[python]  랜덤 6자리 문자열을 생성하고, 중복되지 않도록 파일명을 지정한 후 이미지 캡차를 저장. captcha

import random
import string
import os
from captcha.image import ImageCaptcha  # ImageCaptcha 라이브러리를 사용해야 합니다.

# 랜덤 6자리 문자열 생성 함수
def generate_random_string(length=6):
    characters = string.ascii_letters + string.digits  # 영문 대소문자 + 숫자
    return ''.join(random.choices(characters, k=length))

# 중복되지 않는 파일명 생성 함수
def get_unique_filename(base_name, extension, directory="./img/"):
    counter = 1
    new_filename = f"{base_name}.{extension}"
    
    # 경로 내 파일명이 중복되면 새로운 파일명 생성
    while os.path.exists(os.path.join(directory, new_filename)):
        new_filename = f"{base_name}_{counter}.{extension}"
        counter += 1
    
    return new_filename

# 메인 로직
def main():
    # 캡차 이미지 생성기 설정
    image = ImageCaptcha(width=280, height=90)

    # 랜덤 6자리 문자열 생성
    captcha_text = generate_random_string()
    print(f"\n랜덤 6자리 문자열: {captcha_text}")

    # 이미지 생성
    data = image.generate(captcha_text)

    # 이미지 저장 경로 지정
    img_directory = "./img/"
    os.makedirs(img_directory, exist_ok=True)  # img 폴더가 없을 경우 생성

    # 중복되지 않는 파일명 생성
    unique_filename = get_unique_filename(captcha_text, 'png', directory=img_directory)

    # 이미지 파일 저장
    image.write(captcha_text, os.path.join(img_directory, unique_filename))

    print(f"이미지가 {unique_filename}으로 저장되었습니다.")

# 프로그램 실행
if __name__ == "__main__":
    main()

수정 내용:

1. generate_random_string() 함수: 랜덤한 6자리 문자열을 생성합니다.
2. get_unique_filename() 함수: 파일명 중복을 방지하기 위해, 기존에 존재하는 파일이 있을 경우 숫자를 추가하여 고유 파일명을 생성합니다.
3. 폴더 생성 (os.makedirs()): 이미지 저장 경로(./img/)가 존재하지 않으면 자동으로 폴더를 생성하도록 os.makedirs()를 사용합니다.
4. os.path.exists(): 파일이 존재하는지 확인하고 중복 파일명을 방지합니다.
5. 경로 및 파일명 결합 (os.path.join()): OS에 관계없이 적절한 경로를 결합하기 위해 os.path.join()을 사용합니다.

실행 결과:

• ./img/ 폴더에 랜덤한 문자열을 포함한 이미지가 저장됩니다.
• 파일명이 중복되면 자동으로 _1, _2 등의 숫자가 추가됩니다.

[python] Generate Captcha Using Python

https://www.geeksforgeeks.org/generate-captcha-using-python/

In this article, we are going to see how to generate a captcha using Python package captcha to generate our own CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) in picture form. CAPTCHA is a form of challenge-response authentication security mechanism. CAPTCHA prevents automated systems from reading the distorted characters in the picture.

Installation:

pip install captcha

Generating image captcha: 

Here we are going to generate an image captcha:

Stepwise implementation:

Step 1: Import module and create an instance of ImageCaptcha().

image = ImageCaptcha(width = 280, height = 90)

Step 2: Create image object with image.generate(CAPTCHA_Text).

data = image.generate(captcha_text)  

Step 3: Save the image to a file image.write().

image.write(captcha_text, 'CAPTCHA.png')

Below is the full implementation:

Output:

Image CAPTCHA

Generating Audio captcha:

Here we are going to generate an audio captcha:

Stepwise implementation:

Step 1: Import module and create an instance of AudioCaptcha().

image = audioCaptcha(width = 280, height = 90)

Step 2: Create an audio object with audio.generate(CAPTCHA_Text).

data = audio.generate(captcha_text)  

Step 3: Save the image to file audio.write().

audio.write(captcha_text, audio_file)

Below is the full implementation:

Output:

https://ipython.org/install.html

Installing IPython

There are multiple ways of installing IPython. This page contains simplified installation instructions that should work for most users. Our official documentation contains more detailed instructions for manual installation targeted at advanced users and developers.

If you are looking for installation documentation for the notebook and/or qtconsole, those are now part of Jupyter.

I already have Python

If you already have Python installed and are familiar with installing packages, you can get IPython with pip:

pip install ipython

I am getting started with Python

For new users who want to install a full Python environment for scientific computing and data science, we suggest installing the Anaconda or Canopy Python distributions, which provide Python, IPython and all of its dependences as well as a complete set of open source packages for scientific computing and data science.

