is integral.

Join us in the coming weeks to explore the new features in PyCharm, test them out, and provide feedback on the new additions. Your engagement is what helps us shape the evolution of PyCharm.


New Graph OptionsFirst Parent and No Merges

Git has a useful option for viewing the history of changes in a branch: –first-parent. Use it with the git log command. This option simplifies the log by hiding individual commits that came with the merge, making it easier to track changes. 

We’ve also added filtering with the –no merges command, which displays the history without any merge commits. 

Both options can be selected under the Graph Options button in the Git tool window.

And that wraps up the first week! To see all the changes in this EAP build, check out the full release notes.

Keep an eye on our blog for weekly updates leading up to the major release. Your feedback is incredibly important to us, so please share your thoughts on the new features. You can leave a comment under his blog post or contact our team on X (formerly Twitter). If you encounter any bugs in this build, please report them through our issue tracker.

May 17, 2024 08:06 AM UTC

May 16, 2024

Robin Wilson

Some matplotlib tips – a reblog

I was looking through my past blog posts recently, and thought a few of them were worth ‘reblogging’ so that more people could see them (now that my blog posts are getting more readers). So, here are a few posts on matplotlib tips.

Matplotlib titles have configurable locations – and you can have more than one at once!

This post explains how to create matplotlib titles in various locations.

Easily hiding items from the legend in matplotlib

This post explains how to easily hide items from the legend in matplotlib.

Easily specifying colours from the default colour cycle in matplotlib

This post shows how to specify colours from the default colour cycle – ranging from a very simple way to more complex methods that might work in other situations.

May 16, 2024 09:14 AM UTC

Armin Ronacher

Using Rust Macros for Custom VTables

Given that building programming languages and interpreters is the developer's most favorite hobby, I will never stop writing templating engines. About three years ago I first wanted to see if I can make an implementation of my Jinja2 template engine for Rust. It's called MiniJinja and very close in behavior to Jinja2. Close enought that I have seen people pick it up more than I thought they would. For instance the Hugging Face Text Generation Inference uses it for chat templates.

I wrote it primarily just to see how you would introduce dynamic things into a language that doesn't have much of a dynamic runtime. A few weeks ago I released a major new version of the engine that has a very different internal object model for values and in this post I want to share a bit how it works, and what you can learn from it. At the heart of it is a type_erase! macro originally contributed by Sergio Benitez. This post goes into the need and usefulness of that macro.

Runtime Values

To understand the problem you first need to understand that a template engine like Jinja2 has requirements for runtime types that are a bit different from how Rust likes to think about data. The runtime is entirely dynamic and requires a form of garbage collection for those values. In case of a simple templating engine like Jinja2 you can largely get away with reference counting. The way this works in practice is that MiniJinja has a type called Value which can be cloned to increment the refcount, and when it's dropped the refcount is decremented. The value is the basic type that can hold all kinds of things (integers, strings, functions, sequences, etc.). In MiniJinja you can thus do something like this:

use minijinja::Value;

// primitives
let int_val = Value::from(42);
let str_val = Value::from("Maximilian");
let bool_val = Value::from(true);

// complex objects
let vec_val = Value::from(vec![1, 2, 3]);

// reference counting
let vec_val2 = vec_val.clone();   // refcount = 2
drop(vec_val);                    // refcount = 1
drop(vec_val2);                   // refcount = 0 -> gone

Inside the engine these objects have all kinds of behaviors to make templates like this work:

{{ int_val }}
{{ str_val|upper }}
{{ not bool_val }}
{{ vec_val }}
    [1, 2, 3]
{{ vec_val|reverse }}
    [3, 2, 1]

Some of that functionality is also exposed via Rust APIs. So for instance you can iterate over values if they contain sequences:

let vec_val = Value::from(vec![1, 2, 3]);
for value in vec_val.try_iter()? {
    println!("{} ({})", value, value.kind());

If you run this, this will print the following:

1 (number)
2 (number)
3 (number)

So each value in this object has itself been “boxed” in a value. As far as the engine is concerned, everything is a value.


But how do you get something interesting into these values that is not just a basic type that could be hardcoded (such as a vector)? Imagine you have a custom object that you want to efficently expose to the engine. This is in fact even something the engine itself needs to do internally. For instance Jinja has first class functions in the form of macros so it needs to expose that into the engine as well. Additionally Rust functions passed to the engine also need to be represented.

This is why a Value type can hold objects internally. These objects also support downcasting:

// box a vector in a value
let value = Value::from_object(vec![1i32, 2, 3]);
println!("{} ({})", value, value.kind());

// downcast it back into a reference of the original object
let v: &Vec<i32> = value.downcast_object_ref().unwrap();
println!("{:?}", value);

In order to do this, MiniJinja provides a trait called Object which if a type implements can be boxed into a value. All the dynamic operations of the value are forwarded into the internal Object. These operations are the following:

  • repr(): returns the “representation” of the object. The representation define is how the object is represented (serialized) and how it behaves. Valid representations are Seq (the object is a list or sequence), Map (the object is a struct or map), Iterable (the object can be iterated over but not indexed), Plain (the object is just a plain object, for instance used for functions)
  • get_value(key): looks up a key in the object
  • enumerate(): returns the contents of the object if there are any

Additionally there is quite a few extra API (to render them to strings, to make them callable etc.) but we can ignore this for now. In addition there are a few more but some of them just have default implementations. For instance the “length” of an object by default comes from the length of the enumerator returned by enumerate().

So how would one design a trait like this? For sake of keeping this post brief let's pretend there is only repr, get_value and enumerate. Remember that we need to reference count, so we might be encouraged to make a trait like the following:

pub trait Object: Debug + Send + Sync {
    fn repr(self: &Arc<Self>) -> ObjectRepr {

    fn get_value(self: &Arc<Self>, key: &Value) -> Option<Value> {

    fn enumerate(self: &Arc<Self>) -> Enumerator {

This trait looks pretty appealing. The self receiver type is reference counted (thanks to &Arc<Self>) and the interface is pretty minimal. [1] Enumerator maybe needs a bit of explanation before we go further. In Rust usually when you iterate over an object you have something called an Iterator. Iterators usually borrow and you use traits to give the iterator additional functionality. For instance a DoubleEndedIterator can be reversed. In a template engine like Jinja we however need to do everything dynamically and we also need to ensure that we do not end up borrowing with lifetimes from the object. The engine needs to be able to hold on to the iterator independent of the object that you iterate. To simplify this process the engine uses this Enumerator type internally. It looks a bit like the following:

pub enum Enumerator {
    // object cannot be enumerated
    // object is empty
    // iterate over static strings
    Str(&'static [&'static str]),
    // iterate over an actual dynamic iterator
    Iter(Box<dyn Iterator<Item = Value> + Send + Sync>),
    // iterate by calling `get_value` in senquence from 0 to `usize`

There are many more versions (for instance for DoubleEndedIterators and a few more) but again, let's keep it simple.

Why Arc Receiver?

So why do you need an &Arc<Self> as receiver? Because in a lot of cases you really need to bump your own refcount to do something useful. For instance here is how the iteration of an object is implemented for sequences:

fn try_iter(self: &Arc<Self>) -> Option<Box<dyn Iterator<Item = Value> + Send + Sync>>
    Self: 'static,
    match self.enumerate() {
        Enumerator::Seq(l) => {
            let self_clone = self.clone();
            Some(Box::new((0..l).map(move |idx| {
        // ...

If we did not have a way to bump our own refcount, we could not implement something like this.

