Lo-Fi Python

Copying a Cell Value to a New Google Sheet With Apps Script

I've been messing around with Apps Script lately and am finding it useful. The below Apps Script was created with a few prompts to Bing Copilot. First, I asked Bing:

Write a Google sheets formula that copies the value in cell B10 to another sheet and also fills another column with today's date

...and a later prompt:

how can i make this script add a new row each time in the target sheet and appending to existing rows?

I refined with some followup prompts to write a script. It copies cell B10's value from a sheet then fills in a row in a different sheet with cell B10's value and today's date.

AI allows me to do things that would have taken more time to figure out before. For example, Bing just tapped the appendRow function to handle the updating of values without overwriting existing data. We don't need to know the specific Apps Script functions to write code in a new language anymore. AI can just fill in those details for us.

Apps Script to Copy a Cell Value and Create New Row with Today's Date

In this example, one sheet is named "Summary" and the other target sheet is named "Daily" with 2 column names.

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function copyTotalValue() {
  var ss = SpreadsheetApp.getActiveSpreadsheet();
  var sourceSheet = ss.getSheetByName("Summary");
  var targetSheet = ss.getSheetByName("Daily");

  // Get the value from B10 in Summary
  var value = sourceSheet.getRange("B10").getValue();

  // Get today's date
  var today = new Date();

  // Append a new row to the Daily sheet with the value and today's date
  targetSheet.appendRow([value, today]);
}

According to Bing, Apps Script is a JavaScript dialect:

Google Apps Script is based on JavaScript. It uses a subset of JavaScript and provides additional built-in functions...

Automate The Script From the Apps Script Jobs Dashboard

Go to the Apps Script Jobs dashboard.

Add a new script to run Apps Script.

Click the blue "Add Trigger" button to create a new job.

Supplementary Reading

Apps Script Reference Overview

Apps Script API

Apps Script Spreadsheet Service

Importing and Analyzing Data With Google Sheets Functions

This post focuses on Google Sheets formulas, rather than Python. If you're interested in accessing Google Sheets with Python, check out this post I wrote about the pygsheets library. I haven't personally tried it, but xlwings also looks like a decent option if you're looking for Google Sheets Python libraries.

Here are some functions I recently discovered to analyze data in Sheets. There a few different strategies you can use to import data into Google Sheets with the functions available. Below, you'll see some different approaches you use to get data into Google Sheets and then analyze it. The Google Sheets formulas rabbit hole is deep. Lots of power can be harnessed by getting familiar with the formulas it has built-in.

Year To Date SPARKLINE of a Stock

=SPARKLINE(GOOGLEFINANCE("Nasdaq:NVDA", "price", DATE(YEAR(TODAY()), 1, 1), TODAY(), "daily"), {"charttype", "line"; "linewidth", 2; "color", "green"})

Year to Date SPARKLINE of USD to MXN Currency Value with GOOGLEFINANCE + SPARKLINE USING IF to Dynamically Color the SPARKLINE

=SPARKLINE(GOOGLEFINANCE("CURRENCY:USDMXN", "price", TODAY()-365, TODAY(), "DAILY"), {"charttype", "line"; "linewidth", 2; "color", if(A2>0,"green","red")})

Above, we use an if condition to check another cell and set the color to green if > 0 or red if < 0.

Market Cap with GOOOGLEFINANCE

=GOOGLEFINANCE("Nasdaq:NVDA", "marketcap")

Price/Earnings Ratio

=GOOGLEFINANCE("Nasdaq:MSFT", "pe")

Daily % Change of a Stock

=GOOGLEFINANCE("Nasdaq:TSLA","changepct") &"%"

Import the Price of ADA Cryptocurrency with IMPORTDATA

=IMPORTDATA("https://cryptoprices.cc/ADA")

This function imports the price of Cardano cryptocurrency from cryptoprices.cc.

Import the Market Cap of ADA Cryptocurrency with IMPORTDATA

=IMPORTDATA("https://cryptoprices.cc/ADA/MCAP")

This function imports the current market cap of Cardano cryptocurrency.

