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@VintageAppMaker
Created July 3, 2023 13:33
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prompt로배우는파이썬기초.ipynb
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{
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"metadata": {
"colab": {
"provenance": [],
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"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/VintageAppMaker/7d54eb523ad5336ec6a95fa0196c0415/prompt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"## 1. 프롬프트로 파이썬 공부하기\n",
"> 목적: 생성 AI의 프롬프트를 활용하여 파이썬을 빠르고 쉽게 공부하기\n",
"\n",
"- 파이썬의 학습목차를 가져온다.\n",
"- 학습목차대로 ChatGPT나 Bard같은 생성AI에게 **명령**한다.\n",
"- 맥락(Context)을 만드는 프롬프트를 연습한다. \n",
"- Colab의 ipython 파일로 **노트**하며 학습노트를 만든다.\n",
"\n",
"\n",
"올바른 생성AI의 프롬프트를 활용하면 **혼자**서도 재미있게 프로그래밍이 가능하다. 사람들에게 물어보는 것보다 더 편리하게 학습이 가능하다. \n",
"\n"
],
"metadata": {
"id": "nUZohiCXGXvF"
}
},
{
"cell_type": "markdown",
"source": [
"### 1. 파이썬에 대한 이해\n",
"> 파이썬은 어떤 언어인가?\n",
"\n",
"- ChatGPT(🤖)에게 물어보기\n",
"\n",
"```\n",
"파이썬은 어떤 언어인가?\n",
"```"
],
"metadata": {
"id": "VufuiJt2Gqqd"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"\n",
"\n",
"Person:\n",
"- 파이썬은 어떤 언어인가?\n",
"\n",
"ChatGPT:\n",
"- 파이썬은 인터프리터 방식으로 동작하는 고수준 프로그래밍 언어입니다. 1991년에 귀도 반 로섬(Guido van Rossum)에 의해 개발되었으며, 가독성과 간결성을 강조하는 철학을 가지고 있습니다.\n",
"\n",
"파이썬은 문법이 간결하고 사용하기 쉬우며, 다양한 플랫폼에서 동작할 수 있습니다. 이는 파이썬을 배우기 쉽고 생산성이 높은 언어로 만들어주는 특징입니다. 또한, 다양한 용도로 활용될 수 있는 범용 프로그래밍 언어로서 많은 사람들에게 사랑받고 있습니다.\n",
"\n",
"파이썬은 대규모 프로젝트에서도 사용될 수 있는 강력한 기능을 제공합니다. 다양한 라이브러리와 프레임워크를 통해 데이터 분석, 웹 개발, 인공지능, 기계 학습 등 다양한 분야에서 사용되고 있습니다. 파이썬의 생태계는 매우 활발하며, 수많은 개발자들이 다양한 패키지와 도구를 공유하고 있어 빠른 개발과 문제 해결에 도움을 줍니다.\n",
"\n",
"또한, 파이썬은 오픈 소스 언어로서 무료로 사용할 수 있으며, 개발자들이 커뮤니티를 통해 기여할 수 있는 개방성을 가지고 있습니다. 이러한 특징들로 인해 파이썬은 많은 사람들에게 인기 있는 프로그래밍 언어로 자리잡았습니다.\n",
"\n",
"\n"
],
"metadata": {
"id": "fF46B1lCLXXJ"
}
},
{
"cell_type": "code",
"source": [
"from IPython.display import YouTubeVideo, display\n",
"video = YouTubeVideo(\"Y8Tko2YC5hA\", width=500)\n",
"display(video)"
],
"metadata": {
"id": "ay5v04ujMRw3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 321
},
"outputId": "1dd51200-92fa-467c-bc67-f4a9a126ccc0"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x7f893a1a3c70>"
],
"text/html": [
"\n",
" <iframe\n",
" width=\"500\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/Y8Tko2YC5hA\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from IPython.display import YouTubeVideo, display\n",
"video = YouTubeVideo(\"MYpu8Qb3mZw\", width=500)\n",
"display(video)"
],
"metadata": {
"id": "23JQt2JMSI_E",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 321
},
"outputId": "a7c24855-d19e-462c-9776-c9477bfbd01e"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x7f76001adff0>"
],
"text/html": [
"\n",
" <iframe\n",
" width=\"500\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/MYpu8Qb3mZw\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"image/jpeg": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"### 2. 파이썬은 왜 사용해야 하는가?\n",
"> 파이썬은 어떤 언어인가?\n",
"\n",
"- ChatGPT(🤖)에게 물어보기\n",
"\n",
"```\n",
"파이썬은 누가 사용하며 어떤 목적으로 사용하는가?\n",
"```"
],
"metadata": {
"id": "RqajTSelGbn9"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬은 누가 사용하며 어떤 목적으로 사용하는가?\n",
"\n",
"ChatGPT:\n",
"- 파이썬은 다양한 사용자들에 의해 널리 사용되는 프로그래밍 언어입니다. 주요 사용자 그룹은 다음과 같습니다:\n",
"1. 개발자들: 파이썬은 문법이 간결하고 읽기 쉽기 때문에 개발자들 사이에서 인기가 많습니다. 파이썬은 웹 개발, 데이터베이스 관리, 네트워크 프로그래밍, 인공지능 및 머신러닝 등 다양한 분야에서 사용됩니다.\n",
"2. 데이터 과학자들: 파이썬은 데이터 분석 및 데이터 과학 분야에서 널리 사용됩니다. 데이터 과학자들은 파이썬을 사용하여 데이터 수집, 데이터 정제, 분석, 시각화 등 다양한 작업을 수행합니다. 인기있는 라이브러리인 NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow 등을 통해 데이터 과학 작업을 쉽게 할 수 있습니다.\n",
"3. 교육자들: 파이썬은 프로그래밍을 배우는 데에 매우 적합한 언어로 여겨집니다. 문법이 간결하고 가독성이 좋기 때문에 초보자들이 쉽게 접근할 수 있습니다. 파이썬은 학교에서 프로그래밍 교육에 널리 사용되며, 기본 프로그래밍 개념을 이해하고 응용할 수 있는 능력을 키우는 데 도움이 됩니다.\n",
"4. 시스템 관리자들: 파이썬은 시스템 관리 작업에 유용한 도구로 사용됩니다. 파일 조작, 자동화, 네트워크 서비스 관리 등 시스템 관리 작업을 간편하게 수행할 수 있습니다. 또한 파이썬은 다른 언어와의 통합이 용이하여 시스템 관리 작업에 유연성을 제공합니다.\n",
"\n",
"또한 파이썬은 개인적인 프로젝트부터 대규모 엔터프라이즈 소프트웨어까지 다양한 목적으로 사용됩니다. 그 이유는 파이썬이 다양한 라이브러리와 프레임워크를 지원하며, 생산성이 높고 커뮤니티가 활발하기 때문입니다.\n"
],
"metadata": {
"id": "oR9KEreTpqjr"
}
},
{
"cell_type": "markdown",
"source": [
"### 3. 파이썬을 공부할 수 있는 사이트\n",
"> 목적: 파이썬을 다양하게 공부할 수 있는 효율적인 사이트 정리\n",
"\n",
"- [w3schools](https://www.w3schools.com/python/)\n",
"- [codecademy](https://www.codecademy.com/catalog)\n",
"- [구름](https://accounts.goorm.io/login?return_url=aHR0cHM6Ly9pZGUuZ29vcm0uaW8vbXkvZGFzaGJvYXJk&keep_login=false)\n"
],
"metadata": {
"id": "EM5ukVunIAlE"
}
},
{
"cell_type": "markdown",
"source": [
"### 4. 구글 Colab이란?\n",
"> 목적: Colab에 대한 이해 및 활용\n",
"\n",
"- Colab은 [마크다운](https://namu.wiki/w/%EB%A7%88%ED%81%AC%EB%8B%A4%EC%9A%B4)으로 문서처럼 저장되며 파이썬 코드를 실행가능하다.\n",
"- 우측에는 목차, 찾기, 지금 실행된 파이썬 기능들(개발자 지식필요함), 그리고 파일이 있다.\n",
"- 파일은 linux에서 사용하는 파일이다.\n",
"- colab은 linux 환경을 제공하는 클라우드 서비스이다.\n"
],
"metadata": {
"id": "BLQbzX7zILoz"
}
},
{
"cell_type": "markdown",
"source": [
"#### 빠르게 기능 맛보기\n",
"1. 목차만들기(메뉴사용 만들기, 마크업으로 #으로 조절하기, 이동하기)\n",
"2. 파일관리(파일업로드, 삭제, 생성)\n",
"3. 변수 및 코드실행\n",
"4. 파이썬 초기화"
],
"metadata": {
"id": "cNNk0XwIY0N1"
}
},
{
"cell_type": "markdown",
"source": [
"목차만들기\n",
"> 첫문장의 제목을 #,##, ###, #####로 계층구조로 만들어 목차로 사용가능하다. 접고 펼칠 수 있다. 메뉴에서는 수정이 힘들고 문서 내(.ipynb)에서 copy & paste가 아니라 화살표 기능으로 이동이 가능하다. \n",
"\n",
"![image.png](data:image/png;base64,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)"
],
"metadata": {
"id": "zFbt6i5ucKV6"
}
},
{
"cell_type": "markdown",
"source": [
"파일관리\n",
"> 저장된 파일은 세션이 종료되면 사라진다. 그러므로 잠시 사용할 수 있는 파일이다.\n",
"\n",
"\n",
"![image.png](data:image/png;base64,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)"
],
"metadata": {
"id": "XsEyKgEmbrxL"
}
},
{
"cell_type": "markdown",
"source": [
"변수 및 코드실행\n",
"> 지금 문서에서 실행된 파이썬의 변수정보를 알려준다.\n",
"파이썬 코드를 만들고자 한다면 shift + enter를 누르거나 상단의 +코드라는 메뉴를 누르면 선택한 항목의 하단에 파이썬 코드를 입력 및 실행할 수 있는 항목이 생성된다.\n",
"\n",
"\n",
"\n",
"\n",
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)"
],
"metadata": {
"id": "O28MXUtke6oB"
}
},
{
"cell_type": "code",
"source": [
"print (\"안녕하세요. 파이썬입니다. 좌측 버튼을 눌러주세요\")"
],
"metadata": {
"id": "9P4PWx6ie-fy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"좌측 버튼을 누르면 실행이 되고 결과화면의 좌측 메뉴에서 X를 누르면 결과가 삭제된다."
