part5-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3578b3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#auto-mpg.csv 파일을 load\n",
    "#horsepower의 데이터 타입을 float형으로 형변환\n",
    "#형변환 할 수 없는 자료의 행을 제거\n",
    "#origin 1, 2, 3 -> 각 usa eu jpn으로 변경, category 형변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "035ab660",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('./dataset/auto-mpg.csv', header=None)\n",
    "\n",
    "df.columns=['mpg','cylinders', 'displacement','horsepower','weight','acceleration',\n",
    "           'model year', 'origin','name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bdf2f800",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "float64\n",
      "category\n"
     ]
    }
   ],
   "source": [
    "print(df.horsepower.dtype)\n",
    "df['horsepower'].replace('?',np.nan, inplace = True)\n",
    "df.dropna(subset=['horsepower'], axis=0, inplace =True)\n",
    "df['horsepower'] = df['horsepower'].astype('float')\n",
    "print(df.horsepower.dtype)\n",
    "df['origin'].replace({1:'USA',2:'EU',3:'JPN'},inplace=True)\n",
    "df['origin'] = df['origin'].astype('category')\n",
    "print(df.origin.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "35bae2fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[257 103  32] [ 46.         107.33333333 168.66666667 230.        ]\n",
      "    horsepower hp_bin\n",
      "0        130.0   보통출력\n",
      "1        165.0   보통출력\n",
      "2        150.0   보통출력\n",
      "3        150.0   보통출력\n",
      "4        140.0   보통출력\n",
      "5        198.0    고출력\n",
      "6        220.0    고출력\n",
      "7        215.0    고출력\n",
      "8        225.0    고출력\n",
      "9        190.0    고출력\n",
      "10       170.0    고출력\n",
      "11       160.0   보통출력\n",
      "12       150.0   보통출력\n",
      "13       225.0    고출력\n",
      "14        95.0    저출력\n",
      "category\n"
     ]
    }
   ],
   "source": [
    "#np.histogram() 함수를 활용하면 각 구간의 경계선을 구할 수 있음\n",
    "count, bin_dividers = np.histogram(df['horsepower'],bins=3) \n",
    "#count는 각 구간의 데이터 갯수를 반환\n",
    "print(count, bin_dividers)\n",
    "\n",
    "#pd.cut() 함수로 각각 데이터를 3개의 빈에 할당\n",
    "bin_names = ['저출력','보통출력','고출력']\n",
    "\n",
    "df['hp_bin']=pd.cut(x=df['horsepower'], # 데이터 배열\n",
    "                   bins=bin_dividers, #경계값리스트\n",
    "                   labels=bin_names, # bin 이름\n",
    "                   include_lowest=True) #첫 경계값포함\n",
    "print(df[['horsepower','hp_bin']].head(15))\n",
    "print(df.hp_bin.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d085f8b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>저출력</th>\n",
       "      <th>보통출력</th>\n",
       "      <th>고출력</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    저출력  보통출력  고출력\n",
       "0     0     1    0\n",
       "1     0     1    0\n",
       "2     0     1    0\n",
       "3     0     1    0\n",
       "4     0     1    0\n",
       "5     0     0    1\n",
       "6     0     0    1\n",
       "7     0     0    1\n",
       "8     0     0    1\n",
       "9     0     0    1\n",
       "10    0     0    1\n",
       "11    0     1    0\n",
       "12    0     1    0\n",
       "13    0     0    1\n",
       "14    1     0    0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hp_dummies = pd.get_dummies(df['hp_bin'])\n",
    "hp_dummies.head(15) # 0 or 1 one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "650fb874",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 0 0 0 0 0 0 1 1 0 2]\n",
      "[[1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [2]]\n",
      "  (0, 1)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 1)\t1.0\n",
      "  (3, 1)\t1.0\n",
      "  (4, 1)\t1.0\n",
      "  (5, 0)\t1.0\n",
      "  (6, 0)\t1.0\n",
      "  (7, 0)\t1.0\n",
      "  (8, 0)\t1.0\n",
      "  (9, 0)\t1.0\n",
      "  (10, 0)\t1.0\n",
      "  (11, 1)\t1.0\n",
      "  (12, 1)\t1.0\n",
      "  (13, 0)\t1.0\n",
      "  (14, 2)\t1.0\n"
     ]
    }
   ],
   "source": [
    "#머신러닝 분석을 위한 라이브러리 sklearn -> 딥러닝 tensorflow의 keras\n",
    "from sklearn import preprocessing\n",
    "#전처리를 위한 encoder\n",
    "label_encoder = preprocessing.LabelEncoder() # 클래스instance생성 labelencoder\n",