Boxing Up Objects

We can now use this to implement a custom struct for instance (say a 2D point with two attributes: x and y):

struct Point(f32, f32);

impl Object for Point {
    fn repr(self: &Arc<Self>) -> ObjectRepr {

    fn get_value(self: &Arc<Self>, key: &Value) -> Option<Value> {
        match key.as_str()? {
            "x" => Some(Value::from(self.0)),
            "y" => Some(Value::from(self.1)),
            _ => None,

    fn enumerate(self: &Arc<Self>) -> Enumerator {
        Enumerator::Str(&["x", "y"])

Or alternatively as a custom sequence:

struct Point(f32, f32);

impl Object for Point {
    fn repr(self: &Arc<Self>) -> ObjectRepr {

    fn get_value(self: &Arc<Self>, key: &Value) -> Option<Value> {
        match key.as_usize()? {
            0 => Some(Value::from(self.0)),
            1 => Some(Value::from(self.1)),
            _ => None,

    fn enumerate(self: &Arc<Self>) -> Enumerator {

Now that we have the object, we need to box it up into an Arc. Unfortunatley this is where we hit a hurdle:

error[E0038]: the trait `Object` cannot be made into an object
   --> src/
29  |     let val = Arc::new(Point(1.0, 2.5)) as Arc<dyn Object>;
    |               ^^^^^^^^^^^^^^^^^^^^^^^^^ `Object` cannot be made into an object
note: for a trait to be "object safe" it needs to allow building a
      vtable to allow the call to be resolvable dynamically

The reason it cannot be made into an object is because we declare the receiver as &Arc<Self> instead of &Self. This is a limitation because Rust is not capable of building a vtable for us. A vtable is nothing more than a struct that holds a field with a function pointer for each method on the trait. So our plan of using Arc<dyn Object> won't work, but we can in fact build out own version of this. To accomplish this we just need to build something like a DynObject which internally implements trampolines to call into the original methods and to manage the refcounting for us.

Macro Magic

Since this requires a lot of unsafe code, and we want to generate all the necessary trampolines to put into the vtable automatically, we will use a macro. The invocation of that macro which generates the final type looks like this:

type_erase! {
    pub trait Object => DynObject {
        fn repr(&self) -> ObjectRepr;
        fn get_value(&self, key: &Value) -> Option<Value>;
        fn enumerate(&self) -> Enumerator;

You can read this as “map trait Object into a DynObject smart pointer”. The actual macro has a few extra things (it also supports building the necessary vtable entries for fmt::Debug and other traits) but let's focus on the simple pieces. This macro generates some pretty wild output.

I cleaned it up and added some comments about what it does. Later I will show you the macro that generates it. First let's start with the definition of the fat pointer:

use std::sync::Arc;
use std::mem::ManuallyDrop;
use std::any::{type_name, TypeId};

pub struct DynObject {
    /// ptr points to the payload of the Arc<T>
    ptr: *const (),
    /// this points to our vtable.  The actual type is hidden
    /// (`VTable`) in a local scope.
    vtable: *const (),

And this is the implementation of the vtable and the type:

// this is a trick that is useful for generated macros to hide a type
// at a local scope
const _: () = {
    /// This is the actual vtable.
    struct VTable {
        // regular trampolines
        repr: fn(*const ()) -> ObjectRepr,
        get_value: fn(*const (), key: &Value) -> Option<Value>,
        enumerate: fn(*const ()) -> Enumerator,
        // method to return the type ID of the internal type for casts
        __type_id: fn() -> TypeId,
        // method to return the type name of the internal type
        __type_name: fn() -> &'static str,
        // method used to drop the refcount by one
        __drop: fn(*const ()),

    /// Utility function to return a reference to the real vtable.
    fn vt(e: &DynObject) -> &VTable {
        unsafe { &*(e.vtable as *const VTable) }

    impl DynObject {

        /// Takes ownership of an Arc<T> and boxes it up.
        pub fn new<T: Object + 'static>(v: Arc<T>) -> Self {
            // "shrinks" an Arc into a raw pointer.  This returns the
            // address of the payload it carries, just behind the
            // refcount.
            let ptr = Arc::into_raw(v) as *const T as *const ();

            let vtable = &VTable {
                // example trampoline that is generated for each method
                repr: |ptr| unsafe {
                    // now take ownership of the ptr and put it in a
                    // ManuallyDrop so we don't have to manipulate the
                    // reference count.
                    let arc = ManuallyDrop::new(Arc::<T>::from_raw(ptr as *const T));
                    // and invoke the original method via the arc
                    <T as Object>::repr(&arc)
                get_value: |ptr, key| unsafe {
                    let arc = ManuallyDrop::new(Arc::<T>::from_raw(ptr as *const T));
                    <T as Object>::get_value(&arc, key)
                enumerate: |ptr| unsafe {
                    let arc = ManuallyDrop::new(Arc::<T>::from_raw(ptr as *const T));
                    <T as Object>::enumerate(&arc)
                // these are pretty trivial, they are modelled after
                // rust's `Any` type.
                __type_id: || TypeId::of::<T>(),
                __type_name: || type_name::<T>(),
                // on drop take ownership of the pointer (decrements
                // refcount by one)
                __drop: |ptr| unsafe {
                    Arc::from_raw(ptr as *const T);
            Self {
                vtable: vtable as *const VTable as *const (),

        /// DynObject::repr() just calls via the vtable into the
        /// original type.
        pub fn repr(&self) -> ObjectRepr {

        pub fn get_value(&self, key: &Value) -> Option<Value> {
            (vt(self).get_value)(self.ptr, key)

        pub fn enumerate(&self) -> Enumerator {


At this point the object is functional, but it's kind of problematic because it does not yet have memory management so we would just leak memory. So we need to add that:

Memory management:

/// Clone just increments the strong refcount of the Arc.
impl Clone for DynObject {
    fn clone(&self) -> Self {
        unsafe {
        Self { ptr: self.ptr, vtable: self.vtable }

/// Drop decrements the refcount via a method in the vtable.
impl Drop for DynObject {
    fn drop(&mut self) {

Additionally to make the object useful, we need to add support for downcasting which is surprisingly easy at this point. If the type ID matches we're good to cast:

impl DynObject {
    pub fn downcast_ref<T: 'static>(&self) -> Option<&T> {
        if (vt(self).__type_id)() == TypeId::of::<T>() {
            unsafe {
                return Some(&*(self.ptr as *const T));

    pub fn downcast<T: 'static>(&self) -> Option<Arc<T>> {
        if (vt(self).__type_id)() == TypeId::of::<T>() {
            unsafe {
                Arc::<T>::increment_strong_count(self.ptr as *const T);
                return Some(Arc::<T>::from_raw(self.ptr as *const T));

    pub fn type_name(&self) -> &'static str {

The Macro

So now that we know what we want, we can actually use a Rust macro to generate this stuff for us. I will leave most of this undocumented given that you know now what it expands to. Here just some notes to better understand what is going on:

  1. The const _:() = { ... } trick is useful as macros today cannot generate custom identifiers. Unlike with C macros where you can concatenate identifiers to create temporary names, that is unavailable in Rust. But you can use that to hide a type in a local scope as we are doing with the VTable struct.
  2. Since we cannot prefix identifiers, there is a potential conflict with the names in the struct for the methods and the internal names (__type_id etc.) To reduce the likelihood of collision the internal names are prefixed with two underscores.
  3. All names are fully canonicalized (eg: std::sync::Arc instead of Arc) to make the macro work without having to bring types into scope.