],
"metadata": {
"id": "7q20-wQrf0pY"
}
},
{
"cell_type": "markdown",
"source": [
"초기화\n",
"> 실행된 파이썬의 메모리 정보(Conetext)와 저장된 파일을 삭제한다. 이런 정보가 쌓이면 할당된 RAM과 디스크에 과부하가 생기기 때문이다.\n",
"\n",
"![image.png](data:image/png;base64,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)"
],
"metadata": {
"id": "r6gqMq_Jhzid"
}
},
{
"cell_type": "markdown",
"source": [
"## 2. 파이썬 시작하기\n",
"> 파이썬을 공부하기 위한 목차를 만들고 생성 AI에게 프롬프트를 던지며 자료를 보고 학습하기.\n",
"\n",
"- **초보라는 설정**하에 궁금한 것은 생성AI에 명령(prompt)하여 얻어내기\n",
"- 원하는 결과를 찾지 못하면 youtube가 Googling을 통해 얻어내기"
],
"metadata": {
"id": "Fr3YgkTBiPf-"
}
},
{
"cell_type": "markdown",
"source": [
"### 파이썬 학습용 목차만들기\n",
"> 파이썬 학습을 위해서 ChatGPT에게 목차를 요청한다\n",
"\n",
"ChatGPT(🤖)을 프롬프트 한다.\n",
"\n",
"```\n",
"파이썬 학습을 위해 기초적인 목차를 알려주세요\n",
"```\n"
],
"metadata": {
"id": "1EJh5UGCke4c"
}
},
{
"cell_type": "markdown",
"source": [
"- [ChatGPT(🤖)에게 프롬프트한 내용](https://chat.openai.com/share/78f006fc-41be-41e6-95d9-1859b824e62d\n",
")\n",
"\n",
"- [Bard에게 미리 프롬프트한 Quiz의 결과를 표로 정리](https://docs.google.com/document/d/1uNwf6rGBwq6UWhkuN5o7rr8Xn2AltkDWmKF8ZfJgYY8/edit?usp=sharing)\n"
],
"metadata": {
"id": "-Ur2utHdlWUr"
}
},
{
"cell_type": "markdown",
"source": [
"#### 위의 프롬프트로 만든 목차\n",
"Person(😀):\n",
"- 파이썬 기초학습을 위해 목차를 알려주세요\n",
"\n"
],
"metadata": {
"id": "SXdXE5BH-iSV"
}
},
{
"cell_type": "markdown",
"source": [
"ChatGPT(🤖):\n",
"- 아래는 파이썬의 기초 학습을 위한 일반적인 목차 예시입니다. 이 목차는 파이썬의 기본 문법, 데이터 구조, 함수, 조건문, 반복문 등에 대한 기초적인 내용을 다룹니다. 물론 목차는 학습자의 수준과 요구사항에 따라 다를 수 있습니다.\n",
"1. 소개\n",
"- 파이썬 소개\n",
"- 파이썬 설치\n",
"2. 기본 문법\n",
"- 주석\n",
"- 변수\n",
"- 데이터 타입\n",
"- 연산자\n",
"- 조건문\n",
"- 반복문\n",
"- 함수\n",
"3. 데이터 구조\n",
"- 리스트\n",
"- 튜플\n",
"- 딕셔너리\n",
"- 집합\n",
"- 문자열\n",
"4. 파일 다루기\n",
"- 파일 읽기\n",
"- 파일 쓰기\n",
"- 파일 입출력 예외 처리\n",
"5. 모듈과 패키지\n",
"- 모듈의 개념\n",
"- 모듈 사용하기\n",
"- 패키지의 개념\n",
"- 패키지 사용하기\n",
"6. 예외 처리\n",
"- 예외의 개념\n",
"- 예외 처리하기\n",
"- 예외 발생시키기\n",
"7. 객체 지향 프로그래밍\n",
"- 클래스와 객체\n",
"- 상속\n",
"- 다형성\n",
"8. 추가 주제\n",
"- 내장 함수\n",
"- 람다 함수\n",
"- 제너레이터\n",
"- 데코레이터\n",
"- 가상 환경과 패키지 관리\n",
"\n",
"위의 목차는 파이썬의 기초를 다루는 주제들을 포함하고 있습니다. 학습자의 흥미와 요구에 따라 추가적인 주제를 포함시킬 수 있습니다. 또한, 실제 학습 과정에서는 예제 코드와 실습을 통해 실제로 파이썬을 작성하고 실행해보는 것이 중요합니다.\n"
],
"metadata": {
"id": "-B1Ogx98-3rW"
}
},
{
"cell_type": "markdown",
"source": [
"[TODO]위의 목차로 프롬프트하며 나만의 학습노트 만들기"
],
"metadata": {
"id": "hGipG6ssoXmx"
}
},
{
"cell_type": "markdown",
"source": [
"#### 1. 주석\n",
"주석은 파이썬 코드에서 프로그래머가 작성한 메모나 설명으로, 프로그램 실행에는 영향을 주지 않습니다. 주석은 코드의 가독성을 높이고, 다른 개발자들과 코드를 공유하거나 유지보수를 위해 중요한 역할을 합니다. 파이썬에서는 주석을 작성할 때 # 기호를 사용합니다.\n",
"주석은 코드의 특정 부분을 설명하거나, 임시로 코드를 비활성화할 때 사용될 수 있습니다. 주석은 단일 라인이나 여러 줄에 걸쳐 작성할 수 있습니다. 다음은 주석을 사용한 예제입니다.\n"
],
"metadata": {
"id": "0HoSGsF9vYac"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"```python\n",
"# 이것은 단일 라인 주석입니다.\n",
"\n",
"print(\"안녕하세요!\") # 이 줄은 코드이며, 이 부분은 주석입니다.\n",
"\n",
"# 다음은 여러 줄에 걸친 주석입니다.\n",
"# 이 주석은\n",
"# 여러 줄을\n",
"# 설명하기 위해 사용됩니다.\n",
"\n",
"\"\"\"\n",
"이것은 여러 줄 주석의 다른 형식입니다.\n",
"큰 따옴표 세 개로 감싸서 사용할 수 있습니다.\n",
"여러 줄 주석을 사용할 때 주로 문서화에 활용됩니다.\n",
"\"\"\"\n",
"\n",
"# 아래 코드는 실행되지 않습니다.\n",
"# print(\"이 부분은 주석 처리되었기 때문에 실행되지 않습니다.\")\n",
"\n",
"```\n",
"위의 예제에서 볼 수 있듯이, 주석은 # 기호 뒤에 작성되며, 해당 줄의 끝까지 주석으로 처리됩니다. 여러 줄에 걸친 주석은 \"\"\" 또는 '''로 감싸서 사용할 수 있습니다. 이러한 주석은 문서화에 자주 사용되며, 함수나 클래스의 동작 설명 등을 기술할 때 유용합니다. 주석은 필요에 따라 자세하게 작성하여 코드의 이해를 돕는 것이 좋습니다.\n"
],
"metadata": {
"id": "1vrdqqZz_Pn2"
}
},
{
"cell_type": "code",
"source": [
"# 이것은 단일 라인 주석입니다.\n",
"\n",
"print(\"안녕하세요!\") # 이 줄은 코드이며, 이 부분은 주석입니다.\n",
"\n",
"# 다음은 여러 줄에 걸친 주석입니다.\n",
"# 이 주석은\n",
"# 여러 줄을\n",
"# 설명하기 위해 사용됩니다.\n",
"\n",
"\"\"\"\n",
"이것은 여러 줄 주석의 다른 형식입니다.\n",
"큰 따옴표 세 개로 감싸서 사용할 수 있습니다.\n",
"여러 줄 주석을 사용할 때 주로 문서화에 활용됩니다.\n",
"\"\"\"\n",
"\n",
"# 아래 코드는 실행되지 않습니다.\n",
"# print(\"이 부분은 주석 처리되었기 때문에 실행되지 않습니다.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
},
"id": "BOYa2ApewdON",
"outputId": "bdb52061-5d54-4419-9d31-de5c97e044d2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"안녕하세요!\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'\\n이것은 여러 줄 주석의 다른 형식입니다.\\n큰 따옴표 세 개로 감싸서 사용할 수 있습니다.\\n여러 줄 주석을 사용할 때 주로 문서화에 활용됩니다.\\n'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 2. 변수\n",
"변수는 값을 저장하는 데 사용되는 이름이며, 데이터를 참조하고 조작하는 데에 필요한 기본적인 요소입니다. 파이썬에서 변수를 사용하여 데이터를 저장하고, 이를 활용하여 계산하거나 조작할 수 있습니다. 변수는 다양한 데이터 타입을 가질 수 있으며, 값이 변경될 수 있는 특징을 가지고 있습니다.\n",
"\n",
"변수를 사용하기 위해서는 변수를 선언하고 값을 할당해야 합니다. 파이썬에서는 변수 선언을 별도로 할 필요가 없으며, 변수에 값을 할당하는 과정에서 변수가 자동으로 생성됩니다. 변수 할당은 등호(=)를 사용하여 수행되며, 오른쪽의 값을 왼쪽의 변수에 저장합니다.\n",
"\n",
"다음은 변수를 사용한 예제입니다.\n"
],
"metadata": {
"id": "a1mjX83Gymvw"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"```python\n",
"# 변수 할당\n",
"name = \"Alice\"\n",
"age = 25\n",
"pi = 3.14\n",
"is_student = True\n",
"\n",
"# 변수 값 출력\n",
"print(name) # \"Alice\" 출력\n",
"print(age) # 25 출력\n",
"print(pi) # 3.14 출력\n",
"print(is_student) # True 출력\n",
"\n",
"# 변수 값 변경\n",
"name = \"Bob\"\n",
"age = age + 1\n",
"pi = pi * 2\n",
"is_student = False\n",
"\n",
"# 변경된 변수 값 출력\n",
"print(name) # \"Bob\" 출력\n",
"print(age) # 26 출력\n",
"print(pi) # 6.28 출력\n",
"print(is_student) # False 출력\n",
"\n",
"```\n",
"\n",
"위의 예제에서는 name, age, pi, is_student라는 변수를 선언하고 값을 할당했습니다. 각 변수에는 문자열, 정수, 부동 소수점 숫자, 불리언(Boolean) 값이 할당되었습니다. 변수 값은 print() 함수를 사용하여 출력되었습니다.\n",
"\n",
"또한, 예제의 후반부에서는 변수의 값을 변경하였습니다. 변수는 언제든지 새로운 값을 할당하여 내용을 업데이트할 수 있습니다. 위의 예제에서는 name 변수의 값을 \"Alice\"에서 \"Bob\"으로, age 변수의 값을 25에서 26으로, pi 변수의 값을 3.14에서 6.28로, is_student 변수의 값을 True에서 False로 변경하였습니다.\n",
"\n",
"변수는 파이썬 프로그래밍에서 매우 중요한 개념이며, 계산, 조건문, 반복문 등 다양한 상황에서 활용됩니다."
],
"metadata": {
"id": "QxaW4P-E_ZqO"
}
},
{
"cell_type": "code",
"source": [
"# 변수 할당\n",
"name = \"Alice\"\n",
"age = 25\n",
"pi = 3.14\n",
"is_student = True\n",
"\n",
"# 변수 값 출력\n",
"print(name) # \"Alice\" 출력\n",
"print(age) # 25 출력\n",
"print(pi) # 3.14 출력\n",
"print(is_student) # True 출력\n",
"\n",
"# 변수 값 변경\n",
"name = \"Bob\"\n",
"age = age + 1\n",
"pi = pi * 2\n",
"is_student = False\n",
"\n",
"# 변경된 변수 값 출력\n",
"print(name) # \"Bob\" 출력\n",
"print(age) # 26 출력\n",
"print(pi) # 6.28 출력\n",
"print(is_student) # False 출력\n"
],
"metadata": {
"id": "3-qF-bp8zIf5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"미션)\n",
"ChatGPT(🤖)에게 물어보거나 소스코드를 만든다.\n",
"```\n",
"a는 3이고\n",
"b는 4이다\n",
"a와 b를 더한 것을\n",
"c에 입력하는\n",
"파이썬 코드를 만들어주세요\n",
"```"
],
"metadata": {
"id": "tTcsR25Tzrjo"
}
},
{
"cell_type": "code",
"source": [
"a = 3\n",
"b = 4\n",
"c = a + b\n",
"print(c)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yu3qXz-azzG8",
"outputId": "3fa0ab7b-8719-4147-8c54-c3de08d9af79"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"7\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 3. 데이터 타입\n",
"파이썬에서 데이터 타입은 변수가 저장할 수 있는 값의 종류를 의미합니다. 파이썬은 동적 타이핑 언어로, 변수를 선언할 때 데이터 타입을 명시적으로 지정해주지 않아도 됩니다. 대신 변수에 할당되는 값에 따라 자동으로 데이터 타입이 결정됩니다.\n",
"\n",
"파이썬에서 주요한 데이터 타입은 다음과 같습니다.\n",
"\n"
],
"metadata": {
"id": "CuvVdj3H3hHj"
}
},
{
"cell_type": "markdown",
"source": [
"숫자(Number) 타입:\n",
"\n",
"- 정수(int):\n",
"\n",
"정수 값을 나타냅니다. 예: 1, 100, -10\n",
"\n",
"- 실수(float):\n",
"\n",
"부동소수점 숫자를 나타냅니다. 예: 3.14, 2.718, -0.5\n",
"문자열(String) 타입:\n",
"\n",
"- 문자열(str):\n",
"\n",
"문자들의 시퀀스로 이루어진 데이터 타입입니다. 작은 따옴표(')나 큰 따옴표(\")로 둘러싸서 표현합니다. 예: 'Hello', \"Python\", '123'\n",
"\n",
"- 불리언(Boolean) 타입:\n",
"\n",
"불리언(bool):\n",
"참(True) 또는 거짓(False) 값을 가지는 데이터 타입입니다. 조건문과 논리 연산에 주로 사용됩니다.\n",
"리스트(List) 타입:\n",
"\n",
"- 리스트(list):\n",
"\n",
"값들의 순서가 있는 변경 가능한 시퀀스입니다. 대괄호([ ])로 둘러싸서 표현하며, 각 값은 쉼표로 구분됩니다. 예: [1, 2, 3], ['apple', 'banana', 'orange']\n",
"\n",
"- 튜플(Tuple) 타입:\n",
"\n",
"튜플(tuple): 값들의 순서가 있는 변경 불가능한 시퀀스입니다. 소괄호(( ))로 둘러싸서 표현하며, 각 값은 쉼표로 구분됩니다. 예: (1, 2, 3), ('apple', 'banana', 'orange')\n",
"\n",
"- 딕셔너리(Dictionary) 타입:\n",
"\n",
"딕셔너리(dict): 키와 값의 쌍으로 이루어진 데이터 타입입니다. 중괄호({ })로 둘러싸서 표현하며, 각 쌍은 콜론(:)으로 구분되고, 각 쌍은 쉼표로 구분됩니다. 예: {'name': 'John', 'age': 25, 'city': 'Seoul'}\n",
"\n",
"- 집합(Set) 타입:\n",
"\n",
"집합(set): 고유한 값을 가지는 변경 가능한 데이터 타입입니다. 중괄호({ })로 둘러싸서 표현하며, 각 값은 쉼표로 구분됩니다. 예: {1, 2, 3, 4, 5}\n",
"아래는 각 데이터 타입의 예제입니다.\n",
"\n",
"```python\n",
"# 숫자(Number) 타입\n",
"age = 25\n",
"pi = 3.14\n",
"\n",
"# 문자열(String) 타입\n",
"name = 'John'\n",
"greeting = \"Hello, World!\"\n",
"\n",
"# 불리언(Boolean) 타입\n",
"is_true = True\n",
"is_false = False\n",
"\n",
"# 리스트(List) 타입\n",
"numbers = [1, 2, 3, 4, 5]\n",
"fruits = ['apple', 'banana', 'orange']\n",
"\n",
"# 튜플(Tuple) 타입\n",
"point = (10, 20)\n",
"colors = ('red', 'green', 'blue')\n",
"\n",
"# 딕셔너리(Dictionary) 타입\n",
"person = {'name': 'John', 'age': 25, 'city': 'Seoul'}\n",
"\n",
"# 집합(Set) 타입\n",
"unique_numbers = {1, 2, 3, 4, 5}\n",
"\n",
"```\n",
"\n",
"위의 예제에서는 각 데이터 타입의 변수를 선언하고, 값을 할당하였습니다. 파이썬은 변수에 값을 할당할 때 해당 값의 데이터 타입을 자동으로 추론하여 변수에 할당합니다."
