part2-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e9c4291",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e93affab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[   Unnamed: 0  c0  c1  c2  c3\n",
       " 0           0   0   1   4   7\n",
       " 1           1   1   2   5   8\n",
       " 2           2   2   3   6   9,\n",
       "          name  year        developer  opensource\n",
       " 0       NumPy  2006  Travis Oliphant        True\n",
       " 1  matplotlib  2003   John D. Hunter        True\n",
       " 2      pandas  2008    Wes Mckinneye        True]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tables = pd.read_html('./dataset/sample.html')\n",
    "tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0a66df30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "   Unnamed: 0  c0  c1  c2  c3\n",
      "0           0   0   1   4   7\n",
      "1           1   1   2   5   8\n",
      "2           2   2   3   6   9\n",
      "\n",
      "         name  year        developer  opensource\n",
      "0       NumPy  2006  Travis Oliphant        True\n",
      "1  matplotlib  2003   John D. Hunter        True\n",
      "2      pandas  2008    Wes Mckinneye        True\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# table 갯수 확인\n",
    "print(len(tables))\n",
    "\n",
    "for i in range(len(tables)):\n",
    "    print(tables[i])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2355eb04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         name  year        developer  opensource\n",
      "0       NumPy  2006  Travis Oliphant        True\n",
      "1  matplotlib  2003   John D. Hunter        True\n",
      "2      pandas  2008    Wes Mckinneye        True\n",
      "            year        developer  opensource\n",
      "name                                         \n",
      "NumPy       2006  Travis Oliphant        True\n",
      "matplotlib  2003   John D. Hunter        True\n",
      "pandas      2008    Wes Mckinneye        True\n"
     ]
    }
   ],
   "source": [
    "df = tables[1]\n",
    "print(df)\n",
    "\n",
    "\n",
    "# name 컬럼을 인덱스로 설정 기존 데이터 변경\n",
    "df.set_index('name',inplace=True)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c484a190",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3 entries, NumPy to pandas\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   year        3 non-null      int64 \n",
      " 1   developer   3 non-null      object\n",
      " 2   opensource  3 non-null      bool  \n",
      "dtypes: bool(1), int64(1), object(1)\n",
      "memory usage: 183.0+ bytes\n"
     ]
    },
    {
     "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>year</th>\n",
       "      <th>developer</th>\n",
       "      <th>opensource</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NumPy</th>\n",
       "      <td>2006</td>\n",
       "      <td>Travis Oliphant</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pandas</th>\n",
       "      <td>2008</td>\n",
       "      <td>Wes Mckinneye</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year        developer  opensource\n",
       "name                                     \n",
       "NumPy   2006  Travis Oliphant        True\n",
       "pandas  2008    Wes Mckinneye        True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'numpy' 인덱스행의 정보만 출력\n",
    "df.loc['NumPy',:]\n",
    "# year가 2006 이후인 자료만 검색\n",
    "df.info()\n",
    "df.loc[df.year >= 2006, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "776c41e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터프레임 자료를 csv파일로 저장\n",
    "data = {'name':['Jerry','Riah','paul'],\n",
    "       'algol':['a','b','c'],\n",
    "       'basic':['c','b','c'],\n",
    "       'c++':['b','c','d']}\n",
    "df = pd.DataFrame(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f4fa272a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"./dataset/to_csv_sa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1cd41e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 여러개의 파일을 엑셀로 저장\n",
    "# 판다스 DataFrame() 함수로 데이터프레임 변환. 변수 df1, df2에 저장\n",
    "data1 = {'name' : [ 'Jerry', 'Riah', 'Paul'],\n",
    "         'algol' : [ \"A\", \"A+\", \"B\"],\n",
    "         'basic' : [ \"C\", \"B\", \"B+\"],\n",
    "          'c++' : [ \"B+\", \"C\", \"C+\"]}\n",
    "\n",
    "data2 = {'c0':[1,2,3], \n",
    "         'c1':[4,5,6], \n",
    "         'c2':[7,8,9], \n",
    "         'c3':[10,11,12], \n",
    "         'c4':[13,14,15]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78ff3bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(data1)\n",
    "df2 = pd.DataFrame(data2)\n",
    "\n",
    "#df1의 'name' 컬럼을 인덱스로 설정\n",
    "df1.set_index('name',inplace=True)\n",
    "#df2의 'name' 컬럼을 인덱스로 설정\n",
    "df2.set_index('c0',inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f71c1d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ExcelWriter를 사용하여 두 개의 파일을 하나의 엑셀파일에 저장\n",
    "writer = pd.ExcelWriter(\"./dataset/two_excel.xlsx\")\n",
    "df1.to_excel(writer, sheet_name = 'df1')\n",
    "df2.to_excel(writer, sheet_name = 'df2')\n",
    "writer.save()"
   ]
  }
 ],
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