Programing for language analysis/변민욱마인_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7\n",
       "1    3\n",
       "2    5\n",
       "3    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "pd.Series([7,3,5,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "서울    7\n",
      "대구    3\n",
      "부산    5\n",
      "광주    8\n",
      "dtype: int64\n",
      "['광주', '대구', '부산', '서울']\n",
      "[3, 5, 7, 8]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "광주    8\n",
       "대구    3\n",
       "부산    5\n",
       "서울    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=pd.Series([7,3,5,8], index=['서울','대구','부산','광주'])\n",
    "print(x)\n",
    "x[['서울','대구']]\n",
    "x.index\n",
    "x.values\n",
    "print(sorted(x.index))\n",
    "print(sorted(x.values))\n",
    "\n",
    "x = x.reindex(sorted(x.index))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "광주     NaN\n",
       "대구    15.0\n",
       "대전     NaN\n",
       "부산    18.0\n",
       "서울    13.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.Series([3,5,8,9], index =['서울', '대구', '부산', '광주'])\n",
    "y = pd.Series([10,10,10,10], index =['대구', '부산', '서울', '대전'])\n",
    "x + y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 3 2 4]\n"
     ]
    }
   ],
   "source": [
    "medal = [1,3,2,4,2,3]\n",
    "x = pd.Series(medal)\n",
    "print(pd.unique(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "민준    23\n",
       "현우    43\n",
       "서연    12\n",
       "동현    45\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = {'민준':23, '현우':43, '서연':12, '동현':45}\n",
    "x = pd.Series(age)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    민준\n",
      "1    서연\n",
      "2    현우\n",
      "3    민서\n",
      "4    동현\n",
      "5    수빈\n",
      "dtype: object\n",
      "수빈\n"
     ]
    }
   ],
   "source": [
    "names = ['민준', '서연','현우', '민서', '동현', '수빈']\n",
    "pdata = pd.Series(names)\n",
    "print(pdata)\n",
    "\n",
    "a = pdata[3:6]\n",
    "# print(a[2])\n",
    "print(a[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    175.3\n",
       "1    180.3\n",
       "2    165.8\n",
       "3    172.7\n",
       "Name: height, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    'age':[23,43,12,45],\n",
    "    'name':['민준', '서연','현우', '민서'],\n",
    "    'height':[175.3, 180.3, 165.8, 172.7]\n",
    "}\n",
    "\n",
    "x = pd.DataFrame(data, columns =['name', 'age', 'height'])\n",
    "\n",
    "x.name\n",
    "x.height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "   first  second\n",
       "0      1       2\n",
       "1      3       4\n",
       "2      5       6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
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    }
   ],
   "source": [
    "ary = [[1,2], [3,4], [5,6]] \n",
    "data = pd.DataFrame(ary, columns = ['first','second'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "Name: second, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1]\n",
    "data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
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       "   First  Second\n",
       "0      1       2\n",
       "1      3       4\n",
       "2      5       6"
      ]
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     "execution_count": 28,
     "metadata": {},
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   ],
   "source": [
    "array = [[1,2], [3,4], [5,6], [7,8], [9,10]]\n",
    "data = pd.DataFrame(ary, columns =['First','Second'])\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     4\n",
       "2     6\n",
       "4    10\n",
       "Name: Second, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ary = [[1,2], [3,4], [5,6], [7,8], [9,10]]\n",
    "data = pd.DataFrame(ary, columns = ['First', 'Second'])\n",
    "bools = [False, True, True, False, True]\n",
    "data.Second[bools]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age        30.750\n",
      "height    173.525\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\보람!\\AppData\\Local\\Temp/ipykernel_14872/2132887366.py:9: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(x.mean())\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    'age':[23,43,12,45],\n",
    "    'name':['민준', '서연','현우', '민서'],\n",
    "    'height':[175.3, 180.3, 165.8, 172.7]\n",
    "}\n",
    "\n",
    "x = pd.DataFrame(data, columns =['name', 'age', 'height'])\n",
    "\n",
    "print(x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  name  age  height\n",
      "0   민준   23   175.3\n",
      "2   현우   12   165.8\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    'age':[23,43,12,45],\n",
    "    'name':['민준', '서연','현우', '민서'],\n",
    "    'height':[175.3, 180.3, 165.8, 172.7]\n",
    "}\n",
    "\n",
    "x = pd.DataFrame(data, columns =['name', 'age', 'height'])\n",
    "index = [True,False,True,False]\n",
    "print(x[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "        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>구분</th>\n",
       "      <th>국가</th>\n",
       "      <th>시작일</th>\n",
       "      <th>종료일</th>\n",
       "      <th>성격</th>\n",
       "      <th>비고</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>단독형</td>\n",
       "      <td>말레이시아</td>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>B2B</td>\n",
       "      <td>온라인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>단독형</td>\n",
       "      <td>태국</td>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>B2B</td>\n",
       "      <td>온라인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>단독형</td>\n",
       "      <td>프랑스</td>\n",
       "      <td>2021-09-09</td>\n",
       "      <td>2021-09-10</td>\n",
       "      <td>B2B</td>\n",
       "      <td>온라인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>단독형</td>\n",
       "      <td>호주</td>\n",
       "      <td>2021-07-14</td>\n",
       "      <td>2021-07-15</td>\n",
       "      <td>B2B</td>\n",
       "      <td>온라인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>연계형</td>\n",
       "      <td>베트남</td>\n",
       "      <td>2021-11-04</td>\n",
       "      <td>2021-11-05</td>\n",
       "      <td>B2B</td>\n",
       "      <td>온라인</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    구분     국가         시작일         종료일   성격   비고\n",
       "0  단독형  말레이시아  2021-06-29  2021-06-29  B2B  온라인\n",
       "1  단독형     태국  2021-07-29  2021-07-30  B2B  온라인\n",
       "2  단독형    프랑스  2021-09-09  2021-09-10  B2B  온라인\n",
       "3  단독형     호주  2021-07-14  2021-07-15  B2B  온라인\n",
       "4  연계형    베트남  2021-11-04  2021-11-05  B2B  온라인"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "food = pd.read_csv('food.csv', encoding='cp949')\n",
    "food.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('name.txt', 'w' )\n",
    "while True:\n",
    "    name = input('name: ')\n",
    "    if not name: break\n",
    "    f.write(name)\n",
    "f.close()"
   ]
  }
 ],
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