17day.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  5]\n",
      " [ 8 10]\n",
      " [12 15]]\n",
      "(3, 2)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "v = np.array([1,2,3])\n",
    "w = np.array([4,5])  \n",
    "# v*w\n",
    "\n",
    "x = np.reshape(v, (3, 1)) * w\n",
    "print(x)   # (3, 1)*(2,) => (3,1)*(3,2) => (3,2)*(3,2) => (3,2)\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 행렬의 곱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[19 22]\n",
      " [43 50]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "lst1 = [\n",
    "    [1,2],\n",
    "    [3,4]\n",
    "]\n",
    " \n",
    "lst2 = [\n",
    "    [5,6],\n",
    "    [7,8]\n",
    "]\n",
    "a = np.array(lst1)\n",
    "b = np.array(lst2)\n",
    " \n",
    "c = np.dot(a, b)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n",
      "[ 4 10 18]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "# a = np.array([1,2,3])\n",
    "# b = np.array([4,5])\n",
    "\n",
    "a = np.array([1,2,3])\n",
    "b = np.array([4,5,6])\n",
    "\n",
    "c = np.dot(a, b)  # (3,)(3,)\n",
    "print(c)\n",
    "\n",
    "d = a*b     \n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3)\n",
      "(3,)\n",
      "[32]\n",
      "[[ 4 10 18]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "a = np.array([[1,2,3]])\n",
    "b = np.array([4,5,6])\n",
    "print(a.shape)  # (1,3)\n",
    "print(b.shape)  # (3,)\n",
    "\n",
    "c = np.dot(a, b)  # (1,3)(3,)\n",
    "print(c)  # (1,)\n",
    "\n",
    "d = a*b        # (1,3) * (1,3) = (1,3)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1,)\n",
      "(1, 3)\n",
      "[12 15 18]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "a = np.array([3])\n",
    "b = np.array([[4,5,6]])\n",
    "print(a.shape)  # (1,)\n",
    "print(b.shape)  # (1,3)\n",
    "\n",
    "c = np.dot(a, b)  # (1,)(1,3)\n",
    "print(c)  # (3,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 1)\n",
      "(1, 3)\n",
      "[[ 4  5  6]\n",
      " [ 8 10 12]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "a = np.array([[1],[2]])\n",
    "b = np.array([[4,5,6]])\n",
    "print(a.shape)  # (2,1)\n",
    "print(b.shape)  # (1,3)\n",
    "\n",
    "c = np.dot(a, b)  # (2,1)(1,3)\n",
    "print(c)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 2)\n",
      "(2, 3)\n",
      "[[18 21 24]\n",
      " [40 47 54]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "a = np.array([[1,2],[3,4]])\n",
    "b = np.array([[4,5,6],[7,8,9]])\n",
    "print(a.shape)  # (2,2)\n",
    "print(b.shape)  # (2,3)\n",
    "\n",
    "c = np.dot(a, b)  # (2,2)(2,3)\n",
    "print(c) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 행렬의 전치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n",
      "[[1 3]\n",
      " [2 4]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1,2], [3,4]])\n",
    "print(x)    \n",
    "           \n",
    "print(x.T)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "v = np.array([1,2,3])\n",
    "print(v)    \n",
    "print(v.T) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "[4 6]\n",
      "[3 7]\n",
      "24\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "a = np.array([[1,2],[3,4]])\n",
    " \n",
    "s = np.sum(a)\n",
    "print(s)   \n",
    " \n",
    "s = np.sum(a, axis=0)\n",
    "print(s)   \n",
    " \n",
    "s = np.sum(a, axis=1)\n",
    "print(s)   \n",
    " \n",
    "s = np.prod(a)\n",
    "print(s) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "<class 'pandas.core.frame.DataFrame'> \n",
      "\n",
      "   c0  c1\n",
      "0   1   6\n",
      "1   2   7\n",
      "2   3   8\n",
      "3   4   9\n",
      "4   5  10 \n",
