Intro-to-Data-Science/WEEK2/week2_numpy_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🦉COSADAMA Introduction to Data Science Study\n",
    "\n",
    "- 작성자: 박하람 \n",
    "- 참고자료: 파이썬 머신러닝 완벽가이드(권철민), 비쥬얼프로그래밍 수업(김승태), 파이썬으로 데이터 주무르기(민형기)\n",
    "\n",
    "## 2. What are NumPy, matplotlib?\n",
    "\n",
    "이번 주차에는 **수치 데이터 처리 모듈 Numpy, 시각화 모듈 matplotlib**의 간단한 문법을 학습하려 합니다. 프로젝트를 먼저 실시하기에 앞서 이러한 모듈들을 어느정도 학습하는 것이 여러분들에게 더 도움이 되겠다 판단하여 아주 간단하게 모두를 다뤄보겠습니다.\n",
    "\n",
    "### 2.1 수치 데이터 처리 - NumPy\n",
    "\n",
    "NumPy는 데이터/수치 분석 모듈로 Numerical Python을 의미합니다. 파이썬에서 선형대수 기반의 프로그램을 쉽게 만들 수 있도록 지원하는 대표적인 패키지입니다. 루프(loop)를 사용하지 않고 대량 데이터의 배열 연산을 가능하게 하므로 빠른 배열 연산 속도를 보장합니다. 대량 데이터 기반의 과학과 공학 프로그램은 빠른 계산 능력이 매우 중요합니다. 때문에 파이썬 기반의 많은 과학과 공학 패키지는 넘파이에 의존하고 있습니다. 그러나 **수학 연산을 돕는 도구이지, 분석 도구는 아닙니다.** 넘파이는 내용이 상당히 방대하니 넘파이를 정복하겠다는 생각은 하지 맙시다. 데이터 분석에 우선적으로 필요한 부분만 최대한 빨리 학습하여 부족한 부분은 후에 채워가면서 학습하면 됩니다. \n",
    "\n",
    "아, **과제 문제가 3개 있으니 잊지 말고 제출해 주세요!**\n",
    "\n",
    "#### NumPy import 하기 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 다차원 배열 ndarray \n",
    "- NumPy의 중요 자료형 \n",
    "- 표준 파이썬의 array와 비슷하지만 별개임\n",
    "- 다차원(Multi-dimentsion) 배열을 쉽게 생성하고 다양한 연산을 수행할 수 있음."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "list1 = [1,2,3]\n",
    "print(type(list1))   # 파이썬의 list\n",
    "array1 = np.array(list1)\n",
    "print(type(array1))  # 넘파이의 ndarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- array() 함수는 파이썬의 리스트와 같은 다양한 인자를 입력 받아서 ndarray로 변환하는 기능을 수행. \n",
    "- 생성된 ndarray 배열의 shape 변수는 ndarray의 크리, 즉 행과 열의 수를 튜플 형태로 가지고 있으며 이를 통해 ndarray 배열의 차원까지 알 수 있음."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1 = np.array([1,2,3])\n",
    "array1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(array1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1.shape # 쉼표 뒤가 없다면 1차원이라는 의미 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [2, 3, 4]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2차원 ndarray 만들기 \n",
    "array2 = np.array([[1,2,3],\n",
    "                 [2,3,4]])\n",
    "array2   # 항상 데이터를 보는 습관 들이기!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(array2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array2.shape # 2개의 row와 3개의 column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ndarray의 중요 속성\n",
    "- **ndarray.ndim** : ndarray의 차원\n",
    "- **ndarray.shape**: 배열의 모양 \n",
    "- **ndarray.size**: 배열의 원소의 개수 \n",
    "- **ndarray.dtype** : 원소의 자료형"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1.ndim  # 1차원이구나!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array2.ndim # 2차원이구나!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array2.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 배열 생성하기 \n",
    "\n",
    "- [numpy의 random 모듈 학습](https://nittaku.tistory.com/443)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.33657423,  0.89653759, -2.11299124],\n",
       "       [-0.33322897, -0.58722101, -1.4667888 ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.randn(2,3) #0에서 1사이의 균일 분포에서 난수 생성\n",
    "data # 항상 이렇게 변수명을 적어 데이터가 어떻게 나오는지 보는 습관 들이기!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.36574235,   8.96537594, -21.1299124 ],\n",
       "       [ -3.33228973,  -5.87221009, -14.66788802]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.67314847,  1.79307519, -4.22598248],\n",
       "       [-0.66645795, -1.17444202, -2.9335776 ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data + data # 배열 더하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ndarray 배열의 모양 즉, 2개의 row와 3개의 column으로 구성\n",