    "onehot_encoder = preprocessing.OneHotEncoder() # onehot encoder생성\n",
    "\n",
    "#label encoder로 문자형 범주를 숫자형 범주로 변환\n",
    "onehot_labeled = label_encoder.fit_transform(df['hp_bin'].head(15))\n",
    "print(onehot_labeled)\n",
    "\n",
    "#2차워 배열로 변경\n",
    "onehot_reshape = onehot_labeled.reshape(len(onehot_labeled),1)\n",
    "# 2차원 배열로 변환\n",
    "print(onehot_reshape)\n",
    "#희소행렬로 변환\n",
    "onehot_fitted = onehot_encoder.fit_transform(onehot_reshape)\n",
    "print(onehot_fitted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c2ee9ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>horsepower</th>\n",
       "      <th>h_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.706522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>165.0</td>\n",
       "      <td>0.896739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150.0</td>\n",
       "      <td>0.815217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150.0</td>\n",
       "      <td>0.815217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>140.0</td>\n",
       "      <td>0.760870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>86.0</td>\n",
       "      <td>0.467391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0.282609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.456522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>79.0</td>\n",
       "      <td>0.429348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>82.0</td>\n",
       "      <td>0.445652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     horsepower       h_1\n",
       "0         130.0  0.706522\n",
       "1         165.0  0.896739\n",
       "2         150.0  0.815217\n",
       "3         150.0  0.815217\n",
       "4         140.0  0.760870\n",
       "..          ...       ...\n",
       "393        86.0  0.467391\n",
       "394        52.0  0.282609\n",
       "395        84.0  0.456522\n",
       "396        79.0  0.429348\n",
       "397        82.0  0.445652\n",
       "\n",
       "[392 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정규화 : 모든 실수를 0~1또는 -1~1사이의 수로 변환 상대숫자간 영향의 균형을\n",
    "#위해\n",
    "#.describe() 통계data\n",
    "# 해당 열의 최대값으로 모든 데이터를 나누어 저장하는 방법 abs는 절대값\n",
    "df['h_1']= df.horsepower / abs(df.horsepower.max())\n",
    "df[['horsepower','h_1']]\n",
    "# 해당 열의 (최대값-최소값)으로 모든 데이터를 나누어 저장\n",
    "df['h_1']= df.horsepower / abs(df.horsepower.max() - df.horsepower.min())\n",
    "df[['horsepower','h_1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a7b3e5a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Date    20 non-null     object\n",
      " 1   Close   20 non-null     int64 \n",
      " 2   Start   20 non-null     int64 \n",
      " 3   High    20 non-null     int64 \n",
      " 4   Low     20 non-null     int64 \n",
      " 5   Volume  20 non-null     int64 \n",
      "dtypes: int64(5), object(1)\n",
      "memory usage: 1.1+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   Date      20 non-null     object        \n",
      " 1   Close     20 non-null     int64         \n",
      " 2   Start     20 non-null     int64         \n",
      " 3   High      20 non-null     int64         \n",
      " 4   Low       20 non-null     int64         \n",
      " 5   Volume    20 non-null     int64         \n",
      " 6   new_Date  20 non-null     datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(5), object(1)\n",
      "memory usage: 1.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# 시계열 데이터\n",
    "df = pd.read_csv('./dataset/stock-data.csv') #header= 0 안하면 첫열이 컬럼명\n",
    "df.info()\n",
    "\n",
    "#Date 컬럼의 자료를 new_Date 로 datetime형식으로\n",
    "\n",