      "\n",
      "    c0  c1\n",
      "r0   1   6\n",
      "r1   2   7\n",
      "r2   3   8\n",
      "r3   4   9\n",
      "r4   5  10 \n",
      "\n",
      "   0  1  2  3   4\n",
      "0  1  2  3  4   5\n",
      "1  6  7  8  9  10 \n",
      "\n",
      "    c0  c1  c2  c3  c4\n",
      "r0   1   2   3   4   5\n",
      "r1   6   7   8   9  10 \n",
      "\n",
      "(5, 2)\n",
      "(2, 5)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dict_data = {'c0':[1,2,3,4,5], 'c1':[6,7,8,9,10]} \n",
    "print(type(dict_data))\n",
    "df1 = pd.DataFrame(dict_data)  \n",
    "print(type(df1), \"\\n\") \n",
    "print(df1, \"\\n\")\n",
    "\n",
    "df2 = pd.DataFrame(dict_data, index=['r0', 'r1', 'r2', 'r3', 'r4'])  \n",
    "print(df2, \"\\n\")\n",
    "\n",
    "list_of_list_data = [[1,2,3,4,5], [6,7,8,9,10]]\n",
    "df3 = pd.DataFrame(list_of_list_data) \n",
    "print(df3, \"\\n\")\n",
    "\n",
    "df4 = pd.DataFrame(list_of_list_data, \n",
    "                   index=['r0', 'r1'], \n",
    "                   columns=['c0', 'c1', 'c2', 'c3', 'c4'])  \n",
    "print(df4, \"\\n\")\n",
    "\n",
    "print(df2.shape)\n",
    "print(df4.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                통계구분                    통계지표      시점         원자료   단위\n",
      "0   국민소득 ? 경기 ? 기업경영    경제성장률(실질, 계절조정 전기대비)   20211         1.7    %\n",
      "1   국민소득 ? 경기 ? 기업경영  민간소비증감률(실질, 계절조정 전기대비)   20211         1.2    %\n",
      "2   국민소득 ? 경기 ? 기업경영  설비투자증감률(실질, 계절조정 전기대비)   20211         6.1    %\n",
      "3   국민소득 ? 경기 ? 기업경영  건설투자증감률(실질, 계절조정 전기대비)   20211         1.3    %\n",
      "4   국민소득 ? 경기 ? 기업경영           GDP(명목, 계절조정)   20211   501,248.1  십억원\n",
      "..               ...                     ...     ...         ...  ...\n",
      "95         경제관련 사회통계          고령인구비율(65세 이상)    2021        16.5    %\n",
      "96         경제관련 사회통계                   합계출산율    2020       0.840    명\n",
      "97         경제관련 사회통계                 자동차등록대수  202105  24,587,343    대\n",
      "98         경제관련 사회통계                   주택보급률    2019       104.8    %\n",
      "99         경제관련 사회통계                    지니계수    2019       0.339  NaN\n",
      "\n",
      "[100 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "csv_file = 'bok_statistics_CD.csv'\n",
    "\n",
    "# df1 = pd.read_csv(csv_file)\n",
    "# print(df1)\n",
    "# print('\\n')\n",
    " \n",
    "# df2 = pd.read_csv(csv_file, header=None)\n",
    "# print(df2)\n",
    "# print('\\n')\n",
    "\n",
    "# df3 = pd.read_csv(csv_file, index_col=0)\n",
    "# print(df3)\n",
    "# print('\\n')\n",
    "\n",
    "# df4 = pd.read_csv(csv_file, index_col=0, header=None)\n",
    "# print(df4)\n",
    "# print('\\n')\n",
    "\n",
    "excel_file = 'report_Key100Stat.xls'\n",
    "\n",
    "df5 = pd.read_excel(excel_file)\n",
    "print(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0     1     2\n",
      "0  2014  2.49 -0.23\n",
      "1  2015  1.76 -0.73\n",
      "2  2016  1.49 -0.27\n",
      "3  2017  1.44 -0.05\n",
      "4  2018  1.68  0.24\n",
      "\n",
      "\n",
      "      0     1     2\n",
      "0  2014  2.49 -0.23\n",
      "1  2015  1.76 -0.73\n",
      "2  2016  1.49 -0.27\n",
      "\n",
      "\n",
      "            0    1    2\n",
      "15  2021.7.19  0.7  0.0\n",
      "16  2021.7.20  0.7  0.0\n",
      "17  2021.7.21  0.7  0.0\n",
      "18  2021.7.22  0.7  0.0\n",
      "19  2021.7.23  0.7  0.0\n",
      "\n",
      "\n",
      "            0    1    2\n",
      "17  2021.7.21  0.7  0.0\n",
      "18  2021.7.22  0.7  0.0\n",
      "19  2021.7.23  0.7  0.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('bok_statistics_CD.csv', header=None)  \n",