The macro is surprisingly only a bit awful:

macro_rules! type_erase {
    ($v:vis trait $t:ident => $erased_t:ident {
        $(fn $f:ident(&self $(, $p:ident: $t:ty $(,)?)*) $(-> $r:ty)?;)*
    }) => {
        $v struct $erased_t {
            ptr: *const (),
            vtable: *const (),

        const _: () = {
            struct VTable {
                $($f: fn(*const (), $($p: $t),*) $(-> $r)?,)*
                $($($f_impl: fn(*const (), $($p_impl: $t_impl),*) $(-> $r_impl)?,)*)*
                __type_id: fn() -> std::any::TypeId,
                __type_name: fn() -> &'static str,
                __drop: fn(*const ()),

            fn vt(e: &$erased_t) -> &VTable {
                unsafe { &*(e.vtable as *const VTable) }

            impl $erased_t {
                $v fn new<T: $t + 'static>(v: std::sync::Arc<T>) -> Self {
                    let ptr = std::sync::Arc::into_raw(v) as *const T as *const ();
                    let vtable = &VTable {
                            $f: |ptr, $($p),*| unsafe {
                                let arc = std::mem::ManuallyDrop::new(
                                    std::sync::Arc::<T>::from_raw(ptr as *const T));
                                <T as $t>::$f(&arc, $($p),*)
                        __type_id: || std::any::TypeId::of::<T>(),
                        __type_name: || std::any::type_name::<T>(),
                        __drop: |ptr| unsafe {
                            std::sync::Arc::from_raw(ptr as *const T);
                    Self { ptr, vtable: vtable as *const VTable as *const () }

                    $v fn $f(&self, $($p: $t),*) $(-> $r)? {
                        (vt(self).$f)(self.ptr, $($p),*)

                $v fn type_name(&self) -> &'static str {

                $v fn downcast_ref<T: 'static>(&self) -> Option<&T> {
                    if (vt(self).__type_id)() == std::any::TypeId::of::<T>() {
                        unsafe {
                            return Some(&*(self.ptr as *const T));


                $v fn downcast<T: 'static>(&self) -> Option<Arc<T>> {
                    if (vt(self).__type_id)() == std::any::TypeId::of::<T>() {
                        unsafe {
                            std::sync::Arc::<T>::increment_strong_count(self.ptr as *const T);
                            return Some(std::sync::Arc::<T>::from_raw(self.ptr as *const T));


            impl Clone for $erased_t {
                fn clone(&self) -> Self {
                    unsafe {

                    Self {
                        ptr: self.ptr,
                        vtable: self.vtable,

            impl Drop for $erased_t {
                fn drop(&mut self) {

The full macro that is in MiniJinja is a bit more feature rich. It also generates documentation and implementations for other traits. If you want to see the full one look here:

Putting it Together

So now that we have this DynObject internally it's trivially possible to use it in the internals of our value type:

pub(crate) enum ValueRepr {
    String(Arc<str>, StringType),

pub struct Value(pub(crate) ValueRepr);

And make the downcasting and construction of such types directly available:

impl Value {
    pub fn from_object<T: Object + Send + Sync + 'static>(value: T) -> Value {

    pub fn downcast_object_ref<T: 'static>(&self) -> Option<&T> {
        match self.0 {
            ValueRepr::Object(ref o) => o.downcast_ref(),
            _ => None,

    pub fn downcast_object<T: 'static>(&self) -> Option<Arc<T>> {
        match self.0 {
            ValueRepr::Object(ref o) => o.downcast(),
            _ => None,

What do we learn from this? Not sure. I at least learned that just because Rust tells you that you cannot make something into an object does not mean that you actually can't. It just requires some creativity and the willingness to actually use unsafe code. Another thing is that this yet again makes a pretty good argument in favor of compile time introspection. Zig programmers will laugh / cry about this since comptime is a much more powerful system to make something like this work compared to the ridiculous macro abuse necessary in Rust.

Anyways. Maybe this is useful to you.

[1]Important note: You can actually make an Arc<Self> object safe but that involves moving the object which means manipulating the reference count. If you are okay with the implication that this requires, you can avoid most of what this blog post talks about and just use Arc<Self>.

May 16, 2024 12:00 AM UTC

Matt Layman

Settings and Billing Portal - Building SaaS with Python and Django #190

In this episode, I worked on the settings page for the user. This was a vital addition because it allows users to access the Stripe billing portal and close their account if they no longer wish to use JourneyInbox.

May 16, 2024 12:00 AM UTC

May 15, 2024

Mike Driscoll

An Intro to Logging with Python and Loguru

Python’s logging module isn’t the only way to create logs. There are several third-party packages you can use, too. One of the most popular is Loguru. Loguru intends to remove all the boilerplate you get with the Python logging API.

You will find that Loguru greatly simplifies creating logs in Python.

This chapter has the following sections:

Let’s find out how much easier Loguru makes logging in Python!


Before you can start with Loguru, you will need to install it. After all, the Loguru package doesn’t come with Python.

Fortunately, installing Loguru is easy with pip. Open up your terminal and run the following command:

python -m pip install loguru

Pip will install Loguru and any dependencies it might have for you. You will have a working package installed if you see no errors.

Now let’s start logging!

Logging Made Simple

Logging with Loguru can be done in two lines of code. Loguru is really that simple!

Don’t believe it? Then open up your Python IDE or REPL and add the following code:


from loguru import logger

logger.debug("Hello from loguru!")"Informed from loguru!")

One import is all you need. Then, you can immediately start logging! By default, the log will go to stdout.

Here’s what the output looks like in the terminal:

2024-05-07 14:34:28.663 | DEBUG    | __main__:<module>:5 - Hello from loguru!
2024-05-07 14:34:28.664 | INFO     | __main__:<module>:6 - Informed from loguru!

Pretty neat! Now, let’s find out how to change the handler and add formatting to your output.

Handlers and Formatting

Loguru doesn’t think of handlers the way the Python logging module does. Instead, you use the concept of sinks. The sink tells Loguru how to handle an incoming log message and write it somewhere.

Sinks can take lots of different forms:

To see how this works, create a new file called in your Python IDE. Then add the following code:


from loguru import logger

fmt = "{time} - {name} - {level} - {message}"

logger.add("formatted.log", format=fmt, level="INFO")
logger.debug("This is a debug message")"This is an informational message")

If you want to change where the logs go, use the add() method. Note that this adds a new sink, which, in this case, is a file. The logger will still log to stdout, too, as that is the default, and you are adding to the handler list. If you want to remove the default sink, add logger.remove() before you call add().

When you call add(), you can pass in several different arguments:

There are several more, but those are the ones you would use the most. If you want to know more about add(), you should check out the documentation.

You might have noticed that the formatting of the log records is a little different than what you saw in Python’s own logging module.

Here is a listing of the formatting directives you can use for Loguru:

You can also change the time formatting in the logs. In this case, you would use a subset of the formatting from the Pendulum package. For example, if you wanted to make the time exclude the date, you would use this: {time:HH:mm:ss} rather than simply {time}, which you see in the code example above.

See the documentation for details on formating time and messages.

When you run the code example, you will see something similar to the following in your log file:

2024-05-07T14:35:06.553342-0500 - __main__ - INFO - This is an informational message

You will also see log messages sent to your terminal in the same format as you saw in the first code example.

Now, you’re ready to move on and learn about catching exceptions with Loguru.

Catching Exceptions

Catching exceptions with Loguru is done by using a decorator. You may remember that when you use Python’s own logging module, you use logger.exception in the except portion of a try/except statement to record the exception’s traceback to your log file.

When you use Loguru, you use the @logger.catch decorator on the function that contains code that may raise an exception.

Open up your Python IDE and create a new file named Then enter the following code:


from loguru import logger

def silly_function(x, y, z):
    return 1 / (x + y + z)

def main():
    fmt = "{time:HH:mm:ss} - {name} - {level} - {message}"
    logger.add("exception.log", format=fmt, level="INFO")"Application starting")
    silly_function(0, 0, 0)"Finished!")

if __name__ == "__main__":

According to Loguru’s documentation, the’ @logger.catch` decorator will catch regular exceptions and also work with applications with multiple threads. Add another file handler on top of the stream handler and start logging for this example.

Then you call silly_function() with a bunch of zeroes, which causes a ZeroDivisionError exception.

Here’s the output from the terminal:

Loguru Exception Handling

If you open up the exception.log, you will see that the contents are a little different because you formatted the timestamp and also because logging those funny lines that show what arguments were passed to the silly_function() don’t translate that well:

14:38:30 - __main__ - INFO - Application starting
14:38:30 - __main__ - ERROR - An error has been caught in function 'main', process 'MainProcess' (8920), thread 'MainThread' (22316):
Traceback (most recent call last):

  File "C:\books\11_loguru\", line 17, in <module>
    â”” <function main at 0x00000253B01AB7E0>

> File "C:\books\11_loguru\", line 13, in main
    silly_function(0, 0, 0)
    â”” <function silly_function at 0x00000253ADE6D440>

  File "C:\books\11_loguru\", line 7, in silly_function
    return 1 / (x + y + z)
                │   │   └ 0
                │   └ 0
                â”” 0

ZeroDivisionError: division by zero
14:38:30 - __main__ - INFO - Finished!

