开发者

If you've ever seen usernames like 𝓙𝓸𝓱𝓷, 🅹🅾🅷🅽, or 𝕵𝖔𝖍𝖓 on Instagram, Discord, or gaming platforms, you might have wondered how they are created. Many people think these are custom fonts, but they aren't. They're Actually Unicode Characters Most "font generators" don't generate new fonts. Instead, they replace standard letters with Unicode characters that resemble different writing styles. For example: Hello → 𝐇𝐞𝐥𝐥𝐨 Hello → 𝓗𝓮𝓵𝓵𝓸 Hello → 𝕳𝖊𝖑𝖑𝖔 Hello → 🅷🅴🅻🅻🅾 Because these are Unicode characters, they can often be copied and pasted directly into social media profiles, chat applications, and games. How Does a Text Generator Work? The process is surprisingly simple: Read the user's input. Map each character to its Unicode equivalent. Preserve spaces and punctuation where possible. Output the transformed text for copying. Most generators use predefined character mappings stored as JSON or arrays. Things to Keep in Mind Not every platform supports every Unicode character. Some games or websites may replace unsupported symbols with blank squares or question marks, so it's a good idea to test the generated text before using it everywhere. Building Your Own If you're interested in experimenting with Unicode text styles or want to see how different mappings look, I built a free tool that converts regular text into multiple Unicode styles. ( https://fontedeletra.com/letras-diferentes/ I'm also curious—if you've built a Unicode or text transformation tool, how did you organize your character mappings? I'd love to hear different implementation approaches.

I recently completed an exploratory data analysis project on the NHANES (National Health and Nutrition Examination Survey) dataset from Kaggle. It's a real-world health survey collected by the CDC covering body measurements, lifestyle habits, and demographic data from thousands of US adults. In this article I'll walk you through exactly what I did — from loading and cleaning the data all the way to running statistical tests — and share what I found along the way. The Dataset The dataset has 5,735 rows and 28 columns , but for this project I focused on 8 columns that were relevant to the questions I wanted to answer: Column Description smoking Has the person smoked at least 100 cigarettes? gender Male or Female age Age in years education Highest level of education weight Weight in kg height Height in cm bmi Body Mass Index Step 1 — Loading and Selecting Columns import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns db = pd . read_csv ( ' NHANES.csv ' ) data = db . loc [:, ( ' SEQN ' , ' SMQ020 ' , ' RIAGENDR ' , ' RIDAGEYR ' , ' DMDEDUC2 ' , ' BMXWT ' , ' BMXHT ' , ' BMXBMI ' )] data = data . rename ( columns = { ' SEQN ' : ' id ' , ' SMQ020 ' : ' smoking ' , ' RIAGENDR ' : ' gender ' , ' RIDAGEYR ' : ' age ' , ' DMDEDUC2 ' : ' education ' , ' BMXWT ' : ' weight ' , ' BMXHT ' : ' height ' , ' BMXBMI ' : ' bmi ' }) One thing worth knowing about NHANES: all the columns come in as numeric codes. 1 means Male, 2 means Female. 1 means the person smoked, 2 means they didn't. You have to map these to readable labels before doing any analysis, otherwise your charts are meaningless. Step 2 — Cleaning the Data Drop the ID column and remove nulls data . drop ( ' id ' , axis = 1 , inplace = True ) data . dropna ( inplace = True ) This brought us from 5,735 rows down to 5,406 — about 6% lost, which is acceptable. Remove outliers using the IQR method The IQR (Interquartile Range) method flags values that fall too far outside the middle 50% of

submitted by /u/verdagon [link] [留言]

After several years of mandatory DRM lockdowns from most commercial book sources, now authors have a choice when it comes to DRM for their books. Pick authors and books that are DRM-free, or download DRM-free classics that are out of copyright. https://frequal.com/Perspectives/DrmFreeAuthors.html

Hello everyone! Me and my friend. We are still learning, and recently we tried to make a very simple, hobby antivirus/security project written in C. We call it, F4us. It is far from perfect and still in a very early development phase. We just wanted to learn how some basic security concepts work under the hood. What we tried to build: A simple honeypot script to log suspicious access. A basic entropy function to check file structures. A background daemon loop to monitor files. Since we are still beginners, we know there are probably a lot of bugs and messy code in it. We just wanted to share this hobby journey here. Thank you for reading! here is the link(github), sorry if include many bugs or critical,it just an experiment: https://github.com/CornelI5/F4us my friend was out, he are in crashout cause this project is too hard. btw, dont use this in your real computer, trust me. just use VM(Virtual Machine),or you will get BSOD(blue screen of death) or kernel panic.