DEV Community: George Karanikolas

Stop Writing 20 @Preview Functions — Build a Runtime Sandbox Instead

George Karanikolas — Tue, 10 Mar 2026 19:00:07 +0000

Every Compose developer hits this wall eventually. You start with one @Preview. Then one for dark mode. Then one for large fonts. Then one per locale. Before you know it, you're scrolling through two dozen nearly-identical functions just to check whether a button label wraps in German.

The real problem isn't laziness — it's that static previews don't scale. Dark × 3 locales × 4 font scales × RTL is 24 permutations for a single screen. And your IDE renders all of them every build.

So I built PreviewSandbox. Instead of multiplying preview functions, you get an interactive control panel that lets you toggle every configuration axis at runtime — no rebuilds, no system-setting changes.

What It Looks Like

The API is intentionally minimal:

@Preview
@Composable
private fun ScreenPreview() {
    PreviewSandbox(
        theme = { state, content ->
            AppTheme(darkTheme = state.isDark) { content() }
        }
    ) {
        MyScreen()
    }
}

That's one preview. One function. A ⚙ icon appears at runtime and opens a control panel where you can toggle:

Dark / light theme
Language — cycles through your supported locales
Font scale — 1.0×, 1.3×, 1.5×, 1.8×, 2.0×
RTL layout direction
Background color
Wallpaper (for transparent UIs like widgets or overlays)
Dynamic Color (Material You)
Bold text
High contrast mode

The control panel only shows the toggles that are relevant to your app. Everything else stays out of the way.

Read the full deep-dive on Medium.

Introduction to Kotlin

George Karanikolas — Fri, 26 Mar 2021 15:02:48 +0000

Introduction to Kotlin

History

In July 2011, JetBrains unveiled Project Kotlin, a new language for the JVM.
Kotlin was released on 15 February 2016.
Kotlin is sponsored by Google, announced as one of the official languages for Android Development in 2017.
On 2019, Google announced that the Kotlin programming language is now its preferred language for Android app developers.

Intro

Kotlin is an open-source, statically-typed programming language that supports both object-oriented and functional programming. Kotlin provides similar syntax and concepts from other languages, including C#, Java, Groovy and Scala, among many others.

Benefits of Kotlin

Kotlin compiles to JVM bytecode or JavaScript
Kotlin can use all existing Java Frameworks and Libraries
Kotlin can be learned quickly, and it is approachable
Automatic conversion of Java to Kotlin
It Requires Less Code
It’s Secure

Features of Kotlin

Clean, compact syntax
Null Safety
Data Classes
Extension Functions
Smart Casts
Type Inference
Single type system (almost)
Functional Programming

Kotlin VS Java

Applications of Kotlin

Pinterest
Trello
Evernote
Slack
Netflix
And much more!

For many resources, check out my Github:
https://github.com/SeijinD

Tutorial Selenium

George Karanikolas — Fri, 22 May 2020 14:00:35 +0000

Selenium

Selenium is library that can be useful when scraping the web. Unlike the other libraries, Selenium wasn’t originally designed for web scraping. First and foremost, Selenium is a web driver designed to render web pages like your web browser would for the purpose of automated testing of web applications.

Install Selenium

pip install selenium

Use Selenium

from selenium import webdriver

Driver setup

Chrome Link
Firefox Link
Opera Link
Edge Link

# Chrome:
chrome_driver = webdriver.Chrome(“Chrome Driver Path”)
# Firefox:
firefox_driver = webdriver.Firefox(“Firefox Driver Path”)
# Opera:
opera_driver = webdriver.Opera(“Opera Driver Path”)
# Edge:
edge_driver = webdriver.Edge(“Edge Driver Path”)
# Safari
safari_driver = webdriver.Safari() # The SafariDeriver is integrated in Safari.

Open A Website

url = "https://www.example.com"
driver.get(url)

For many resources on Selenium or anything else on Python, check out Github:
https://github.com/SeijinD/Python-World/blob/master/main/extends_libraries/selenium.md

How to Download YouTube videos in Python.

George Karanikolas — Thu, 21 May 2020 13:14:21 +0000

Hello everyone!

This tutorial is "How to Download Youtube videos in Python.

_________________________________________________

Download Pytube Library

pip install pytube # python2

pip3 install pytube # python3

pip install pytube3 # if not work with pytube.