    "\n",
    "# print(df)\n",
    "print(df.head())\n",
    "print('\\n')\n",
    "print(df.head(3))\n",
    "print('\\n')\n",
    "print(df.tail())\n",
    "print('\\n')\n",
    "print(df.tail(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0     1     2\n",
      "0  2014  2.49 -0.23\n",
      "1  2015  1.76 -0.73\n",
      "2  2016  1.49 -0.27\n",
      "3  2017  1.44 -0.05\n",
      "4  2018  1.68  0.24\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   0       20 non-null     object \n",
      " 1   1       20 non-null     float64\n",
      " 2   2       20 non-null     float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 608.0+ bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('bok_statistics_CD.csv', header=None)\n",
    "\n",
    "print(df.head())\n",
    "print('\\n')\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0     1     2\n",
      "0  2014  2.49 -0.23\n",
      "1  2015  1.76 -0.73\n",
      "2  2016  1.49 -0.27\n",
      "3  2017  1.44 -0.05\n",
      "4  2018  1.68  0.24\n",
      "\n",
      "\n",
      "               1          2\n",
      "count  20.000000  20.000000\n",
      "mean    1.028500  -0.089500\n",
      "std     0.530097   0.246992\n",
      "min     0.660000  -0.770000\n",
      "25%     0.700000  -0.052500\n",
      "50%     0.715000   0.000000\n",
      "75%     1.452500   0.010000\n",
      "max     2.490000   0.240000\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('bok_statistics_CD.csv', header=None) \n",
    "\n",
    "print(df.head())\n",
    "print('\\n')\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    c0  c1\n",
      "r0   1   4\n",
      "r1   2   5\n",
      "r2   3   6 \n",
      "\n",
      "r0    1\n",
      "r1    2\n",
      "r2    3\n",
      "Name: c0, dtype: int64 \n",
      "\n",
      "r0    4\n",
      "r1    5\n",
      "r2    6\n",
      "Name: c1, dtype: int64 \n",
      "\n",
      "    c0  c1  c2\n",
      "r0   1   4   7\n",
      "r1   2   5   8\n",
      "r2   3   6   9 \n",
      "\n",
      "    c0  c1  c2  c3\n",
      "r0   1   4   7   0\n",
      "r1   2   5   8   0\n",
      "r2   3   6   9   0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dict_data = {'c0':[1,2,3], 'c1':[4, 5, 6]} \n",
    "df = pd.DataFrame(dict_data, index=['r0', 'r1', 'r2'])  \n",
    "print(df, \"\\n\") \n",
    "\n",
    "col0 = df['c0']\n",
    "col1 = df.c1\n",
    "\n",
    "print(col0, \"\\n\")\n",
    "print(col1, \"\\n\")\n",
    "\n",
    "df['c2'] = 7, 8, 9\n",
    "print(df, \"\\n\") \n",
    "\n",
    "df['c3'] = 0\n",
    "print(df, \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    c0  c1  c2  c3  c4\n",
      "r0   1   4   7   0   0\n",
      "r1   2   5   8   0   0\n",
      "r2   3   6   9   0   0 \n",
      "\n",
      "    c0  c1  c2  c3  c4\n",
      "r0   1   4   7  10   0\n",
      "r1   2   5   8  11   0\n",
      "r2   3   6   9  12   0 \n",
      "\n",
      "    c0  c1  c2  c3  c4\n",
      "r0   1   4   7   0   0\n",
      "r1   2   5   8   0   0\n",
      "r2   3   6   9   0   0 \n",
      "\n",
      "    c0  c1  c2  c3\n",
      "r0   1   4   7   0\n",
      "r1   2   5   8   0\n",
      "r2   3   6   9   0 \n",
      "\n",
      "    c0  c2\n",
      "r0   1   7\n",
      "r1   2   8\n",
      "r2   3   9\n"
     ]
    }
   ],
   "source": [
    "df['c4'] = df['c3']\n",
    "print(df, \"\\n\") \n",
    "\n",
    "df['c3'] = 10, 11, 12\n",
    "print(df, \"\\n\") \n",
    "\n",
    "df['c3'] = 0\n",
    "print(df, \"\\n\") \n",
    "\n",
    "df.drop('c4', axis=1, inplace=True)\n",
    "print(df, \"\\n\") \n",
    "\n",