1. Download and install Continuum’s Anaconda or the free edition of Enthought’s Canopy.
2. Update IPython to the current version using the Terminal:

Anaconda:

conda update conda
conda update ipython

https://pypi.org/project/ipython/

 pip install sympy

https://pypi.org/project/sympy/

2차 방정식은 2차 다항식 방정식으로, 일반적인 형태는 다음과 같습니다:

Python으로 2차 방정식 풀기

아래는 Python을 사용하여 2차 방정식의 해를 구하는 예제 코드입니다:

import cmath  # 복소수 계산을 위해 cmath 모듈 사용

def solve_quadratic(a, b, c):
    # 판별식 계산
    discriminant = b**2 - 4*a*c
    
    # 근의 공식 사용
    root1 = (-b + cmath.sqrt(discriminant)) / (2 * a)
    root2 = (-b - cmath.sqrt(discriminant)) / (2 * a)
    
    return root1, root2

# 예시: a, b, c 값 입력
a = 1  # x^2의 계수
b = -3  # x의 계수
c = 2  # 상수항

# 함수 호출하여 근 구하기
roots = solve_quadratic(a, b, c)

# 결과 출력
print(f"방정식의 근: {roots[0]} 과 {roots[1]}")

[python] html table을 Markdown으로 변경하기. 

htmltabletomdhtml 테이블을 마크다운으로 변환하기 위한 것입니다. 또한, 테이블 셀 내부의 내용은 HTML이 포함되어 있는 경우 마크다운으로 변환할 수 있으며, 이를 위해 라이브러리를 사용합니다 htmltomarkdown.

pip install htmltabletomd

https://pypi.org/project/htmltabletomd/

from bs4 import BeautifulSoup
from markdownify import markdownify as md
import os

def convert_html_table_to_md(html_file_path, output_md_file_path):
    # HTML 파일 읽기
    with open(html_file_path, 'r', encoding='utf-8') as file:
        html_content = file.read()

    # BeautifulSoup을 사용하여 HTML 파싱
    soup = BeautifulSoup(html_content, 'html.parser')

    # HTML을 Markdown으로 변환
    markdown_content = md(str(soup))

    # 결과를 Markdown 파일로 저장
    with open(output_md_file_path, 'w', encoding='utf-8') as file:
        file.write(markdown_content)
    
    print(f"Converted HTML table to Markdown and saved as: {output_md_file_path}")

# 사용 예시
#html_file_path = 'path_to_your_html_file.html'  # 변환할 HTML 파일 경로
html_file_path = 'test_001.html'  # 변환할 HTML 파일 경로
output_md_file_path = 'output_markdown_file.md'  # 저장할 Markdown 파일 경로

convert_html_table_to_md(html_file_path, output_md_file_path)

Ibis는 모든 쿼리 엔진에서 실행되는 Python 데이터프레임 API를 정의합니다. 현재 20개 이상의 백엔드가 있는 모든 백엔드 데이터 플랫폼의 프런트엔드입니다. 이를 통해 Ibis는 연결된 백엔드만큼 뛰어난 성능을 일관된 사용자 경험과 함께 제공할 수 있습니다.

https://ibis-project.org/why

Ibis defines a Python dataframe API that executes on any query engine – the frontend for any backend data platform, with 20+ backends today. This allows Ibis to have excellent performance – as good as the backend it is connected to – with a consistent user experience.

What is Ibis?

Ibis is the portable Python dataframe library.

We can demonstrate this with a simple example on a few local query engines:

import ibis

ibis.options.interactive = True

1con = ibis.connect("duckdb://")

t = con.read_parquet("penguins.parquet")
t.limit(3)

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

t.group_by(["species", "island"]).agg(count=t.count()).order_by("count")

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Biscoe    │    44 │
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Dream     │    56 │
│ Chinstrap │ Dream     │    68 │
│ Gentoo    │ Biscoe    │   124 │
└───────────┴───────────┴───────┘

Who is Ibis for?

Ibis is for data engineers, data analysts, and data scientists (or any title that needs to work with data!) to use directly with their data platform(s) of choice. It also has benefits for data platforms, organizations, and library developers.

Ibis for practitioners

You can use Ibis at any stage of your data workflow, no matter your role.

Data engineers can use Ibis to:

• write and maintain complex ETL/ELT jobs
• replace fragile SQL string pipelines with a robust Python API
• replace PySpark with a more Pythonic API that supports Spark and many other backends

Data analysts can use Ibis to:

• use Ibis interactive mode for rapid exploration
• perform rapid exploratory data analysis using interactive mode
• create end-to-end analytics workflows
• work in a general-purpose, yet easy to learn, programming language without the need for formatting SQL strings

Data scientists can use Ibis to:

• extract a sample of data for local iteration with a fast local backend
• prototype with the same API that will be used in production
• preprocess and feature engineer data before training a machine learning model

Ibis for data platforms

Data platforms can use Ibis to quickly bring a fully-featured Python dataframe library with minimal effort to their platform. In addition to a great Python dataframe experience for their users, they also get integrations into the broader Python and ML ecosystem.