    "data.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 차원의 수\n",
    "data.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 배열 생성 array 함수\n",
    "- **array()**: 데이터를 기반으로 배열(ndarray)를 생성하는 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 2.5, 3. , 4.5, 5. ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alist = [1,2.5,3,4.5,5]\n",
    "arr1 = np.array(alist) # ndarray로 바꾸어준 것!\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dalist = [[1,2,3,4,5],\n",
    "         [6,7,8,9,10]]\n",
    "arr2 = np.array(dalist)\n",
    "arr2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ndarray를 편리하게 생성하기 - arange, zeros, ones\n",
    "\n",
    "특정 크기와 차원을 가진 ndarray를 연속값이나 0또는 1로 초기화해 쉽게 생성해야 할 필요가 있는 경우가 발생할 수 있음. 이 경우 **arange(), zeros(), ones()**를 이용해 쉽게 ndarray를 생성할 수 있음.\n",
    "\n",
    "- **np.arange()**: range의 numpy 버전. 0부터 함수 인자 값 -1까지의 값을 순차적으로 ndarray의 데이터값으로 변환해줌.\n",
    "- **np.zeros()**: 함수 인자로 튜플 형태의 shape 값을 입력하면 모든 값을 0으로 채운 해당 shape를 가진 ndarray를 반환함. \n",
    "- **np.ones()**: 함수 인자로 튜플 형태의 shape 값을 입력하면 모든 값을 1로 채운 해당 shape을 가진 ndarray를 반환함."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 꿀팁! 내가 잘 모르는 게 나왔다! \n",
    "np.arange?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 괄호 대신 **?**를 적어주면 도움말이 뜹니다! 그러면 영어로 옵션값(파라미터들)도 볼 수 있고, 어떤 함수인지도 알 수 있습니다. 무엇보다 가장 좋은 것은 제일 아래에 **examples**를 통해 이 함수가 어떤 기능을 하는지 직관적으로 알 수 있어요! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예시를 하나 가져왔어요. 아, 이 함수는 파이썬의 range()와 비슷하구나~\n",
    "np.arange(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_array = np.arange(10)\n",
    "sequence_array  # 항상 데이터 확인하는 것 잊지말기!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_array.shape  # 1차원이고, 칼럼이 10개네~!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 이 3개짜리는 무엇을 의미할까요?\n",
    "question = np.arange(0,10,2)\n",
    "question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_array = np.zeros((3,2), dtype='int32')\n",
    "zero_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_array = np.ones((3,2))\n",
    "one_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ndarray의 차원과 크기를 변경하는 reshape()\n",
    "\n",
    "- **np.reshape()**: ndarray를 특정 차원 및 크기로 변환 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(12).reshape(4,3) # 4개의 로우, 3개의 컬럼으로!\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.arange(24).reshape(2,3,4) # 3차원으로 바꿔주세욤!\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 수학 개념 함수 / 통계용 메서드들\n",
    "- sum(): 전체 또는 특정 축의 원소의 합계\n",
    "- mean(): 산술평균\n",
    "- std(): 표준편차\n",
    "- var(): 분산\n",
    "- min(), max(): 최소값, 최대값\n",
    "- cumsum(): 누적합\n",
    "- comprod(): 누적곱\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.30804036, 0.70411508, 0.08159497],\n",
       "       [0.43746412, 0.5387499 , 0.32312268]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.random((2,3))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3930871116957118"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08159497080581557"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7041150766704284"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.74550449, 1.24286497, 0.40471765])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum(axis=0)   # 열의 합(아래 방향)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.09375041, 1.2993367 ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum(axis=1)   # 행의 합(오른쪽 방향)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.30804036, 1.01215544, 1.09375041],\n",
