Chap11_판다스의전역함수로_DataFrame로딩하기.ipynb

{
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "id, name, price, desc \n",
      "1, iphone, 890000, iphone x 12\n",
      "2, android, 990000, samsung phone\n",
      "3, winphone, 450000, microsoft winphone \n"
     ]
    }
   ],
   "source": [
    "#Chap11_판다스의_전역함수로_DataFrame로딩하기\n",
    "!type c:\\work\\Chap11_ex1.csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:4: SyntaxWarning: invalid escape sequence '\\w'\n",
      "<>:4: SyntaxWarning: invalid escape sequence '\\w'\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_8060\\499457248.py:4: SyntaxWarning: invalid escape sequence '\\w'\n",
      "  df = pd.read_csv(\"c:\\work\\Chap11_ex1.csv\")\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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>iphone</td>\n",
       "      <td>890000</td>\n",
       "      <td>iphone x 12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>android</td>\n",
       "      <td>990000</td>\n",
       "      <td>samsung phone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>winphone</td>\n",
       "      <td>450000</td>\n",
       "      <td>microsoft winphone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id       name   price                 desc \n",
       "0   1     iphone  890000           iphone x 12\n",
       "1   2    android  990000         samsung phone\n",
       "2   3   winphone  450000   microsoft winphone "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DataFrame으로 로딩합니다. \n",
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv(\"c:\\work\\Chap11_ex1.csv\")\n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1, iphone, 890000, iphone x 12 \n",
      "2, android, 990000, samsung phone\n",
      "3, winphone, 450000, microsoft winphone \n"
     ]
    }
   ],
   "source": [
    "#두번째파일 \n",
    "!type c:\\work\\Chap11_ex2.csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>iphone</th>\n",
       "      <th>890000</th>\n",
       "      <th>iphone x 12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>android</td>\n",
       "      <td>990000</td>\n",
       "      <td>samsung phone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>winphone</td>\n",
       "      <td>450000</td>\n",
       "      <td>microsoft winphone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1     iphone   890000          iphone x 12 \n",
       "0  2    android   990000         samsung phone\n",
       "1  3   winphone   450000   microsoft winphone "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1행이 컬럼명으로 잘못 로딩된 경우 \n",
    "df2 = pd.read_csv(\"c:\\\\work\\\\Chap11_ex2.csv\")\n",
    "df2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>iphone</td>\n",
       "      <td>890000</td>\n",
       "      <td>iphone x 12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>android</td>\n",
       "      <td>990000</td>\n",
       "      <td>samsung phone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>winphone</td>\n",
       "      <td>450000</td>\n",
       "      <td>microsoft winphone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id       name   price                  desc\n",
       "0   1     iphone  890000          iphone x 12 \n",
       "1   2    android  990000         samsung phone\n",
       "2   3   winphone  450000   microsoft winphone "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#누락된 컬럼명을 지정해서 로딩한 경우 \n",
    "df2 = pd.read_csv(\"c:\\\\work\\\\Chap11_ex2.csv\",\n",
    "    names=[\"id\",\"name\",\"price\",\"desc\"])\n",
    "df2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data1 data2 data3 \n",
      "1.2     2.3     1.3\n",
      "0.1     1.5     2.5\n",
      "3.1     3.2     3.3\n"
     ]
    }
   ],
   "source": [
    "#공백문자가 구분자인 경우\n",
    "!type Chap11_ex3.txt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:1: SyntaxWarning: invalid escape sequence '\\s'\n",
      "<>:1: SyntaxWarning: invalid escape sequence '\\s'\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_8060\\3869429848.py:1: SyntaxWarning: invalid escape sequence '\\s'\n",
      "  df3 = pd.read_csv(\"c:\\\\work\\\\Chap11_ex3.txt\", sep=\"\\s+\")\n"
     ]
    },
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "      <th>data3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1  data2  data3\n",
       "0    1.2    2.3    1.3\n",
       "1    0.1    1.5    2.5\n",
       "2    3.1    3.2    3.3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.read_csv(\"c:\\\\work\\\\Chap11_ex3.txt\", sep=\"\\s+\")\n",
    "df3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id::name::price::desc \n",
      "1::iphone::980000::iphone x 12 13 \n",
      "2::android::880000::samsung phone \n",
      "3::window phone::450000::window phone \n",
      "\n"
     ]
    }
   ],
   "source": [
    "!type c:\\work\\Chap11_ex4.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>iphone</td>\n",
       "      <td>980000</td>\n",
       "      <td>iphone x 12 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>android</td>\n",
       "      <td>880000</td>\n",
       "      <td>samsung phone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>window phone</td>\n",
       "      <td>450000</td>\n",
       "      <td>window phone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id          name   price            desc\n",
       "0   1        iphone  980000  iphone x 12 13\n",
       "1   2       android  880000   samsung phone\n",
       "2   3  window phone  450000    window phone"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#구분자를 지정한 경우(::)\n",
    "df4 = pd.read_csv(\"c:\\\\work\\\\Chap11_ex4.csv\", sep=\"::\", engine=\"python\")\n",
    "df4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>사번</th>\n",
       "      <th>성별</th>\n",
       "      <th>나이</th>\n",
       "      <th>판매갯수</th>\n",
       "      <th>판매금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E001</td>\n",
       "      <td>M</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "      <td>2500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E002</td>\n",
       "      <td>F</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>4500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E003</td>\n",
       "      <td>F</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E004</td>\n",
       "      <td>M</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>1500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E005</td>\n",
       "      <td>F</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E006</td>\n",
       "      <td>M</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E007</td>\n",
       "      <td>M</td>\n",
       "      <td>32</td>\n",
       "      <td>14</td>\n",
       "      <td>1200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E008</td>\n",
       "      <td>F</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>2000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>E009</td>\n",
       "      <td>M</td>\n",
       "      <td>32</td>\n",
       "      <td>50</td>\n",
       "      <td>4800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>E010</td>\n",
       "      <td>M</td>\n",
       "      <td>36</td>\n",
       "      <td>60</td>\n",
       "      <td>5800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     사번 성별  나이  판매갯수     판매금액\n",
       "0  E001  M  34    10  2500000\n",
       "1  E002  F  40    20  4500000\n",
       "2  E003  F  37     5   500000\n",
       "3  E004  M  30    15  1500000\n",
       "4  E005  F  44    10   800000\n",
       "5  E006  M  36    11   900000\n",
       "6  E007  M  32    14  1200000\n",
       "7  E008  F  26    20  2000000\n",
       "8  E009  M  32    50  4800000\n",
       "9  E010  M  36    60  5800000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#엑셀파일 로딩하기 \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel('c:\\\\work\\\\Chap11_demo.xlsx', '직원명부')\n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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       "        38.85714286, 41.42857143, 44.        ]),\n",
       " <BarContainer object of 7 artists>)"
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     "execution_count": 11,
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     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.hist(df['나이'], bins = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}