Often, data platforms evolve to support Python in some sequence like:

1. Develop a fast query engine with a SQL frontend
2. Gain popularity and need to support Python for data science and ML use cases
3. Develop a bespoke pandas or PySpark-like dataframe library and ML integrations

This third step is where Ibis comes in. Instead of spending a lot of time and money developing a bespoke Python dataframe library, you can create an Ibis backend for your data platform in as little as four hours for an experienced Ibis developer or, more typically, on the order of one or two months for a new contributor.

Ibis for organizations

Organizations can use Ibis to standardize the interface for SQL and Python data practitioners. It also allows organizations to:

• transfer data between systems
• transform, analyze, and prepare data where it lives
• benchmark your workload(s) across data systems using the same code
• mix SQL and Python code seamlessly, with all the benefits of a general-purpose programming language, type checking, and expression validation

Ibis for library developers

Python developers creating libraries can use Ibis to:

• instantly support 20+ data backends
• instantly support pandas, PyArrow, and Polars objects
• read and write from all common file formats (depending on the backend)
• trace column-level lineage through Ibis expressions
• compile Ibis expressions to SQL or Substrait
• perform cross-dialect SQL transpilation (powered by SQLGlot)

How does Ibis work?

Most Python dataframes are tightly coupled to their execution engine. And many databases only support SQL, with no Python API. Ibis solves this problem by providing a common API for data manipulation in Python, and compiling that API into the backend’s native language. This means you can learn a single API and use it across any supported backend (execution engine).

Ibis broadly supports two types of backend:

1. SQL-generating backends
2. DataFrame-generating backends

 

 

 

 

As you can see, most backends generate SQL. Ibis uses SQLGlot to transform Ibis expressions into SQL strings. You can also use the .sql() methods to mix in SQL strings, compiling them to Ibis expressions.

While portability with Ibis isn’t perfect, commonalities across backends and SQL dialects combined with years of engineering effort produce a full-featured and robust framework for data manipulation in Python.

In the long-term, we aim for a standard query plan Intermediate Representation (IR) like Substrait to simplify this further.

Python + SQL: better together

For most backends, Ibis works by compiling Python expressions into SQL:

g = t.group_by(["species", "island"]).agg(count=t.count()).order_by("count")
ibis.to_sql(g)

SELECT
  *
FROM (
  SELECT
    `t0`.`species`,
    `t0`.`island`,
    COUNT(*) AS `count`
  FROM `ibis_read_parquet_pp72u4gfkjcdpeqcawnpbt6sqq` AS `t0`
  GROUP BY
    1,
    2
) AS `t1`
ORDER BY
  `t1`.`count` ASC NULLS LAST

You can mix and match Python and SQL code:

sql = """
SELECT
  species,
  island,
  COUNT(*) AS count
FROM penguins
GROUP BY species, island
""".strip()

con = ibis.connect("duckdb://")
t = con.read_parquet("penguins.parquet")
g = t.alias("penguins").sql(sql)
g

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Biscoe    │    44 │
│ Adelie    │ Dream     │    56 │
│ Gentoo    │ Biscoe    │   124 │
│ Chinstrap │ Dream     │    68 │
└───────────┴───────────┴───────┘

g.order_by("count")

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Biscoe    │    44 │
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Dream     │    56 │
│ Chinstrap │ Dream     │    68 │
│ Gentoo    │ Biscoe    │   124 │
└───────────┴───────────┴───────┘

This allows you to combine the flexibility of Python with the scale and performance of modern SQL.

Scaling up and out

Out of the box, Ibis offers a great local experience for working with many file formats. You can scale up with DuckDB (the default backend) or choose from other great options like Polars and DataFusion to work locally with large datasets. Once you hit scaling issues on a local machine, you can continue scaling up with a larger machine in the cloud using the same backend and same code.

If you hit scaling issues on a large single-node machine, you can switch to a distributed backend like PySpark, BigQuery, or Trino by simply changing your connection string.

Stream-batch unification

As of Ibis 8.0, the first stream processing backends have been added. Since these systems tend to support SQL, we can with minimal changes to Ibis support both batch and streaming workloads with a single API. We aim to further unify the batch and streaming paradigms going forward.

Ecosystem

Ibis is part of a larger ecosystem of Python data tools. It is designed to work well with other tools in this ecosystem, and we continue to make it easier to use Ibis with other tools over time.

Ibis already works with other Python dataframes like:

• pandas
• Dask
• Polars

Ibis already works well with visualization libraries like:

• matplotlib
• seaborn
• Plotly
• Vega-Altair
• plotnine

Ibis already works well with dashboarding libraries like:

• Streamlit
• Dash
• Quarto dashboards
• Shiny

Ibis already works well with machine learning libraries like:

• scikit-learn
• XGBoost
• LightGBM
• PyTorch

Supported backends

You can install Ibis and a supported backend with pip, conda, mamba, or pixi.

pip install 'ibis-framework[bigquery]'

See the backend support matrix for details on operations supported. Open a feature request if you’d like to see support for an operation in a given backend. If the backend supports it, we’ll do our best to add it quickly!

Community

Community discussions primarily take place on GitHub and Zulip.

Getting started

If you’re interested in trying Ibis we recommend the getting started tutorial.