],
"metadata": {
"id": "w_DFtqwa_sWO"
}
},
{
"cell_type": "code",
"source": [
"# 숫자(Number) 타입\n",
"age = 25\n",
"pi = 3.14\n",
"\n",
"# 문자열(String) 타입\n",
"name = 'John'\n",
"greeting = \"Hello, World!\"\n",
"\n",
"# 불리언(Boolean) 타입\n",
"is_true = True\n",
"is_false = False\n",
"\n",
"# 리스트(List) 타입\n",
"numbers = [1, 2, 3, 4, 5]\n",
"fruits = ['apple', 'banana', 'orange']\n",
"\n",
"# 튜플(Tuple) 타입\n",
"point = (10, 20)\n",
"colors = ('red', 'green', 'blue')\n",
"\n",
"# 딕셔너리(Dictionary) 타입\n",
"person = {'name': 'John', 'age': 25, 'city': 'Seoul'}\n",
"\n",
"# 집합(Set) 타입\n",
"unique_numbers = {1, 2, 3, 4, 5}"
],
"metadata": {
"id": "F0255olO35Qd"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"미션) ChatGPT(🤖)에게 물어보거나 직접하기\n",
"\n",
"```\n",
"\n",
"고양이, 강아지, 침팬지,고래를 넣는 배열을 동물들이라고 정의한다.\n",
"파이썬으로 코드를 만들어주세요\n",
"\n",
"```"
],
"metadata": {
"id": "IJNJ-BOu4OdD"
}
},
{
"cell_type": "code",
"source": [
"animals = [\"cat\", \"dog\", \"chimpanzee\", \"whale\"]\n",
"print(animals)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4MnjjeGS4e5P",
"outputId": "a72e2829-a7b8-47ad-f2a7-42104f3728e3"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['cat', 'dog', 'chimpanzee', 'whale']\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 4. 연산자\n",
"파이썬에서 연산자는 값을 조작하거나 연산을 수행하는 데 사용되는 기호입니다. 파이썬은 다양한 종류의 연산자를 제공하여 수치 연산, 논리 연산, 비교 연산 등을 수행할 수 있습니다.\n"
],
"metadata": {
"id": "kuIn9qau-XZj"
}
},
{
"cell_type": "markdown",
"source": [
"1. 산술 연산자:\n",
"- `+` : 덧셈 연산을 수행합니다.\n",
"- `-` : 뺄셈 연산을 수행합니다.\n",
"- `*` : 곱셈 연산을 수행합니다.\n",
"- `/` : 나눗셈 연산을 수행하고 결과를 실수형으로 반환합니다.\n",
"- `//` : 나눗셈 연산을 수행하고 결과를 정수형으로 반환합니다.\n",
"- `%` : 나머지 연산을 수행합니다.\n",
"- `**` : 거듭제곱 연산을 수행합니다.\n",
"\n",
"```python\n",
"\n",
"a = 10\n",
"b = 3\n",
"\n",
"print(a + b) # 13\n",
"print(a - b) # 7\n",
"print(a * b) # 30\n",
"print(a / b) # 3.3333333333333335\n",
"print(a // b) # 3\n",
"print(a % b) # 1\n",
"print(a ** b) # 1000\n",
"```\n",
"\n",
"\n",
"1. 할당 연산자:\n",
"- `=` : 변수에 값을 할당합니다.\n",
"- `+=` : 현재 변수에 값을 더하여 결과를 변수에 할당합니다.\n",
"- `-=` : 현재 변수에서 값을 빼고 결과를 변수에 할당합니다.\n",
"- `*=` : 현재 변수에 값을 곱하여 결과를 변수에 할당합니다.\n",
"- `/=` : 현재 변수를 나누고 결과를 변수에 할당합니다.\n",
"- `//=` : 현재 변수를 나눈 뒤 정수형으로 변환하여 결과를 변수에 할당합니다.\n",
"- `%=` : 현재 변수를 나눈 뒤 나머지를 결과로 변수에 할당합니다.\n",
"\n",
"```python\n",
"\n",
"x = 10\n",
"\n",
"x += 5 # x = x + 5\n",
"print(x) # 15\n",
"\n",
"x -= 3 # x = x - 3\n",
"print(x) # 12\n",
"\n",
"x *= 2 # x = x * 2\n",
"print(x) # 24\n",
"\n",
"x /= 4 # x = x / 4\n",
"print(x) # 6.0\n",
"\n",
"x //= 2 # x = x // 2\n",
"print(x) # 3\n",
"\n",
"x %= 2 # x = x % 2\n",
"print(x) # 1\n",
"```\n",
"\n",
"\n",
"1. 비교 연산자:\n",
"- `==` : 두 값이 같은지 비교합니다.\n",
"- `!=` : 두 값이 다른지 비교합니다.\n",
"- `>` : 왼쪽 값이 오른쪽 값보다 큰지 비교합니다.\n",
"- `<` : 왼쪽 값이 오른쪽 값보다 작은지 비교합니다.\n",
"- `>=` : 왼쪽 값이 오른쪽 값보다 크거나 같은지 비교합니다.\n",
"- `<=` : 왼쪽 값이 오른쪽 값보다 작거나 같은지 비교합니다.\n",
"\n",
"```python\n",
"\n",
"a = 5\n",
"b = 3\n",
"\n",
"print(a == b) # False\n",
"print(a != b) # True\n",
"print(a > b) # True\n",
"print(a < b) # False\n",
"print(a >= b) # True\n",
"print(a <= b) # False\n",
"```\n",
"\n",
"\n",
"1. 논리 연산자:\n",
"- `and` : 두 조건식이 모두 참일 때 참을 반환합니다.\n",
"- `or` : 두 조건식 중 하나라도 참이면 참을 반환합니다.\n",
"- `not` : 조건식의 결과를 반대로 반환합니다.\n",
"\n",
"```python\n",
"\n",
"x = 5\n",
"y = 3\n",
"z = 7\n",
"\n",
"print(x > y and x < z) # True\n",
"print(x > y or x > z) # True\n",
"print(not(x > y)) # False\n",
"```\n",
"\n",
"\n",
"\n",
"위의 예제에서는 주요한 연산자들을 다루었습니다. 파이썬은 다양한 연산자를 제공하므로 필요에 따라 추가적인 연산자를 학습하고 활용할 수 있습니다."
],
"metadata": {
"id": "cjoM2ZOSDgIl"
}
},
{
"cell_type": "code",
"source": [
"a = 10\n",
"b = 3\n",
"\n",
"print(a + b) # 13\n",
"print(a - b) # 7\n",
"print(a * b) # 30\n",
"print(a / b) # 3.3333333333333335\n",
"\n",
"x = 10\n",
"\n",
"x += 5 # x = x + 5\n",
"print(x) # 15\n",
"\n",
"x -= 3 # x = x - 3\n",
"print(x) # 12\n",
"\n",
"a = 5\n",
"b = 3\n",
"\n",
"print(a == b) # False\n",
"print(a != b) # True\n",
"print(a > b) # True\n",
"print(a < b) # False\n",
"print(a >= b) # True\n",
"print(a <= b) # False"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "s7Ghmu_4-s9b",
"outputId": "5a9c0c59-6b34-458b-f530-e4d419334f2a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"13\n",
"7\n",
"30\n",
"3.3333333333333335\n",
"15\n",
"12\n",
"False\n",
"True\n",
"True\n",
"False\n",
"True\n",
"False\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"미션) ChatGPT(🤖)에게 물어보세요\n",
"\n",
"```\n",
"파이썬에서\n",
"a는 3\n",
"b는 5\n",
"비교연산자로 a >b의 값은\n",
"무엇인지 출력해줘\n",
"```"
],
"metadata": {
"id": "EKDM-OJL_HVf"
}
},
{
"cell_type": "code",
"source": [
"a = 3\n",
"b = 5\n",
"\n",
"print(a > b)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a1VsJOUx_OJF",
"outputId": "1ff0583b-0c3f-408a-8444-402e16880ac9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"False\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 5. 조건문\n",
"\n",
"파이썬에서 조건문은 주어진 조건에 따라 코드 블록을 실행하거나 건너뛰는 데 사용됩니다. 주로 `if`, `elif`, `else` 키워드를 사용하여 작성합니다. 조건문은 프로그램이 특정 조건을 충족할 때 특정 동작을 수행하도록 하는데 유용합니다. 아래 예시를 통해 조건문을 자세히 살펴보겠습니다.\n"
],
"metadata": {
"id": "JIzJx4SFB1f4"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"```python\n",
"\n",
"age = 18\n",
"\n",
"if age >= 20:\n",
" print(\"성인입니다.\")\n",
"elif age >= 13:\n",
" print(\"청소년입니다.\")\n",
"else:\n",
" print(\"어린이입니다.\")\n",
"```\n",
"\n",
"\n",
"\n",
"위의 예제에서는 `age` 변수에 값을 할당하고, 해당 값에 따라 조건문이 실행됩니다.\n",
"- 첫 번째 조건 `age >= 20`은 참이 아니므로 건너뛰고 다음 조건으로 넘어갑니다.\n",
"- 두 번째 조건 `age >= 13`은 참이므로 해당 코드 블록이 실행되고 \"청소년입니다.\"라는 메시지가 출력됩니다.\n",
"- 마지막으로 `else` 구문은 앞선 조건들이 모두 거짓일 경우 실행되며, 위 예제에서는 해당되지 않으므로 실행되지 않습니다.\n",
"\n",
"조건문은 단일 `if` 문으로 시작할 수도 있고, 여러 개의 `elif` 문과 선택적인 `else` 문을 포함할 수도 있습니다. 이를 통해 여러 가지 조건을 처리하고 원하는 결과를 얻을 수 있습니다.\n",
"\n",
"아래 예시는 조건문을 사용하여 성적에 따라 학점을 부여하는 예제입니다.\n",
"\n",
"```python\n",
"\n",
"score = 85\n",
"\n",
"if score >= 90:\n",
" grade = 'A'\n",
"elif score >= 80:\n",
" grade = 'B'\n",
"elif score >= 70:\n",
" grade = 'C'\n",
"elif score >= 60:\n",
" grade = 'D'\n",
"else:\n",
" grade = 'F'\n",
"\n",
"print(\"학점:\", grade)\n",
"```\n",
"\n",
"\n",
"\n",
"위 예제에서는 `score` 변수에 값을 할당하고, 해당 값에 따라 조건문이 실행됩니다. `if`와 `elif` 조건들은 참인 경우 해당되는 학점을 `grade` 변수에 할당하고, `else` 조건은 모든 이전 조건들이 거짓일 경우 실행되어 'F'를 `grade` 변수에 할당합니다. 마지막으로, 학점이 출력됩니다.\n",
"\n",
"이러한 방식으로 조건문을 사용하여 프로그램의 흐름을 제어하고, 주어진 조건에 따라 다른 동작을 수행할 수 있습니다."
],
"metadata": {
"id": "4FnoSoo2EIjd"
}
},
{
"cell_type": "code",
"source": [
"score = 85\n",
"\n",
"if score >= 90:\n",
" grade = 'A'\n",
"elif score >= 80:\n",
" grade = 'B'\n",
"elif score >= 70:\n",
" grade = 'C'\n",
"elif score >= 60:\n",
" grade = 'D'\n",
"else:\n",
" grade = 'F'\n",
"\n",
"print(\"학점:\", grade)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yozbDK4BCBCv",
"outputId": "ff20ad33-49f3-4183-d50f-dcc75c5eb410"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"학점: B\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"미션) ChatGPT(🧚🏻‍♂️)\n",
"\n",
"```\n",
"점수=80\n",
"80점이하:\n",
" 출력 \"시험에 불합격하셨습니다\"\n",
"80점이상:\n",
" 출력 \"시험에 합격하셨습니다\"\n",
"100점이상:\n",
" 출력 \"멋지세요. 100입니다\" \n",
"\n",
"파이썬 코드로 만들어주세요\n",
"```"
],
"metadata": {
"id": "s66frPwhCbaP"
}
},
{
"cell_type": "code",
"source": [
"score = 100\n",
"\n",
"if score < 80:\n",
" print(\"시험에 불합격하셨습니다.\")\n",
"elif score >= 80 and score < 100:\n",
" print(\"시험에 합격하셨습니다.\")\n",
"else:\n",
" print(\"멋지세요. 100입니다.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0vNiPRVdDs4W",
"outputId": "29a1f445-ad53-41d2-f5a4-23ffad4a73dc"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"멋지세요. 100입니다.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 6. 반복문\n",
"파이썬에서 반복문은 특정한 코드 블록을 반복적으로 실행하는데 사용됩니다. 주로 리스트나 튜플과 같은 데이터 구조의 요소들을 하나씩 접근하거나, 특정한 횟수만큼 반복 작업을 수행할 때 사용됩니다. 파이썬에서는 `for`문과 `while`문이 두 가지 주요한 반복문입니다.\n"
],
"metadata": {
"id": "Hqzopuf6GdGd"
}
},
{
"cell_type": "markdown",
"source": [
"1. `for`문:\n",
"`for`문은 리스트, 튜플, 문자열과 같은 iterable한 객체의 요소를 하나씩 순회하면서 반복 작업을 수행합니다. `for`문의 기본 구조는 다음과 같습니다:\n",
"\n",
"```python\n",
"\n",
"for 요소 in iterable:\n",
" # 반복적으로 실행할 코드\n",
"```\n",
"\n",
"\n",
"\n",
"이제 예제를 통해 `for`문을 설명해드리겠습니다. 다음 예제는 리스트의 요소들을 순회하며 출력하는 간단한 예제입니다:\n",
"\n",
"```python\n",
"\n",
"fruits = ['apple', 'banana', 'cherry']\n",
"\n",
"for fruit in fruits:\n",
" print(fruit)\n",
"```\n",
"\n",
"\n",
"\n",
"출력 결과는 다음과 같습니다:\n",
"\n",
"```\n",
"\n",
"apple\n",
"banana\n",
"cherry\n",
"```\n",
"2. `while`문:\n",
"`while`문은 조건식이 참인 동안에 코드 블록을 반복적으로 실행합니다. `while`문의 기본 구조는 다음과 같습니다:\n",
"\n",
"```python\n",
"\n",
"while 조건식:\n",
" # 반복적으로 실행할 코드\n",
"```\n",
"\n",
"\n",
"\n",
"이제 예제를 통해 `while`문을 설명해드리겠습니다. 다음 예제는 변수를 이용하여 1부터 5까지의 숫자를 출력하는 예제입니다:\n",
"\n",
"```python\n",
"\n",
"count = 1\n",
"\n",
"while count <= 5:\n",
" print(count)\n",
" count += 1\n",
"```\n",
"\n",
"\n",
"\n",
"출력 결과는 다음과 같습니다:\n",
"\n",
"```\n",
"\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"```\n",
"\n",
"\n",
"\n",
"`while`문은 조건식을 주의해서 작성해야 합니다. 조건식이 항상 참이 되는 경우 무한 루프에 빠질 수 있으므로, 적절한 종료 조건을 설정해야 합니다.\n",
"\n",
"이렇게 파이썬의 반복문인 `for`문과 `while`문을 간단하게 설명하고 예제를 보여드렸습니다. 이를 통해 파이썬에서 반복 작업을 수행하는 방법에 대해 이해하셨을 것입니다."