    "df['new_Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0b56aad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='new_Date'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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atQuAcePGUVdXF0/JzXTljvs6sBHIdvfvBl5T1QdF5G53/y4RmQwsBKYAxcBfRGSCqoaAXwA3A38DXgIuAV4GbgIOqep4EVkIPARcLSJ5wD3ALECBVSLyvKpa0J4BSCRybE9qFKkZyRSNy2HnuirOWDAuVtIMj7LvX/6FwMbYhhlPnTSRYd/5TpfzXX/99fzsZz/j/PPP5/vf/z4/+MEP+OlPf0pDQwOHDx9m+fLlzJo1i+XLl3POOecwZMgQMjIyYqq9u3SqRiEiI4DPAI9FmRcAT7rbTwJXRNmXqGpAVXcAW4E5IlIEZKvqu+qETfxVizyRsp4FLnJrG/OBpapa7TqHpTjOxRiAdHUtirYYXZbPwd1HOXooEAtZhtEhtbW11NTUcP755wOwaNEi3nzzTQDOOuss3n77bd58802+853v8Oabb7J8+XLOPffceEo+gc7ecT8FvgVET2kdqqp7AVR1r4gMce3DcWoMEcpdW5O73dIeybPbLSsoIrVAfrS9lTzNiMjNODUVRo2yxWn6K11ZBrU9Rpfl8+4ft7FrfRWTzymOhTTDo3Tnyb+vOffcc1m+fDk7d+5kwYIFPPTQQ4gIl112WbylNdNhjUJELgMqVXVVJ8tsLQiPtmPvbp7jBtXFqjpLVWcVFhZ2UqaRaDTWdT1ybGvkFWeSlZdqixkZfUZOTg6DBw9m+fLlAPz6179url2cd955/OY3v6G0tBSfz0deXh4vvfQSZ599djwln0Bn7rizgctF5FIgDcgWkd8A+0WkyK1NFAGVbvpyIHq9vhFAhWsf0Yo9Ok+5iCQBOUC1a5/bIs8bnb46o1/RnRDjrSEijC4rYPN7+wg1hfEn2+A/I7bU1dUxYsTxv7s777yTJ598kltuuYW6ujrGjh3L//zP/wBQUlICOA4D4JxzzqG8vJzBgwefVG686PCOU9VvA98GEJG5wD+p6hdF5MfAIuBB9/05N8vzwG9F5Cc4ndmlwPuqGhKRIyJyBvAecD3ws6g8i4B3gSuB11VVReRV4F+iRlTNi2gxBh6NzU1PXY/11JKSsnzWv7mHim01jJyY1+PyDCOacDjcqj2ySFFLIiOdAL7zne/wHY81mfXk0exB4BkRuQnYBVwFoKrrReQZYAMQBG53RzwB3Ao8AaTjjHZ62bX/Evi1iGzFqUksdMuqFpEfAivcdPepanUPNBsJTKAuCAIpXViLoi2GnzIYf5KPnWurzFEYRgd0yVGo6hu4TT+qWgVc1Ea6+4H7W7GvBMpasTfgOppWjj0OPN4VnUb/JFDvhO8QX88XHkpO9TP8lFx2rqvinKtKY6DOMPov1jhrJAw9iRzbGqPL8qnZX0ftAW9MajIMr2KOwkgYehLnqTVGlxUA2Ognw+gAcxRGwtBY3/3Isa2RU5hO7tAMW8zIMDrAHIWRMATqur8WRVuMnprPns01NAVCHSc2jAGKOQojYQjUN8W0RgFOP0UoGKZ8k4UPM2LD3LlzefXVV0+w/fSnP+W2225rNX1JSQkHD3o7SKU5CiNhaKwLkhLjGkXx+FySU/3sXOvtG9VIHK655hqWLFlygm3JkiVcc801cVLUc8xRGAlBOKw0NoRiXqPwJ/kYOSmPneuqcGJVGkbPuPLKK3nhhRcIBJygk5988gkVFRWUl5czdepUysrKuOuuu07K98knn1BWdnz2wMMPP8y9994LOLWUb3zjG5x33nlMmjSJFStW8PnPf57S0lK++93vNuf5zW9+w5w5c5gxYwZf/epXCYVi06Qa27vOMHqJxhiF72iN0VPz2b76ANUVx8gf3r9WJhvoLH9mMwd3H41pmQUjszj37ya0eTw/P585c+bwyiuvsGDBApYsWcL8+fO56667WLVqFYMHD2bevHn86U9/4oorruj0eVNSUnjzzTf5j//4DxYsWMCqVavIy8tj3LhxfOMb36CyspKnn36at99+m+TkZG677Taeeuoprr/++h5fs9UojISgMUaRY1tj9JR8wIbJGrEjuvlpyZIljBgxgrlz51JYWEhSUhLXXXddc5jxznL55ZcDMHXqVKZMmUJRURGpqamMHTuW3bt389prr7Fq1Spmz57NjBkzeO2119i+fXtMrsdqFEZC0BxiPL3ncZ5akpmbSsHILD5Ze5CZ80fHvHwjfrT35N+bXHHFFdx555188MEH1NfXM336dLZt29ZunqSkpBNiRDU0NJxwPDU1FQCfz9e8HdkPBoOoKosWLeKBBx6I4ZW454h5iYbRCzSHGO+FGgU4o5/2bT/cXHMxjJ6QlZXF3Llz+fKXv8w111zD6aefzl//+lcOHjxIKBTid7/7XXOY8QhDhw6lsrKSqqoqAoEAL7zwQpfOedFFF/Hss89SWekE8q6urmbnzp0xuR5zFEZCcLxG0TuOonh8LhpWKncd6ZXyjYHHNddcw0cffcTChQspKirigQce4IILLmD69OnMnDmTBQsWnJA+OTmZ73//+5x++ulcdtllTJw4sUvnmzx5Mj/60Y+YN28e06ZN4+KLL2bv3r0xuRbpbyM9Zs2apStXroy3DCPGbHxnL6//aiNf+tGZZBekx7z8hqNN/PKflnPm58ZZ81OCs3HjRiZNmhRvGZ6mtc9IRFap6qzW0luNwkgIenPUE0BaVjLZhelUfnK4V8o3jETGHIWREDSvbpfW87Uo2mJoSTb7zVEYxkmYozASgsa6IMlpfnz+3vvJDi3J5uihAMdqA712DsNIRMxRGAlBb8R5asmQkmwAKndah7ZhRGOOwkgIeiNybEsKRmYhPrF+CsNogTkKIyFojPGiRa2RnOInf3im9VMYRgvMURgJQaA+SGpG7Gdlt2RISTaVnxy2AIFGt1FVzjnnHF5++eVm2zPPPMMll1wSR1U9wxyFkRA4NYreG/EUYWhJNoG6ILUH6nv9XEb/RER49NFHufPOO2loaODYsWP88z//M4888ki3yotVBNieYI7CSAgCdcFeifPUkiGj3Q5tjzU/hUPhjhMZnqGsrIzPfvazPPTQQ/zgBz/gi1/8Ivfffz+zZ8/m1FNP5bnnngOc0OLnnnsuM2fOZObMmbzzzjsAvPHGG1xwwQVce+21TJ06NZ6XAlhQQCMB0LA662X3cmc2QF5RBkkpPvZ/cpgJc4b1+vk6w64NVbz86Fouu306w08Z3OPy9u2opfzjQ8z6dEnPxXmcZU8spnJnbCKoRhgyeiwX3HBzh+nuueceZs6cSUpKCpdddhkXXnghjz/+ODU1NcyZM4dPfepTDBkyhKVLl5KWlsaWLVu45ppriESWeP/991m3bh1jxoyJqf7u0GGNQkTSROR9EflIRNaLyA9ce56ILBWRLe774Kg83xaRrSKySUTmR9lPE5G17rH/FBFx7aki8rRrf09ESqLyLHLPsUVEFsX06o2EoCkQQrX3ZmVH4/P7KBw1yDM1iqqKo7y6eB3BxjCfxCgM+uqlu3nvue0WALGXyczM5Oqrr+ZLX/oSS5cu5cEHH2TGjBnMnTuXhoYGdu3aRVNTE1/5yleYOnUqV111FRs2bGjOP2fOHE84CehcjSIAXKiqR0UkGXhLRF4GPg+8pqoPisjdwN3AXSIyGVgITAGKgb+IyARVDQG/AG4G/ga8BFwCvAzcBBxS1fEishB4CLhaRPKAe4BZgAKrROR5VbUFjgcQgV5ci6I1hpZks/avewiFwvh7cYJfR9QdbuTFR9aQlOInKy+NvVtrelymqlLhllNVcYyicTk9LtPLdObJvzfx+Xz4fD5Uld///veccsopJxy/9957GTp0KB999BHhcJi0tLTmY5mZmX0tt006vAvUIbJEVLL7UmAB8KRrfxK4wt1eACxR1YCq7gC2AnNEpAjIVtV31RlS8qsWeSJlPQtc5NY25gNLVbXadQ5LcZyLMYAI1PVu5NiWDCnJJtQUpnrPsT45X2sEm0K8/Oga6g83cult0yiZWsCBnUdoCvSsY7O2sp76w40AVO2J7cpvRtvMnz+fn/3sZ82j6T788EMAamtrKSoqwufz8etf/9oTHdet0anHJRHxi8hqoBLnj/s9YKiq7gVw34e4yYcDu6Oyl7u24e52S/sJeVQ1CNQC+e2U1VLfzSKyUkRWHjhwoDOXZCQQvR0QsCVD3Rna8ZpPoaq8/quP2bf9MJ+6cTJDS7IpGp9DOKzs31Hbo7IjtQkRqK6InyMcaHzve9+jqamJadOmUVZWxve+9z0AbrvtNp588knOOOMMNm/e7KlaRDSduvPcZqMZIpIL/FFEytpJLq0V0Y69u3mi9S0GFoMTZrwdbUYC0tdNT4Py00jLSnb6Kc476bmk11nxwg62rNjPGVeMZdxM5/mraFwOCOzdVsuIiXndLnvvlhrSspLJKUy3GkUfcO+99zZv/9d//ddJx0tLS1mzZk3zfmR1urlz5zJ37tzeltdputQAq6o1wBs4zT/73eYk3PdKN1k5MDIq2wigwrWPaMV+Qh4RSQJygOp2yjIGEI11TUDf1ShEJG6RZDe9t48VL37CxLOKTlgXIzUjmfzhWVRsqelR+RVbaygen0v+8CyqKo7axEKjU3Rm1FOhW5NARNKBTwEfA88DkVFIi4