On the whole, using the @logger.catch is a nice way to catch exceptions.

Now, you’re ready to move on and learn about changing the color of your logs in the terminal.

Terminal Logging with Color

Loguru will print out logs in color in the terminal by default if the terminal supports color. Colorful logs can make reading through the logs easier as you can highlight warnings and exceptions with unique colors.

You can use markup tags to add specific colors to any formatter string. You can also apply bold and underline to the tags.

Open up your Python IDE and create a new file called After saving the file, enter the following code into it:

import sys
from loguru import logger

fmt = ("<red>{time}</red> - "
       "<yellow>{name}</yellow> - "
       "{level} - {message}")

logger.add(sys.stdout, format=fmt, level="DEBUG")
logger.debug("This is a debug message")"This is an informational message")

You create a special format that sets the “time” portion to red and the “name” to yellow. Then, you add() that format to the logger. You will now have two sinks: the default root handler, which logs to stderr, and the new sink, which logs to stdout. You do formatting to compare the default colors to your custom ones.

Go ahead and run the code. You should see something like this:

Changing terminal output colors with Loguru

Neat! It would be best if you now spent a few moments studying the documentation and trying out some of the other colors. For example, you can use hex and RGB colors and a handful of named colors.

The last section you will look at is how to do log rotation with Loguru!

Easy Log Rotation

Loguru makes log rotation easy. You don’t need to import any special handlers. Instead, you only need to specify the rotation argument when you call add().

Here are a few examples:

These demonstrate that you can set the rotation at 100 megabytes at noon daily or even rotate weekly.

Open up your Python IDE so you can create a full-fledged example. Name the file and add the following code:


from loguru import logger

fmt = "{time} - {name} - {level} - {message}"

           rotation="50 B")
logger.debug("This is a debug message")"This is an informational message")

Here, you set up a log format, set the level to DEBUG, and set the rotation to every 50 bytes. When you run this code, you will get a couple of log files. Loguru will add a timestamp to the file’s name when it rotates the log.

What if you want to add compression? You don’t need to override the rotator like you did with Python’s logging module. Instead, you can turn on compression using the compression argument.

Create a new Python script called and add this code for a fully working example:


from loguru import logger

fmt = "{time} - {name} - {level} - {message}"

           rotation="50 B",
logger.debug("This is a debug message")"This is an informational message")
for i in range(10):"Log message {i}")

The new file is automatically compressed in the zip format when the log rotates. There is also a retention argument that you can use with add() to tell Loguru to clean the logs after so many days:

             rotation="100 MB",
             retention="5 days")

If you were to add this code, the logs that were more than five days old would get cleaned up automatically by Loguru!

Wrapping Up

The Loguru package makes logging much easier than Python’s logging library. It removes the boilerplate needed to create and format logs.

In this chapter, you learned about the following:

Loguru can do much more than what is covered here, though. You can serialize your logs to JSON or contextualize your logger messages. Loguru also allows you to add lazy evaluation to your logs to prevent them from affecting performance in production. Loguru also makes adding custom log levels very easy. For full details about all the things Loguru can do, you should consult Loguru’s website.

The post An Intro to Logging with Python and Loguru appeared first on Mouse Vs Python.

May 15, 2024 02:08 PM UTC

Real Python

Python's Built-in Exceptions: A Walkthrough With Examples

Python has a complete set of built-in exceptions that provide a quick and efficient way to handle errors and exceptional situations that may happen in your code. Knowing the most commonly used built-in exceptions is key for you as a Python developer. This knowledge will help you debug code because each exception has a specific meaning that can shed light on your debugging process.

You’ll also be able to handle and raise most of the built-in exceptions in your Python code, which is a great way to deal with errors and exceptional situations without having to create your own custom exceptions.

In this tutorial, you’ll:

  • Learn what errors and exceptions are in Python
  • Understand how Python organizes the built-in exceptions in a class hierarchy
  • Explore the most commonly used built-in exceptions
  • Learn how to handle and raise built-in exceptions in your code

To smoothly walk through this tutorial, you should be familiar with some core concepts in Python. These concepts include Python classes, class hierarchies, exceptions, tryexcept blocks, and the raise statement.

Get Your Code: Click here to download the free sample code that you’ll use to learn about Python’s built-in exceptions.

Errors and Exceptions in Python

Errors and exceptions are important concepts in programming, and you’ll probably spend a considerable amount of time dealing with them in your programming career. Errors are concrete conditions, such as syntax and logical errors, that make your code work incorrectly or even crash.

Often, you can fix errors by updating or modifying the code, installing a new version of a dependency, checking the code’s logic, and so on.

For example, say you need to make sure that a given string has a certain number of characters. In this case, you can use the built-in len() function:

>>> len("Pythonista") = 10
  File "<input>", line 1
SyntaxError: cannot assign to function call here.
    Maybe you meant '==' instead of '='?

In this example, you use the wrong operator. Instead of using the equality comparison operator, you use the assignment operator. This code raises a SyntaxError, which represents a syntax error as its name describes.

Note: In the above code, you’ll note how nicely the error message suggests a possible solution for correcting the code. Starting in version 3.10, the Python core developers have put a lot of effort into improving the error messages to make them more friendly and useful for debugging.

To fix the error, you need to localize the affected code and correct the syntax. This action will remove the error:

>>> len("Pythonista") == 10

Now the code works correctly, and the SyntaxError is gone. So, your code won’t break, and your program will continue its normal execution.

There’s something to learn from the above example. You can fix errors, but you can’t handle them. In other words, if you have a syntax error like the one in the example, then you won’t be able to handle that error and make the code run. You need to correct the syntax.

On the other hand, exceptions are events that interrupt the execution of a program. As their name suggests, exceptions occur in exceptional situations that should or shouldn’t happen. So, to prevent your program from crashing after an exception, you must handle the exception with the appropriate exception-handling mechanism.

To better understand exceptions, say that you have a Python expression like a + b. This expression will work if a and b are both strings or numbers:

>>> a = 4
>>> b = 3

>>> a + b

In this example, the code works correctly because a and b are both numbers. However, the expression raises an exception if a and b are of types that can’t be added together:

>>> a = "4"
>>> b = 3

>>> a + b
Traceback (most recent call last):
  File "<input>", line 1, in <module>
    a + b
TypeError: can only concatenate str (not "int") to str

Because a is a string and b is a number, your code fails with a TypeError exception. Since there is no way to add text and numbers, your code has faced an exceptional situation.

Python uses classes to represent exceptions and errors. These classes are generically known as exceptions, regardless of what a concrete class represents, an exception or an error. Exception classes give us information about an exceptional situation and also errors detected during the program’s execution.

The first example in this section shows a syntax error in action. The SyntaxError class represents an error but it’s implemented as a Python exception. This could be confusing, but Python uses exception classes for both errors and exceptions.

Read the full article at »

[ 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 ]

May 15, 2024 02:00 PM UTC

How to Get the Most Out of PyCon US

Congratulations! You’re going to PyCon US!

Whether this is your first time or not, going to a conference full of people who love the same thing as you is always a fun experience. There’s so much more to PyCon than just a bunch of people talking about the Python language, and that can be intimidating for first-time attendees. This guide will help you navigate all there is to see and do at PyCon.

PyCon US is the biggest conference centered around the Python language. Originally launched in 2003, this conference has grown exponentially and has even spawned several other PyCons and workshops around the world.

Everyone who attends PyCon will have a different experience, and that’s what makes the conference truly unique. This guide is meant to help you, but you don’t need to follow it strictly.

By the end of this article, you’ll know:

  • How PyCon consists of tutorials, conference, and sprints
  • What to do before you go
  • What to do during PyCon
  • What to do after the event
  • How to have a great PyCon

This guide will have links that are specific to PyCon 2024, but it should be useful for future PyCons as well.

Free Download: Get a sample chapter from Python Tricks: The Book that shows you Python’s best practices with simple examples you can apply instantly to write more beautiful + Pythonic code.

What PyCon Involves

Before considering how to get the most out of PyCon, it’s important to first understand what PyCon involves.