],
"metadata": {
"id": "ZJm0b_ZSasFN"
}
},
{
"cell_type": "code",
"source": [
"fruits = ['apple', 'banana', 'cherry']\n",
"\n",
"for fruit in fruits:\n",
" print(fruit)\n",
"\n",
"\n",
"count = 1\n",
"\n",
"while count <= 5:\n",
" print(count)\n",
" count += 1"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "57xXVq3LGjFy",
"outputId": "2c6f4e03-bdd0-4db4-acad-4a467df799a4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"apple\n",
"banana\n",
"cherry\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"mission)\n",
"\n",
"ChatGPT나 Bard에게 프롬프트 해보기\n",
"```\n",
"count = 1\n",
"\n",
"while count <= 5:\n",
" print(count)\n",
" count += 1\n",
"\n",
"에서 count가 10보다 작을경우 루프를 실행해주세요 \n",
"```"
],
"metadata": {
"id": "zvmLGrdIG2tj"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "Zo8xwmz5HA0O"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#### 7. 함수\n",
"파이썬에서 함수는 일련의 작업을 수행하는 코드 블록으로, 재사용 가능한 코드 조각입니다. 함수는 특정한 작업을 수행하고 그 결과를 반환할 수 있습니다. 함수는 코드의 구조화와 모듈화를 도와주며, 코드의 가독성과 유지보수성을 향상시킵니다.\n",
"\n",
"함수는 다음과 같은 형식으로 정의됩니다:\n",
"\n"
],
"metadata": {
"id": "8jmSrtMrLgNV"
}
},
{
"cell_type": "markdown",
"source": [
"```python\n",
"\n",
"def 함수이름(매개변수):\n",
" # 함수의 동작을 정의하는 코드\n",
" # 필요에 따라 매개변수를 활용하고, 결과를 반환할 수 있음\n",
" return 결과값\n",
"```\n",
"\n",
"\n",
"\n",
"위의 형식에서 매개변수는 함수에 전달되는 입력 값이며, 함수 내부에서 사용될 수 있습니다. 결과값은 함수가 실행된 후 반환되는 값입니다.\n",
"\n",
"다음은 간단한 함수의 예제입니다. 이 함수는 두 개의 숫자를 더한 결과를 반환합니다:\n",
"\n",
"```python\n",
"\n",
"def add_numbers(a, b):\n",
" sum = a + b\n",
" return sum\n",
"```\n",
"\n",
"\n",
"\n",
"위의 예제에서 `add_numbers` 함수는 `a`와 `b`라는 두 개의 매개변수를 받고, 덧셈 연산을 수행한 뒤 그 결과를 `return` 키워드를 통해 반환합니다.\n",
"\n",
"이제 이 함수를 호출하여 결과를 확인해봅시다:\n",
"\n",
"```python\n",
"\n",
"result = add_numbers(3, 5)\n",
"print(result)\n",
"```\n",
"\n",
"\n",
"\n",
"출력 결과는 `8`이 될 것입니다. 함수를 호출할 때 인자로 `3`과 `5`를 전달하면, 함수 내부에서는 `a`에 `3`, `b`에 `5`가 할당되어 덧셈 연산이 수행되고, 결과로 `8`이 반환됩니다.\n",
"\n",
"이처럼 함수는 입력을 받아서 어떠한 작업을 수행하고 결과를 반환하는 구조로 사용됩니다. 실제로 함수는 더 복잡한 작업을 수행할 수 있으며, 필요에 따라 매개변수를 조정하거나 반환값을 다양하게 활용할 수 있습니다."
],
"metadata": {
"id": "9add1J9REeoF"
}
},
{
"cell_type": "code",
"source": [
"def add_numbers(a, b):\n",
" sum = a + b\n",
" return sum\n",
"\n",
"result = add_numbers(3, 5)\n",
"print(result)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H3_9AvqvLwr9",
"outputId": "8e64237b-04ec-4175-a0eb-679c4f10a1d6"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"8\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"미션) ChatGPT(🤖)에게 물어보세요\n",
"\n",
"```\n",
"def 더하기함수:\n",
" sum = 파라메터1 + 파라메터2\n",
" return sum\n",
"\n",
"result = 더하기함수(113, 5)\n",
"print(result)\n",
"\n",
"위의 소스에서 파라메터1이 100보다 크면 100 대입하고\n",
"파라메터 2가 100보다 적으면 0으로 대입해서\n",
"sum을 리턴하게 수정해주세요\n",
"\n",
"```"
],
"metadata": {
"id": "GBkMlJqwMII3"
}
},
{
"cell_type": "code",
"source": [
"def 더하기함수(파라메터1, 파라메터2):\n",
" if 파라메터1 > 100:\n",
" 파라메터1 = 100\n",
" if 파라메터2 < 100:\n",
" 파라메터2 = 0\n",
" sum = 파라메터1 + 파라메터2\n",
" return sum\n",
"\n",
"result = 더하기함수(113, 5)\n",
"print(result)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DHxIu-4vMY-t",
"outputId": "c0baf23c-e64a-4e1e-c6bd-84e106af4da9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"100\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 8. 리스트\n",
"파이썬에서 리스트(List)는 여러 개의 값을 저장하는 데이터 구조입니다. 리스트는 대괄호([])로 둘러싸여 있으며, 각 값은 쉼표로 구분하여 나열됩니다. 리스트는 순서가 있으며, 값의 추가, 삭제, 수정 등이 가능합니다. 아래는 리스트의 간단한 설명과 예제입니다."
],
"metadata": {
"id": "3HQBbelZQUBx"
}
},
{
"cell_type": "markdown",
"source": [
"리스트를 만들기 위해서는 대괄호([])를 사용하고, 리스트 내부의 값들은 쉼표(,)로 구분합니다. 예를 들어, 1부터 5까지의 숫자를 저장하는 리스트를 만들어보겠습니다.\n",
"\n",
"```python\n",
"\n",
"numbers = [1, 2, 3, 4, 5]\n",
"```\n",
"\n",
"\n",
"\n",
"위의 코드에서 `numbers`라는 이름의 리스트를 생성하였습니다. 이 리스트에는 1, 2, 3, 4, 5라는 값이 순서대로 저장되어 있습니다.\n",
"\n",
"리스트는 다양한 데이터 타입을 저장할 수 있습니다. 예를 들어, 숫자, 문자열, 불리언 값 등을 혼합하여 리스트에 저장할 수 있습니다.\n",
"\n",
"```python\n",
"\n",
"mixed_list = [1, \"Hello\", True, 3.14]\n",
"```\n",
"\n",
"\n",
"\n",
"위의 예제에서 `mixed_list`라는 이름의 리스트를 생성하였습니다. 이 리스트에는 숫자 1, 문자열 \"Hello\", 불리언 값 True, 그리고 실수 3.14가 순서대로 저장되어 있습니다.\n",
"\n",
"리스트에는 인덱스(Index)를 사용하여 개별 요소에 접근할 수 있습니다. 인덱스는 0부터 시작하며, 대괄호([]) 안에 인덱스 값을 넣어주면 해당 인덱스에 위치한 요소를 가져올 수 있습니다.\n",
"\n",
"```python\n",
"\n",
"numbers = [1, 2, 3, 4, 5]\n",
"print(numbers[0]) # 첫 번째 요소에 접근\n",
"print(numbers[2]) # 세 번째 요소에 접근\n",
"```\n",
"\n",
"\n",
"\n",
"위의 코드는 `numbers` 리스트의 첫 번째 요소와 세 번째 요소를 출력하는 예제입니다. 출력 결과는 각각 1과 3이 됩니다.\n",
"\n",
"리스트에는 다양한 메서드(method)가 제공되어 값의 추가, 삭제, 검색 등을 수행할 수 있습니다. 예를 들어, `append()` 메서드를 사용하여 리스트의 끝에 값 하나를 추가할 수 있습니다.\n",
"\n",
"```python\n",
"\n",
"fruits = [\"apple\", \"banana\", \"cherry\"]\n",
"fruits.append(\"orange\") # \"orange\"를 리스트의 끝에 추가\n",
"print(fruits)\n",
"```\n",
"\n",
"\n",
"\n",
"위의 코드에서 `fruits` 리스트에 \"orange\"를 추가하는 예제입니다. `append()` 메서드를 사용하여 \"orange\"를 리스트의 끝에 추가하였습니다. 출력 결과는 `[\"apple\", \"banana\", \"cherry\", \"orange\"]`가 됩니다.\n",
"\n",
"리스트에 대한 다양한 작업과 메서드에 대해서는 파이썬의 공식 문서나 파이썬 학습 자료에서 자세히 확인할 수 있습니다."
],
"metadata": {
"id": "D3vArk43E0TE"
}
},
{
"cell_type": "code",
"source": [
"numbers = [1, 2, 3, 4, 5]\n",
"print(numbers[0]) # 첫 번째 요소에 접근\n",
"print(numbers[2]) # 세 번째 요소에 접근\n",
"\n",
"fruits = [\"apple\", \"banana\", \"cherry\"]\n",
"fruits.append(\"orange\") # \"orange\"를 리스트의 끝에 추가\n",
"print(fruits)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FdIKPeWrQeIr",
"outputId": "050931d6-4c25-464f-9376-5990cbb2e6a7"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1\n",
"3\n",
"['apple', 'banana', 'cherry', 'orange']\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"미션) ChatGPT(🤖)나 코딩을 통해서 문장이 요구하는 결과를 만들어주세요\n",
"```\n",
"데이터=[1, 2, 3, 4, \"명식이\", False]\n",
"데이터의 요소값을 하나씩 가져온다:\n",
" 만약 숫자라면 요소를 출력\n",
" 아니면 데이터 type을 출력\n",
"```"
],
"metadata": {
"id": "RlZoGAjGQ0c2"
}
},
{
"cell_type": "code",
"source": [
"데이터=[1, 2, 3, 4, \"명식이\", False]\n",
"for 요소 in 데이터:\n",
" if isinstance(요소, int):\n",
" print(요소)\n",
" else:\n",
" print(type(요소))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8edd1011-51fc-4089-cb00-112f58add359",
"id": "aG1Fkn_xYpU4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"<class 'str'>\n",
"False\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"위의 버그 코드를 공유함\n",
"```\n",
"# 버그이유\n",
"데이터=[1, 2, 3, 4, \"명식이\", True]\n",
"for 요소 in 데이터:\n",
" # 이곳에서 False를 int로 인식했다.\n",
" # 그러므로 값을 출력한 것이다.\n",
" if isinstance(요소, int):\n",
" print(요소)\n",
" else:\n",
" print(type(요소))\n",
"\n",
"# 버그는 복합리스트에서 발생하는 것 같다.\n",
"# 같은 데이터의 리스트에서는 발생되지 않는다.\n",
"# - 버그아님\n",
"다른데이터 = [False, True, False]\n",
"print(type( 다른데이터[2] ))\n",
"\n",
"# 버그는 복합리스트 같다. - 버그아님\n",
"데이터=[1, 2, 3, 4, \"명식이\", False]\n",
"print(type( 데이터[5] ))\n",
"\n",
"\n",
"```\n",
"**isinstance가 아닌 type(요소) == \"<class 'int'>\"로 비교해야 함**\n"
],
"metadata": {
"id": "aOFRQk2hV-7g"
}
},
{
"cell_type": "code",
"source": [
"데이터=[1, 2, 3, 4, \"명식이\", False]\n",
"for 요소 in 데이터:\n",
" if str(type(요소)) == \"<class 'int'>\":\n",
" print(요소)\n",
" else:\n",
" print(type(요소))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oPAiSHCvRymP",
"outputId": "a35799b0-6e57-47e5-fb8e-636a97dc2c6b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"<class 'str'>\n",
"<class 'bool'>\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "N5Q7TcZXMmsO"
}
},
{
"cell_type": "markdown",
"source": [
"#### 9. 튜플"
],
"metadata": {
"id": "uZA12fyNMpW4"
}
},
{
"cell_type": "markdown",
"source": [
"Person:\n",
"- 파이썬에서 튜플에 대해서 쉽게 설명해주고 예제를 만들어주세요.\n",