       "       [0.43746412, 0.97621402, 1.2993367 ]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.cumsum(axis=1)  # 행의 누적합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[0,1,2],\n",
    "              [3,4,5],\n",
    "              [6,7,8]])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  3,  6, 10, 15, 21, 28, 36], dtype=int32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  3],\n",
       "       [ 3,  7, 12],\n",
       "       [ 6, 13, 21]], dtype=int32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.cumsum(axis = 1) # column 방향으로~"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "질문이 들어왔는데요, numpy에서 axis=0은 row, axis=1은 column을 의미합니다. 그래서 column 방향이라고 한 것에 헷갈릴 수 있겠지만, 틀린 말이 아닙니다. 참고하세요~!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Indexing \n",
    "\n",
    "- 기본적으로 list와 같은 방식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   8,  27,  64, 125, 216, 343, 512, 729], dtype=int32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)**3\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 27, 64], dtype=int32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1000,     1, -1000,    27, -1000,   125,   216,   343,   512,\n",
       "         729], dtype=int32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:6:2] = -1000  #a[0:6:2]에 모두 -1000을 넣는다\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  729,   512,   343,   216,   125, -1000,    27, -1000,     1,\n",
       "       -1000], dtype=int32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 8, 9])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다차원 ndarray의 인덱싱 \n",
    "arr2d = np.array([[1,2,3],\n",
    "                 [4,5,6],\n",
    "                 [7,8,9]])\n",
    "arr2d[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[0,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_slice = arr[5:8]\n",
    "arr_slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다차원 ndarray의 슬라이싱\n",
    "arr2d = np.array([[1,2,3],\n",
    "                 [4,5,6],\n",
    "                 [7,8,9]])\n",
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2,1:]  # 생략 되어 있다면 0부터!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean Indexing\n",
    "- 조건 필터링과 검색을 동시에 할 수 있어 매우 자주 이용되는 인덱싱 방식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1d = np.arange(start=1, stop=10) #(1,10)과 같음\n",
    "array1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 7, 8, 9])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [ ] 안에 array1d > 5보다 높은 것만 가져오너라!\n",
    "boolean_array1d = array1d[array1d > 5]\n",
    "boolean_array1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1d > 5 # 5이상이면 True로 표시"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 난수 생성 \n",
    "- np.random.rand(): 균등 분포에서 추철 \n",
    "- np.random.randint(): 주어진 범위 내의 임의의 난수 \n",
    "- np.random.randn(): 표준편차가 1이고 평균 0인 정규 분포에서 추출 \n",
    "- np.random.normal(): 정규 분포에서 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.15947163, -0.10381295, -1.40611371, -0.83275307],\n",
       "       [ 0.38743887, -0.74626072, -0.77014419,  0.0822977 ],\n",
       "       [-0.0492367 , -0.97997549, -0.22625832,  0.36076559],\n",
       "       [ 0.82263307, -0.33873647,  1.56631342, -0.97812027]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.normal(size=(4,4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.69453078, -0.77045282,  0.07073747, -0.51623367],\n",