PyCon is broken up into three stages:

  1. Tutorials: PyCon starts with two days of three-hour workshops, during which you get to learn in depth with instructors. These are great to go to since the class sizes are small, and you can ask questions of the instructors. You should consider going to at least one of these if you can, but they do have an additional cost of $150 per tutorial.

  2. Conference: Next, PyCon offers three days of talks. Each presentation lasts for thirty to forty-five minutes, and there are about five talks going on at a time, including a Spanish language charlas track. But that’s not all: there are also open spaces, sponsors, posters, lightning talks, dinners, and so much more.

  3. Sprints: During this stage, you can take what you’ve learned and apply it! This is a four-day exercise where people group up to work on various open-source projects related to Python. If you’ve got the time, going to one or more sprint days is a great way to practice what you’ve learned, become associated with an open-source project, and network with other smart and talented people. Learn more about sprints in this blog post from an earlier year.

Since most PyCon attendees go to the conference part, that’ll be the focus of this article. However, don’t let that deter you from attending the tutorials or sprints if you can!

You may even learn more technical skills by attending the tutorials rather than listening to the talks. The sprints are great for networking and applying the skills that you’ve already got, as well as learning new ones from the people you’ll be working with.

What to Do Before You Go

In general, the more prepared you are for something, the better your experience will be. The same applies to PyCon.

It’s really helpful to plan and prepare ahead of time, which you’re already doing just by reading this article!

Look through the talk schedule and see which talks sound most interesting to you. This doesn’t mean you need to plan out all of the talks that you’re going to see, in every slot possible. But it helps to get an idea of which topics are going to be presented so that you can decide what you’re most interested in.

Getting the PyCon US mobile app will help you plan your schedule. This app lets you view the schedule for the talks and add reminders for the ones that you want to attend. If you’re having a hard time picking which talks to go to, you can come prepared with a question or problem that you need to solve. Doing this can help you focus on the topics that are important to you.

If you can, come a day early to check in and attend the opening reception. The line to check in on the first day is always long, so you’ll save time if you check in the day before. There’s also an opening reception that evening, so you can meet other attendees and speakers, as well as get a chance to check out the various sponsors and their booths.

If you’re brand-new to PyCon, the Newcomer Orientation can help you get caught up on what the conference involves and how you can participate.

Read the full article at »

[ 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 ]

May 15, 2024 02:00 PM UTC


4 Ways to Use ChatGPT API in Python

In this tutorial, we will explain how to use ChatGPT API in Python, along with examples.

Steps to Access ChatGPT API

Please follow the steps below to access the ChatGPT API.

  1. Visit the OpenAI Platform and sign up using your Google, Microsoft or Apple account.
  2. After creating your account, the next step is to generate a secret API key to access the API. The API key looks like this -sk-xxxxxxxxxxxxxxxxxxxx
  3. If your phone number has not been associated with any other OpenAI account previously, you may get free credits to test the API. Otherwise you have to add atleast 5 dollars into your account and charges will be based on the usage and the type of model you use. Check out the pricing details in the OpenAI website.
  4. Now you can call the API using the code below.
To read this article in full, please click here

May 15, 2024 01:56 PM UTC

Real Python

Quiz: What Are CRUD Operations?

In this quiz, you’ll test your understanding of CRUD Operations.

By working through this quiz, you’ll revisit the key concepts and techniques related to CRUD operations. Good luck!

[ 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 ]

May 15, 2024 12:00 PM UTC


PyCon US 2024 Sprints will be here before you know it!

The Development Sprints are coming soon. Make sure you plan ahead:

When: Sprints will take place on May 20, 2024 8:00am through May 23, 2024 11:00pm EST

Where: At PyCon US at the David L. Lawrence Convention Center in rooms 308-311 and 315-321

Project Signups: Get your project listed so that attendees can help support it by signing up here Submit Sprint Project:

What are Sprints?

PyCon Development Sprints are up to four days of intensive learning and development on an open source project(s) of your choice, in a team environment. It's a time to come together with colleagues, old and new, to share what you've learned and apply it to an open source project.

It's a time to test, fix bugs, add new features, and improve documentation. And it's a time to network, make friends, and build relationships that go beyond the conference.

PyCon US provides the opportunity and infrastructure; you bring your skills, humanity, and brainpower (oh! and don't forget your computer).

For those that have never attended a development sprint before or want to brush up on basics, on Sunday, May 19th, there will be an Introduction to Sprinting Workshop that will guide you through the basics of git, github, and what to expect at a Sprint. The Introduction to Sprint Workshop takes place in Room 402 on Sunday, May 19th from 5:30pm - 8:30pm EST.

Who can participate?

You! All experience levels are welcome; sprints are a great opportunity to get connected with, and start contributing to your favorite Python project. Participation in the sprints is free and included in your conference registration. Please go to your attendee profile on your dashboard and indicate the number of sprint days you will be attending. 

Mentors: we are always looking for mentors to help new sprinters get up and running. Reach out to the sprint organizers for more info. 

Which Projects are Sprinting?

Project Leads: Any Python project can signup and invite sprinters to contribute to their project. If you would like your project to be included, add your project to the list. Attendees, check here to see if which projects have signed up so far.  

Thanks to our sponsors and support team!

Have questions? reach out to

May 15, 2024 10:03 AM UTC

Glyph Lefkowitz

How To PyCon

These tips are not the “right” way to do PyCon, but they are suggestions based on how I try to do PyCon. Consider them reminders to myself, an experienced long-time attendee, which you are welcome to overhear.

See Some Talks

The hallway track is awesome. But the best version of the hallway track is not just bumping into people and chatting; it’s the version where you’ve all recently seen the same thing, and thereby have a shared context of something to react to. If you aren’t going to talks, you aren’t going to get a good hallway track.. Therefore: choose talks that interest you, attend them and pay close attention, then find people to talk to about them.

Given that you will want to see some of the talks, make sure that you have the schedule downloaded and available offline on your mobile device, or printed out on a piece of paper.

Make a list of the talks you think you want to see, but have that schedule with you in case you want to call an audible in the middle of the conference, switching to a different talk you didn’t notice based on some of those “hallway track” conversations.

Participate In Open Spaces

The name “hallway track” itself is antiquated, in a way which is relevant and important to modern conferences. It used to be that conferences were exclusively oriented around their scheduled talks; it was called the “hallway” track because the way to access it was to linger in the hallways, outside the official structure of the conference, and just talk to people.

But however, at PyCon and many other conferences, this unofficial track is now much more of an integrated, official part of the program. In particular, open spaces are not only a more official hallway track, they are considerably better than the historical “hallway” experience, because these ad-hoc gatherings can be convened with a prepared topic and potentially a loose structure to facilitate productive discussion.

With open spaces, sessions can have an agenda and so conversations are easier to start. Rooms are provided, which is more useful than you might think; literally hanging out in a hallway is actually surprisingly disruptive to speakers and attendees at talks; us nerds tend to get pretty loud and can be quite audible even through a slightly-cracked door, so avail yourself of these rooms and don’t be a disruptive jerk outside somebody’s talk.

Consult the open space board, and put up your own proposed sessions. Post them as early as you can, to maximize the chance that they will get noticed. Post them on social media, using the conference's official hashtag, and ask any interested folks you bump into help boost it.1

Remember that open spaces are not talks. If you want to give a mini-lecture on a topic and you can find interested folks you could do that, but the format lends itself to more peer-to-peer, roundtable-style interactions. Among other things, this means that, unlike proposing a talk, where you should be an expert on the topic that you are proposing, you can suggest open spaces where you are curious — but ignorant — about something, in the hopes that some experts will show up and you can listen to their discussion.

Be prepared for this to fail; there’s a lot going on and it’s always possible that nobody will notice your session. Again, maximize your chances by posting it as early as you can and promoting it, but be prepared to just have a free 30 minutes to check your email. Sometimes that’s just how it goes. The corollary here is not to always balance attending others’ spaces with proposing your own. After all if someone else proposed it you know at least one other person is gonna be there.

Take Care of Your Body

Conferences can be surprisingly high-intensity physical activities. It’s not a marathon, but you will be walking quickly from one end of a large convention center to another, potentially somewhat anxiously.