Import the Daily % Change of Ethereum Cryptocurrency with IMPORTXML and INDEX

=TEXT(
 IF(
     IMPORTXML("https://coinmarketcap.com/currencies/ethereum/", "//p/@data-change") = "down",
     "-" & INDEX(IMPORTXML("https://coinmarketcap.com/currencies/ethereum/", "//p[@data-change]"), 1, 2),
     INDEX(IMPORTXML("https://coinmarketcap.com/currencies/ethereum/", "//p[@data-change]"), 1, 2)
 ),
 "0.0%"
)

This method uses IMPORTXML to import data to Google Sheets by passing an "XPath query". The first line checks if the direction of the % change is "down". If it is down, then we know the % change is negative, otherwise the % change is positive. To select the HTML element, I right-clicked the number on the page that I wanted to import on coinmarketcap and selected "Inspect" to reference the HTML class names for the paragraph I was targeting.

Conditionally sum a range with SUMIFS, SUMIF and COUNTIF

=SUMIFS('sheet_name'!G:G, 'sheet_name'!N:N, ">0")

In the following examples, "sheet_name" = Your Google sheet's name. This function sums the corresponding cells in column N if column G contains a number greater than 0.

=SUMIF('sheet_name'!N:N, "Some Value",'sheet_name'!G:G)

This function sums all values in column G if the values in column N are equal to "Some Value".

=COUNTIF('sheet_name'!A:A, "Some Value")

Count all the cells in column A that equal "Some Value".

Select Columns from a Dataset

{ 'sheet_name'!A:D, 'sheet_name'!T:X, 'sheet_name'!Z:AA}

Google Sheets recognizes this format of bracket enclosed ranges of columns to select into your dataset. In the next example, you can see this applied.

Conditionally Select a Range of Cells from a Dataset with SORTN, FILTER, and REGEXMATCH

=SORTN(FILTER({'sheet_name'!A:A, 'sheet_name'!E:E, 'sheet_name'!P:P/1,'sheet_name'!F:F}, REGEXMATCH('sheet_name'!P:P, "-")), 5, 0, 3, TRUE)

This formula constructs a dataset, then filters it on a condition using REGEXMATCH to check if the cell contains a hyphen (-). 5 specifies how many rows to return, and 3 specifies which column to sort on, in this case the 3rd column.

Conditionally Select a Range of Cells from a Dataset with SORTN and QUERY with SQL-Like Language

=SORTN(QUERY({'sheet_name'!A:A, 'sheet_name'!E:E, 'sheet_name'!P:P,'sheet_name'!F:F}, "SELECT * WHERE Col3 IS NOT NULL"), 6, 0, 3, FALSE)

This queries rows that are not null, containing data.

Official Function Documentation

Google Sheets Function List

GOOGLEFINANCE

IMPORTDATA

IMPORTXML

SPARKLINE

FILTER

SUMIFS

QUERY

SORTN

INDEX

REGEXMATCH

There is Background Noise in Real Life

While walking down a busy avenue in Mexico, I listened to a conversation in Spanish. I'm not fluent in Spanish, but I have 4 years of high school classes and ~2 more years of real world experience. Spanish is not spoke in a bubble. It needs to be practiced to be learned. I put much effort into listening. There is much more I have to learn. Typically, I try to compensate for some lack of skills with intense focus. Sometimes I know what is said and more often I am closer to guessing.

On this walk down the street, I attempted to listen to my friends speak. In the back of my mind, I was frustrated because the noise of the cars going past us and a city in motion made it near impossible to hear the words. Similarly, attempting to listen in a bar, party or anywhere playing music is very tough.

I used to think, "if only it was more quiet!" while walking and straining to hear what my friends said over the noise. Today I had an epiphany: there's always background noise.

A difference between textbooks and real life: there's usually noise in the background.

We should embrace practicing and growing our skills because there will always be "noise". This is a tidy metaphor for distraction and our focus bandwidth. In the lens of coding, the "noise" might be skill deficiencies and lack of understanding, other co-workers, unimportant projects, meetings, social media, games and anything that misdirects us away from our primary goals.

It's easy to get annoyed when there is excessive noise. It makes it way harder to hear what is being said. Nonetheless, I don't have to let the noise distract my effort and intention to learn. So it is with programming and all skills. There will always be "noise". Embrace the noise and lean heavily on your intention to practice.