First of all, we need to import Pytube library :

import pytube

After that, You need copy the URL of the Youtube video :

url = 'https://www.youtube.com/watch?v=4SFhwxzfXNc'

Load url in function Youtube :

 youtube = pytube.YouTube(url)

Set Streams Resolution :

video = youtube.streams.first()
# or
video = youtube.streams.get_highest_resolution()

Download Video :

video.download() # In Same Folder
# or
video.download('/Downloads') # In Other Folder

Get Information of Video :

video.title # Title
video.video_id # Id
video.age_restricted # Age

Streams Format :

video.streams.all()
stream = video.streams.all()
for i in stream:
  print(i)

Example 1 :

import pytube

url = 'https://www.youtube.com/watch?v=4SFhwxzfXNc'

youtube = pytube.YouTube(url)
video = youtube.streams.first()
video.download('../Video')

Example 2 :

import pytube

print("Give URL:")
url = input()

pytube.YouTube(url).streams.get_highest_resolution().download('../Video')

I hope you liked it!

Library Creator : https://github.com/nficano/pytube

Web Scraping Introduction

George Karanikolas — Tue, 17 Mar 2020 15:39:37 +0000

Why Web Scraping?

Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? To know about this, let’s look at the applications of web scraping:
- Price Comparison
- Email address gathering
- Social Media Scraping
- Research and Development
- Job listings

Why Python For Web Scraping?

Here is the list of features of Python which makes it more suitable for web scraping.
- Ease of Use
- Large Collection of Libraries
- Dynamically typed
- Easily Understandable Syntax
- Small code, large task
- Community

What is Web Scraping?

Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code.

Libraries for Web Scraping

Requests (Full Tutorial)

Requests is a python library designed to simplify the process of making HTTP requests.This is highly valuable for web scraping because the first step in any web scraping workflow is to send an HTTP request to the website’s server to retrieve the data displayed on the target web page.

BeautifulSoup (Full Tutorial)

Unlike Requests, BeautifulSoup is a python library designed to parse data, i.e., to extract data from HTML or XML documents.
Because BeautifulSoup can only parse the data and can’t retrieve the web pages themselves, it is often used with the Requests library. In situations like these, Requests will make the HTTP request to the website to retrieve the web page, and once it has been returned, BeautifulSoup can be used to parse the target data from the HTML page.

Selenium (Full Tutorial)

Scrapy (Web Scraping Framework) (Full Tutorial Coming soon!)

Scrapy is an open source python framework built specifically for web scraping by Scrapinghub co-founders Pablo Hoffman and Shane Evans.
It means that Scrapy is a fully fledged web scraping solution that takes a lot of the work out of building and configuring your spiders, and best of all, it seamlessly deals with edge cases that you probably haven’t thought of yet.
Within minutes of installing the framework, you can have a fully functioning spider scraping the web. Out of the box, Scrapy spiders are designed to download HTML, parse and process the data and save it in either CSV, JSON or XML file formats.

Lxml (Full Tutorial Coming soon!)

lxml is a Python library which allows for easy handling of XML and HTML files, and can also be used for web scraping. There are a lot of off-the-shelf XML parsers out there, but for better results, developers sometimes prefer to write their own XML and HTML parsers. This is when the lxml library comes to play. The key benefits of this library are that it's ease of use, extremely fast when parsing large documents, very well documented, and provides easy conversion of data to Python data types, resulting in easier file manipulation.

Examples

# Example 1:
import requests
from bs4 import BeautifulSoup
import pandas as pd # Pandas is useful library. (Go Extends Liraries)

page = requests.get('https://meteo.gr/lab/book/index.cfm?city_id=1')
soup = BeautifulSoup(page.content, 'html.parser')

# Lists to store the scraped data in
days = []
temperatures = []
# Extract data from individual dayblock
for block in soup.find_all(class_ = 'dayblock'):
    day = block.find(class_ = 'subheader_calendar')
    # Data Number and Month Name
    dom = block.find(class_ = 'datenumber_calendar').get_text()
    month = block.find(class_ = 'month_calendar').get_text()
    daystr = f"{dom} {month}"
    days.append(daystr)
    # Low and High Temperature
    hightemp = block.find(class_ = 'hightemp').get_text()
    lowtemp = block.find(class_ = 'lowtemp').get_text()
    tempstr = f"{lowtemp} {hightemp}"
    temperatures.append(tempstr)

# Print in Table with Pandas
weather_table = pd.DataFrame({
    'Days' : days,
    'Temperatures' : temperatures
})
# Extract to the csv file
weather_table.to_csv('week_weather.csv')

# ==================================================
# Example 2:
from selenium import webdriver
import csv

MAX_PAGE_NUM = 5
MAX_PAGE_DIG = 3

with open('result.csv', 'w', encoding='utf8') as f:
    f.write("Buyers, Price \n")