"\n",
"ChatGPT:\n",
"- 파이썬에서 튜플은 여러 개의 값을 저장하는 자료형입니다. 튜플은 리스트와 비슷하지만, 한 번 생성되면 값을 변경할 수 없습니다. 이러한 특징 때문에 튜플은 불변(immutable)한 자료형이라고도 불립니다.\n",
"\n",
"튜플은 괄호 ( ) 를 사용하여 생성하며, 각 값들은 쉼표 ( , ) 로 구분합니다. 예를 들어, 다음과 같이 튜플을 생성할 수 있습니다:\n",
"\n",
"```python\n",
"\n",
"my_tuple = (1, 2, 3, 'apple', 'banana')\n",
"```\n",
"\n",
"\n",
"\n",
"위의 예제에서 `my_tuple`은 정수와 문자열로 이루어진 튜플입니다. 튜플은 서로 다른 타입의 값들을 함께 저장할 수 있습니다.\n",
"\n",
"튜플은 인덱싱과 슬라이싱을 통해 특정 값을 가져올 수 있습니다. 인덱스는 0부터 시작하며, 음수 인덱스를 사용하여 끝에서부터 접근할 수도 있습니다. 예를 들어:\n",
"\n",
"```python\n",
"\n",
"print(my_tuple[0]) # 1 출력\n",
"print(my_tuple[3]) # 'apple' 출력\n",
"print(my_tuple[-1]) # 'banana' 출력\n",
"print(my_tuple[1:4]) # (2, 3, 'apple') 출력\n",
"```\n",
"\n",
"\n",
"\n",
"튜플은 값을 변경할 수 없기 때문에, 인덱싱을 통해 접근한 값은 수정할 수 없습니다. 하지만, 튜플을 이용한 패킹과 언패킹을 통해 값을 다룰 수 있습니다. 패킹은 여러 값을 하나의 튜플로 묶는 것을 의미하고, 언패킹은 튜플을 개별 변수로 분해하는 것을 의미합니다. 예를 들어:\n",
"\n",
"```python\n",
"\n",
"numbers = (1, 2, 3)\n",
"a, b, c = numbers # 언패킹\n",
"print(a) # 1 출력\n",
"print(b) # 2 출력\n",
"print(c) # 3 출력\n",
"```\n",
"\n",
"\n",
"\n",
"위의 예제에서 `numbers` 튜플의 각 값을 변수 `a`, `b`, `c`에 언패킹하여 할당하였습니다.\n",
"\n",
"튜플은 값의 변경이나 추가가 불가능하기 때문에, 데이터의 불변성을 유지해야할 때 유용합니다. 또한, 함수가 여러 개의 값을 반환할 때 튜플을 사용하여 반환할 수도 있습니다"
],
"metadata": {
"id": "wo18NbtHRX7O"
}
},
{
"cell_type": "code",
"source": [
"my_tuple = (1, 2, 3, 'apple', 'banana')\n",
"print(my_tuple[0]) # 1 출력\n",
"print(my_tuple[3]) # 'apple' 출력\n",
"print(my_tuple[-1]) # 'banana' 출력\n",
"print(my_tuple[1:4]) # (2, 3, 'apple') 출력\n",
"\n",
"numbers = (1, 2, 3)\n",
"a, b, c = numbers # 언패킹\n",
"print(a) # 1 출력\n",
"print(b) # 2 출력\n",
"print(c) # 3 출력"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wjnQTe-qMzWu",
"outputId": "043b3552-4690-4b21-f8d6-63575e17c3aa"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1\n",
"apple\n",
"banana\n",
"(2, 3, 'apple')\n",
"1\n",
"2\n",
"3\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 10. 딕셔너리\n"
],
"metadata": {
"id": "54IaBu8YP4rc"
}
},
{
"cell_type": "markdown",
"source": [
"Person:\n",
"- 파이썬에서 딕셔너리에 대해서 가장 쉽게 설명해주고 예제를 만들어주세요\n",
"\n",
"ChatGPT:\n",
"- 파이썬에서 딕셔너리(Dictionary)는 키(Key)와 값(Value)의 쌍을 저장하는 데이터 구조입니다. 딕셔너리는 중괄호({})를 사용하여 생성하며, 각 키와 값은 콜론(:)으로 구분합니다. 딕셔너리는 키를 사용하여 값을 검색하고, 키와 값의 쌍을 추가, 수정, 삭제할 수 있습니다.\n",
"\n",
"다음은 딕셔너리의 간단한 예제입니다:\n",
"\n",
"```python\n",
"\n",
"# 딕셔너리 생성\n",
"student = {\n",
" '이름': '홍길동',\n",
" '학번': 20210001,\n",
" '학과': '컴퓨터공학',\n",
" '성적': 90\n",
"}\n",
"\n",
"# 딕셔너리 값 읽기\n",
"print(student['이름']) # 출력: 홍길동\n",
"print(student['학번']) # 출력: 20210001\n",
"print(student['성적']) # 출력: 90\n",
"\n",
"# 딕셔너리 값 수정\n",
"student['성적'] = 95\n",
"print(student['성적']) # 출력: 95\n",
"\n",
"# 딕셔너리 값 추가\n",
"student['전화번호'] = '010-1234-5678'\n",
"print(student['전화번호']) # 출력: 010-1234-5678\n",
"\n",
"# 딕셔너리 값 삭제\n",
"del student['학과']\n",
"print(student) # 출력: {'이름': '홍길동', '학번': 20210001, '성적': 95, '전화번호': '010-1234-5678'}\n",
"```\n",
"\n",
"\n",
"\n",
"위의 예제에서 `student` 딕셔너리는 학생의 정보를 저장합니다. 각 키는 이름, 학번, 학과, 성적 등을 나타내고, 각 값은 해당 정보의 실제 값입니다. 딕셔너리를 사용하여 키를 통해 값을 검색하거나, 키와 값을 추가, 수정, 삭제할 수 있습니다."
],
"metadata": {
"id": "aLV2hNNTRhK8"
}
},
{
"cell_type": "code",
"source": [
"# 딕셔너리 생성\n",
"student = {\n",
" '이름': '홍길동',\n",
" '학번': 20210001,\n",
" '학과': '컴퓨터공학',\n",
" '성적': 90\n",
"}\n",
"\n",
"# 딕셔너리 값 읽기\n",
"print(student['이름']) # 출력: 홍길동\n",
"print(student['학번']) # 출력: 20210001\n",
"print(student['성적']) # 출력: 90\n",
"\n",
"# 딕셔너리 값 수정\n",
"student['성적'] = 95\n",
"print(student['성적']) # 출력: 95\n",
"\n",
"# 딕셔너리 값 추가\n",
"student['전화번호'] = '010-1234-5678'\n",
"print(student['전화번호']) # 출력: 010-1234-5678\n",
"\n",
"# 딕셔너리 값 삭제\n",
"del student['학과']\n",
"print(student) # 출력: {'이름': '홍길동', '학번': 20210001, '성적': 95, '전화번호': '010-1234-5678'}"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kQh2M7d0QGpe",
"outputId": "626e2c3b-0bef-4d37-ffc2-6306cc6dc2fd"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"홍길동\n",
"20210001\n",
"90\n",
"95\n",
"010-1234-5678\n",
"{'이름': '홍길동', '학번': 20210001, '성적': 95, '전화번호': '010-1234-5678'}\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"미션) ChatGPT에게 요청하기\n",
"\n",
"```\n",
"학생이라는 딕셔너리를 만든다.\n",
"이름은 \"A\"\n",
"성적은 \"80\"\n",
"학과는 \"컴퓨터공학과\"\n",
"\n",
"딕셔너리에서 성적을 지우고 출력해줘\n",
"```"
],
"metadata": {
"id": "0euLG1SRQaXc"
}
},
{
"cell_type": "code",
"source": [
"student = {\n",
" \"name\": \"A\",\n",
" \"grade\": \"80\",\n",
" \"department\": \"Computer Science\"\n",
"}\n",
"\n",
"del student[\"grade\"]\n",
"\n",
"print(student)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kVfbv7atQkHG",
"outputId": "c90c710d-711e-4140-e593-504fa7bc8dfd"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'name': 'A', 'department': 'Computer Science'}\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 11. 집합\n"
],
"metadata": {
"id": "KHSPK46SVu1L"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬에서 집합에 대해서 가장 쉽게 설명해주고 예제를 만들어주세요\n",
"\n",
"ChatGPT:\n",
"- 파이썬에서 집합(set)은 중복되지 않는 요소들의 모임입니다. 집합은 중괄호({})를 사용하여 표현하며, 각 요소는 쉼표로 구분됩니다. 아래는 간단한 집합 예제입니다.\n",
"\n",
"```python\n",
"\n",
"# 빈 집합 생성\n",
"empty_set = set()\n",
"print(empty_set) # 출력: set()\n",
"\n",
"# 요소가 있는 집합 생성\n",
"fruits = {'apple', 'banana', 'orange', 'apple'}\n",
"print(fruits) # 출력: {'apple', 'banana', 'orange'}\n",
"\n",
"# 집합에 요소 추가하기\n",
"fruits.add('grape')\n",
"print(fruits) # 출력: {'apple', 'banana', 'orange', 'grape'}\n",
"\n",
"# 집합에서 요소 제거하기\n",
"fruits.remove('banana')\n",
"print(fruits) # 출력: {'apple', 'orange', 'grape'}\n",
"\n",
"# 집합 연산 수행하기\n",
"set1 = {1, 2, 3, 4, 5}\n",
"set2 = {4, 5, 6, 7, 8}\n",
"\n",
"# 합집합 구하기\n",
"union = set1.union(set2)\n",
"print(union) # 출력: {1, 2, 3, 4, 5, 6, 7, 8}\n",
"\n",
"# 교집합 구하기\n",
"intersection = set1.intersection(set2)\n",
"print(intersection) # 출력: {4, 5}\n",
"\n",
"# 차집합 구하기\n",
"difference = set1.difference(set2)\n",
"print(difference) # 출력: {1, 2, 3}\n",
"\n",
"# 대칭 차집합 구하기\n",
"symmetric_difference = set1.symmetric_difference(set2)\n",
"print(symmetric_difference) # 출력: {1, 2, 3, 6, 7, 8}\n",
"```\n",
"\n",
"\n",
"\n",
"위의 예제에서 볼 수 있듯이, 집합은 중복된 요소를 허용하지 않으며, 요소의 순서가 보장되지 않습니다. 집합은 요소의 존재 유무를 효율적으로 확인하고, 집합 연산을 수행하는데 유용한 자료 구조입니다.\n"
],
"metadata": {
"id": "hk5SsRAaV0hD"
}
},
{
"cell_type": "code",
"source": [
"# 요소가 있는 집합 생성\n",
"fruits = {'apple', 'banana', 'orange', 'apple', 'apple', 'apple'}\n",
"print(fruits) # 출력: {'apple', 'banana', 'orange'}\n",
"\n",
"# 집합에 요소 추가하기\n",
"fruits.add('grape')\n",
"print(fruits) # 출력: {'apple', 'banana', 'orange', 'grape'}\n",
"\n",
"# 집합에서 요소 제거하기\n",
"fruits.remove('banana')\n",
"print(fruits) # 출력: {'apple', 'orange', 'grape'}\n",
"\n",
"# 집합 연산 수행하기\n",
"set1 = {1, 2, 3, 4, 5}\n",
"set2 = {4, 5, 6, 7, 8}\n",
"\n",
"# 합집합 구하기\n",
"union = set1.union(set2)\n",
"print(union) # 출력: {1, 2, 3, 4, 5, 6, 7, 8}"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mlIo5s3JV_BS",
"outputId": "c6bde2ed-a188-4da9-dd3a-120bb747b1f9"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'apple', 'banana', 'orange'}\n",
"{'apple', 'banana', 'grape', 'orange'}\n",
"{'apple', 'grape', 'orange'}\n",
"{1, 2, 3, 4, 5, 6, 7, 8}\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"미션) ChatGPT에게 물어보기\n",
"\n",
"\n",
"```\n",
"다음은 집합이다.\n",
"animal = {'dog', 'cat', 'bird' }\n",
"animal 집합에서\n",
" dog를 삭제하고\n",
" wolf를 추가해줘\n",
"```"
],
"metadata": {
"id": "MrsAtR-lXB0T"
}
},
{
"cell_type": "code",
"source": [
"animal = {'dog', 'cat', 'bird'}\n",