Image Generated with Bing Image Creator

How to Share a Google Calendar Event URL

You can easily share a Google Calendar event if you know the url syntax Google uses. When the url is opened in a browser, it prompts the person you want to share an event with to save it to their calendar.

Demonstrating Google Calendar URL Arguments in Python

By simply knowing the proper url arguments, you can enable people to quickly save a Google Calendar event. This example uses the Python standard library: urllib to format the Google calendar url parameters and webbrowser to open the url in a browser. This is a handy little trick to keep in your back pocket!

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from urllib.parse import urlencode
import webbrowser

def new_google_calendar_event():
   """
   Pass an event to Google Calendar with url arguments.

   Base URL: https://calendar.google.com/calendar/render

   URL Arguments:

   action: TEMPLATE
   text: Event Title
   dates: start_datetime/end_datetime
   details: event description or link to more info
   location: url to webcast, call or physical location name
   ctz: set the time zone by name, ex: America/New_York
   recur: set a recurring event, ex: RRULE:FREQ%3DWEEKLY;INTERVAL%3D3
   crm: if Free, Busy, or Out of Office, ex: AVAILABLE, BUSY or BLOCKING
   add: add a list of guests by email, ex: [email protected],[email protected]
   """
   parameters = {
      "action": "TEMPLATE",
      "text": "Title of Event",
      "dates": "20240504T123000Z/20240504T133000Z",
      "details": "Event Details: https://lofipython.com",
      "location": "link to webcast, call or physical location",
      "ctz": "America/Chicago",
      "crm": "BUSY"
   }
   # Returns str of URL encoded parameters.
   url_parameters = urlencode(parameters)
   url = f"https://calendar.google.com/calendar/render?{url_parameters}"
   print(url)
   return url

url = new_google_calendar_event()
webbrowser.open_new(url)

I was struggling to find any official documentation, so I figured Google's Gemini AI model might know where this is documented.

Using the app on my phone, Gemini informed me of a useful Google Calendar Help thread response from Neil@GCalTools.

The official documentation says to use "https://calendar.google.com/calendar/render" instead of "https://calendar.google.com/calendar/event", but they both work, at least for now.

- Neil@GCalTools, Google Calendar Help

Relevant Links

Read more documentation of possible url arguments in the add-event-to-calendar-docs Github repo.

Wikipedia Time Zone List

Recurrence Rule Syntax

Kalinka Google Calendar Link Generator

Troubleshooting Tracebacks to Resurrect a Python Web2py App

Back in 2016, I built a web2py app as my first web application. It accepts a prompt from the user and primitively attempts to match that text to a line of Kevin Parker's lyrics written for the band Tame Impala.

I didn't look at the app's code for many years. I just needed to log into PythonAnywhere every 3 months and hit a button to keep it running on their free plan. Until I decided to update it recently.

Tame Impala released a new album and lots of additional tracks to import to the app. Just need to update the database with new music. No big deal, right?

Part of the problem with updating the app was that I didn't remember where the important code like the controller default.py and relevant HTML files were after not seeing it for 5+ years. It took some time to remember the folder structure of a web2py project. Since it was my first project ever, documentation was nonexistent. I could have saved myself a lot of grief if I'd wrote down some notes when I made the app.

web2py is relatively easy to grasp for Python developers. One thing I like is that once it is installed, the development server is easily started by running the web2py.py file:

cd web2py
python3.10 web2py.py

web2py Python Errors Solved

I installed web2py locally with the help of a DigitalOcean blog post. After I failed to push a new version of the app to production, for some reasons it was in a broken state. Python version issues surfaced, requiring some heady navigation. Enjoy these gritty details of the tracebacks that transpired.

ModuleNotFoundError: No module named 'formatter'

This error showed up in my app's WSGI error logs. Initially, I researched and attempted to install the formatter module. I believe this was caused by attempting to run Python code compiled to a .w2p file on Python 3.11 on a Python 3.10 interpreter. However, I didn't know how to solve it until after I saw the next error.

SystemError: compiled code is incompatible

After reading this error, I consulted Bing about it. One of the options that Bing suggested was that my Python code had incompatible versions. This was caused by a mismatch between my development and production Python versions.