Hydrate, hydrate, hydrate. Bring a water bottle, and have it with you at all times. It might be helpful to set repeating timers on your phone to drink water, since it can be easy to forget in the middle of engaging conversations. If you take advantage of the hallway track as much as you should, you will talk more than you expect; talking expels water from your body. All that aforementioned walking might make you sweat a bit more than you realize.


More generally, pay attention to what you are eating and drinking. Conference food isn’t always the best, and in a new city you might be tempted to load up on big meals and junk food. You should enjoy yourself and experience the local cuisine, but do it intentionally. While you enjoy the local fare, do so in whatever moderation works best for you. Similarly for boozy night-time socializing. Nothing stings quite as much as missing a morning of talks because you’ve got a hangover or a migraine.

This is worth emphasizing because in the enthusiasm of an exciting conference experience, it’s easy to lose track and overdo it.

Meet Both New And Old Friends: Plan Your Socializing

A lot of the advice above is mostly for first-time or new-ish conferencegoers, but this one might be more useful for the old heads. As we build up a long-time clique of conference friends, it’s easy to get a bit insular and lose out on one of the bits of magic of such an event: meeting new folks and hearing new perspectives.

While open spaces can address this a little bit, there's one additional thing I've started doing in the last few years: dinners are for old friends, but lunches are for new ones. At least half of the days I'm there, I try to go to a new table with new folks that I haven't seen before, and strike up a conversation. I even have a little canned icebreaker prompt, which I would suggest to others as well, because it’s worked pretty nicely in past years: “what is one fun thing you have done with Python recently?”2.

Given that I have a pretty big crowd of old friends at these things, I actually tend to avoid old friends at lunch, since it’s so easy to get into multi-hour conversations, and meeting new folks in a big group can be intimidating. Lunches are the time I carve out to try and meet new folks.

I’ll See You There

I hope some of these tips were helpful, and I am looking forward to seeing some of you at PyCon US 2024!

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

  1. In PyCon2024's case, #PyConUS on Mastodon is probably the way to go. Note, also, that it is #PyConUS and not #pyconus, which is much less legible for users of screen-readers. 

  2. Obviously that is specific to this conference. At the O’Reilly Software Architecture conference, my prompt was “What is software architecture?” which had some really fascinating answers. 

May 15, 2024 09:12 AM UTC

Talk Python to Me

#462: Pandas and Beyond with Wes McKinney

This episode dives into some of the most important data science libraries from the Python space with one of its pioneers: Wes McKinney. He's the creator or co-creator of pandas, Apache Arrow, and Ibis projects and an entrepreneur in this space.<br/> <br/> <strong>Episode sponsors</strong><br/> <br/> <a href=''>Neo4j</a><br> <a href=''>Mailtrap</a><br> <a href=''>Talk Python Courses</a><br/> <br/> <strong>Links from the show</strong><br/> <br/> <div><b>Wes' Website</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Pandas</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Apache Arrow</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Ibis</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Python for Data Analysis - Groupby Summary</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Polars</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Dask</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Sqlglot</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Pandoc</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Quarto</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Evidence framework</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>pyscript</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>duckdb</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Jupyterlite</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Djangonauts</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Watch this episode on YouTube</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Episode transcripts</b>: <a href="" target="_blank" rel="noopener"></a><br/> <br/> <b>--- Stay in touch with us ---</b><br/> <b>Subscribe to us on YouTube</b>: <a href="" target="_blank" rel="noopener"></a><br/> <b>Follow Talk Python on Mastodon</b>: <a href="" target="_blank" rel="noopener"><i class="fa-brands fa-mastodon"></i>talkpython</a><br/> <b>Follow Michael on Mastodon</b>: <a href="" target="_blank" rel="noopener"><i class="fa-brands fa-mastodon"></i>mkennedy</a><br/></div>

May 15, 2024 08:00 AM UTC

May 14, 2024

PyCoder’s Weekly

Issue #629 (May 14, 2024)

#629 – MAY 14, 2024
View in Browser »

The PyCoder’s Weekly Logo

Flattening a List of Lists in Python

In this video course, you’ll learn how to flatten a list of lists in Python. You’ll use different tools and techniques to accomplish this task. First, you’ll use a loop along with the .extend() method of list. Then you’ll explore other tools, including reduce(), sum(), itertools.chain(), and more.

What’s New in Python 3.13

Python 3.13 has gone into beta, which means the feature freeze is now in place. This is the official listing of the new features in 3.13. This release includes changes to the REPL, new typing features, experimental support for disabling the GIL, dead battery removal, and more.

[Webinar] Saga Pattern Simplified: Building Sagas with Temporal


Join us on May 30th: we’ll give a brief overview of Sagas, including challenges and benefits. Then we’ll introduce you to Temporal and demonstrate how easy it is to build, test, and run Sagas using our platform and coding in your preferred language. Prior knowledge of Temporal is not required →
TEMPORAL sponsor

Sets as Dictionaries With No Values

A set is a built-in data type that provides fast lookup and insertion with characteristics similar to those of dictionary keys. This article explores the relationship between sets and dictionaries by implementing a set class.

2023 PSF Annual Impact Report


Python Software Foundation Board Election Dates for 2024


Python 3.13.0 Beta 1 Released


Articles & Tutorials

A 100x Speedup With Unsafe Python

This is a deep, in the weeds analysis of how different packages can store the same kinds of data in a different order, and how row-based vs column-based storage order can affect NumPy’s speed to process the data. The not often examined “strides” value of a NumPy array specifies how things are stored and this article shows an interesting approach to getting around this value for speed-up.

The New REPL in Python 3.13

Python 3.13 just hit feature freeze with the first beta release, and it includes a host of improvements to the REPL. Automatic indenting, block-level editing, and more make the built-in REPL more powerful and easier to use.

How to Read and Write Parquet Files With Python

Apache Parquet files are a popular columnar storage format used by data scientists and anyone using the Hadoop ecosystem. By using the pyarrow package, you can read and write Parquet files, this tutorial shows you how.

Generating Fake Django Model Instances With Factory Boy

Writing good tests means having data to test with. The factory-boy library helps you create fake data that you can use with your tests. This article shows you how to use factory-boy with Django ORM models.

Python Sequences: A Comprehensive Guide

This tutorial dives into Python sequences, which is one of the main categories of data types. You’ll learn about the properties that make an object a sequence and how to create user-defined sequences.

How LLMs Work, Explained Without Math

You’ve probably come across articles on Large Language Models (LLMs) and may have tried products like ChatGPT. This article explains how these tools work without resorting to advanced math.

Creating a Calculator With wxPython

wxPython is a GUI toolkit for the Python programming language. This article introduces you to building GUIs by creating a personal calculator.

Asyncio Run Multiple Concurrent Event Loops

Ever wanted to add concurrency to your concurrency? You can run multiple asyncio event loops by using threading. This articles shows you how.

How Python asyncio Works: Recreating It From Scratch

This article explains how asyncio works by showing you how to re-create it using generators and the __await__ method.
JACOB PADILLA • Shared by Jacob Padilla

Comments on Understanding Software

Nat responds to a presentation by C J Silverio on how software gets made at small to medium sized organizations.

Projects & Code

A Raspberry Pi Document Scanner


horus: An OSINT, Digital Forensics Tool


simple-spaced-repetition: Simple Spaced Repetition Scheduler


Best Python Chart Examples


WireViz: Easily Document Cables and Wiring Harnesses



Weekly Real Python Office Hours Q&A (Virtual)

May 15, 2024

PyCon US 2024

May 15 to May 24, 2024

PyData Bristol Meetup

May 16, 2024

PyLadies Dublin

May 16, 2024

Flask Con 2024

May 17 to May 18, 2024

PyGrunn 2024

May 17 to May 18, 2024

Django Girls Ecuador 2024

May 17, 2024

Happy Pythoning!
This was PyCoder’s Weekly Issue #629.
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May 14, 2024 07:30 PM UTC


Python List

In this tutorial, we will learn about Python lists (creating lists, changing list items, removing items, and other list operations) with the help of examples.