"\n",
"animal.remove('dog')\n",
"animal.add('wolf')\n",
"\n",
"print(animal)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "s1tn6p9xX0kj",
"outputId": "8efc02b4-b1d0-448c-9606-d86f1afa6679"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'wolf', 'bird', 'cat'}\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 12. 문자열\n"
],
"metadata": {
"id": "uLhnPAWJZ6hZ"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬에서 문자열에 대해서 설명해주고 문자열 조작하는 예제를 5개만 만들어주세요\n",
"\n",
"ChatGPT:\n",
"- 파이썬에서 문자열은 문자들의 시퀀스로 표현되며, 작은따옴표('')나 큰따옴표(\"\")로 둘러싸서 표현합니다. 문자열은 변경할 수 없는(immutable) 자료형으로, 한 번 생성되면 수정할 수 없습니다. 이제 문자열 조작을 위한 다섯 가지 예제를 살펴보겠습니다.\n",
"1. 문자열 연결하기\n",
"두 개의 문자열을 연결하여 새로운 문자열을 생성합니다. 이를 위해서는 \"+\" 연산자를 사용합니다.\n",
"\n",
"```python\n",
"\n",
"str1 = \"Hello\"\n",
"str2 = \"World\"\n",
"result = str1 + \" \" + str2\n",
"print(result) # 출력: \"Hello World\"\n",
"```\n",
"\n",
"\n",
"1. 문자열 길이 구하기\n",
"len() 함수를 사용하여 문자열의 길이를 구할 수 있습니다.\n",
"\n",
"```python\n",
"\n",
"str1 = \"Hello, World!\"\n",
"length = len(str1)\n",
"print(length) # 출력: 13\n",
"```\n",
"\n",
"\n",
"1. 문자열 슬라이싱\n",
"문자열에서 일부분을 추출하기 위해 슬라이싱을 사용합니다. 슬라이싱은 문자열의 일부분을 선택할 때 사용되며, 인덱스를 기반으로 동작합니다.\n",
"\n",
"```python\n",
"\n",
"str1 = \"Hello, World!\"\n",
"substring = str1[7:12]\n",
"print(substring) # 출력: \"World\"\n",
"```\n",
"\n",
"\n",
"1. 문자열 대체\n",
"문자열에서 특정 부분을 대체하고 싶을 때는 replace() 메서드를 사용합니다.\n",
"\n",
"```python\n",
"\n",
"str1 = \"Hello, World!\"\n",
"new_str = str1.replace(\"Hello\", \"Hi\")\n",
"print(new_str) # 출력: \"Hi, World!\"\n",
"```\n",
"\n",
"\n",
"1. 문자열 분할\n",
"문자열을 특정 구분자를 기준으로 분할하여 리스트로 변환할 수 있습니다. split() 함수를 사용하여 이 작업을 수행할 수 있습니다.\n",
"\n",
"```python\n",
"\n",
"str1 = \"Hello, World!\"\n",
"words = str1.split(\", \")\n",
"print(words) # 출력: ['Hello', 'World!']\n",
"```\n",
"\n",
"\n",
"\n",
"이러한 예제들은 파이썬에서 문자열 조작에 대한 기본적인 개념과 사용법을 보여줍니다. 문자열은 파이썬 프로그래밍에서 매우 중요한 부분이며, 다양한 기능과 메서드를 활용하여 다양한 작업을 수행할 수 있습니다.\n"
],
"metadata": {
"id": "lnix2ui2Z-3C"
}
},
{
"cell_type": "code",
"source": [
"# 문자열 더하기\n",
"str1 = \"Hello\"\n",
"str2 = \"World\"\n",
"result = str1 + \" \" + str2\n",
"print(result) # 출력: \"Hello World\"\n",
"\n",
"#문자열 길이구하기\n",
"str1 = \"Hello, World!\"\n",
"length = len(str1)\n",
"print(length) # 출력: 13\n",
"\n",
"#특정구간 문자열 자르기\n",
"str1 = \"Hello, World!\"\n",
"substring = str1[7:12]\n",
"print(substring) # 출력: \"World\"\n",
"\n",
"#문자열 치환\n",
"str1 = \"Hello, World!\"\n",
"new_str = str1.replace(\"Hello\", \"Hi\")\n",
"print(new_str) # 출력: \"Hi, World!\"\n",
"\n",
"#문자열 delimeter 기준으로 자르기\n",
"str1 = \"Hello, World!\"\n",
"words = str1.split(\", \")\n",
"print(words) # 출력: ['Hello', 'World!']"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6MD3k1weaE7o",
"outputId": "98c25308-a47b-4cb3-d47e-6bfa28404481"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Hello World\n",
"13\n",
"World\n",
"Hi, World!\n",
"['Hello', 'World!']\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"미션)\n",
"\n",
"```\n",
"데이터 =\"고양이:강아지:캥거루:비둘기\"\n",
"데이터에서 \":\"로 문자를 split해서\n",
"동물들이라는 list에 넣는다.\n",
"동물들 list를 출력하고 갯수를 표시한다.\n",
"\n",
"```"
],
"metadata": {
"id": "icnd4DnMbrbY"
}
},
{
"cell_type": "code",
"source": [
"data = \"고양이:강아지:캥거루:비둘기\"\n",
"animals = data.split(\":\")\n",
"print(animals)\n",
"print(len(animals))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Iq29hY9Nb5Ws",
"outputId": "74d1ea3b-eb6d-466a-d77f-886f15f9a279"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['고양이', '강아지', '캥거루', '비둘기']\n",
"4\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 13. 파일다루기"
],
"metadata": {
"id": "ja1ZYFprhi-t"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬에서 파일 다루기에 대해서 설명해주고 필수 예제를 만들어주세요\n",
"\n",
"ChatGPT:\n",
"- 파이썬에서 파일 다루기는 매우 중요한 작업입니다. 파일 다루기를 통해 데이터를 읽고 쓰며, 파일을 조작하고, 파일 시스템에 접근할 수 있습니다. 이를 위해 파이썬은 `open()` 함수를 제공합니다. `open()` 함수를 사용하여 파일을 열고, 파일 객체를 얻어 작업을 수행한 후에는 `close()` 메서드를 사용하여 파일을 닫아야 합니다. 하지만 좀 더 효율적인 방법으로 파일을 다루는 것이 좋으며, 이를 위해 `with` 문을 사용할 수 있습니다. `with` 문을 사용하면 파일을 열고 닫는 과정을 자동으로 처리할 수 있습니다.\n",
"\n",
"다음은 파일을 열고 내용을 읽어오는 예제입니다:\n",
"\n",
"```python\n",
"\n",
"with open('파일경로/파일이름', 'r') as file:\n",
" contents = file.read()\n",
" print(contents)\n",
"```\n",
"\n",
"\n",
"\n",
"위 예제에서 `'파일경로/파일이름'`은 열고자 하는 파일의 경로와 이름을 나타내는 부분입니다. `'r'`은 파일을 읽기 모드로 열라는 의미입니다. `file.read()` 메서드를 사용하여 파일의 내용을 읽어올 수 있습니다. 그 후 `print()` 함수를 사용하여 파일의 내용을 출력합니다. `with` 문을 사용했으므로, 파일을 닫을 필요가 없습니다.\n",
"\n",
"파일에 새로운 내용을 작성하는 예제는 다음과 같습니다:\n",
"\n",
"```python\n",
"\n",
"with open('파일경로/파일이름', 'w') as file:\n",
" file.write('새로운 내용을 작성합니다.')\n",
"```\n",
"\n",
"\n",
"\n",
"위 예제에서 `'w'`는 파일을 쓰기 모드로 열라는 의미입니다. `file.write()` 메서드를 사용하여 파일에 텍스트를 작성할 수 있습니다.\n",
"\n",
"또한 파일을 추가 모드로 열어 기존 내용 뒤에 새로운 내용을 추가할 수도 있습니다:\n",
"\n",
"```python\n",
"\n",
"with open('파일경로/파일이름', 'a') as file:\n",
" file.write('새로운 내용을 추가합니다.')\n",
"```\n",
"\n",
"\n",
"\n",
"위 예제에서 `'a'`는 파일을 추가 모드로 열라는 의미입니다.\n",
"\n",
"파일을 한 줄씩 읽는 예제는 다음과 같습니다:\n",
"\n",
"```python\n",
"\n",
"with open('파일경로/파일이름', 'r') as file:\n",
" for line in file:\n",
" print(line)\n",
"```\n",
"\n",
"\n",
"\n",
"위 예제에서 `for line in file`을 사용하여 파일을 한 줄씩 읽을 수 있습니다. 각 줄은 `line` 변수에 할당되며, 이를 `print()` 함수를 사용하여 출력합니다.\n",
"\n",
"이 외에도 파이썬은 파일과 관련된 다양한 기능을 제공합니다. 파일을 읽고 쓰는 방법뿐만 아니라 파일을 조작하고 파일 시스템에 대한 작업도 수행할 수 있습니다. 이를 위해 `os` 모듈과 `shutil` 모듈 등을 사용할 수 있습니다. 파일 및 파일 시스템 조작에 대한 자세한 내용은 파이썬 공식 문서를 참조하시기 바랍니다.\n",
"\n",
"Person:\n",
"- 파일 삭제하는 방법도 알려주세요\n",
"\n",
"ChatGPT:\n",
"- 파이썬에서 파일을 삭제하기 위해서는 `os` 모듈의 `remove()` 함수를 사용할 수 있습니다. 다음은 파일을 삭제하는 예제입니다:\n",
"\n",
"```python\n",
"\n",
"import os\n",
"\n",
"# 파일 삭제\n",
"os.remove('파일경로/파일이름')\n",
"```\n",
"\n",
"\n",
"\n",
"위 예제에서 `'파일경로/파일이름'`은 삭제하려는 파일의 경로와 이름을 나타냅니다. `os.remove()` 함수를 호출하여 해당 파일을 삭제할 수 있습니다.\n",
"\n",
"파일 삭제 전에는 항상 주의해야 합니다. 삭제하려는 파일이 중요한 데이터를 담고 있을 수 있으므로, 파일을 삭제하기 전에 반드시 확인해야 합니다. 또한 파일 삭제에는 권한과 보안에 대한 문제도 고려해야 합니다. 파일을 삭제할 때에는 주의하여 진행하시기 바랍니다.\n",
"\n",
"또 다른 유용한 방법은 `os.path` 모듈을 사용하여 파일의 존재 여부를 확인한 후에 삭제하는 것입니다. 다음은 해당 파일이 존재하는지 확인한 후 삭제하는 예제입니다:\n",
"\n",
"```python\n",
"\n",
"import os\n",
"\n",
"file_path = '파일경로/파일이름'\n",
"\n",
"if os.path.exists(file_path):\n",
" os.remove(file_path)\n",
" print(f\"{file_path}가 삭제되었습니다.\")\n",
"else:\n",
" print(f\"{file_path}가 존재하지 않습니다.\")\n",
"```\n",
"\n",
"\n",
"\n",
"위 예제에서 `os.path.exists()` 함수를 사용하여 파일이 존재하는지 확인합니다. 파일이 존재하는 경우에만 `os.remove()` 함수를 호출하여 파일을 삭제합니다. 파일이 존재하지 않을 경우에는 해당 파일이 존재하지 않음을 알리는 메시지를 출력합니다.\n",
"\n",
"파일 삭제는 신중히 진행해야 하며, 필요한 파일인지 확인한 후에 삭제하도록 주의해야 합니다.\n"
],
"metadata": {
"id": "kPiKdDZXhnwz"
}
},
{
"cell_type": "code",
"source": [
"# 파일 읽어오기\n",
"# colab에서는 좌측에서 파일을 a.txt를 만들고 그 안에\n",
"# 아무값이나 넣고 저장한다.\n",
"# \"r\"을 파라메터로 넘기면 읽기를 요청\n",
"with open('a.txt', 'r') as file:\n",
" contents = file.read()\n",
" print(contents)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zZuF46FviJ0O",
"outputId": "23b130eb-86e4-4c59-d9b0-05023134601c"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"안녕하세요 반갑습니다. \n",
"안녕하세요 반갑습니다. \n",
"안녕하세요 반갑습니다. \n",
"안녕하세요 반갑습니다. \n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# 파일 만들기\n",
"# b.txt로 파일을 정한다.\n",
"# 파라메터를 \"w\"로 넘기면 쓰기를 요청\n",
"with open('b.txt', 'w') as file:\n",
" file.write('새롭게 만든파일입니다.')"
],
"metadata": {
"id": "1vWlduHLi1Kk"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# 파일에 내용추가\n",
"# \"a\"파라메터를 넘기면 추가모드를 요청함\n",
"with open('b.txt', 'a') as file:\n",
" file.write('\\n a 모드로 새로운 내용을 추가합니다.')"