       "       [ 1.17230171, -1.75608976, -1.06318371, -0.28396412],\n",
       "       [ 1.67912299,  0.26406609,  0.06496775, -1.61263553],\n",
       "       [-0.19099145, -0.89348338,  1.54734076,  0.74781568]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.random.randn(4,4)\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 실습1: numpy을 왜 사용할까? \n",
    "\n",
    "다음 프로그램을 작성해보자. \n",
    "- 1 또는 -1의 값을 랜덤하게 생성한다. 1이 나오면 한 칸 전진하고 -1이 나오면 한 칸 후진한다. \n",
    "- 이 과정을 1000회 반복한다. 그 때 나의 위치는 어디인가? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy를 모르고 파이썬 프로그래밍만으로 만들 때 \n",
    "import random\n",
    "position = 0\n",
    "walk = [position]\n",
    "for i in range(1000):\n",
    "    step = 1 if random.randint(0,1) else -1\n",
    "    position + step\n",
    "    walk.append(position)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이것을 numpy 버전으로 만드시오. 단, 변수는 draws, steps, walk 이 3가지만 사용하여야 한다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 실습2: numpy를 왜 사용할까? \n",
    "𝒚 = 𝒙^𝟑 +𝟑𝒙^𝟐 +𝟑𝒙 + 𝟏의 그래프를 그려라. x와 y에 값을 넣어 에러를 해결하세요!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name '_________' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-60-88a47a7f80d7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_________\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_________\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name '_________' is not defined"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "x = _________\n",
    "y = _________\n",
    "\n",
    "df = pd.DataFrame(x,y)\n",
    "plt.plot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기까지가 수치 데이터 처리에 사용되는 NumPy였습니다! \n",
    "지금 바로 당장에는 사용하지 않을 것 같아요. 당장에 많이 사용되는 것은 pandas겠지만 그래도 pandas 배우는 김에 numpy도 배우고 넘어가야하지 않겠어요. \n",
    "\n",
    "**과제방에 실습 1,2를 올려주시면 됩니다!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🦉COSADAMA Introduction to Data Science Study\n",
    "\n",
    "- 작성자: 박하람 \n",
    "- 참고자료: 파이썬 머신러닝 완벽가이드(권철민), 비쥬얼프로그래밍 수업(김승태), 파이썬으로 데이터 주무르기(민형기)\n",
    "\n",
    "\n",
    "### 2.2. 시각화 - Matplotlib\n",
    "\n",
    "시각화는 **수집한 데이터의 의미를 명확히 이해할 수 있도록 단순하고 직관적이게 표현**해줍니다. 수많은 줄글 보다 하나의 표가 더 직관적으로 정보를 이해할 수 있죠. 신문기사를 봐도 단순히 수치를 얘기한 것보다 그래프를 이용해 제시한 것이 훨씬 더 이해가 잘 가는 것처럼요. \n",
    "파이썬 기반의 대표적인 시각화로는 **matplotlib과 seaborn**이 있습니다. 판다스 자체에서도 그래프를 표시할 수 있는 방법이 있지만, matplotlib가 더욱 편합니다. 그러나 matplotlib보다 seaborn이 더 고급화된 시각화 도구라 seaborn 또한 많이 쓰입니다. 우리는 간단하게 matplotlib부터 학습해 볼겁니다. \n",
    "\n",
    "- **주교재 56-65쪽과 함께 보기**\n",
    "\n",
    "#### matplotlib 라이브러리 구축하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib 중 그래프를 그리는 모듈은 matplotlib.pyplot입니다. **%matplotlib inline**은 현재 이 페이지에 그래프를 내장해서 보여주는 아주 편리한 방식입니다. 보통 이 두 가지는 필수로 적어두고 시각화를 진행합니다.\n",
    "\n",
    "\n",
    "1. 라이브러리를 import 한다. \n",
    "2. 가시화할 데이터를 로딩(생성)한다. \n",
    "3. 그래프의 속성을 설정한다. (레이블, 색상, 각 축의 제목 등) \n",
    "4. 그래프를 그린다. \n",
    "5. 그래프를 출력한다. \n",
    "\n",
    "이런 순서로 시각화가 진행됩니다.\n",
    "\n",
    "#### 아주 간단한 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "plt.plot([1.5,3.5,-2,1.6,1.5,3.5,-2,1.6])\n",
    "plt.plot(np.random.randn(50).cumsum(), 'k--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "축하합니다! 여러분은 방금 첫 그래프를 그려보았습니다. 매우 간단하죠? 그럼 어떻게 해서 이런 2개의 그래프가 나올 수 있었는지 알아봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1.5,3.5,-2,1.6,1.5,3.5,-2,1.6])  # 선그래프 그리기\n",
