00-Python/11-파이썬-패키지-모듈.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "4e6uTqLq2OWr"
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pV39e4IM1jR1"
   },
   "source": [
    "## 패키지와 모듈의 관계"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "03Wyuv9-25Hr"
   },
   "source": [
    "* **함수**들이 뭉쳐진 하나의 .py 파일 안에 이루어진 것을 **모듈**이라고 합니다.\n",
    "* 여러 개의 모듈을 그룹화 하면 **패키지**가 됩니다.\n",
    "* **패키지**는 종종 **라이브러리**라고도 불리웁니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "jgkanXLh10Lv",
    "outputId": "ec5c29ed-e0f7-4520-c801-10bde0b6bd8a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 출처: pythonstudy.xyz\n",
    "Image('http://pythonstudy.xyz/images/basics/python-package.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "즉, 하나의 .py 파일은 **모듈**이며, 모듈을 포함하는 디렉토리(directory)는 **패키지**로 정의됩니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZaHyvfbF3cfx"
   },
   "source": [
    "## 모듈 import\n",
    "\n",
    "- 모듈 import는 외장 라이브러리의 모듈을 불러오는 유용한 기능입니다.\n",
    "- 모듈 import를 통해 는 다른 누군가가 이미 만들어 놓은 기능을 쉽게 가져와 사용할 수 있습니다.\n",
    "- 파이썬 모듈을 가져와 사용하기 위해서는 `import` 구문을 사용합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저, 실습에 사용할 사용자 정의 모듈을 생성합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing calculator.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile calculator.py\n",
    "name = '계산기'\n",
    "\n",
    "def add(a, b):\n",
    "    return a + b\n",
    "\n",
    "def sub(a, b): \n",
    "    return a - b\n",
    "\n",
    "def multiply(a, b):\n",
    "    return a * b\n",
    "\n",
    "def divide(a, b):\n",
    "    return a / b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정의한 모듈 **import** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import calculator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모듈의 **변수**에 접근"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'계산기'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculator.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모듈의 **함수 실행**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculator.add(10, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 별칭(alias) 지정\n",
    "\n",
    "- `as` 구문으로 긴 이름의 모듈을 축약하여 별칭(alias)으로 지정할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import calculator as calc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc.multiply(10, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모듈의 다양한 import\n",
    "\n",
    "- 파이썬 모듈을 import 하는 방법은 여러가지 방식이 존재합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9R28ydc_49GW"
   },
   "source": [
    "`random` 모듈 import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1, 2, 3, 4, 5]\n",
    "\n",
    "# random 모듈의 shuffle 함수를 실행\n",
    "random.shuffle(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 1, 5, 3, 2]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`random`모듈을 `rd` 별칭(alias) 지정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as rd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = [1, 2, 3, 4, 5]\n",
    "\n",
    "# rd 별칭으로 지정한 random 모듈의 shuffle 함수를 실행\n",
    "rd.shuffle(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 1, 2, 4, 3]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`random` 모듈의 `shuffle()` 함수 import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "UoBQDPI35TQ7"
   },
   "outputs": [],
   "source": [
    "from random import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = [1, 2, 3, 4, 5]\n",
    "\n",
    "# import한 shuffle 함수를 실행\n",
    "shuffle(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 1, 3, 4, 2]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`random` 모듈의 `shuffle()` 함수를 `sf` 별칭(alias) 지정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import shuffle as sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = [1, 2, 3, 4, 5]\n",
    "\n",
    "# import한 sf 함수를 실행\n",
    "sf(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 5, 4, 3, 1]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UgNkGgBA7MLi"
   },
   "source": [
    "## 자주 사용하는 파이썬 데이터 분석 모듈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "gbMS9Y9C7QL2"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u6EfObpV7aTu"
   },
   "source": [
    "- numpy: 과학 계산을 위한 패키지\n",
    "- pandas: 데이터 분석을 할 때 가장 많이 쓰이는 모듈\n",
    "- matplotlib: 시각화를 위한 모듈\n",
    "- seaborn: 시각화를 위한 모듈 (통계 시각화 기능을 제공하며 matplotlib 에 기반한 모듈 라이브러리)\n",
    "- random: 난수 생성 관련 모듈"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### random 모듈을 활용한 예시"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "pxV8WMg9ohaE",
    "outputId": "c100ef5d-27aa-42ce-e91a-e66275d00a6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7728598504638505"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0.0에서부터 1.0 사이의 실수(float) 난수 생성\n",
    "random.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "ut3ZDKpJoxst"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shuffle\n",
    "a = [1, 2, 3, 4]\n",
    "random.shuffle(a)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "6lvPirFfoz9D"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# start포함 end미만 사이에서 랜덤한 정수 난수 생성\n",
    "random.randrange(3, 10)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "11-파이썬-패키지와 모듈",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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