The hottest Python Substack posts right now

And their main takeaways
Top Technology Topics
MOHAMMED JAMAL 204 HN points 12 May 24
  1. Lisp is a programming language known for treating code and data as interchangeable, a concept called homoiconicity, allowing for unique expression within the language.
  2. By translating Lisp expressions into Python constructs like function calls and lists, programmers can maintain the essence of Lisp in a more familiar syntax.
  3. Incorporating features like lambdas in Python, inspired by Lisp, provides the ability to define and call functions recursively, essential for creating Turing complete languages.
Bite code! 1467 implied HN points 03 Mar 24
  1. Redis is a powerful companion for Python, used for caching, sharing states, and creating queues.
  2. Redis is easy to use and highly efficient, widely employed in the industry for tasks like caching and inter-process communication.
  3. With basic features like lists, hashes, and sets, Redis has vast utility in tasks such as caches, queues, and inter-process communication.
Data at Depth 59 implied HN points 13 May 24
  1. GPT-4 can be useful for generating data cleaning and visualization code in Python when combined with libraries like pandas and plotly
  2. Using GPT-4, you can learn how to clean datasets, create choropleth maps, and even animated choropleth maps to visualize data over time
  3. Interactive geospatial data visualizations that tell stories over time can be quickly created with Plotly by using GPT-4 prompts
Data at Depth 39 implied HN points 16 May 24
  1. The author shares insights on their data analysis for the past 2 weeks, highlighting significant growth on Substack, experiences on Medium and LinkedIn, and struggles with Twitter-X.
  2. The author emphasizes the importance of taking time to read and detach from the pressure of creating content, as well as the value of ownership and direct engagement through Substack newsletters.
  3. A tutorial is provided on creating interactive Python Plotly dashboards for data visualizations, specifically focusing on a bubble map and bar chart to showcase data on global undernourishment.
Ron Friedhaber 3 HN points 26 May 24
  1. Math notation focuses on simplification, not optimization, unlike in computer programming where efficiency is crucial.
  2. In math, statements are mostly immutable and remain so until proven true, contrasting with programs that are mutable to accommodate bugs and user requests.
  3. Python initially succeeded with dynamic typing for prototyping but has gradually shifted towards typed Python, reflecting a broader trend in the language's evolution.
Get a weekly roundup of the best Substack posts, by hacker news affinity:
Bite code! 1100 implied HN points 28 Feb 24
  1. Astral released a new Python package manager called uv, which aims to replace existing package and virtual env managers, with smartly integrated features and community contributions.
  2. Stand Alone Python project by indygreg compiles Python for various platforms, offering archives that can be run without installation, providing a consistent experience across different machines and platforms.
  3. A new lock file proposal by Brett Canon aims to tackle the challenge of pinned dependencies for Python projects, with previous attempts in 2021 and the latest proposal focusing on source distribution support and a new file format.
Bite code! 1467 implied HN points 21 Jan 24
  1. Decorators provide syntactic sugar for code reuse of callback processing.
  2. Popular usages for decorators include intercepting calls, registering functions, and enriching function behavior.
  3. Decorators can be combined and used creatively to enhance your code, but be mindful not to overdo it.
Data at Depth 79 implied HN points 03 May 24
  1. Python Streamlit is great for creating interactive maps from GIS point data, allowing for more engaging data storytelling.
  2. Interactive maps offer a better way to present data compared to static maps, enabling users to interact and explore the information further.
  3. Streamlit is a useful tool for creating interactive maps with user input functionalities, making it ideal for data visualization projects.
Bite code! 1100 implied HN points 15 Jan 24
  1. The splat operator ( ) in Python is used for unpacking function arguments and defining infinite (variadic) parameters.
  2. Differentiate between arguments and parameters in Python; parameters are defined variables in a function signature, while arguments are values passed to those parameters when calling the function.
  3. Positional and keyword arguments in Python allow passing arguments by position or by name in a function call.
Bite code! 856 implied HN points 30 Jan 24
  1. A new Python video game, JOY OF PROGRAMMING, is available on Steam for learning programming interactively.
  2. Pyodide, a Webassembly CPython port, now has experimental support from urllib3, enabling Python to run in the browser.
  3. Numpy 2 is set to release soon, with changes that may impact compatibility, so users should prepare by checking and updating dependencies.
Data at Depth 79 implied HN points 26 Apr 24
  1. In data visualization, choosing the right chart is crucial to effectively communicate complex information in a clear and simple manner.
  2. Starting with techniques like small multiples, heat maps, and stacked area charts can help in learning how to select the right visualization for specific types of data.
  3. Experimenting with different visualization types and customizing them to the audience's needs can lead to impactful data storytelling.
Mostly Python 524 implied HN points 22 Feb 24
  1. When creating a test suite, consider the constraints of your project and think about how to handle testing for non-traditional outputs like images or sound files.
  2. Use pytest to optimize your test suite by utilizing features like parametrization, fixtures, parallel test execution, and custom CLI arguments.
  3. An effective test suite should not only focus on passing tests but also consider failure scenarios, the need for assertions about test setup, and testing across platforms early on.