Dn3O3ngYXuSKYxQCnwvts8dUREznD7H65vkSdS1pXA624/xqvAPBEZ7I6qmufajAFEX9coAIaMHsShvcdobOi7kUF7t9bw+q83Ulyay9xrT8EdFNhM8fhc9u043O05FUcPBTh8sIGi8TnkD88kcCxIXW1jLKQb/ZzO1CiKgGUisgZYgdNH8QLwIHCxiGwBLnb3UdX1wDPABuAV4Ha36QrgVuAxnA7ubTgjngB+CeSLyFbgTpwRVKhqNfBD97wrgPtcmzGAiHRm91WNApwObVU4uLtvIsnWHqjnpUfXMigvjU9/dSr+pJNvzaLxOQQDIQ7s7l6TUWTUVHFpLnnFWYAz/LY/YjWltunOZ9Phnaeqa4BTW7FXARe1ked+4P5W7CuBk/o3VLUBuKqNsh4HHu9Ip9F/CdQHSUr19+lQ1eYO7R1HKC7t+SS39gjUNfHiIx+hYeWy26eTltX6DPTi8bmA84cf0dcVKrbWkJzqp2BEVnMtrWrPMUZNzu+2di+SlpZGVVUV+fn5J9XKBjqqSlVV1QnDcDuDzcw2PE9jfZDUXlzZrjXSB6WQXZDW6/0UoVCYVxavo/ZAPZd/bQa5QzPaTJuZm0p2QRp7t9Yy41NdP1fFlhqKxuXg8/tIz0ohIzuF6n7YoT1ixAjKy8uxEZCtk5aWxogRIzpOGIU5CsPzNNYFSemDyLEtGTK6dzu0VZXlSzZT/vEhLrx+YqfCcxSPz2Xn+ipUtUtPyw3HmqiuOEbprKHNtvzhmVT1wyGyycnJnpnR3F+woICG5wnUB/tssl00Q0qyOVLVQN3h3unw/ei13axfXsHM+aOZdFZxp/IUjc+l/kgTNfvrunSu4/0Tx2di5xVnUb33GOGwtecb7WOOwvA8fbG6XWsMbV4aNfa1ih0fHeDt329l3KmFnLFgbKfzFY13/ui7Oky2YmstviRpXu4VnBpFqCnMYQupbnSAOQrD8wT6YHW71igcNQiR2M/QPrD7CH9+fANDRg3iohsnI77ONyHlDs0gfVAye7d2bYZ2pAM8Kfl4X0/+cHfkUz/spzBiizkKw/M0xqlGkZzqJ684k8pPYjdE9lhNgBcfWUNaRhKX3jaN5JSuddKLCEXjc9m7rabTeZoCIQ7sPEKRO2oqwuCiTBD6ZT+FEVvMURieRlX7ZL3stojl0qhNgRAv/nwNjfVBPnP7NDJzUrtVTvH4XA4fbODooUCn0u/bUUs4rBSX5p5gT07xk1OQ3i9HPhmxxRyF4WmCjWHCYY1LZzY4/RQNx5o4fLChR+VoWFn6+HoO7j7CvJumUDBiULfLivRTdDbs+N4tNYhA0diTQ4o7oTysRmG0jzkKw9M0hxiPQ9MT0Nz529OFjN790zZ2fHSQs68spWRaQY/KKhiRRXKqvzkSbEdUbK0hf0RWq7WyvOGZ1FbWEWz0ZnhrwxuYozA8TaC+bwMCtiSvOBN/sq9HHdob3q7gwz/vYsp5w5l2YdcmOrWGz+9j2NjsTnVoh4Jh9m8/fFKzU4T84ixU4dC+rg23NQYW5igMT9MY5xqF3++jcOSgbg+RLd90iL8+tYmRk/M49+rSmIWUKBqfS1XFUQJuZN22OLDrCMGmcHP4j5bkD3fWP7CRT0Z7mKMwPE1z5Nj0vp+ZHWFoSTYHdh7pctTWQ/uO8cp/rSVnaAbzv1IW01hVxeNzQZ31KdojMt+i5YinCDmF6fiTfOYojHYxR2F4muOr2/VtrKdohowZRLApTPXeznf6Nhxt4oVH1uDzC5fdPi3mnfFDxmTj80uHHdp7t9aQOzSDjOyUVo/7/D4GF2XYandGu5ijMDzN8c7s+NUohoyORJLtXPNTqCnMy/+1lmOHAnz6lmlkF6THXFNyip/CUYOo2NJ2jULDyt5ttRSPP3m0UzT5xVlWozDaxRyF4WkCHqhR5BSmk5qRROXOjifeqSpvPPUxFVtquHDRRGcJ016ieHwulTsPtzliqariGIG6YJsd2RHyhmdyrLaRhmPt93cYAxdzFIanaawL4k/2nRB6oq/pytKoq17Zycd/28fsy8YwYfawXtVVVJpLOKRtdrRHmqXa6p+IEAnlUd1PFzEyeo45CsPTxCtybEuGlGRTXXGMpkDb8w22rqrkvee2Uzp7KLM/U9LrmiIT6NpqfqrYUkPW4FQG5be/SE1+cWTkk/VTGK1jjsLwNI318Ynz1JIhJdloWDnQxtKo+3cc5i9PbGDY2BwuvH5in6yslpaVTF5xZqsd2qpKxdYaisbndqglMzeV1Iwk66cw2sQcheFp4hU5tiVDRjshN1qboX2kuoEXf7GGzJwULr11ap82kxWNz2Xv9tqT1pSoPVBPXW1jh/0T4DSt5RVnWo3CaBNzFIanCdR5o+kpMyeVrLzUkxxFY32QFx/5iFBjiM/cNp30Qa0PQ+0tisfn0NQQoqr8xNrA8f6JznWm5xdnUV1xNCbBD43+hzkKw9M01gdJ8UDTE3BSh3Y4FObPv1xP9d46Lrl5KnluW39fEumobhn3qWJrLWmZyeQN65ym/OGZNDaEOh2R1hhYmKMwPE2grskTNQpw5lMcPthA/VFnadS3n93KznVVnLdwAiMn58VF06C8NLLyUk/qp6jYUkPR+JxOL4qUZ4sYGe1gjsLwNAGPdGZD9NKoR1j7RjlrlpUz/aKRlJ03PK66isfnUrG1trnZ6FhtgMMH6jscFhtNZOSTzdA2WqNDRyEiI0VkmYhsFJH1IvJ1154nIktFZIv7Pjgqz7dFZKuIbBKR+VH200RkrXvsP8UdjiEiqSLytGt/T0RKovIscs+xRUQWxfTqDU8TbAoRDqonOrMBCkcPAoHVS3ex/OnNlEwr4KwvjI+3LIrG51J/uJHaSmft60h8p850ZEdIzUgma3Cq1SiMVulMjSII/KOqTgLOAG4XkcnA3cBrqloKvObu4x5bCEwBLgF+LiKRYSC/AG4GSt3XJa79JuCQqo4H/h14yC0rD7gHOB2YA9wT7ZCM/o0XwndEk5KWRF5RJuUfHyJveBYXf3kyvi6sd91bRCLDRpZH3bulhqRUP4Ujs7pUTl5xlo18MlqlQ0ehqntV9QN3+wiwERgOLACedJM9CVzhbi8AlqhqQFV3AFuBOSJSBGSr6rvq1JF/1SJPpKxngYvc2sZ8YKmqVqvqIWApx52L0c/xQkDAlgyfMJjM3FQ+c9s0UtK8UdMZPCyD1MwkKtz1KSq21lI0NhtfF6PV5g/P5NC+Y4S6GCXX6P906ZfkNgmdCrwHDFXVveA4E2CIm2w4sDsqW7lrG+5ut7SfkEdVg0AtkN9OWS113SwiK0Vk5YEDB7pySYaHaa5RxDHEeEvO+btSrrvvDAbltT/buS8Rn1A0Lpe9W2poONZEVcXRLvVPRMgvziQcUmr318depJHQdNpRiEgW8HvgH1S1vaA3rdXFtR17d/McN6guVtVZqjqrsLCwHWlGItG8FoVHOrMBfD4hOcU7NZwIxeNzqT1Qz/YPD4DS5kJF7dE88sliPhkt6JSjEJFkHCfxlKr+wTXvd5uTcN8rXXs5MDIq+wigwrWPaMV+Qh4RSQJygOp2yjIGAJHV7bzSme1likqdiXUfvLoTn18YOia7y2XkDctEfGId2sZJdGbUkwC/BDaq6k+iDj0PREYhLQKei7IvdEcyjcHptH7fbZ46IiJnuGVe3yJPpKwrgdfdfoxXgXkiMtjtxJ7n2owBgBdrFF6lcNQgkpJ91B6oZ8jobJK6UevxJ/vIHZJuQ2SNk+jMHXg28CVgrYisdm3fAR4EnhGRm4BdwFUAqrpeRJ4BNuCMmLpdVSMhN28FngDSgZfdFziO6NcishWnJrHQLataRH4IrHDT3aeq1d27VCPRaGxeBtUcRUf4/T6Gjs1mz6Yaiku7vwZGXnEWB3Z1b31wo//S4R2oqm/Rel8BwEVt5LkfuL8V+0qgrBV7A66jaeXY48DjHek0+h+BuiA+v+BPtnmhnaFofC57NtV0qyM7Qv7wTLZ9UEljQ9Azo7qM+GN3oOFZIrOy+yJkd3/glNOHUTp7KMNP6f5Uo+ZFjLqwPrjR/zFHYXiWxrom68juArlDMph305QejcrKs1AeRiuYozA8i1dWtxtI5BSkk5Tis5FPPaCxIci2Dyo5fLD/zEexu9DwLF5Z3W4gIT4hr8gWMeoODUeb+GjZbtYuK2+eLDpsbDals4cx/rQhZGT37VolscTuQsOzBOqCZOZ6Zwb0QCF/eBafrD0YbxkJw9FDDaxeupv1b+0h2BhmzPQCys4bzoHdR9iyopLlT2/mrf/dwsiJgymdPZSxMwoTrkk1sdQaAwqn6cl7s6D7O3nFmWx8Zy91hxsT+im4tzm07xgf/nkXm97bhypMmD2UU+ePIr/YGRAwako+p11SQtWeo2xZsZ/NK/bz2pMbeeO3myiZms+E2cMYVZbXp0vndhdzFIZnaawLkuKRyLEDifyoUB4Z2fFZkMnLVO48zAev7mTbhwfwJ/mYcu5wZlw8kuz89FbT5w/PIn94FqcvGMv+HYfZ/P5+tq7az7YPDpCSnsTYUwuZ4I5W80I04tYwR2F4klBTmGBT2Dqz40DzENk9xxg50RxFhKOHArzx1MfsXFdFSnoSp80fzbQLR3a61iUiDBubw7CxOZxz1XjKNx1iy/v72fZBJR+/s5eM7BTGzxrChNnDGFIyyFPDwu0uNDyJhe+IHxnZKaQPSrbggFFsX32A13+9kVBQOeOKsUw9f0SP+hl8fh+jJuczanI+5zeG2Lmuis0r9rPuzT2seb2c7MJ0JsweSunsoeQV9f1a7C2xu9DwJMfXorCfaDzIK7aRTwDBxhBv/34r6/66h8JRg5h30xRyh2bE9BxJKX7GzRzCuJlDCNQ1sX31ATa/v59VL3/Cypc+oWBkFqWzh1I6a2jcwtvbXWh4EqtRxJf84iw2vLMXDSvi0Xbz3qZqz1H+/Mv1VFccY8anRnLGFePwJ/Xu1LPUjGQmnVXMpLOKOVYbYOuqSras2M+7f9jGu3/YRtH4HCbMGca4mYWkZ/XdQAO7Cw1PYiHG40v+8CyCgRCHqxrIKWy9k7a/oqqsf3MPbz27lZT0JD57x3RGTcnvcx2ZOalMv3Ak0y8cSe2BOrasqGTz+/v46283sXzJZkZOyWPC7KGUTCvo9bhcdhcaniRgkWPjSiSUR9WeowPKUTQcbeL1X29kx0cHGTUlj4sWTfbEEOGcwgxmXVrCaZ8eTdWeo2x+fz9bVuxn6doqklJ8jJlWQOmcYYyanNcrtR67Cw1PEqhrAqzpKV4cj/l0lLEz2l41UlUJB7VfRPjds+kQS/9nA/VHGjn7yvFMv3Ck55rdRISCEYMoGDGIM68Yx97ttWx5f7/TRLWykvwRWSz87pyYn9fuQsOTBKwzO66kpCWRXZDG1lWV1B1porE+2PwKNG+HaGwIEg4po6fmM+/LUxL2+1r9l128/fut5A7J4DO3zaJw1KB4S+oQ8QnF43MpHp/LOVeXsntDNU0NoY4zdoPE/FaNfk9jfRDxCcmp3p+12l8ZOSmP9W9VcKSqgZT0JFIzkkhJTyIzN5XBwzJJTXf2Q6Ewa14v5w8Pr+Izt0+P28ic7lJ3uJF3/7SNkrJ8Lr5pSkKuw+H3+yiZWtBr5SfeJ2IMCBrrnMixXpp0NNCYe91Ezr/mlE41v4yenM8ri9fy7EMruez26QnxRB5h/fI9hIPKWV8Yn5BOoi9I/IZFo18SqA+SYnGe4k5n2+hHTs7j8988DZ9f+MO/fcAnaxIjqGAoGGbdX/cwakoeg4fFf2KbVzFHYXgSZ3U7i/OUSOQPz+LKu2YxeGgGL/1iDWuW7Y63pA7ZuqqSusONTL9wZLyleBpzFIYnaawLJmzH6EAmMyeVz/3jTEqmFbD86S0sf3oz4bDGW1arqCprXt/N4GEZjJxsMa3awxyF4UkCtmhRwpKc6ueSr05l+oUjWbOsnJcfXUtToHdG4/SEfdtqqdx5hGkXjLC+sA4wR2F4kkZbBjWh8fmEc/6ulHOvnsDOtQf54799wLHaQLxlncBHr5eTmpHEKWcUxVuK5zFHYXiSQF2QFKtRJDzTLhjBpbdO49D+Op59cKVn1uI+Ut3A9tUHmHx2sQ3B7gQdOgoReVxEKkVkXZQtT0SWisgW931w1LFvi8hWEdkkIvOj7KeJyFr32H+KW9cTkVQRedq1vyciJVF5Frnn2CIii2J21YanCYfCNAVCVqPoJ5RMK+Dz/ziTcFj5/Y9XsWtDVbwlsfaNclClbO7weEtJCDpTo3gCuKSF7W7gNVUtBV5z9xGRycBCYIqb5+ciEnHXvwBuBkrdV6TMm4BDqjoe+HfgIbesPOAe4HRgDnBPtEMy+i+N9U57tnVm9x8KRw3iyrtmkZ2fxgv/bw3rl++Jm5amQIgNb1Uw9tTCNlelM06kQ0ehqm8C1S3MC4An3e0ngSui7EtUNaCqO4CtwBwRKQKyVfVdVVXgVy3yRMp6FrjIrW3MB5aqarWqHgKWcrLDMvohgXqL89QfGZSXxuf/6TRGThrMG09t4t0/bkXjMCJq03v7CNQFmWZDYjtNd/sohqrqXgD3fYhrHw5ED54ud23D3e2W9hPyqGoQqAXy2ynrJETkZhFZKSIrDxw40M1LMrxCpEZhTU/9j5T0JD5z2zSmnFvMB6/u4tXH1hNs7LsRUZEhsYWjBlE0LqfPzpvoxLozu7UxZtqOvbt5TjSqLlbVWao6q7Cw7UiXRmJgkWP7Nz6/j/OvPYWzvjCebR9W8txPP6T+SGOfnHv3xmoO7atj+oU2JLYrdNdR7Hebk3DfK117ORBdnxsBVLj2Ea3YT8gjIklADk5TV1tlGf2c45FjbWZ2f0VEOPXiUVzylTIO7D7Ksw+t5NC+3l969aPXyknPTmH8aUN7/Vz9ie46iueByCikRcBzUfaF7kimMTid1u+7zVNHROQMt//h+hZ5ImVdCbzu9mO8CswTkcFuJ/Y812b0cwLNq9vZsMX+zriZQ7jizlNpCoT4/b+uYs/mQ712rkP7jrFrfRVTzx/eL9bP6Es6Mzz2d8C7wCkiUi4iNwEPAheLyBbgYncfVV0PPANsAF4BblfVSAPkrcBjOB3c24CXXfsvgXwR2QrciTuCSlWrgR8CK9zXfa7N6Oc0Nq+XbTWKgcCwMTlcedcsMrJTeP4/VrPpb3t75Txrl5XjSxKmnGtDYrtKh43AqnpNG4cuaiP9/cD9rdhXAmWt2BuAq9oo63Hg8Y40Gv2LQF0QBFJsItSAIbsgnc9/8zReWbyWvzyxkdoD9cy+bEzM+hECdU1s/Ns+Jswa6omlTRMNq38ZniMSvsNry1AavUtaZjKfvWMGE88cxooXP+EvT2wg1BSOSdkb3t5LMBCyIbHdxIaVGJ7DWYvCfpoDEX+Sjwuvn0ROYTrvPb+Do9UBPn3LVNIyu98MGQ6FWbusnOLS3IRaUMlLWI3C8ByBOoscO5AREWZdOoaLvzyZfTtq+f2/rqL2QF23y9ux5iBHqhuYduGIjhMbrWKOwvAcFjnWAJgwZxgLvn4q9UcbefahVezdVtutcta8Xs6g/DTGTLc5Vt3FHIXhOQK2aJHhUlyay5XfmkVqehLP/fuHbFm5v0v5D+w6QsWWGqbOHYHP+ry6jd2NRpfQsBIMhgk1hgk2hQkFQwQbw4SCYee9KUywKeQcawpHvYda7LdtP7T/GAUjs+J9qYZHyB2awRfuOo2Xf7GWPz+2nsMH65k5f3SrI6JCoTAHdjrOoWJLDXu31pCU6mfy2bbmRE8wR2GcRCgUpvzjQ2xbVcmezYdoagwTagwRDIYJB3sWxM2f5CMpxYc/yYc/2UdScuTdjz/ZR0ZGEtn5BXZjGyeQnpXC5f8wg9d/9TF/+9N2Dh+o57xrT4Ew7N95mIrNNVRsOcTe7YcJuqvpDR6WQensoZxyRpHNyekh5igMAELBMLs3VrPtwwPsWH3Aaf5J8zNyUh6pWckkNf+p+6P+3E+2tfzzj07rT/LZkFej2yQl+7n4xslkF6Sx6uWdlG86xLHaxuYhtPnDM5l0xjCKJwymuDTX5kvEEHMUA5hm57Cqkh1rDjY7hzHTCxl32hBGTcqzUAeGpxCfcMaCceQOyWDjO3sZM62Q4gm5FI/PJS3Lag29hTmKfo6qEqgLcvRQgGM1AY4eauBYTYCaynp2rqui0Z2zMGZ6AeNnDmGkOQcjAZh4ZhETz7Tmyb7CHEUCEw4r9YcbjzuBmgbXGQSibIGTZ7cKZGanMHZ6AeNOG8LIieYcDMNoG3MUCYCGla0fVLJvey3HDjl//sdqAhyrbTxphTBfkpCVm0pmbipDRg9izIzC5v2swc57Rk4Kfr85BsMwOoc5Co9TsaWGt/53Cwd2HSE51d/8Zz9i4mCyBqc5DiDKEaRlJduCLIZhxBRzFB6l9kA97/5hK9s+PEDW4FQ+deNkJsweaqOGDMPoc8xRuGhYqao4RlpmMmlZSSQlxyfEdaA+yMqXPmHNst34