Hey Everyone, I Know It's Going To Be A Busy Day For A Lot Of People, But Google Enrolled Everyone Over

Database of Databases

Discover and learn about 924 database management systems

Database of Databases

Database of Databases

The on-line encyclopedia of databases systems from Carnegie Mellon University.

This is an interest resource that aims to catalog the different database systems in existence and allows you to search and filter through them:

It also has "leaderboards" for views and uses:

A lot of the information doesn't seem very deep but it does give you a good overview of the features and support in each one of it's entries.

I found it interesting. Perhaps you will find it useful.

Enjoy! 🤓

Dudes shouldn't have to prove themselves by having spartan greyscale homes with dollar store rubber shower curtains and a mattress on the floor. Do you know what life is like with linen

A rescued pelican in the backseat.

there should be a tax that youtubers pay where 1.5% of all of their revenue goes back to Kevin Macleod for basically supplying YouTube with it’s own soundtrack.

It is with the deepest frustrations that I must report Microsoft has pushed out Copilot onto Microsoft Word no matter what your previous settings were. If you have Office because you paid for it/are on a family plan/have a work/school account, you can disable it by going to Options -> click on Copilot -> uncheck 'Enable Copilot'.

(Note, you may not see this option if you haven't updated lately, but Copilot will still pop up. Updating should give you this option. I will kill Microsoft with my bare hands.)

In addition, Google has forced a roll-out of it's Gemini AI on all American accounts of users over 18 (these settings are turned off by default for EU, Japan, Switzerland, and UK, but it doesn't hurt to check).

To remove this garbage, you must go to Manage Workspace smart feature settings for all your Gmail/Drive/Chat and turn them off. Go to Settings -> See all settings -> find under "Genera" the "Google Workspace smart features" -> turn smart feature setting off for both Google Workspace and all other Google products and hit save. (If you turned off the smart settings in your Gmail, it never hurts to open Drive and double-check that they're set to off there too.)

Quick Edit: I found the easiest way to get to the Smart Feature settings following the instructions above was to do it through Drive. Try that route first.

Now is the time to consider switching to Libre Office if you haven't already.

i learned that in August 2014 Women’s Voices for the Earth commissioned testing of four types of Always menstrual pads, manufactured by Procter & Gamble. The certified laboratory STAT Analysis Corporation analyzed the products for volatile organic compound.

“The results of the testing indicate that both scented and unscented Always pads emit toxic chemicals, including chemicals identified by the U.S. Department of Health and Human Services National Toxicology Program, the Agency for Toxic Substances and Disease Registry, and the State of California Environmental Protection Agency as carcinogens, and reproductive and developmental toxins. None of these chemicals are disclosed on the product by the manufacturer.” (x)

Your Essential Guide to Python Libraries for Data Analysis

Here’s an essential guide to some of the most popular Python libraries for data analysis:

 1. Pandas

- Overview: A powerful library for data manipulation and analysis, offering data structures like Series and DataFrames.

- Key Features:

  - Easy handling of missing data

  - Flexible reshaping and pivoting of datasets

  - Label-based slicing, indexing, and subsetting of large datasets

  - Support for reading and writing data in various formats (CSV, Excel, SQL, etc.)

 2. NumPy

- Overview: The foundational package for numerical computing in Python. It provides support for large multi-dimensional arrays and matrices.

- Key Features:

  - Powerful n-dimensional array object

  - Broadcasting functions to perform operations on arrays of different shapes

  - Comprehensive mathematical functions for array operations

 3. Matplotlib

- Overview: A plotting library for creating static, animated, and interactive visualizations in Python.

- Key Features:

  - Extensive range of plots (line, bar, scatter, histogram, etc.)

  - Customization options for fonts, colors, and styles

  - Integration with Jupyter notebooks for inline plotting

 4. Seaborn

- Overview: Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive statistical graphics.

- Key Features:

  - Simplified syntax for complex visualizations

  - Beautiful default themes for visualizations

  - Support for statistical functions and data exploration

 5. SciPy

- Overview: A library that builds on NumPy and provides a collection of algorithms and high-level commands for mathematical and scientific computing.

- Key Features:

  - Modules for optimization, integration, interpolation, eigenvalue problems, and more

  - Tools for working with linear algebra, Fourier transforms, and signal processing

 6. Scikit-learn

- Overview: A machine learning library that provides simple and efficient tools for data mining and data analysis.

- Key Features:

  - Easy-to-use interface for various algorithms (classification, regression, clustering)

  - Support for model evaluation and selection

  - Preprocessing tools for transforming data

 7. Statsmodels

- Overview: A library that provides classes and functions for estimating and interpreting statistical models.

- Key Features:

  - Support for linear regression, logistic regression, time series analysis, and more

  - Tools for statistical tests and hypothesis testing

  - Comprehensive output for model diagnostics

 8. Dask

- Overview: A flexible parallel computing library for analytics that enables larger-than-memory computing.

- Key Features:

  - Parallel computation across multiple cores or distributed systems

  - Integrates seamlessly with Pandas and NumPy

  - Lazy evaluation for optimized performance

 9. Vaex

- Overview: A library designed for out-of-core DataFrames that allows you to work with large datasets (billions of rows) efficiently.

- Key Features:

  - Fast exploration of big data without loading it into memory

  - Support for filtering, aggregating, and joining large datasets

 10. PySpark

- Overview: The Python API for Apache Spark, allowing you to leverage the capabilities of distributed computing for big data processing.

- Key Features:

  - Fast processing of large datasets

  - Built-in support for SQL, streaming data, and machine learning

 Conclusion

These libraries form a robust ecosystem for data analysis in Python. Depending on your specific needs—be it data manipulation, statistical analysis, or visualization—you can choose the right combination of libraries to effectively analyze and visualize your data. As you explore these libraries, practice with real datasets to reinforce your understanding and improve your data analysis skills!

So when are people going to realize that this map is really fucking bad and stupid

I want to tell a story to the artists and would-be artists out there.

When I was 19, I made a large oil painting of the nerd I would eventually marry. I poured all my attention and care into this painting. It's the only art I have from back then that still holds up as a work I'm proud of today.

I entered it into a judged show at the local art center. It got an honorable mention. I went to see the show with my beloved model. One of the judges came up to talk to me, and highlighted that all the judges really liked the painting. It would have placed, except, you see, the feet were incorrect. They were too wide and short, and if I just studied a bit more anatomy-

I called over my future wife, and asked her to take off her shoe. Being already very used to humoring me, she did. The judge looked at her very short, very wide little foot. Exactly as I'd lovingly rendered it. I would never edit her appearance in any way.

The judge looked me in the eye, and to his credit, he really looked like he meant it when he said "Oh I'm so sorry."

Anyways the moral of the story is that all of those anatomy books that teach you proportions are either showing you averages, or a very specific idea of an idealized body. Actual bodies are much more varied than that.

So don't forget to draw from observation, and remember that humans aren't mass produced mannequins. Delight in our variation. Because it's supposed to be there.