Data at Depth 79 implied HN points 23 Apr 24
  1. GPT-4 can create choropleth and heatmaps from datasets if you know the right questions to ask
  2. Integrating GPT-4 into data visualization workflows can be beneficial for exploration and learning new libraries such as Python folium
  3. GPT-4 can be used to enhance code generation for data visualization projects by providing responses and solutions to specific coding challenges
Mostly Python 314 implied HN points 25 Jan 24
  1. The post discusses testing a game project, Alien Invasion, which may seem challenging to test at first.
  2. Testing the book's code against different versions of Python is important to identify bugs and incompatibilities.
  3. Using tools like pyenv to switch between Python versions can make testing with different versions easier.
Data at Depth 5 HN points 15 May 24
  1. Creating an interactive Streamlit dashboard can be done step by step with a modular approach, allowing users to select a year, view a global choropleth map, and see a horizontal bar chart of top 10 countries.
  2. By using Python libraries like Streamlit, Pandas, and Plotly Express, you can efficiently build interactive data visualizations for a dashboard project.
  3. Data preprocessing steps, such as filtering, cleaning, and extracting necessary information, are essential before visualizing data on the dashboard using tools like Plotly Express for map and chart creation.
Confessions of a Code Addict 293 HN points 06 Dec 23
  1. Each type in Python implements functions for the operators it supports and populates a function pointer table in its header.
  2. The CPython Virtual Machine calls a function in the abstract object interface based on the operator being executed.
  3. The abstract object interface performs function pointer table lookup in the object headers to call the right function for dynamic dispatch.
Data at Depth 39 implied HN points 01 Apr 24
  1. GPT-4 can be used with simple modular prompts to generate Python code for data cleaning and visualization quickly.
  2. Combining GPT-4 with libraries like Pandas and Plotly enables the creation of interactive and visually appealing visuals rapidly.
  3. Consider subscribing to Data at Depth for more insightful content and to support the author's work.
Data at Depth 79 implied HN points 15 Feb 24
  1. Creating an interactive Python Plotly dashboard can help in deeper storytelling by combining data visuals like bubble charts and horizontal bar charts.
  2. Python's Plotly Dash framework allows developers to easily create web-based applications directly from Python code, without needing additional web development skills.
  3. By using the UN food security dataset, the tutorial demonstrates step-by-step how to load, filter, and visualize data, as well as set up dropdown menus for interactive exploration.
Bite code! 733 implied HN points 09 May 23
  1. PDB is a basic but useful debugger that comes with Python by default, allowing you to pause programs, enter a debugging shell, and analyze program states.
  2. Learning to use PDB is valuable because it helps you understand debugging fundamentals, and knowing PDB can translate to using other debuggers as well.
  3. PDB offers various helpful commands like 'help', 'quit', 'list', 'next', 'continue', 'until line', 'jump line', 'display', 'step', 'return', 'up', and 'down' for effective debugging in Python.
Mostly Python 628 implied HN points 30 Mar 23
  1. Copying a list in Python can lead to unexpected behavior if the items in the list are mutable objects.
  2. To create a true copy of a list with mutable objects, use the deepcopy() function from the copy module.
  3. When working with Python lists, consider the nature of the items in the list to decide between using list[:], list.copy(), or deepcopy().
Data at Depth 19 implied HN points 06 Apr 24
  1. Understanding Python data visualization libraries like Matplotlib, Seaborn, and Plotly can help you create different types of visualizations.
  2. Learning data cleaning and preprocessing techniques with Pandas is crucial to ensure accurate and meaningful visualizations.
  3. Mastering Modular Prompting with tools like ChatGPT can speed up coding tasks by generating code snippets based on specific instructions.
Data at Depth 79 implied HN points 28 Jan 24
  1. The value of data often lies in its comparability - Edward Tufte emphasizes this point in data visualization.
  2. Data visualization helps distill complex information into clear insights, especially with the abundance of data available today.
  3. Comparative analysis using tools like Python Plotly can enhance understanding and interpretation of data sets.
Data at Depth 79 implied HN points 20 Jan 24
  1. OpenAI's GPT-4 has a new tool that can analyze and interpret image data, including complex data visualizations.
  2. The image analysis tool from GPT-4 is capable of performing accurate analysis on intricate data representations.
  3. Consider becoming a subscriber to Data at Depth to get access to more insightful posts and to support the author's work.
Mostly Python 419 implied HN points 14 Mar 23
  1. Programming languages may offer multiple ways to accomplish the same task for different use cases.
  2. Python emphasizes having one clear and obvious way to do things to promote readability and maintainability.
  3. Ending a while loop in Python can be done using the while statement, break statement, or a flag - each with its own unique advantages.
Tim's Tech Things 2 HN points 09 May 24
  1. Creating a healthy sourdough starter involves feeding it with flour and water until it's ready to use in baking, which contributes to the delicious taste and texture of the bread.
  2. Monitoring the rise of sourdough starter is crucial to ensure there are enough active yeast cells to create CO2 bubbles, which make the bread light and fluffy.
  3. Using computer vision with Python, ffmpeg, and algorithms like rolling averages and derivatives can help automate the process of determining when sourdough is ready for baking.
Data Engineering Central 294 implied HN points 10 Apr 23
  1. Airflow has been a dominant tool for data orchestration, but new tools like Prefect and Mage are challenging its reign.
  2. Prefect focuses on using Python for defining tasks and workflows, but may not offer enough differentiation from Airflow.
  3. Mage stands out for its focus on engineering best practices and providing a smoother developer experience, making it a compelling choice over Airflow for scaling up data pipelines.