fMKcz45hxsWjSE6xkNuGYcQHcxQuDceaePpH7zfvJ6X6SctMchxHZjJpWcmkZyaTmpV8oi1qPznN3+1mn3AozIa3Knjv/3bQcKyJiWcM44wF48jMTY3VJRqGYXQLcxQuSal+5n+ljIZjTc7raNMJ20eqG2g41uQsqtPG5GSfX0h1ncZxB5JEWlYyqZknOpU0dzs1M5ndG6t5+9mtHNp7jOLSXM65qtTCIRuG4RnMUbgkp/gZf9qQDtOFw0qgLuJIgq4jaaThaPC4Y3GdS01lXbPDCYfaD32RU5jOp2+ZypjpBdYZbRiGpzBH0UV8PiE9K4X0rM6HB1BVmgKh47WUFrWVjJxUJp1VhD/JhqwahuE9zFH0ASJCSloSKWlJZBekx1uOYRhGlzBHYSQMTeEmAsEAhwP11NTXcThQTygcJBRWlDBhVUIaRlUJq7OvhAmHw4QUwhokHA5BsAkNNqBNTRBshMYABJuQUBMK+FDwKYKghPELIIrgvPtE8PkFn9+Pz+/D7/PhE/D5fPhE3H1xt519QUAEcZeAiTQvHt/HffdFNk5IKwhpablkpOeTmVlIeno+Pp+NhDP6BnMULrt3rOedr1/d6jEFBNAOug7aO65t9YC3OJFEbQNIVLaTjkVlPZ5OW7G1lu7EQtXd1mabcyByTZ0qq0u2k43R16e0eJ3wOWhzmf5wi1cIklraWhzz9SxS+kmEJerlcz6zsDjvIYGmVuxtpQ/7IMyJdmdbCPog5IegH4I+UD+oD/A5wfLwC+LzEfY5TinsE1R8qE9Qn7MPPsJ+HyqChBWfOg5Qws5nKnriOzjn0Ogy3e1IuSo+EOd7Pv4datTvU0/4bTvlatS2eyzqN3xiWSf+1k/Ydzy042B9fsTnQ8Tv2Px+xOdsq/vbVolsi/Muzmer4pYlgvicFz7B53evze9DxPmcnc/Uh+J+HkTti4+wOF+KuBcVuZ2E5ruqWX9kWlTE7ot8KBr18AAnlBVdxgnlCeQMGsZnPvP9Nn6p3ccchYs0BSk+EGr7eE/K7uCPSYj+g466X6QNe7NNWrGdmO648zrhtj3J0qxRj//4Itpbc4DRGk861llbO2U4N5bzTC2Ic5MCPvfp2udu4xc0RZwb1f3T0CR32+8j6PPT5Bfwu38Yfj8a2U7ygz/J2U9KAp9zNlVpdkyqEfGKht1vKgwaDkNYT3pHwxBy3jWsji0cdgpqTqNI5JgqEg47f8xhxa+OXaLfQ84xXziILxTCHw7hC4fxhRRfOIw/hPMeBn8oFHNHaCQOu4YJDFRHISKXAP8B+IHHVPXBWJ9jxITpjHh3Y6yLNYw+RyNOKRRCQyE0FIawsx2xEWyCpGTE54OkJKcpzO8H8SFujQPxEXadnIRCEAo6Di8YdMoNOjYNRT9gSbPjF5HoNrXmbY3sR5xxC7uING9Hnr1PfNBwazRhdZoVQyHCTU0EGxtpCjYQCgSc98YGwk0NBIONbq0pjA9xnLnrtAXFp2EIhVENEw6FCYXChEPOQl0adlZxdM7j2CWs+AgjGnJqZBpCNIxPFcIhfLjlS9SDnPN84VSu3GsPRz2SNT+n6fHtMFEPKOI8VxxPH/UwE2VLK+iddcE97yhExA88AlwMlAMrROR5Vd0Q63Mte2IxlTu3x7pYwzAGHO21QWiL99gxJCMt5mUCJMJ4zDnAVlXdrqqNwBJgQZw1GYZhDBg8X6MAhgO7o/bLgdOjE4jIzcDNAKNGjer2iS644eZu5zUMw+ivJEKNorU63Al1NlVdrKqzVHVWYWHvtNEZhmEMVBLBUZQDI6P2RwAVcdJiGIYx4EgER7ECKBWRMSKSAiwEno+zJsMwjAGD5/soVDUoIn8PvIozPPZxVV0fZ1mGYRgDBs87CgBVfQl4Kd46DMMwBiKJ0PRkGIZhxBFzFIZhGEa7iLYSnC2REZEDwM4eFFEAHIyRnFhj2rqHl7WBt/WZtu4TS319ca2jVbXV+QX9zlH0FBFZqaqz4q2jNUxb9/CyNvC2PtPWfWKpL97Xak1PhmEYRruYozAMwzDaxRzFySyOt4B2MG3dw8vawNv6TFv3iaW+uF6r9VEYhmEY7WI1CsMwDKNdzFEYhmEY7WKOwoOISHvLYxkJiIgkx1tDImP3RHwZcI7CXVrVcz88cfiGiIxQj3YciUipiPTOWos9RESmiUhWvHW0xP1e7wX+IbIfV0Et8Or9AN6/J2J9P3j5uxgwjkJEbhCRD4Gvx1tLS0TkemAZcCpw2Gs/FBFZICLbgPuAx0QkL96aIojIdSKyBvgB8LQbit4TiMgXcb7X64EvAnjlD8/L9wN4+56I9f3g9e8CBoijEJGJwG3AC8B5IjJWVVVE4n79InI28ATwT6p6vaoejvyZeOHmcG+C/w+4VlWvASqBfxaRCfFVBiLyaeCrwK2q+jlgHPBZ91jcPjsR8YvITcBXgG+p6lhgj4hMiZemaLx8P4C374lY3w9e/y4ieEpMLBGRQZFtVf0Y56nu34ENwN+79rAHtL2NszjTJPfY3SLyWRHJitfTZ7S+iAmIfFZLgC8Al8bj6T1SPXd5Q1XPU9W3RSQH2O6mkXh8dhFtqhoCnlPV81X1fRGZBByh9WV9+0pbc7Oc1+4HOEmfp+6JVpo0e3Q/ePm/qS36paMQkbuBD0XkIRG5wTVvUtVq4I/AOBE5z03bp59BC203uebbgCdFZDWQC9wB/Nh92uhTovT9q4hc635ma4FFIjIYmAWsBIYBw/tY233A90UkErgs4NqH4qxXUoNz0/b5ZxelbQiAqh507aKqG4ESYIZr6+vf3LeAN9zv9HpX38deuB9a0Xeja/bEPRGl7ccishA4RA/uBy//N7WLqvarF3Ah8CYwBrgA2AtMizqehdOx+FSUzR9HbTPdY7cDs9ztQuBPwHwPfHYTgNHAT4AXgaeAKcAbQEkf6UoFvo0TFfiPwLxW0uS473k4S+Ve6gVtkd8W8DXg0T7+PvNxmnCewXFSVwLvAcOj0sTzfmhL32j3+O3Aae52n94TrWi7ytWWD4ztzv3g5f+mjl7e8VixIxn4UFV3qOoy4D+AB6KOHwOeBY6KyA9F5F9xnvbipe1fAFT1EVVd6W4fAKpx/vT6kpb6fgb8m6ruVNU7gZtU9Tp1lqIt70N9TThtuJOBvwEXiMiY6ASqWuu+V+O0Gw/2gjZ1mqHAqf3UikNf3XfHgD+r6t+p6mrgNZyn4REt0sTrfmhL33BovidWudt9fU+01PYXYB0wVlW3d/N+8PJ/U7v0R0eRAeSLO2xNVR8EikTkKndfgQZgKnArcEBVt8VR25CINnA6y0Tk34BpOO20fUlLff8CDBeRq939fSIyUkQewbmZN/WFKHXaazer6jHgaZw/ujkikgrHOzjdz+5hnCfAPvnsOqEt0qfyMXCjOvRJ+7OqNgD/F2UK4nw2e11tEs/7oR195dHp4nFPtKFtOrA/Kk1X7wcv/ze1S8I6ChH5kohMbWlX1T/ijH65LMr8r8CdUfsPAOuBUar6Y69oc59Ef4fz5HG+qm6NtbZu6vuHqP3/B/iBz7h/jn2lLeC+fwK8BZwPTIzKNw2nmSDy2W32graoGsU7wL+ISFJvjNxpR9uRqN18oFJVd7nHIh3DD9GL90N39bn5xuJ0GPfaPdFdbS6P0OJ+EJF/FJF57nbzf6wX/pu6Tbzbvrr6wvHqH+G0V06PsguQ6m4vxGkLLHH3R+F8oYPc/TQPaksB0oA8j352We5+Rhy0+dztSHt/Nk6z2LXAl4DLXHuhx7R9Efhcb32f3dB2Dm77NzAftz8FSPeovrnudr4HtUU+u8yofPOAV3GaPn/Tory4/jf19JVE4nEp8IiqnhB2V51POeA+gTyN02b8XXEmsnwW+ETdJwR1qpVe09boJu8tbT3Vd9RNWxcHbepqqwZqVPWwiGzBucGqcDqKUacd25PaepFOawPOA1JE5Bc4zRvfcdPWe1DfNOBuN22VV7UBdeIMi/0+Tk3yAZyHvtkikgSE1WlqjPd/U4/wfNNTK9X0icA+99g3ROQSEcl19+8C3sfx/v8G/A8wB3hNVW8ZSNq8rq8b2t4BznQ7gyfiNIf9WFXHq+pLpq1dbWe7aacApwMfq+o5qvpmLLXFWN/Zqrrcy9oifTzuQ95zqnqu+30fAhaqalBVw+JMwLwbZ9RUn93/scTT61GIyN8DFwF/Bf5XVfeIyAPANuAzOF/yYJw2wh8BZwJPq+qhqDL8erydeEBo87q+nmpzOwP92jt9JP1Z22XAW6paE2ttXtcXa21R5b0JLFHVyACBZFVtEpGlwE9U9WURKQYWuOn65P6POfFu+2rrBXwOZ4TDBTje9xGc9ryFwOs4T2zgfLGvA5+NyuvHdYIDTZvX9fVQW5KHPzcva0vuTW1e1xdrba2U9zPcPg6c/og84DFan+/T6/d/b7y83PR0OvALdcYb34szoenbqroEx/sni8hQdTzyOzijCSJD/kLqfisDUJvX9fVEW7AXdfVnbU29rM3r+mKtrWV5n+AG9FOHaiAdmOuW448qry/u/5jjOUcR1Y64HWfkCKq6E2e2baGInAP8GGei07dF5Hs4Mzr/6qbttS/By9q8rs+09T9tXtcXa23tlPcikCkil0cl/w1wuoikuQ6o17+L3iTujkKcYG6R7ehgbs/ijChY4O7vxakWnqWqH+KMLvgYZxLLp1zbgNHmdX2mrf9p87q+WGvrYnlvAJOjnEk6zvyPxOiD6IC4DY8VkdNxhucFROQVnDHKgagOnkM4sXNuFZHnVbVWRDKBTGgO1fDoQNPmdX2mrf9p87q+WGvrZnlZOHMlIs7kOU2UjupOEJcahTizaB/B8czP4gTLGg8nzGRNx5m8shdYLM7IgVOBxpMKHCDavK7PtPU/bV7XF2ttPSyvuZ+qPzkJiF/T02nAVlX9NbAUZ0byrki1TUR+iOOxhwL/iBNf5bc4E18eHMDavK7PtPU/bV7XF2ttXr7W+KF9MLQKZ8bi6VH7Q3Ciad6PEwDsb8DjwD/hfDG/Bca3KKO3Qkd4VpvX9Zm2/qfN6/pirc3L1+qlV+8WDoOAP+BMg38cGBx1bCJOMLLro76wl3DXZ3BtvoGozev6TFv/0+Z1fbHW5uVr9eKrt5ueGnFGF3wRqMBZ/ANoXgJwIsdDCq9y00SqeD7t3XDMXtbmdX2mrf9p87q+WGvz8rV6jpg7ChG5XkTOF5FcdcIvP4az6MdmYJacuAj5n4F73Pa/hUAZcBB6Z81YL2vzuj7T1v+0eV1frLV5+Vq9TkxiPbkf5jCc9rswTvyUTODrenzt4FJgERBQ1R+6tnRgMU67oB/4mqpu6LGgBNHmdX2mrf9p87q+WGvz8rUmFD1tu+J4vPYJuDHYceZn/Az4fYu0nwN+DpTidgC5aYf1Rrual7V5XZ9p63/avK4v1tq8fK2J9ur2hDtxYq3fB/hF5CWcBVsiU9WDIvI1oEJEzlfVyJT4P4rIJOBlIEtELlDVjbihfmOFl7V5XZ9p63/avK4v1tq8fK0JS3e8C84ogI+AXwBfwQm1ewmwC5gTle5WYFnU/lU4C4j/NzCkNzyfl7V5XZ9p63/avK4v1tq8fK2J/Orul3su8KWo/Z+7H/wNwCrX5sNpG3wGGBOV79xevSAPa/O6PtPW/7R5XV+stXn5WhP51d0vNwNI5Xgb4HXAA+72auAOd3sW8Ls+vSAPa/O6PtPW/7R5XV+stXn5WhP51a3hsapap6oBPR7P5GIgsl7xjcAkEXkB+B3wQXfO0V28rM3r+kxb/9PmdX2x1ubla01kehQ9VpwFORQn7snzrvkITuTFMmCHqu7pkcJ+qA28rc+09T9t4G19sdbm5WtNRHo64S4MJONMRJnmeurvAWFVfSvOX4SXtYG39Zm2/qcNvK0v1tq8fK2JR0/broAzcL6Ut4Cb4t2WlijavK7PtPU/bV7XF2ttXr7WRHv1eGa2iIwAvgT8RJ1p8Z7By9rA2/pMW/fwsjbwtr5Ya/PytSYaMQnhYRiGYfRf4r5mtmEYhuFtzFEYhmEY7WKOwjAMw2gXcxSGYRhGu5ijMAzDMNrFHIVhGIbRLuYoDKOPEZEbROSAiHwoIltE5FUROasT+a4Qkcl9odEwojFHYRjx4WlVPVVVS4EHgT+4C+e0xxWAOQqjzzFHYRhRiEiJiGwUkf8WkfUi8mcRSReRcSLyioisEpHlIjJRRPwisl0cckUkLCLnueUsF5HxnTmnqi7DWZ/5ZjfvV0RkhYh8JCK/F5EMt8ZxOfBjEVnt6jlJU299LsbAxhyFYZxMKfCIqk4BaoAv4PyR36GqpwH/BPxcnVDWm3Ge8s8BVgHnikgqMEJVt3bhnB8AkT/6P6jqbFWdDmzEiVP0Dk4U1G+q6gxV3daaph5dtWG0QY/CjBtGP2WHqq52t1cBJcBZwP+KSCRNqvu+HDgPGAM8gLP85l+BFV08p0Rtl4nIj4BcIAt49aTEIlntaDKMmGKOwjBOJjqAXAhnTYMaVZ3RStrlwC1AMfB94JvAXJy1mrvCqTi1B4AngCtU9SMRucEtryW+djQZRkyxpifD6JjDwA4RuQrA7ZOY7h57D+fJPqyqDTjLbX4Vx4F0ChE5H6d/4r9d0yBgr4gk4yzlGeGIewxVbU+TYcQUcxSG0TmuA24SkY+A9cACADd89W7gb2665Th/5ms7KO9qt1N6M86qa19Q1UiN4ns4Dmgp8HFUniXAN91htePa0mQYscbCjBuGYRjtYjUKwzAMo12sM9swegkRuRH4egvz26p6ezz0GEZ3saYnwzAMo12s6ckwDMNoF3MUhmEYRruYozAMwzDaxRyFYRiG0S7/Pw+AyfOrIkHfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#new_Date 컬럼을 인덱스로 설정\n",
    "#Date 컬럼을 삭제\n",
    "df.set_index('new_Date', inplace=True)\n",
    "df.drop('Date',axis=1, inplace=True)\n",
    "df.head()\n",
    "\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fa1b1bba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2019-01', '2020-03', '2021-06'], dtype='period[M]')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = ['2019-01-01','2020-03-01','2021-06-01']\n",
    "\n",
    "#str을 timestamp로 변환 : datetime\n",
    "ts_dates = pd.to_datetime(dates)\n",
    "type(ts_dates[0])\n",
    "\n",
    "ts_day = ts_dates.to_period(freq='M') # day 반환 freq='D'날짜, 'M' 월, 'A'연도\n",
    "ts_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f5dc5e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Start</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>10100</td>\n",
       "      <td>10850</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29</th>\n",
       "      <td>10700</td>\n",
       "      <td>10550</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-28</th>\n",
       "      <td>10400</td>\n",
       "      <td>10900</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-27</th>\n",
       "      <td>10900</td>\n",
       "      <td>10800</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-26</th>\n",
       "      <td>10800</td>\n",
       "      <td>10900</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-25</th>\n",
       "      <td>11150</td>\n",
       "      <td>11400</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-22</th>\n",
       "      <td>11300</td>\n",