    "plt.show()  # 그래프 보여줘라!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 그래프는 이 전의 파란색 그래프입니다. x축이 달라서 그렇지 실제로는 앞의 파란색 그래프와 같은 그래프에요. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.random.randn(50).cumsum(), 'k--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "어, 이건 왜 앞의 것을 그래로 붙였는데 앞과 그래프 분포가 다르죠? 넘파이를 배웠다면 random.randn() 때문이란 걸 아실 겁니다. 그런데 왜 검은 색 점선일까요? 옵션을 **'k--'**로 선택했기 때문에 그래요. k는 black의 k이고, --는 점선을 의미해요.\n",
    "\n",
    "#### 히스토그램 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mu = 100\n",
    "sigma = 15\n",
    "x = np.random.normal(mu, sigma, 10000)\n",
    "\n",
    "ax = plt.gca()\n",
    "\n",
    "# the histogram of the data \n",
    "ax.hist(x, bins=35, color='r')  #히스토그램을 그리자\n",
    "ax.set_xlabel('Values')       #x축을 values로 설정!\n",
    "ax.set_ylabel('Frequency')    #y축을 Frequency로 설정!\n",
    "\n",
    "# title을 이렇게 적어주세요~\n",
    "ax.set_title(r'$\\mathrm{Histogram:}\\ \\mu=%d,\\ \\sigma=%d$' % (mu, sigma)) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "짠! 이렇게 히스토그램이 그려졌습니다. matplotlib에 있는 그래프는 수만가지로 매우 다양합니다. 그래서 하나씩 변경해보면서 어떻게 그릴 수 있을까 직접 해보는 것이 좋아요. 이건 막 외우는게 아닙니다. 이 데이터를 보니까 내가 이런 그래프가 필요하겠구나 - 이런 그래프를 만드려면 어떤 코드를 넣어야 하지 이렇게 그 때 그 때 찾아가면서 맞게 적용하면 되는 겁니다. 코드를 복붙해서 하나하나씩 기능을 알아보세요! \n",
    "\n",
    "#### 파이 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# make a square figure and axes\n",
    "# pie chart looks best in square figures \n",
    "# otherwise it looks like ellipses \n",
    "plt.figure(1, figsize=(8, 8))\n",
    "ax = plt.axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "# The slices will be ordered and plotted counter-clockwise. \n",
    "labels = 'Spring', 'Summer', 'Autumn', 'Winter'\n",
    "values = [15, 16, 16, 28]\n",
    "explode =[0.1, 0.1, 0.1, 0.1]\n",
    "\n",
    "# make a pie\n",
    "plt.pie(values, explode=explode, labels=labels,autopct='%1.1f%%', startangle=67)\n",
    "\n",
    "plt.title('Rainy days by season')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "오, 파이 그래프는 이렇게 그리는 것이군요! 이것도 하나씩 수치를 바꿔가거나 구글링해서 특성이 무엇인지 파악하세요. 이게 matplotlib를 학습하는 쉬운 길입니다. \n",
    "\n",
    "#### 여러 개의 그래프를 한번에 그리기 \n",
    "그래프를 그릴 때에는 figure를 생성해서 사용합니다. 하나의 큰 figure에 subplot(각각의 그래프)들이 들어가는 거에요. 쉽게 말해 figure는 그래프를 포함할 수 있는 윈도우(껍데기)이며, 그래프는 또 그 안에 별도로 생성해주어야 합니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ccd56b0730>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()   #figure 생성 \n",
    "\n",
    "ax1 = fig.add_subplot(2,2,1)   #2*2 크기의 1번 서브플롯 \n",
    "ax2 = fig.add_subplot(2,2,2)   #2*2 크기의 2번 서브플롯 \n",
    "ax3 = fig.add_subplot(2,2,3)   #2*2 크기의 3번 서브플롯\n",
    "\n",
    "ax1.hist(np.random.randn(100), bins=20, color='k', alpha=0.3)\n",
    "ax2.scatter(np.arange(30), np.arange(30) + 3 * np.random.randn(30))\n",
    "ax3.plot([1.5,3.5,-2,1.6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아하! ax1에는 히스토그램, ax2에는 산점도, ax3에는 선 그래프를 그린 것이구나! 그런데 히스토그램 속의 옵션 값은 무엇을 의미하는거지? 숫자를 바꿔가면서 이리저리 그려보세요 그러면 무엇을 의미하는지 알 수 있어요. 그래도 모르겠다?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "우리에게는 **도움말**이 있죠? 이를 통해 파라미터의 의미를 알 수 있습니다. \n",
    "\n",
    "산점도를 더 쉬운 예제로 이해해보죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ccd5735730>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = [1,2,3,4,5]\n",
    "y_values = [1,4,9,16,25]\n",
    "\n",
    "plt.scatter(x_values, y_values, s = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ccd5793df0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x78Y+SU9HxPe6OV5cSg8ASWU7hAIAKFDgAJAUBQ4ASVHgAJAUBQ4ASVHgAJAUBQ4ASf0/9Ow3CdWtzC8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = [1,2,3,4,5]\n",
    "y_values = [1,4,9,16,25]\n",
    "\n",