# Set driver with Firefox webdriver
driver = webdriver.Firefox(executable_path="C:\\Python\\Tools\\geckodriver.exe")

# Make loop for run all pages
for i in range(1, MAX_PAGE_NUM + 1):
    page_num = (MAX_PAGE_DIG - len(str(i))) * "0" +str(i)
    url = "https://econpy.pythonanywhere.com/ex/" + page_num + ".html"
    driver.get(url)

    # Get buyers and prices by xpath
    buyers = driver.find_elements_by_xpath('//div[@title="buyer-name"]')
    prices = driver.find_elements_by_xpath('//span[@class="item-price"]')

    # Write all buyers+prices in result.csv
    with open('result.csv', 'a', encoding='utf8') as f:
        f.write("Page" + str(i) + "\n")
        for j in range(len(buyers)):
            f.write(str(j) + ") " + buyers[j].text + " : " + prices[j].text + "\n")

# Close driver
driver.close()

Tutorial NumPy

George Karanikolas — Wed, 11 Mar 2020 16:41:03 +0000

What is NumPy?

Numpy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.

NumPy is often used along with packages like SciPy (Scientific Python) and Matplotlib (plotting library). This combination is widely used as a replacement for MatLab, a popular platform for technical computing. However, Python alternative to MatLab is now seen as a more modern and complete programming language.

Install NumPy

pip install numpy

Import NumPy

import numpy as np

Create Arrays

a = np.array([1,2,3])
b = np.array([(1.5,2,3), (4,5,6)], dtype = float)
c = np.array([[(1.5,2,3), (4,5,6)], [(3,2,1), (4,5,6)]], dtype = float)
d = np.array( [ [1,2], [3,4] ], dtype=complex )

Print Arrays

>>> a = np.arange(6)                         # 1d array
>>> print(a)
[0 1 2 3 4 5]
>>>
>>> b = np.arange(12).reshape(4,3)           # 2d array
>>> print(b)
[[ 0  1  2]
 [ 3  4  5]
 [ 6  7  8]
 [ 9 10 11]]
>>>
>>> c = np.arange(24).reshape(2,3,4)         # 3d array
>>> print(c)
[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]
 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]

For many resources on NumPy or anything else on Python, check out Github:
https://github.com/SeijinD/Python-World/blob/master/main/extends_libraries/numpy.md

Tutorial Matplotlib

George Karanikolas — Wed, 11 Mar 2020 16:29:28 +0000

What is Matplotlib?

Matplotlib is one of the most popular Python packages used for data visualization. It is a cross-platform library for making 2D plots from data in arrays. It provides an object-oriented API that helps in embedding plots in applications using Python GUI toolkits such as PyQt, WxPythonotTkinter. It can be used in Python and IPython shells, Jupyter notebook and web application servers also.
Matplotlib is often used along with package like NumPy.

Install Matplotlib

pip install matplotlib

Import Matplotlib

import matplotlib.pyplot as plt

Create Title

plt.title("Name")

Create X and Y Labels

plt.xlabel('Name')
plt.ylabel('Name')

plt.xlabel('Name', color='red')
plt.ylabel('Name', color='#ff0000')

Create Plot

plt.plot()

plt.plot(name_x, name_y)

plt.plot(name_x, name_y, color='#ff0000')

plt.plot(name_x, name_y, color='#ff0000', linestyle='--')

plt.plot(name_x, name_y, color='#ff0000', linestyle='--', label="Name")

Add Labels in Plot

plt.legend()

Show Plot

plt.show()

Save Plot In Image

plt.savefig('name.png')

plt.savefig('name.png', transparent = True) # Transparent Figure

Close And Clear

plt.cla() # Clear an axis
plt.clf() # Clear the entire figure
plt.close() # Close a window

Examples:

Example 1:

import matplotlib.pyplot as plt

years_x = [2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018]
greece_y = [8.4,7.8,9.6,12.7,17.9,24.5,27.5,26.5,24.9,23.6,21.5,19.3]
europe_y = [7.2,7,9,9.6,9.7,10.5,10.9,10.2,9.4,8.6,7.6,6.8]

plt.title("Comparison of Unemployment")

plt.plot(years_x, greece_y, color='#ff0000', label="Greece")
plt.plot(years_x, europe_y, color='#00ff00', label="Europe", linestyle='--')

plt.xlabel('Years', color='#ff0000')
plt.ylabel('Percentage %', color='#ff0000')

plt.savefig('plot.png')

plt.legend()
plt.show()

Github: https://github.com/SeijinD/Python-World/blob/master/main/extends_libraries/matplotlib.md

Python World

George Karanikolas — Sat, 07 Mar 2020 09:05:11 +0000

Hey everyone! This is my effort for make a Big Guide for Python and anything for it.
I will uploading content frequently and upgrading the old.

https://github.com/SeijinD/Python-World

Thanks! :)