Installing Python 3.10 in Development Environment

Originally, I compiled the updated web2py app in Python 3.11 on my Chromebook. My PythonAnywhere environment was running Python 3.10. Therefore, I need to build the development code in Python 3.10 to match the production environment on PythonAnywhere. I entered a handful of commands from Bing Copilot to build Python 3.10 on my Ubuntu development environment:

sudo apt-get install build-essential
sudo apt-get install zlib1g-dev
sudo apt-get install libsqlite3-dev
wget https://www.python.org/ftp/python/3.10.5/Python-3.10.5.tgz
tar zxvf Python-3.10.5.tgz
cd Python-3.10.5
./configure --enable-optimizations --enable-loadable-sqlite-extensions
make && sudo make install

The lesson I took away is to consider your production environment's Python version before you begin working on a project. In most cases, you'll want to match that version in your development environment to avoid errors like this.

After compiling the new development Python 3.10 version, I exported the app to a new .w2p file. Next, I imported the .w2p file containing the updated app to PythonAnywhere in the admin interface app importer.

After syncing my development and production environment versions, the app showed a different error. Progress!

Since I was using a .w2p file from 5+ years ago, it contained old Python web2py code written in earlier Python versions with a few more bugs. Despite these version inconveniences, I was happy to see the "compiled code is incompatible" and "formatter module missing" errors stopped. One problem solved, two more discovered in its wake, am I right?

SyntaxError: multiple exception types must be parenthesized

This error showed up in my appadmin.py. At some point this unparenthesized syntax was phased out of Python. The fix is add parentheses to the exception statements:

Incorrect

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except Exception, e:

Correct

1

except (Exception, e):

unable to parse csv file: iterator should return strings, not bytes (the file should be opened in text mode)

In order to import the new Tame Impala songs to the SQLlite database, web2py provides a GUI interface in its admin panel or the DAL (Database Abstraction Layer). I chose to use the GUI. In the GUI, you can either manually enter each song or use its csv import widget. To save time, I imported via the csv widget. However, this error slowed me down. It stemmed from the need for TextIOWrapper to convert the csv data to a required format.

The solution I found was to use the fix suggested by AnooshaAviligonda. In web2py/gluon/packages/dal/pydal/objects.py, I swapped in this code:

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csv_reader = csv.reader(TextIOWrapper(utf8_data,encoding), dialect=dialect, **kwargs)

After adding the above code to my web2py app's objects.py file, the csv importer completed my new Tame Impala songs database import. Also, I was able to export an app from my development environment and deploy it into PythonAnywhere via the admin interface. Mission accomplished.

I imported the new songs to my app and brought the code forward into future Python versions. Keeping up with this project over the years shows how maintaining an app across different Python versions can cause unexpected challenges. With these Python tracebacks conquered, the app is back on the web. Now with all of Tame Impala's new lyrics!

Check out my Tame Impala web2py app here:

tameimpala.pythonanywhere.com/tameimpala

Run Python in Your HTML Via a Custom Django Template Tags Filter

This post shows how to set up a custom Django template tag filter. With help from Django's load built-in, you can execute a Python function from your app's HTML. In this example, the function returns the quotient, or result of dividing two numbers. Mathematics and Python for the win!

I worked some of this out with help from Bing and following along with the Django custom template tags documentation.

Install Django Python Library

pip install Django

Create templatetags.py

I created a "templatetags" folder in the app folder and placed templatetags.py within it. The Django docs recommend your app's folder. Below, the @register.filter decorator registers the divide function so Django knows it exists.

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from django import template

register = template.Library()

@register.filter
def divide(value, divisor):
   """A Django filter that accepts 2 arguments:
   1. value, number to be divided
   2. divisor, number to divide by

   Returns the quotient in hundredths decimal format.
   """
   # Check if the argument is zero to avoid division by zero error.
   if divisor == 0:
      return None
   quotient = value / divisor
   return f"{quotient:.2f}"