    "plt.scatter(x_values, y_values, s = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아하! 옵션값 s가 점의 크기를 말하는 것이었군요! x와 y 데이터는 저렇게 공급해주는 거군요! 이렇게 이해하면 됩니다. \n",
    "\n",
    "#### 그래프 꾸미기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ccd67d1700>]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1,2,3,4,5]\n",
    "y = [1,4,9,16,25]\n",
    "\n",
    "plt.plot(x, y, 'g--') # green색 점선"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Markers**\n",
    "\n",
    "``'.'``          point marker\n",
    "``','``          pixel marker\n",
    "``'o'``          circle marker\n",
    "``'v'``          triangle_down marker\n",
    "``'^'``          triangle_up marker\n",
    "``'<'``          triangle_left marker\n",
    "``'>'``          triangle_right marker\n",
    "``'1'``          tri_down marker\n",
    "``'2'``          tri_up marker\n",
    "``'3'``          tri_left marker\n",
    "``'4'``          tri_right marker\n",
    "``'s'``          square marker\n",
    "``'p'``          pentagon marker\n",
    "``'*'``          star marker\n",
    "``'h'``          hexagon1 marker\n",
    "``'H'``          hexagon2 marker\n",
    "``'+'``          plus marker\n",
    "``'x'``          x marker\n",
    "``'D'``          diamond marker\n",
    "``'d'``          thin_diamond marker\n",
    "``'|'``          vline marker\n",
    "``'_'``          hline marker\n",
    "\n",
    "**Line Styles**\n",
    "\n",
    "``'-'``          solid line style\n",
    "``'--'``         dashed line style\n",
    "``'-.'``         dash-dot line style\n",
    "``':'``          dotted line style\n",
    "\n",
    "\n",
    "등등등등..... 그 외에도 도움말을 보면 다양하게 그래프를 꾸밀 수 있습니다!\n",
    "\n",
    "#### 범례 추가하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ccd682fcd0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "ax.plot(np.random.randn(1000).cumsum(), 'k', label='one')\n",
    "ax.plot(np.random.randn(1000).cumsum(), 'g--', label='two')\n",
    "ax.plot(np.random.randn(1000).cumsum(), 'r.', label='three')\n",
    "\n",
    "ax.legend(loc='best')  #범례 위치가 best인 곳에 놓아라"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "범례가 여기 보면, 왼쪽 제일 아래에 one, two, three로 잘 표시되어 있네요. 얘가 생각하기에 여기 위치가 범례가 있기에 제일 best라고 생각하는 것 같아요! \n",
    "\n",
    "#### 그래프에 한글 표시하기 \n",
    "\n",
    "matplotlib이 기본으로 가진 **폰트는 한글을 지원하지 않기 때문에** matplotlib의 폰트를 변경할 필요가 있어요. 그래서 아래와 같이 항상 써줘야 합니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 이거는 그냥 가져다 붙이면 됩니다. 이해 할 필요 없어요.\n",
    "import platform \n",
    "\n",
    "from matplotlib import font_manager, rc \n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "if platform.system() == 'Darwin':\n",
    "    rc('font', family='AppleGothic')\n",
    "elif platform.system() == 'Windows':\n",
    "    path = \"c:/Windows/Fonts/malgun.ttf\"\n",
    "    font_name = font_manager.FontProperties(fname=path).get_name()\n",
    "    rc('font', family=font_name)\n",
    "else:\n",
    "    print('Unknown system')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 해서 matplotlib 사용법에 대해 아주 간단하게 알아보았습니다. 주교재의 설명과 함께 보시면 더욱 더 잘 이해할 수 있을 겁니다! \n",
    "\n",
    "더 다양한 그래프를 보고 싶다면 [matplotlib 홈페이지](https://matplotlib.org/index.html)에 examples에 들어가시면 됩니다. 좀 몇개 그래프가 내가 쓸만하다 하는 것을 골라놓은 다음에 내용을 숙지하고 써먹으면 됩니다.\n",
    "\n",
    "#### 실습과제: 득표율 그래프 그리기\n",
    "\n",
    "방송국의 개표 결과가 나왔다. 1번 후보 홍길동이 20.0%, 2번 후보 심청이 37.3%, 3번 후보 이순신이 21.3%, 4번 후보 강감찬이 21.3%를 차지했다. 이를 기반으로 파이 그래프를 그려라. \n",
    "\n"
   ]
  }
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