],
"metadata": {
"id": "Uk44v_tMjo4c"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# 파일에서 1줄씩 읽어서 프린트한다.\n",
"# 그럴경우 for line in file:와 같은 코드를 사용한다.\n",
"with open('b.txt', 'r') as file:\n",
" for line in file:\n",
" print(line)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "P4SPc2xfkbcd",
"outputId": "2af7253e-5f95-4fd9-c92d-98755f52aaf1"
},
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"새롭게 만든파일입니다.a 모드로 새로운 내용을 추가합니다.a 모드로 새로운 내용을 추가합니다.a 모드로 새로운 내용을 추가합니다.\n",
"\n",
" a 모드로 새로운 내용을 추가합니다.\n",
"\n",
" a 모드로 새로운 내용을 추가합니다.\n",
"\n",
" a 모드로 새로운 내용을 추가합니다.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 14. 모듈에 대한 개념"
],
"metadata": {
"id": "tmb74LqTmLm6"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬에서 모듈에 대해 간략히 설명해주고 예제를 보여주세요\n",
"\n",
"ChatGPT:\n",
"- 파이썬에서 모듈은 함수, 변수, 클래스 등을 포함한 파이썬 코드의 집합입니다. 모듈을 사용하면 코드를 논리적으로 구성하고 재사용 가능한 단위로 분할할 수 있습니다. 모듈은 확장자가 `.py`인 파이썬 파일로 저장되며, 다른 파이썬 프로그램에서 `import` 문을 사용하여 모듈을 불러올 수 있습니다.\n",
"\n",
"모듈을 사용하면 다른 프로그램에서 코드를 재사용할 수 있습니다. 예를 들어, 여러 프로그램에서 공통으로 사용되는 함수나 클래스를 모듈로 작성하여 필요할 때마다 가져와서 사용할 수 있습니다.\n",
"\n",
"다음은 간단한 모듈의 예제입니다. `greetings.py`라는 파일에 다음과 같은 코드를 작성해보겠습니다.\n",
"\n",
"```python\n",
"\n",
"# greetings.py\n",
"\n",
"def say_hello(name):\n",
" print(f\"Hello, {name}!\")\n",
"\n",
"def say_goodbye(name):\n",
" print(f\"Goodbye, {name}!\")\n",
"```\n",
"\n",
"\n",
"\n",
"위의 코드는 `say_hello`와 `say_goodbye`라는 두 개의 함수를 정의한 모듈입니다. 이제 다른 파이썬 프로그램에서 이 모듈을 가져와 사용해보겠습니다.\n",
"\n",
"```python\n",
"\n",
"# main.py\n",
"\n",
"import greetings\n",
"\n",
"greetings.say_hello(\"Alice\")\n",
"greetings.say_goodbye(\"Bob\")\n",
"```\n",
"\n",
"\n",
"\n",
"위의 코드에서 `import greetings`는 `greetings.py` 모듈을 불러온 후에 해당 모듈의 함수를 사용할 수 있게 합니다. 실행 결과는 다음과 같을 것입니다:\n",
"\n",
"```\n",
"\n",
"Hello, Alice!\n",
"Goodbye, Bob!\n",
"```\n",
"\n",
"\n",
"\n",
"이와 같이 모듈을 사용하면 코드를 구조화하고 관련 기능을 하나의 파일로 묶을 수 있으며, 필요할 때마다 다른 프로그램에서 가져와 사용할 수 있습니다.\n",
"\n",
"Person:\n",
"- 위의 예제를 colab에서 사용할 수 있도록 예제를 보여줘\n",
"\n",
"Save & Submit\n",
"Cancel\n",
"\n",
"ChatGPT:\n",
"- 물론입니다! 파이썬 모듈을 Colab에서 사용하려면 먼저 모듈 파일을 Colab 환경에 업로드해야 합니다. 다음 예제를 따라해보세요:\n",
"1. 로컬 컴퓨터에 `greetings.py` 파일을 생성하고 아래와 같이 코드를 작성합니다:\n",
"\n",
"```python\n",
"\n",
"# greetings.py\n",
"\n",
"def say_hello(name):\n",
" print(f\"Hello, {name}!\")\n",
"\n",
"def say_goodbye(name):\n",
" print(f\"Goodbye, {name}!\")\n",
"```\n",
"\n",
"\n",
"1. Colab에 모듈 파일을 업로드하기 위해 다음 코드를 실행합니다:\n",
"\n",
"```python\n",
"\n",
"from google.colab import files\n",
"uploaded = files.upload()\n",
"```\n",
"\n",
"\n",
"1. 파일 선택 창이 나타나면, 로컬 컴퓨터에서 `greetings.py` 파일을 선택합니다.\n",
"2. 파일이 성공적으로 업로드되면, 다음과 같이 모듈을 사용하는 코드를 작성합니다:\n",
"\n",
"```python\n",
"\n",
"import greetings\n",
"\n",
"greetings.say_hello(\"Alice\")\n",
"greetings.say_goodbye(\"Bob\")\n",
"```\n",
"\n",
"\n",
"1. 코드를 실행하면 모듈에서 정의한 함수가 호출되어 결과가 출력됩니다.\n",
"\n",
"Colab에서 모듈을 사용하는 방법은 로컬 환경에서 사용하는 방법과 동일합니다. 단지 모듈 파일을 Colab에 업로드해야 한다는 차이점이 있습니다.\n",
"\n",
"Person:\n",
"- 위의 예제 말고 %%를 사용하여 파일을 만들고 저장하는 방법을 알려주세요\n",
"\n",
"ChatGPT:\n",
"- Colab에서 `%%writefile` 매직 명령어를 사용하여 파일을 만들고 저장할 수 있습니다. 다음 예제를 따라해보세요:\n",
"1. 새로운 코드 셀을 추가하고 다음과 같이 작성합니다:\n",
"\n",
"```python\n",
"\n",
"%%writefile greetings.py\n",
"\n",
"def say_hello(name):\n",
" print(f\"Hello, {name}!\")\n",
"\n",
"def say_goodbye(name):\n",
" print(f\"Goodbye, {name}!\")\n",
"```\n",
"\n",
"\n",
"1. 위의 코드를 실행하면 현재 디렉토리에 `greetings.py` 파일이 생성되고 코드가 저장됩니다.\n",
"2. 이제 다른 코드 셀에서 모듈을 가져와 사용할 수 있습니다:\n",
"\n",
"```python\n",
"\n",
"import greetings\n",
"\n",
"greetings.say_hello(\"Alice\")\n",
"greetings.say_goodbye(\"Bob\")\n",
"```\n",
"\n",
"\n",
"1. 코드를 실행하면 모듈에서 정의한 함수가 호출되어 결과가 출력됩니다.\n",
"\n",
"`%%writefile` 매직 명령어를 사용하면 Colab에서 직접 파일을 생성하고 내용을 작성할 수 있습니다. 이를 활용하여 모듈 파일을 만들 수 있습니다.\n"
],
"metadata": {
"id": "5GY5QTmzoMEh"
}
},
{
"cell_type": "code",
"source": [
"\n",
"# colab에서 파일을 만드는 방법은 아래와 같습니다.\n",
"#writefile 이후, greeings.py 파일이 생성됩니다.\n",
"%%writefile greetings.py\n",
"\n",
"def say_hello(name):\n",
" print(f\"Hello, {name}!\")\n",
"\n",
"def say_goodbye(name):\n",
" print(f\"Goodbye, {name}!\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "V6jof6d9ootZ",
"outputId": "98df20fa-156f-4254-ce32-03adaa24d119"
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Writing greetings.py\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# 새롭게 만들어진 greetings.py를\n",
"# 같은 폴더에 있습니다.\n",
"# import로 불러와 사용합니다.\n",
"\n",
"import greetings\n",
"\n",
"greetings.say_hello(\"Alice\")\n",
"greetings.say_goodbye(\"Bob\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_kx5RP6TpFZj",
"outputId": "8278ec97-d414-4c94-a85a-b534defa6886"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Hello, Alice!\n",
"Goodbye, Bob!\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 15. 패키지 개념\n",
"> 모듈의 확장으로 디렉토리 개념으로 정리함\n",
"- 나중에 시간이 될 때, 차근히 보기를 권장함"
],
"metadata": {
"id": "VBkOHJLkq-DZ"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬에서 패키지의 개념에 대해 쉽게 설명해주고 한 폴더에서 처리하는 예제를 보여주세요\n",
"\n",
"ChatGPT:\n",
"- 파이썬에서 패키지는 관련된 모듈들을 그룹화하고 구조화하는 방법입니다. 이를 통해 코드의 모듈성과 재사용성을 높일 수 있습니다. 패키지는 폴더의 형태로 구성되며, 폴더 내에는 하나 이상의 파이썬 모듈이 포함될 수 있습니다.\n",
"\n",
"예를 들어, 'math'라는 패키지를 생성하고 폴더 내에 'addition.py'와 'subtraction.py'라는 두 개의 모듈을 만들어 보겠습니다.\n",
"1. 'math'라는 이름의 폴더를 생성합니다.\n",
"2. 'addition.py' 파일을 생성하고 다음과 같은 코드를 작성합니다:\n",
"\n",
"```python\n",
"\n",
"def add(a, b):\n",
" return a + b\n",
"```\n",
"\n",
"\n",
"1. 'subtraction.py' 파일을 생성하고 다음과 같은 코드를 작성합니다:\n",
"\n",
"```python\n",
"\n",
"def subtract(a, b):\n",
" return a - b\n",
"```\n",
"\n",
"\n",
"\n",
"이제 'math' 패키지를 사용하여 이 두 개의 모듈을 불러와서 사용할 수 있습니다. 다음은 사용 예제입니다:\n",
"\n",
"```python\n",
"\n",
"from math import addition, subtraction\n",
"\n",
"result1 = addition.add(2, 3)\n",
"print(result1) # 출력: 5\n",
"\n",
"result2 = subtraction.subtract(5, 2)\n",
"print(result2) # 출력: 3\n",
"```\n",
"\n",
"\n",
"\n",
"이 예제에서는 'math' 패키지에서 'addition' 모듈과 'subtraction' 모듈을 불러와 사용했습니다. 각각의 모듈 내에 정의된 함수를 호출하여 결과를 출력하였습니다.\n",
"\n",
"이와 같이 패키지는 파이썬 코드를 모듈화하고 구조화하여 관리하기 쉽게 만들어 줍니다. 패키지를 사용하면 코드의 가독성과 유지보수성을 향상시킬 수 있습니다.\n"
],
"metadata": {
"id": "rOpZXcWxrRPI"
}
},
{
"cell_type": "markdown",
"source": [
"#### 16. 예외처리\n"
],
"metadata": {
"id": "5EajEG0-v8Yn"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬에서 예외 처리 개념에 대해 쉽게 설명해주고 필수 예제를 보여주세요\n",
"\n",
"ChatGPT:\n",
"- 파이썬에서 예외 처리는 프로그램 실행 중에 발생할 수 있는 오류를 감지하고 처리하는 방법입니다. 예외는 일반적으로 프로그램이 실행 중에 예상치 못한 상황이 발생했을 때 발생합니다. 예외 처리를 통해 이러한 상황에 대처하고, 프로그램의 안정성과 신뢰성을 높일 수 있습니다.\n",
"\n",
"파이썬에서 예외 처리는 try-except 블록을 사용하여 구현됩니다. try 블록 안에는 예외가 발생할 가능성이 있는 코드를 작성하고, except 블록은 예외가 발생했을 때 실행될 코드를 작성합니다. 예외가 발생하지 않으면 except 블록은 건너뛰고 프로그램의 실행이 계속됩니다.\n",
"\n",
"아래는 예외 처리의 기본적인 형태입니다:\n",
"\n",
"```python\n",
"\n",
"try:\n",
" # 예외가 발생할 수 있는 코드\n",
"except 예외종류:\n",
" # 예외가 발생했을 때 실행될 코드\n",
"```\n",
"\n",
"예외 처리에서 가장 중요한 부분은 예외 종류입니다. 예외 종류는 예외의 유형을 지정하는데 사용되며, 특정 예외에 대한 처리 코드를 작성할 때 사용됩니다. 예를 들어, ZeroDivisionError 예외는 0으로 나누기 오류를 나타내며, FileNotFoundError 예외는 파일을 찾을 수 없는 오류를 나타냅니다.\n",
"\n",
"다음은 예외 처리의 필수 예제입니다. 아래 코드는 사용자로부터 두 개의 숫자를 입력받고, 첫 번째 숫자를 두 번째 숫자로 나눈 결과를 출력합니다. 그러나 두 번째 숫자가 0이라면 ZeroDivisionError 예외가 발생합니다. 이 예외를 처리하여 프로그램이 비정상적으로 종료되지 않도록 합니다.\n",
"\n",
"```python\n",
"\n",
"try:\n",
" dividend = int(input(\"나눌 숫자를 입력하세요: \"))\n",
" divisor = int(input(\"나누는 숫자를 입력하세요: \"))\n",
" result = dividend / divisor\n",
" print(\"나눗셈 결과:\", result)\n",
"except ZeroDivisionError:\n",
" print(\"0으로 나눌 수 없습니다.\")\n",
"```\n",
"\n",
"\n",
"\n",
"위의 코드에서, 사용자가 0을 입력하면 \"0으로 나눌 수 없습니다.\"라는 메시지가 출력됩니다. 이것은 ZeroDivisionError 예외를 처리하는 except 블록에서 실행되는 코드입니다.\n",
"\n",
"이 예제는 단순한 형태의 예외 처리를 보여줍니다. 복잡한 프로그램에서는 다양한 예외 종류와 예외 처리 방법을 사용해야 할 수도 있습니다.\n"
],
"metadata": {
"id": "DmZf2d1mv_oG"
}
},
{
"cell_type": "code",
"source": [
"# 예외처리를 한 경우\n",
"# 에러가 발생해도 프로그램이 종료되지 않는다.\n",
"try:\n",
" dividend = int(input(\"나눌 숫자를 입력하세요: \"))\n",
" divisor = int(input(\"나누는 숫자를 입력하세요: \"))\n",
" result = dividend / divisor\n",
" print(\"나눗셈 결과:\", result)\n",
"except ZeroDivisionError:\n",
" print(\"0으로 나눌 수 없습니다.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-kxEHcGpwI1O",
"outputId": "89284330-e223-4c37-8435-5ed93d0e5e5e"
},
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"나눌 숫자를 입력하세요: 200\n",
"나누는 숫자를 입력하세요: 0\n",
"0으로 나눌 수 없습니다.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# 예외처리를 하지 않은경우\n",
"# 에러가 발생시 프로그램이 죽습니다.\n",
"dividend = int(input(\"나눌 숫자를 입력하세요: \"))\n",
"divisor = int(input(\"나누는 숫자를 입력하세요: \"))\n",
"result = dividend / divisor\n",
"print(\"나눗셈 결과:\", result)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 253
},
"id": "Sb6d-WZjwv7m",
"outputId": "be2665b6-42a0-4912-cd75-413aa4a84f17"
},
"execution_count": 26,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"나눌 숫자를 입력하세요: 100\n",
"나누는 숫자를 입력하세요: 0\n"
]
},
{
"output_type": "error",
"ename": "ZeroDivisionError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-26-85c0d848ecd4>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdividend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"나눌 숫자를 입력하세요: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdivisor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"나누는 숫자를 입력하세요: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdividend\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdivisor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"나눗셈 결과:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 17. 객체지향프로그래밍\n",
"\n",
"> **절대로 단시간에 이해할 수 없습니다.** 대략적인 사용의도만 파악하고 나중에 시간을 할당하여 집중적으로 공부하기를 권장합니다."