    "df.drop(['c1','c3'], axis=1, inplace=True)\n",
    "print(df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    c0  c1\n",
      "r0   1   4\n",
      "r1   2   5\n",
      "r2   3   6 \n",
      "\n",
      "c0    1\n",
      "c1    4\n",
      "Name: r0, dtype: int64 \n",
      "\n",
      "c0    2\n",
      "c1    5\n",
      "Name: r1, dtype: int64 \n",
      "\n",
      "c0    3\n",
      "c1    6\n",
      "Name: r2, dtype: int64 \n",
      "\n",
      "    c0  c1\n",
      "r0   1   4\n",
      "r1   2   5\n",
      "r2   3   6\n",
      "r3  10  20 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dict_data = {'c0':[1,2,3], 'c1':[4, 5, 6]} \n",
    "df = pd.DataFrame(dict_data, index=['r0', 'r1', 'r2'])  \n",
    "print(df, \"\\n\") \n",
    "\n",
    "row0 = df.iloc[0]\n",
    "row1 = df.iloc[1]\n",
    "row2 = df.loc['r2']\n",
    "\n",
    "print(row0, \"\\n\")\n",
    "print(row1, \"\\n\")\n",
    "print(row2, \"\\n\")\n",
    "\n",
    "df.loc['r3'] = 10, 20\n",
    "print(df, \"\\n\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    c0  c1\n",
      "r0   1   4\n",
      "r1   2   5\n",
      "r2   3   6\n",
      "r3  10  20\n",
      "r4   0   0 \n",
      "\n",
      "    c0  c1\n",
      "r0   1   4\n",
      "r1   2   5\n",
      "r2   3   6\n",
      "r3   0   0\n",
      "r4   0   0 \n",
      "\n",
      "    c0  c1\n",
      "r0   1   4\n",
      "r1   2   5\n",
      "r2   3   6\n",
      "r3   0   0 \n",
      "\n",
      "    c0  c1\n",
      "r0   1   4\n",
      "r2   3   6\n",
      "    c0  c1\n",
      "r0   1   4\n"
     ]
    }
   ],
   "source": [
    "df.loc['r4'] = 0\n",
    "print(df, \"\\n\") \n",
    "\n",
    "df.loc['r3'] = df.loc['r4']\n",
    "print(df, \"\\n\") \n",
    "\n",
    "df.drop('r4', axis=0, inplace=True)\n",
    "print(df, \"\\n\") \n",
    "\n",
    "df.drop(['r1','r3'], axis=0, inplace=True)\n",
    "print(df) \n",
    "\n",
    "df.drop(['r2'], axis=0, inplace=True)\n",
    "print(df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    c0  c1\n",
      "r0   1   4\n",
      "r1   2   5\n",
      "r2   3   6 \n",
      "\n",
      "    c0  c1\n",
      "r0   1   4\n",
      "r2   3   6\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dict_data = {'c0':[1,2,3], 'c1':[4, 5, 6]} \n",
    "df = pd.DataFrame(dict_data, index=['r0', 'r1', 'r2'])  \n",
    "print(df, \"\\n\") \n",
    "\n",
    "df.drop(['r1'], axis=0, inplace=True)\n",
    "print(df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    c0  c1\n",
      "r0   1   4\n",
      "r1   2   5\n",
      "r2   3   6 \n",
      "\n",
      "4 \n",
      "\n",
      "5 \n",
      "\n",
      "6 \n",
      "\n",
      "    c0  c1\n",
      "r1   2   5\n",
      "r2   3   6 \n",
      "\n",
      "    c0\n",
      "r1   2 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dict_data = {'c0':[1,2,3], 'c1':[4, 5, 6]} \n",
    "df = pd.DataFrame(dict_data, index=['r0', 'r1', 'r2'])  \n",
    "print(df, \"\\n\") \n",
    "\n",
    "el_01 = df.iloc[0,1]\n",
    "print(el_01, \"\\n\")\n",
    "\n",
    "el_11 = df.iloc[1,1]\n",
    "print(el_11, \"\\n\")\n",
    "\n",
    "el_21 = df.loc['r2','c1']\n",
    "print(el_21, \"\\n\")\n",
    "\n",
    "el_12_01 = df.loc['r1':'r2','c0':'c1']\n",
    "print(el_12_01, \"\\n\")\n",
    "\n",
    "temp = df.iloc[1:2,0:1]\n",
    "print(temp, \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    c0  c1\n",
      "r1   2   5\n",
      "r2   3   6 \n",
      "\n",
      "    c0  c1\n",
      "r0   1  40\n",
      "r1   2   5\n",
      "r2   3   6 \n",
      "\n",
      "    c0  c1\n",
      "r0   1  40\n",
      "r1   0   0\n",
      "r2   0   0\n"