       "      <td>11250</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-21</th>\n",
       "      <td>11200</td>\n",
       "      <td>11350</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-20</th>\n",
       "      <td>11550</td>\n",
       "      <td>11200</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-19</th>\n",
       "      <td>11300</td>\n",
       "      <td>11850</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-18</th>\n",
       "      <td>12000</td>\n",
       "      <td>13400</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-15</th>\n",
       "      <td>13400</td>\n",
       "      <td>13600</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-14</th>\n",
       "      <td>13450</td>\n",
       "      <td>13200</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-12</th>\n",
       "      <td>13200</td>\n",
       "      <td>12200</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-11</th>\n",
       "      <td>11950</td>\n",
       "      <td>12000</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-08</th>\n",
       "      <td>11950</td>\n",
       "      <td>11950</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-07</th>\n",
       "      <td>11950</td>\n",
       "      <td>12200</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-05</th>\n",
       "      <td>12150</td>\n",
       "      <td>11800</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-04</th>\n",
       "      <td>11900</td>\n",
       "      <td>11900</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>11900</td>\n",
       "      <td>11800</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close  Start  Year  Month  Day\n",
       "new_Date                                  \n",
       "2018-07-02  10100  10850  2018      7    2\n",
       "2018-06-29  10700  10550  2018      6   29\n",
       "2018-06-28  10400  10900  2018      6   28\n",
       "2018-06-27  10900  10800  2018      6   27\n",
       "2018-06-26  10800  10900  2018      6   26\n",
       "2018-06-25  11150  11400  2018      6   25\n",
       "2018-06-22  11300  11250  2018      6   22\n",
       "2018-06-21  11200  11350  2018      6   21\n",
       "2018-06-20  11550  11200  2018      6   20\n",
       "2018-06-19  11300  11850  2018      6   19\n",
       "2018-06-18  12000  13400  2018      6   18\n",
       "2018-06-15  13400  13600  2018      6   15\n",
       "2018-06-14  13450  13200  2018      6   14\n",
       "2018-06-12  13200  12200  2018      6   12\n",
       "2018-06-11  11950  12000  2018      6   11\n",
       "2018-06-08  11950  11950  2018      6    8\n",
       "2018-06-07  11950  12200  2018      6    7\n",
       "2018-06-05  12150  11800  2018      6    5\n",
       "2018-06-04  11900  11900  2018      6    4\n",
       "2018-06-01  11900  11800  2018      6    1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#날자를 분리\n",
    "df = pd.read_csv('./dataset/stock-data.csv')\n",
    "df['new_Date'] = pd.to_datetime(df['Date'])\n",
    "#데이터가 datetime 형식일 때 : dt.year,dt.month,dt.day를 쓸 수 있다.\n",
    "df['Year'] = df['new_Date'].dt.year\n",
    "df['Month'] = df['new_Date'].dt.month\n",
    "df['Day'] = df['new_Date'].dt.day\n",
    "df.head()\n",
    "\n",
    "#종가, 시작가, 연, 월, 일, new_Date만 추출 -> \n",
    "#df_stock에 저장, new_date를 인덱스로 설정\n",
    "df_stock=df.loc[:,['Close', 'Start', 'Year', 'Month', 'Day', 'new_Date']]\n",
    "df_stock.set_index('new_Date', inplace=True)\n",
    "df_stock\n",
    "#new_Date를 인덱스로"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ecc1dfad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load hangul.py\n",
    "#!/usr/bin/env python\n",
    "\n",
    "# In[ ]:\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import rc\n",
    "rc('font', family = 'Malgun Gothic')\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4576e80b",
   "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": [
    "fig=plt.figure()\n",
    "\n",
    "ax =fig.add_subplot(1,1,1)\n",
    "ax.plot(df_stock.index,df_stock['Close'],label='종가')\n",
    "ax.plot(df_stock.index,df_stock['Start'],label='시작가')\n",
    "ax.legend(loc='best')\n",
    "# ax.set_xticklabels(df_stock.index,rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}