Edit Your HTML Code to Call the Divide Function

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{% extends 'base.html' %}
{% block content %}
{% load templatetags %}
<div class="container" style="display: inline-block; inline-size: 90%; block-size: auto; writing-mode: horizontal-tb;">
{% for hotel in hotels %}
<div class="row row-cols-3" style="display: flex; justify-content: flex-end; padding: 10px; margin: 10px; background-color: #f0ffff; box-shadow: 5px 5px 10px gray; border-radius: 10px;">
   <div class="col">
   <h5 style="color: green;">Peso: ${{ hotel.price }}</h5>
   <h5 style="color: gray;">USD: ${{ hotel.price|divide:18.9 }}</h5>
   </div></div>
{% endfor %}
</div>
{% endblock %}

In the HTML, call the divide function by loading the templatetags module and then passing two numeric arguments:

{{ hotel.price|divide:18.9 }}

1. A number to be divided, here the hotel price from a "Hotel" DB model
2. The function name to call and a number to divide by. Here we use "divide:18.9" to approximately convert Mexican pesos to US dollars. Recently, the exchange rate fluctuates in the 17-19 pesos per dollar range.

Basic Hotel Model Example

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from django.db import models

class Hotel(models.Model):
   name = models.CharField(max_length=200)
   price = models.DecimalField(max_digits=10, decimal_places=2)
   address = models.CharField(max_length=200)
   city = models.CharField(max_length=200)
   all_inclusive = models.BooleanField()
   photo = models.ImageField(upload_to="media")

Register Model in admin.py

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from django.contrib import admin
from .models import Hotel

admin.site.register(Hotel)

I was pleased to be able to make some on the fly mathematic calculations in my HTML with a custom Django filter!

Find Broken Links in Your .rst Documents With rst-url-validator

The posts on this blog are written in reStructuredText. I recently had an idea to write a Python script to check my .rst document url links for broken tags or urls that are not valid. When I'm using Pelican, it hints when a url tag is malformed and gives a line number. However, it can still be difficult to track down a problematic link when there's an issue in your document.

So I did what any other programmer probably does in 2024, opened up an AI assistant. Bing quickly generated the structure of the Python script from my prompt, but like usual it required some tinkering and refinement to make it work. You can find the complete Python script in the rst-url-validator Github repo.

Bing was also valuable for helping me modify the regex module code for rst-url-validator. It is typically very confusing to reason about regex, but with AI assistance I can just ask it for regex that does something and the AI can generate the code. Coding is getting way easier than it used to be thanks to these new advances in large language models.

Install Python Library Dependencies

The script uses requests to send an HTTP request to the url and rich to print color-coded output to the terminal screen.

pip install requests
pip install rich

Clone The Github Repo

git clone git@github.com:erickbytes/rst-url-validator.git
cd rst-url-validator

Run The CLI Command

python rst-url-validator.py your_file.rst

It parses each url tag, checks for required characters and sends a HEAD request to the url to check if it loads successfully. Some websites return status codes like 403 (permission denied), 301 (redirect), or 404 (page not found). If the status code returned is 200, it's safe to say the page loaded. Be aware that some sites like Cloudflare (returns 403) and Amazon (returns 503) do not serve any requests and just return an error. In cases like these, you'll need to check pages like this manually in a browser to see if the page loads.

This script helped me find and fix or remove a bunch of broken links on the older posts on this blog. If you're working with .rst documents, give it a try!

Fixing INSTALL_IMAGE_LOADER_TIMED_OUT Error 52 on Crostini Linux Terminal for Chromebook

When booting up the Ubuntu shell on my Chromebook, it usually just works. However, After I updated to a new version of Chromebook OS, I was getting this error:

vmshell failed:

Error starting crostini for terminal:

52 (INSTALL_IMAGE_LOADER_TIMED_OUT)

First, I restarted my Chromebook but the error persisted after restarting. So I turned to Bing Copilot:

source: Bing

Open Crosh: ctrl + alt + T

Stop The Termina Container in Crosh

vmc stop termina

Start The Termina Container in Crosh

vmc start termina

The vmc command is used to manage Linux containers from Crosh. After I entered these two vmc commands, the error was resolved. My Crostini Linux shell is functional again!

Passing the Webmention Rocks! Webmention Update Test With curl and Setting Up a Webmention.io Endpoint

After discovering Webmentions via a helpful blog post about sending webmentions, I wondered how I might be able to achieve sending and receiving them from my Pelican blog. I discovered the Webmention Rocks! website and the Webmention Protocol. Webmentions are a standard for sending notification of linkbacks, likes, comments and pingbacks via HTTP. For example, if your blog is hosted on Wordpress these things are likely all set up for you. Supporting this recommended standard requires a more creative approach on a static site.