     ]
    }
   ],
   "source": [
    "el_12_01_iloc = df.iloc[1:3, 0:2]\n",
    "print(el_12_01, \"\\n\")\n",
    "\n",
    "df.iloc[0,1] = 40\n",
    "print(df, \"\\n\") \n",
    "\n",
    "df.iloc[1:3, 0:2] = 0\n",
    "print(df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    c0  c1\n",
      "r0   1   4\n",
      "r1   2   5\n",
      "r2   3   6 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dict_data = {'c0':[1,2,3], 'c1':[4, 5, 6]} \n",
    "df = pd.DataFrame(dict_data, index=['r0', 'r1', 'r2'])  \n",
    "print(df, \"\\n\") \n",
    "\n",
    "df.to_csv(\"df.csv\")\n",
    "\n",
    "df.to_excel(\"df.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0     1     2\n",
      "0  2014  2.49 -0.23\n",
      "1  2015  1.76 -0.73\n",
      "2  2016  1.49 -0.27\n",
      "3  2017  1.44 -0.05\n",
      "4  2018  1.68  0.24\n",
      "\n",
      "\n",
      "      CD_rate  change\n",
      "year                 \n",
      "2014     2.49   -0.23\n",
      "2015     1.76   -0.73\n",
      "2016     1.49   -0.27\n",
      "2017     1.44   -0.05\n",
      "2018     1.68    0.24\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 85,
     "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": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('bok_statistics_CD.csv', header=None)  \n",
    "print(df.head())\n",
    "print('\\n')\n",
    "\n",
    "df.columns = ['year', 'CD_rate', 'change']  \n",
    "df.set_index('year', inplace=True)   \n",
    "print(df.head())\n",
    "df.to_csv('bok_statistics_CD_2.csv')\n",
    "print('\\n')\n",
    "\n",
    "df.plot()\n",
    "\n",
    "df['CD_rate'].plot()\n",
    "df['change'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CD_rate  change\n",
      "year                 \n",
      "2014     2.49   -0.23\n",
      "2015     1.76   -0.73\n",
      "2016     1.49   -0.27\n",
      "2017     1.44   -0.05\n",
      "2018     1.68    0.24\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 90,
     "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": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('bok_statistics_CD_2.csv', header=0, index_col=0)  \n",
    "print(df.head())\n",
    "print('\\n')\n",
    "\n",
    "df.plot(kind='bar')\n",
    "\n",
    "# df['CD_rate'].plot(kind='bar')\n",
    "# df['change'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CD_rate  change\n",
      "year                 \n",
      "2014     2.49   -0.23\n",
      "2015     1.76   -0.73\n",
      "2016     1.49   -0.27\n",
      "2017     1.44   -0.05\n",
      "2018     1.68    0.24\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 91,
     "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": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('bok_statistics_CD_2.csv', header=0, index_col=0)  \n",
    "print(df.head())\n",
    "print('\\n')\n",
    "\n",
    "df['CD_rate'].plot(kind='hist')\n",
    "df['change'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CD_rate  change\n",
      "year                 \n",
      "2014     2.49   -0.23\n",
      "2015     1.76   -0.73\n",
      "2016     1.49   -0.27\n",
      "2017     1.44   -0.05\n",
      "2018     1.68    0.24\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CD_rate', ylabel='change'>"
      ]
     },
     "execution_count": 92,
     "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": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('bok_statistics_CD_2.csv', header=0, index_col=0)  \n",
    "print(df.head())\n",
    "print('\\n')\n",
    "\n",
    "df.plot(x='CD_rate', y='change', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CD_rate  change\n",