],
"metadata": {
"id": "BlVCkhUDzh91"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬에서 객체 지향 프로그래밍 개념에 대해 쉽게 설명해주고 초간단 예제를 보여주세요\n",
"\n",
"ChatGPT:\n",
"- 객체 지향 프로그래밍(Object-Oriented Programming, OOP)은 소프트웨어를 개발하기 위한 프로그래밍 패러다임 중 하나입니다. 이는 현실 세계의 개념들을 프로그램 안에서 모델링하고, 이러한 모델들 간의 상호작용을 통해 프로그램을 구성하는 방식입니다. 파이썬은 객체 지향 프로그래밍을 지원하는 언어로, 클래스(class)와 객체(object)를 중심으로 프로그램을 작성할 수 있습니다.\n",
"\n",
"클래스는 객체를 생성하기 위한 설계도와 같은 역할을 합니다. 클래스는 속성(attribute)과 동작(method)으로 구성됩니다. 속성은 객체의 상태를 나타내는 변수로, 동작은 객체의 행위를 정의하는 함수입니다.\n",
"\n",
"아래는 파이썬에서 간단한 클래스와 객체를 생성하는 예제입니다. 예제에서는 \"사각형\"이라는 클래스를 정의하고, 사각형의 너비와 높이를 속성으로 갖는 객체를 생성합니다. 객체는 속성에 접근하여 값을 설정하고 가져올 수 있습니다.\n",
"\n",
"```python\n",
"\n",
"class Rectangle:\n",
" def __init__(self, width, height):\n",
" self.width = width\n",
" self.height = height\n",
"\n",
" def get_area(self):\n",
" return self.width * self.height\n",
"\n",
"\n",
"# 객체 생성\n",
"rect = Rectangle(5, 3)\n",
"\n",
"# 속성 값 출력\n",
"print(rect.width) # 출력: 5\n",
"print(rect.height) # 출력: 3\n",
"\n",
"# 메서드 호출\n",
"area = rect.get_area()\n",
"print(area) # 출력: 15\n",
"```\n",
"\n",
"\n",
"\n",
"위 예제에서 `Rectangle` 클래스는 `__init__`이라는 특별한 메서드를 가지고 있습니다. 이 메서드는 객체가 생성될 때 자동으로 호출되며, 너비와 높이 속성을 초기화합니다. 속성에 접근할 때는 `self` 키워드를 사용하여 접근합니다.\n",
"\n",
"또한, `get_area` 메서드는 사각형의 너비와 높이를 곱하여 면적을 계산하고 반환합니다. 이 메서드를 호출하여 사각형의 면적을 구할 수 있습니다.\n",
"\n",
"이와 같이 객체 지향 프로그래밍은 현실 세계의 개념을 프로그램 안으로 추상화하여 표현하고, 객체들 간의 상호작용을 통해 프로그램을 구성합니다.\n"
],
"metadata": {
"id": "bmz6Ky8CzvjW"
}
},
{
"cell_type": "code",
"source": [
"class Rectangle:\n",
" def __init__(self, width, height):\n",
" self.width = width\n",
" self.height = height\n",
"\n",
" def get_area(self):\n",
" return self.width * self.height\n",
"\n",
"\n",
"# 객체 생성\n",
"rect = Rectangle(5, 3)\n",
"\n",
"# 속성 값 출력\n",
"print(rect.width) # 출력: 5\n",
"print(rect.height) # 출력: 3\n",
"\n",
"# 메서드 호출\n",
"area = rect.get_area()\n",
"print(area) # 출력: 15"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RfKI15AGz1EG",
"outputId": "a9633087-180d-4211-bbcf-42509de033db"
},
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"5\n",
"3\n",
"15\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"미션)\n",
"\n",
"```\n",
"다음과 같은 클래스가 있다.\n",
"클래스 애니멀:\n",
" 소리라는 변수\n",
" 먹이라는 변수\n",
" 함수: 짖어라(){출력 : 먹이변수를 출력}\n",
" 함수: 먹어라(){출력 : 소리변수를 출력}\n",
" 애니멀이 생성될 때:\n",
" 소리에 값을 할당\n",
" 먹이에 값을 할당\n",
"\n",
"강아지 = 애니멀(소리->\"멍멍\", 먹이->\"뼈다귀\" ) \n",
"강아지.짖어라()\n",
"강아지.먹어라()\n",
"를 파이썬 코드로 만들어주세요\n",
"```"
],
"metadata": {
"id": "ygN6GLp-0z6b"
}
},
{
"cell_type": "code",
"source": [
"# bard가 생성한 코드\n",
"# 에러가 있음\n",
"class Animal:\n",
" sound = \"\"\n",
" food = \"\"\n",
"\n",
" def bark(self):\n",
" print(self.food)\n",
"\n",
" def eat(self):\n",
" print(self.sound)\n",
"\n",
"\n",
"dog = Animal(sound=\"멍멍\", food=\"뼈다귀\")\n",
"\n",
"dog.bark()\n",
"dog.eat()"
],
"metadata": {
"id": "fK6-lLUZ09kh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# ChatGPT가 만든 코드\n",
"# ChatGPT가 코드를 더 완성도 있게 만들어주었습니다.\n",
"class Animal:\n",
" def __init__(self, sound, food):\n",
" self.sound = sound\n",
" self.food = food\n",
"\n",
" def bark(self):\n",
" print(self.food)\n",
"\n",
" def eat(self):\n",
" print(self.sound)\n",
"\n",
"dog = Animal(sound=\"멍멍\", food=\"뼈다귀\")\n",
"dog.bark()\n",
"dog.eat()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VqrokSVI2cHk",
"outputId": "ed5e3aaa-10d8-418f-e6e9-0651c8bc1e0a"
},
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"뼈다귀\n",
"멍멍\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#### 18. 객체지향에서 상속, 다형성\n",
"> 이 부분도 나중에 시간이 될 때, 집중적으로 공부하기를 권장함. 절대 단시간내에 이해할 수 없는 내용임."
],
"metadata": {
"id": "g5sAQNpQ3H7z"
}
},
{
"cell_type": "markdown",
"source": [
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)"
],
"metadata": {
"id": "0ZroyjFq4OJE"
}
},
{
"cell_type": "markdown",
"source": [
"#### 19. 람다함수\n",
"> 리스트나 특정환경에서 1줄로 비교하고 처리해야 할 경우, 자주 사용하는 함수"
],
"metadata": {
"id": "DPR8bZpo50Ss"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬에서 람다함수를 간략하게 설명하고 초간단 예제를 보여주세요\n",
"\n",
"ChatGPT:\n",
"- 람다 함수는 파이썬에서 익명 함수를 만들기 위한 간단하고 간결한 방법입니다. 일반적인 함수와 달리, 이름이 없으며 한 줄로 작성됩니다. 주로 간단한 연산이나 함수를 한 번 사용해야 할 때 유용하게 활용됩니다.\n",
"\n",
"람다 함수의 구문은 다음과 같습니다:\n",
"\n",
"```python\n",
"\n",
"lambda 인자: 표현식\n",
"```\n",
"\n",
"\n",
"\n",
"간단한 예제로 두 수를 더하는 람다 함수를 만들어보겠습니다.\n",
"\n",
"```python\n",
"\n",
"add = lambda x, y: x + y\n",
"result = add(3, 5)\n",
"print(result)\n",
"```\n",
"\n",
"\n",
"\n",
"출력 결과는 8입니다. 위 예제에서 `add`라는 변수에 람다 함수를 할당했습니다. 이 람다 함수는 `x`와 `y`라는 인자를 받아서 `x + y`라는 표현식을 계산하고 반환합니다. 그리고 `add(3, 5)`를 호출하여 인자로 3과 5를 전달하면, 람다 함수가 실행되어 3과 5를 더한 결과인 8을 반환합니다.\n",
"\n",
"람다 함수는 일반적인 함수와 마찬가지로 다양한 방법으로 사용될 수 있습니다. 간단한 연산이나 정렬, 필터링 등에 활용되며, 함수를 인자로 받는 함수와 함께 사용되는 경우도 있습니다.\n"
],
"metadata": {
"id": "SYoudeXW54Wj"
}
},
{
"cell_type": "markdown",
"source": [
"#### 20. 제너레이터\n",
"> 시간이 된다면 나중에 집중적으로 공부할 필요가 있는 내용. 지금은 저런 코드가 존재한다는 것만 알아도 됩니다.\n",
"\n",
"\n"
],
"metadata": {
"id": "i6LSijo46RBs"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬에서 제너레이터를 아주쉽고 간략하게 설명해주고 초간단 예제를 보여주세요\n",
"\n",
"ChatGPT:\n",
"- 파이썬에서 제너레이터는 이터레이터(Iterator)를 생성하는 함수입니다. 제너레이터 함수는 일반적인 함수와 비슷하게 정의되지만, `return` 문 대신 `yield` 문을 사용하여 값을 반환합니다. `yield` 문은 함수의 실행을 일시 중단하고 값을 생성하며, 다시 호출되었을 때 중단된 지점부터 다시 실행됩니다.\n",
"\n",
"간단한 제너레이터 함수 예제를 살펴보겠습니다. 아래의 예제는 1부터 시작하여 지정된 범위 내의 홀수를 반환하는 제너레이터 함수입니다.\n",
"\n",
"```python\n",
"\n",
"def generate_odd_numbers(n):\n",
" i = 1\n",
" while i <= n:\n",
" yield i\n",
" i += 2\n",
"```\n",
"\n",
"\n",
"\n",
"위의 예제에서 `generate_odd_numbers` 함수는 `n`이라는 인자를 받습니다. 함수 내부에서는 `while` 루프를 사용하여 `i` 값을 1씩 증가시키면서 홀수를 생성합니다. `yield i` 문은 홀수를 생성하고 함수 실행을 일시 중단합니다.\n",
"\n",
"제너레이터 함수를 호출하면 제너레이터 객체가 반환됩니다. 이 객체는 이터레이터이기 때문에 `next()` 함수를 사용하여 값을 하나씩 가져올 수 있습니다. 예를 들어, 다음과 같이 제너레이터 객체를 생성하고 값을 가져오는 방법을 보여드리겠습니다.\n",
"\n",
"```python\n",
"\n",
"numbers = generate_odd_numbers(10)\n",
"\n",
"print(next(numbers)) # 1\n",
"print(next(numbers)) # 3\n",
"print(next(numbers)) # 5\n",
"```\n",
"\n",
"\n",
"\n",
"`next()` 함수를 호출할 때마다 제너레이터 함수는 `yield` 문까지 실행되고, `yield` 문에서 반환한 값을 가져옵니다. 이런식으로 필요한 만큼의 값을 생성하고 사용할 수 있습니다.\n"
],
"metadata": {
"id": "qOWrLIQn7Seb"
}
},
{
"cell_type": "markdown",
"source": [
"#### 21. 데코레이터\n"
],
"metadata": {
"id": "Zr_6hvEr7ngA"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"Person:\n",
"- 파이썬에서 데코레이터를 간략하게 설명하고 초간단 예제를 보여주세요\n",
"\n",
"ChatGPT:\n",
"- 데코레이터(Decorator)는 파이썬에서 함수나 클래스를 수정하거나 기능을 추가하기 위해 사용되는 기능입니다. 데코레이터는 기존의 코드를 변경하지 않고도 기능을 추가하거나 수정할 수 있어 코드의 재사용성과 가독성을 높일 수 있습니다. 데코레이터는 함수나 클래스 위에 `@데코레이터_함수`와 같은 형태로 사용되며, 해당 함수는 데코레이팅된 함수나 클래스를 인자로 받아 기능을 추가하거나 수정한 뒤 반환합니다.\n",
"\n",
"다음은 더 간단한 예제로 `@uppercase`라는 데코레이터를 사용하여 함수의 반환값을 대문자로 변환하는 예제입니다:\n",
"\n",
"```python\n",
"\n",
"def uppercase(func):\n",
" def wrapper():\n",
" result = func() # 원래 함수 호출\n",
" return result.upper() # 반환값 대문자로 변환\n",
" return wrapper\n",
"\n",
"@uppercase\n",
"def greet():\n",
" return \"hello, world!\"\n",
"\n",
"print(greet())\n",
"```\n",
"\n",
"\n",
"\n",
"위 예제에서 `uppercase` 데코레이터는 `wrapper`라는 내부 함수를 정의하고, 이 함수에서 원래 함수인 `greet`를 호출하고 반환값을 대문자로 변환하여 반환합니다. `@uppercase` 데코레이터를 사용하여 `greet` 함수를 장식하면, `greet` 함수의 반환값이 대문자로 변환된 상태로 출력됩니다.\n",
"\n",
"출력 결과:\n",
"\n",
"```\n",
"\n",
"HELLO, WORLD!\n",
"```\n",
"\n",
"\n",
"\n",
"이처럼 데코레이터를 사용하면 함수나 클래스의 동작을 수정하거나 기능을 추가할 수 있습니다.\n"
],
"metadata": {
"id": "XETLyeyE79Ha"
}
},
{
"cell_type": "code",
"source": [
"def uppercase(func):\n",
" def wrapper():\n",
" result = func() # 원래 함수 호출\n",
" return result.upper() # 반환값 대문자로 변환\n",
" return wrapper\n",
"\n",
"@uppercase\n",
"def greet():\n",
" return \"hello, world!\"\n",
"\n",
"print(greet())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "P2cQkByM8AcJ",
"outputId": "98ed5a78-6463-4b80-f697-b27ee3a97243"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"HELLO, WORLD!\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# bard에서 만든 프롬프트 코드\n",
"def timeit(func):\n",
" def wrapper(*args, **kwargs):\n",
" start = time.time()\n",
" result = func(*args, **kwargs)\n",
" end = time.time()\n",
" print(f\"{func.__name__} took {end - start} seconds to run.\")\n",
" return result\n",
" return wrapper\n",
"\n",
"@timeit\n",
"def sum_to_1000():\n",
" \"\"\"1부터 1000까지 더하는 함수.\"\"\"\n",
" sum = 0\n",
" for i in range(1, 1001):\n",
" sum += i\n",
" return sum\n",
"\n",
"if __name__ == \"__main__\":\n",
" print(sum_to_1000())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 322
},
"id": "M63zHIiI8ZsS",
"outputId": "d54fa49e-665b-473e-99ff-826648f179d0"
},
"execution_count": 31,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-31-cffea90ae88e>\u001b[0m in \u001b[0;36m<cell line: 18>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum_to_1000\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-31-cffea90ae88e>\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'time' is not defined"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ChatGPT에서 생성코드\n",
"import time\n",
"\n",
"def calculate_time(func):\n",
" def wrapper(*args, **kwargs):\n",
" start_time = time.time()\n",
" result = func(*args, **kwargs)\n",
" end_time = time.time()\n",
" execution_time = end_time - start_time\n",
" print(f\"Execution time: {execution_time} seconds\")\n",
" return result\n",
" return wrapper\n",
"\n",
"@calculate_time\n",
"def calculate_sum():\n",
" total_sum = sum(range(1, 1001))\n",
" return total_sum\n",
"\n",
"calculate_sum()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hnnpXuMM8y40",
"outputId": "15d50109-1bdc-4e8a-dd63-08a41a7f7d33"
},
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Execution time: 3.838539123535156e-05 seconds\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"500500"
]
},
"metadata": {},
"execution_count": 32
}
]
}
]
}
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