May 14, 2024 03:04 PM UTC

Real Python

HTML and CSS Foundations for Python Developers

When you want to build websites as a Python programmer, there’s no way around HTML and CSS. Almost every website on the Internet is built with HTML markup to structure the page. To make a website look nice, you can style HTML with CSS.

If you’re interested in web development with Python, then knowing HTML and CSS will help you understand web frameworks like Django and Flask better. But even if you’re just getting started with Python, HTML and CSS can enable you to create small websites to impress your friends.

In this video course, you’ll learn how to:

[ 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 ]

May 14, 2024 02:00 PM UTC


PyCharm at PyCon US 2024: Engage, Learn, and Celebrate!

We’re thrilled to announce that the PyCharm team will be part of the vibrant PyCon US 2024 conference in Pittsburgh, Pennsylvania, USA. Join us for a series of engaging activities, expert talks, live demonstrations, and fun quizzes throughout the event! 

Here’s a sneak peek at what we have lined up for you. Unless otherwise specified, all activities mentioned will take place at the JetBrains PyCharm booth. 

Engaging talks and live demonstrations

1. Lies, damned lies, and large language models
Speaker: Jodie Burchell
Dive into the intricacies of large language models (LLMs) with Jodie Burchell’s talk. Discover the reasons behind LLM hallucinations and explore methods like retrieval augmented generation (RAG) to mitigate these issues.
📅 Date and time: May 18, 1:45 pm – 2:15 pm
📍 Location: Ballroom BC
Learn more about this talk

2. XGBoost demo and book signing with Matt Harrison
Get hands-on with XGBoost in this engaging demo, during which you can ask Matt Harrison any questions you have about the gradient boosting library.
📅 Date and time: May 17, 10:30 am
🎁 Giveaway: The first 20 attendees will get a free signed copy of Effective XGBoost.

3. Build a fully local RAG app for a variety of files in just 20 minutes
Presenter: Maria Khalusova
Watch a live demonstration on setting up a fully local RAG application in just 20 minutes. Gain insights from Maria Khalusova, an expert from Unstructured and former Hugging Face employee.
📅 Date and time: May 17, 4:10 pm

4. PyCharm on-demand demos
Discover how to boost your efficiency in data science and web development projects with PyCharm’s latest features. Have any questions or feedback? Our team lead, product manager, and developer advocates will be there to discuss everything PyCharm with you.

5. Qodana on-demand demos
Enhance your team’s code quality with our Qodana static code analysis tool. Drop by the PyCharm booth for on-demand demos and learn how to maintain high-quality, secure, and maintainable code.

Interactive quizzes and giveaways

1. Data science quizzes with Matt Harrison
Kick off the event with our exciting quiz session during the opening reception. Engage in challenging questions and get a chance to win a copy of Effective Pandas 2.0.
📅 Date and time: May 16, 5:30 pm

2. More quizzes! More prizes!
Continue testing your knowledge on May 17 at 1:20 pm. Win a one-year subscription to PyCharm Professional or a voucher for JetBrains merch.

Special events

1. Share your Python story

Want to share your Python experiences with us? Get interviewed by Paul Everitt at our booth! Our video crew will be on-site throughout the conference to capture your stories and insights. 

2. Talk Python To Me: Live podcast recording
Join Michael Kennedy for a live recording with guests Maria Jose Molina-Contreras, Jessica Greene, and Jodie Burchell.
📅 Date and time: May 18, 10:10 am

2. The Black Python Developers Community’s first anniversary
Celebrate the Black Python Devs’ milestone with Jay Miller.
📅 Date and time: May 18, 12:45 pm

3. Frontend testing office hours with Paul Everitt
Got questions about frontend testing? Paul Everitt has the answers! Visit our booth to engage directly with an expert.
📅 Date and time: May 18, 3:45 pm

Visit our booth

Don’t miss out on these exciting opportunities to deepen your knowledge, network with peers, and celebrate achievements in the Python community. See you there!

May 14, 2024 11:48 AM UTC


Get ready for PyCon US 2024! Tips and tricks from our community.

PyCon US 2024 will kick off later this week. Our staff members are all busy with the final preparation, and we know that many of you are packing and traveling to the conference too.

If you’re wondering about who else you will meet or what to do during your time at the conference, check out this video to hear advice from other experienced PyCon US attendees.

Thank you to our community members for helping us with the creation of the video, and for sharing their experiences and advice to the community:
And special thanks to Georgi Ker for the help with video editing and finishing.

In addition to all the talks, tutorials, Charlas, and keynotes, PyCon US offers additional activities and events, including Open Spaces, Sprints, Summits. Check out the full list on our conference website under the Events menu.

First time attendee at PyCon US are recommended to attend the Newcomer Orientation on Thursday at 4:30 PM.

Also new this year is the Hatchery Programs, which features four new tracks. While some of the Hatchery programs are now full, you will be delighted to know you don’t need to sign up in order to attend FlaskCon. Be sure to check them out.

Thank you everyone! Safe travels, and we look forward to meeting you soon!

Not yet registered for PyCon US? You still have time to register and join us either in-person or online.

Register here:

Check our full schedule:

May 14, 2024 11:47 AM UTC


Community Post: The Invisible Threads that sustained me in STEM/Tech

altThe unconscious influence the Ghanaian tech community has had on my career.

My name is Joana Owusu-Appiah, and I am currently pursuing an MSc degree in Medical Imaging and Applications. I am originally from Ghana, but as my colleague likes to put it, I am currently backpacking through Europe. So depending on when you see this, my location might have changed.

I hold a Bachelor of Science degree in Biomedical Engineering from Kwame Nkrumah University of Science and Technology, Ghana. Prior to commencing my graduate studies, I dabbled in data science and analytics, gaining experience in visualizing and manipulating data using various tools (Python, Power BI, Excel, SQL). My current research focuses on computer vision applications on medical images.

Do I consider myself a woman in tech? I guess if it means knowing how to use a computer (lol) and understanding that photo editing is based on image processing algorithms and deep learning, then I might be close.

Has it always been this way? No.

What changed

I am a first-generation university student. In my country, or how it used to be, growing up, the smarter students were encouraged to pursue General Science in high school because it ultimately ensured job security. In high school, my primary ambition was to attend medical school. However, as a backup plan, I stumbled upon Biomedical Engineering (BME), which fascinated me with its potential. It quickly became my secondary option. Interestingly, everyone I spoke to knew nothing about it. Guess who would jump at any opportunity to give a lecture about this mystery degree? Me!

Side note: My high school biology teacher mentioned that neurons (nerve cells), once damaged, could never be repaired, but he also said that they functioned like wires. I thought to myself, if I merged this pathological accident and the BME I had read about, then I could replace damaged nerves with wires (some day). I ran with this new, uninformed career goal.

Fun fact: I didn&apost get into medical school, but I did get into the BME program. I quickly realised that technical drawing (a requisite course for all engineering freshers) was definitely not going to equip me to fix Neurons, and that the only viable role for BM E graduates in my country was clinical engineering (maintenance and installation of medical equipment - or so I thought). Clinical engineering wasn’t something I wanted to try, so I needed an escape!

Programming looked interesting, but also difficult and meant for very smart people. However, I gave it a shot during covid. PyLadies Ghana was organising a data science boot camp, and I decided to try.

[Heads up: My undergraduate degree had programming courses like Introduction to C and Object-Oriented Programming with Java( I had collaborated with people on some projects then), but for some reason, I couldn&apost get my brain to enjoy it…]

The Real Reason you’re here

During the boot camp, some of the participants were absorbed into the national online community of Python Ghana because more resources and opportunities were being shared there. It turned out:  I was looking for an escape without any destination. Members of the community seemed very vibrant; there was always a job opening up for grabs, a new free online course or banter on trendy tech topics. My main struggle was finding a niche to belong; what was in tech for me?

My interest in health never waned, so you would usually see me reposting information on female health, breast cancer, etc. The PyLadies Ghana Lead, at that time, Abigail Mesrenyame Dogbe noticed it and in October (Breast Cancer Awareness month) she tasked me to help organise a session for the members of PyLadies Ghana. I moderated the session and it was very successful. My very first visible interaction with the community!