Naturally, I'm now thinking about how I will automate this on my Pelican blog. There are also existing Python modules like ronkyuu and the indieweb-utils modules for supporting the Webmention protocol in Python. However, a static site generator presents challenges for automatically executing code to send webmentions. Another option may be to use something like Cloudflare Workers since this blog is hosted on Cloudflare's free plan. Possibly, I could set a worker to trigger and run some javascript everytime I add a new post. I'm thinking using an existing pelican plugin would likely be easier than that.

Thankfully, there are some existing Pelican plugins to enable webmentions. I'm currently testing the pelican-webmention plugin but have not yet verified if it is actually sending the webmentions. Alternatively, the linkbacks plugin is an option for supporting Webmentions on a Pelican blog. Bridgy is another tool written in Python as a "bridge" for social networks to webmentions. There are a lot of interesting options for piecing together a Webmention implementation, which is essentially automating an HTTP request you send when you link to someone else from your website.

In the interim until an automated solution is found I decided to attempt passing the Webmention Rocks! Update test with curl. Often I find when HTTP requests are required, I can better understand it by manually making the requests with curl or Python. Once I have a better grasp after succeeding with curl, it's a little easier to grasp automating the sending of the HTTP requests with Python or other means.

Completing the Webmention Rocks! Update Test #1 With curl

Add a URL Link to Your Blog HTML, AKA The "Webmention"

<a href="https://webmention.rocks/update/1">Part 1 Test</a>

Check Target HTML for Webmention Endpoint with Curl

curl -i -s $target | grep 'rel="webmention"'

Alternative Browser Option: "View Page Source" to Find Webmention Endpoint

Go to the page you want to check for a Webmention endpoint. Right-click anywhere on the page and select "View Page Source" to view the website's HTML. Then, right-click the endpoint url and select "Copy Link Address" to copy the full url of the endpoint.

Send a curl Request Notify the Target Site of Webmention Update

curl -X POST -H "Content-Type: application/x-www-form-urlencoded" -d "source=https://yourblog.com/example-post&target=https://webmention.rocks/update/1" https://webmention.rocks/update/1/part/1/webmention?key=UjJPJoDWZateFb7bTAhB -v

In the curl request, edit the "source" with your blog post containing the link and "target" with the target Webmention endpoint. You'll need to change the "key" url argument. The Webmention Rocks! endpoint changes the live key rapidly, about every 30 seconds. In curl, you can pass the -v argument for more verbose output.

Add URL Link to HTML for Part 2 of the Test

<a href="https://webmention.rocks/update/1/part/2">Part 2 Test</a>

Complete Part 2 of the Test with curl

curl -X POST -H "Content-Type: application/x-www-form-urlencoded" -d "source=https://yourblog.com/example-post&target=https://webmention.rocks/update/1" https://webmention.rocks/update/1/part/2/webmention?key=dfMuwOn4DUuwRSe6BM9o -v

Webmention Update Test Succeeded Confirmation

Check for a Webmention Endpoint and Send the Request in a Bash One-Liner

curl -i -d "source=$your_url&target=$target_url" `curl -i -s $target_url | grep 'rel="http://webmention.org/"' | sed 's/rel="webmention"//' | grep -o -E 'https?://[^ ">]+' | sort | uniq`

source: https://indieweb.org/webmention-implementation-guide

Setting Up Your Blog's Webmentions Endpoint With webmention.io

Webmention.io is a free service to set up your own Webmention endpoint so other people can send you Webmentions. I chose to authenticate with Github. There are also options to authenticate via email and other ways. If you choose to authenticate with Github, make sure the url of your website is in your Github profile.

Add Github HTML Link to Your Website

First, add the Github HTML link to your website identify yourself to webmention.io.

<link href="https://github.com/your_username" rel="me">

Go to Webmention.io to Authorize Indie Login to Your Github Account

Once you successfully connect your Github account to webmention.io, you can copy your HTML code from the webmention.io dashboard to your website HTML:

<link rel="webmention" href="https://webmention.io/yourblog.com/webmention" />

With an active endpoint linked in your website HTML, you're able to receive webmentions from the Webmention.io dashboard or with curl.