      "year                 \n",
      "2014     2.49   -0.23\n",
      "2015     1.76   -0.73\n",
      "2016     1.49   -0.27\n",
      "2017     1.44   -0.05\n",
      "2018     1.68    0.24\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 93,
     "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": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('bok_statistics_CD_2.csv', header=0, index_col=0)  \n",
    "print(df.head())\n",
    "print('\\n')\n",
    "\n",
    "df.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CD_rate  change\n",
      "year                 \n",
      "2014     2.49   -0.23\n",
      "2015     1.76   -0.73\n",
      "2016     1.49   -0.27\n",
      "2017     1.44   -0.05\n",
      "2018     1.68    0.24\n",
      "\n",
      "\n"
     ]
    },
    {
     "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 pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('bok_statistics_CD_2.csv', header=0, index_col=0)  \n",
    "print(df.head())\n",
    "print('\\n')\n",
    "\n",
    "boxplot = df.plot(kind='box')\n",
    "plt.savefig('boxplot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matplotlib를 이용한 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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",
    "x = np.array([1,2,3])\n",
    "y = np.array([1,2,3])\n",
    "\n",
    "\n",
    "plt.plot(x,y, 'r-' )\n",
    "plt.plot(x,y, 'bo' )\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "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",
    "x = np.array([1,2,3])\n",
    "y = np.array([1,2,3])\n",
    "w = 2\n",
    "b = 0\n",
    "\n",
    "y_hat = np.zeros(3)\n",
    "for i in range(len(x)):\n",
    "    y_hat[i] = w*x[i] + b;\n",
    "\n",
    "plt.plot(x,y, 'o' )\n",
    "plt.plot(x,y, 'r-' )\n",
    "\n",
    "plt.plot(x,y_hat, 'o' )\n",
    "plt.plot(x,y_hat, 'b-' )\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n",
      "[1 3 5]\n",
      "[-1.         -0.55555556 -0.11111111  0.33333333  0.77777778  1.22222222\n",
      "  1.66666667  2.11111111  2.55555556  3.        ]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3,4,5])\n",
    "print(a)\n",
    "\n",
    "a = np.arange(1,6,2)   # range(1, 6)\n",
    "print(a)\n",
    "\n",
    "a = np.linspace(-1,3,10)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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",
    "w = np.linspace(-1,3,100)\n",
    "b = 0\n",
    "\n",
    "j = np.zeros(100)\n",
    "\n",
    "for i in range(len(w)):\n",
    "    y_hat = w[i]*2 + b;\n",
    "    j[i] = 0.5 * (y_hat - 2)**2\n",
    "\n",
    "plt.plot(w,j, '-' )\n",
    "plt.plot(w,j, 'r-' )\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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",
    "def f(x) : \n",
    "    f = 0.5*(2-x*2)**2\n",
    "    return f\n",
    "\n",
    "def Df(x) : \n",
    "    Df = 2*(2-x*2)\n",
    "    return Df\n",
    "\n",
    "def xp1(x,alpha) : \n",
    "    xp1 = x + alpha * Df(x)\n",
    "    return xp1\n",
    "\n",
    "def plot_steps( guess, alpha, nsteps) :\n",
    "    fig, ax = plt.subplots()\n",
    "    x = np.linspace(-1,3,100)\n",
    "    ax.plot(x, f(x))\n",
    "    x = guess\n",
    "    ax.plot(x,f(x), 'o', label='start x=%.2f' %x )\n",
    "    for i in range(nsteps):\n",
    "        xold = x\n",
    "        x = xp1(x,alpha)\n",
    "        ax.plot(x,f(x), 'o')\n",
    "        ax.plot([xold,x],[f(xold),f(x)], '-')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "plot_steps( 3, 0.01, 100 )\n"
   ]
  }
 ],
 "metadata": {
  "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",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}