Abigail asked if I wanted to keep contributing to the Communications team( the comms team is the main organising force of PyLadies Ghana ) or default to being just a member. I opted for the former. In my eyes, this was a big deal; being asked to stay on the team meant a ton, It was a validation of a certain value I had to offer. I made mistakes, I created terrible designs, and I missed deadlines, but I also learned a lot. I learned how to use tools like Canva, schedule virtual calls,  MS Office tools (Excel, Docs), write official emails, organise events, etc. I was helping with social media engagements, and I didn&apost even have a vibrant social media presence. I was recommended to help with Public Relations (PR) and social media for a connected tech community(Ghana Data Science Summit-IndabaX Ghana) that organises annual data science conferences.

Two years later, I got the opportunity to mentor ladies in the very bootcamp that led me into the community. The ripple effects of my involvement with PyLadies Ghana are diverse, ranging from giving a lightning talk to speaking to young girls about STEM, to helping organise Django Girls at PyCon Ghana 2022, and more…

altSTEM outreach for teenage girls on International Women&aposs Day 2023

Unknown to everyone, I had contemplated brushing the study of data science under the carpet as a ‘failed project’ and moving on to something else. Staying committed to the community, watching the members, and participating in events encouraged me to keep trying. I attended conferences, met and saw women who had achieved great things in data science and machine learning, which meant that I could also, through their stories, find a plan to help me get close to what they had done.

I was always fascinated by their work conversations because wow, these women work in tech?! Some community members had secured scholarships and were pursuing higher STEM degrees abroad while others worked for top tech companies.

After covid, my plan for life after school was to either hone my programming skills and get a good job in a Ghanaian tech company and/or find graduate programs that would enable me to work on my Neurons(of course I had developed other interests). I got into a specialised data science and analytics fellowship with Blossom Academy (more about the training here), landed my first tech role through it, and later began my master’s degree.

altThe Intro slide of the Data science bootcamp I mentored at!

The threads that sustained me in tech were the people, the conversations, and the inclusive atmosphere the Ghanaian community created for people with different personalities to thrive. My journey in STEM can be traced back to that pivotal moment in 2020 when I was offered the opportunity to belong and I seized it!

May 14, 2024 10:20 AM UTC

Doug Hellmann

sphinxcontrib-sqltable 2.1.1 - db cursor fix

What’s new in 2.1.1? Access cursor before closing the db connection (contributions by dopas21) use the theme for docs

May 14, 2024 08:11 AM UTC

Python Bytes

#383 Why aren’t devs shipping faster?

<strong>Topics covered in this episode:</strong><br> <ul> <li><a href=""><strong>I asked 100 devs why they aren’t shipping faster. Here’s what I learned</strong></a></li> <li><a href=""><strong>Python 3.13.0 beta 1 released</strong></a></li> <li><a href=""><strong>A theme editor for JupyterLab</strong></a></li> <li><a href=""><strong>rich-argparse</strong></a></li> <li><strong>Extras</strong></li> <li><strong>Joke</strong></li> </ul><a href='' style='font-weight: bold;'data-umami-event="Livestream-Past" data-umami-event-episode="383">Watch on YouTube</a><br> <p><strong>About the show</strong></p> <p>Sponsored by Mailtrap: <a href=""><strong></strong></a></p> <p><strong>Connect with the hosts</strong></p> <ul> <li>Michael: <a href=""><strong></strong></a></li> <li>Brian: <a href=""><strong></strong></a></li> <li>Show: <a href=""><strong></strong></a></li> </ul> <p>Join us on YouTube at <a href=""><strong></strong></a> to be part of the audience. Usually Tuesdays at 10am PT. Older video versions available there too.</p> <p>Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to <a href="">our friends of the show list</a>, we'll never share it.</p> <p><strong>Michael #1:</strong> <a href=""><strong>I asked 100 devs why they aren’t shipping faster. Here’s what I learned</strong></a></p> <ul> <li>by Daksh Gupta (via PyCoders)</li> <li>What’s stopping you from shipping faster? <ul> <li>Dependency bugs </li> <li>Complicated codebase <ul> <li>><em>There is so much undocumented in our service, including poor records of new features, nonexistent or outdated info on our dependencies, or even essential things like best practices for testing, a lot of time is wasted in syncs trying to find the right information</em></li> </ul></li> </ul></li> <li>QA Loops</li> <li>Waiting for spec <ul> <li><em>&gt; At Amazon? Meetings, approval, talking to 10 different stakeholders because changing the color of a button affects 15 micro services</em></li> </ul></li> <li>Writing tests</li> <li>Deployment/build speed</li> <li>Scope creep <ul> <li>> <em>The human tendency to stuff last-minute items into the crevices of their luggage minutes before leaving for the airport manifests itself at software companies as scope creep.</em></li> </ul></li> <li>Unclear requirements</li> <li>Excessive meetings</li> <li>Motivation <ul> <li><em>&gt;honest answer is i was on ads</em></li> <li><em>&gt;and that’s a very old / complicated / large stack</em> <em>(edited)</em></li> <li><em>&gt;and i didn’t understand it</em></li> <li><em>&gt;my friends on younger teams seemed happier, i was miserable</em></li> </ul></li> <li><a href="">DORA metrics</a></li> </ul> <p><strong>Brian #2:</strong> <a href=""><strong>Python 3.13.0 beta 1 released</strong></a></p> <ul> <li>"Python 3.13 is still in development. This release, 3.13.0b1, is the first of four beta release previews of 3.13.”</li> <li>New REPL, featuring multi-line editing, color support, colorized exception tracebacks</li> <li>Cool GIL, JIT, and GC features</li> <li>Typing changes, including typing.TypeIs . <ul> <li>See last weeks episode and <a href=""><strong>TypeIs does what I thought TypeGuard would do in Python</strong></a></li> </ul></li> <li>Some nice dead battery removals</li> <li>and more</li> <li>But seriously, the REPL is cool. Just ask Trey <ul> <li><a href="">The new REPL in Python 3.13</a> - Trey Hunner</li> </ul></li> </ul> <p><strong>Michael #3:</strong> <a href=""><strong>A theme editor for JupyterLab</strong></a></p> <ul> <li>by Florence Haudin</li> <li><strong>A new tool for authoring JupyterLab themes</strong></li> <li>To lower the bar for customizing JupyterLab we created a new tool providing a simple interface for tuning the JupyterLab appearance interactively.</li> <li>See <a href=""><strong>jupyterlab-theme-editor</strong></a> on github</li> </ul> <p><strong>Brian #4:</strong> <a href=""><strong>rich-argparse</strong></a></p> <ul> <li>“Format argparse and optparse help using <a href="">rich</a>.”</li> <li>“<em>rich-argparse</em> improves the look and readability of argparse's help while requiring minimal changes to the code.”</li> <li>They’re not kidding. 2 line code change. <pre><code>from rich_argparse import RichHelpFormatter parser = argparse.ArgumentParser(..., formatter_class=RichHelpFormatter) </code></pre></li> </ul> <p><strong>Extras</strong> </p> <p>Brian:</p> <ul> <li><a href=""><strong>pytest course</strong></a> is now switched to the new platform. <ul> <li>I sent out an email including how to save their spot on the old site and mark that spot complete on the new site.</li> <li>There’s now comments on the course now. Trying that out. If you’ve got a question, just ask in that section. </li> </ul></li> </ul> <p>Michael:</p> <ul> <li>A new Talk Python course: <a href="">Getting Started with NLP and spaCy</a></li> </ul> <p><strong>Joke:</strong> <a href="">Testing holiday</a></p>

May 14, 2024 08:00 AM UTC

May 13, 2024

Tryton News

Release 1.5.0 of python-sql

We are proud to announce the release of the version 1.5.0 of python-sql.

python-sql is a library to write SQL queries in a pythonic way. It is mainly developed for Tryton but it has no external dependencies and is agnostic to any framework or SQL database.

In addition to bug-fixes, this release contains the following improvements:

python-sql is available on PyPI: python-sql 1.5.0.

2 posts - 2 participants

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May 13, 2024 05:05 PM UTC