View Webmentions for Your Blog with curl

curl -X GET https://webmention.io/api/mentions.jf2?target=https://exampleblog.com

Happy webmentioning!

Read More About Webmentions

Webmention Wiki

Webmention.io Github

Webmention.Rocks

Sending Your First Webmention Guide

Make Your Python Installs Faster With uv

For several years, pip and pip-tools have become distinguished in Python packaging for their usability and ubiquity. Recently there has been some interesting new developments in the realm of Python packaging tools. In a trend that started around 2022, there has been an ongoing "Rustification" of Python tooling.

uv is designed as a drop-in replacement for pip and pip-tools, and is ready for production use today in projects built around those workflows.

- Charlie Marsh, "uv: Python Packaging in Rust", https://astral.sh/blog/uv

First, Rye was released in pursuit of a "cargo for Python". Cargo is Rust's package manager. It seems to have inspired Python developers to keep trying to improve on what we have with pip.

While this was happening, in secret Astral Software and Charlie Marsh were also working on yet another hybrid Rust + Python package manager named uv. There's seemingly no end to this man and the Astral team's projects! ruff quickly supplanted the incumbent Python linters to become a favorite among Python developers. Could lightning strike twice for the creators of ruff? Seems they won't be a one-hit wonder when it comes to developing hit Python packages.

Improving Python packaging is an audacious and challenging task. Part of the problem is that out of the box Python installs can be tough to reason about for new Python developers, not to mention the hassle of explaining the purpose of virtual environments in Python coding via venv. One perk of uv is that it includes virtual environments in its workflow.

uv is 8-10x faster than pip and pip-tools without caching, and 80-115x faster when running with a warm cache

- Charlie Marsh, "uv: Python Packaging in Rust", https://astral.sh/blog/uv

A new space of potential optimization is now accessible to Python developers. We can now use uv to make our development environment build faster. A modest 8x speedup in Python library installs might shave off a shocking amount of time it takes your freshly minted Docker image to build, especially if you have lots of Python library dependencies. Now, imagine an 80-115x speedup with caching. Docker images also use caching after an image is built the first time. They are an optimization use case along with building your development environment in general. In some development shops, this could cut a lot of time installing developer tooling. It's a potential incredible improvement we can now make with uv!

In the case of Rye and uv, two developers simultaneously identified the same opportunity and are now combining their efforts. Sounds like a win for all Python developers. Armin Ronacher, the creator of the Flask web framework and Charlie Marsch with the proven success of ruff are converging to tackle one of Python's biggest pain points. They could be merged into a "cargo for Python" super tool eventually:

Will Rye be retired for uv? Not today, but the desire is that these tools eventually converge into one.

- Armin Ronacher, "Rye Grows with uv", https://lucumr.pocoo.org/2024/2/15/rye-grows-with-uv/

Per Armin's recent blog post, Rye is probably not the final solution. He thinks Rye will get absorbed into a more fleshed out project like uv. It seems Python packaging will continue evolving and improving, a welcome sight for Pythonistas!

Image Source: "uv: Python Packaging in Rust", https://astral.sh/blog/uv

Install uv and rye

pip install uv
pip install rye

# Alternative install for uv with curl
curl -LsSf https://astral.sh/uv/install.sh | sh
# Alternative Install for rye on Linux and Mac
curl -sSf https://rye-up.com/get | bash

Create a Virtual Environment With uv

uv venv  # Create a virtual environment at .venv.
# Activate venv on macOS and Linux.
source .venv/bin/activate

Installing a New Module With uv

uv pip install requests

pip sync a requirements.txt file with uv

uv pip sync requirements.txt  # Install from a requirements.txt file.

Optional: Configure Rye on Top of uv

rye config --set-bool behavior.use-uv=true

Create a New Python project With Rye

rye init my-project
rye pin 3.10
rye add black
rye sync
rye run black

uv and rye Documentation and Blog Links

uv: Python Packaging in Rust

uv Github Repo

Rye Grows with uv

Rye User Guide