quiz_22.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A\n",
      "0   1\n",
      "1   2\n",
      "2   2\n",
      "3   3\n",
      "4   4\n",
      "5   5\n",
      "6   5\n",
      "7   5\n",
      "8   6\n",
      "9   7\n",
      "10  7\n"
     ]
    }
   ],
   "source": [
    "# 22. 다음 객체에서 키값 A와 중복된 값이 제거된 1,2,3,4,5,6,7 이 출려되는 코드를 작성\n",
    "df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})\n",
    "print(df)\n",
    "# print(df['A']) <class 'pandas.core.series.Series'>\n",
    "# print(type(df.loc[df['A']])) <class 'pandas.core.frame.DataFrame'>\n",
    "# df.loc[df['A'].shift() != df['A']]"
   ],
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0  0.138540 -0.437271  0.298731\n",
      "1 -0.438612  0.182145  0.256467\n",
      "2  0.056288  0.318248 -0.374536\n",
      "3 -0.093913 -0.085165  0.179078\n",
      "4 -0.211976  0.404651 -0.192675\n"
     ]
    }
   ],
   "source": [
    "# 23. 다음 객체에서 행의 각 요소에서 행의 평균을 뺀 값을 출력\n",
    "df = pd.DataFrame(np.random.random(size=(5, 3)))\n",
    "df = pd.DataFrame(np.random.random(size=(5, 3)))\n",
    "df = df.sub(df.mean(axis = 1), axis =0)\n",
    "print(df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# 24. 다음 객체에서 가장 작은 합계를 가진 숫자열의 열을 출력\n",
    "df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
    "df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
    "df = df.sum(axis = 0).idxmin()\n",
    "# df = df.sum(axis = 0).idxmax()\n",
    "print(df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1  2\n",
      "0  0  1  0\n",
      "1  0  1  0\n",
      "2  1  0  0\n",
      "3  0  1  1\n",
      "4  0  0  0\n",
      "5  1  0  0\n",
      "6  0  1  0\n",
      "7  1  0  1\n",
      "8  1  0  1\n",
      "9  0  1  0\n",
      "   0  1  2\n",
      "3  0  1  1\n",
      "4  0  0  0\n"
     ]
    }
   ],
   "source": [
    "# 25. 다음 객체에서 중복된 값이 없는 유니크한 열의 카운트 출력(중복되지 않은 행은 몇 개..)\n",
    "df = pd.DataFrame(np.random.randint(0, 2, size=(10, 3)))\n",
    "print(df)\n",
    "print(df.drop_duplicates(keep=False))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    e\n",
      "1    c\n",
      "2    d\n",
      "3    h\n",
      "4    d\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 26. 객체의 각 행에 대해 세번째 NaN 값이 들어 있는 열을 찾으시오. 일련의 열 레이블을 반환해야 합니다.\n",
    "# 답은 e, c, d, h, d 입니다.\n",
    "nan = np.nan\n",
    "data = [[0.04,  nan,  nan, 0.25,  nan, 0.43, 0.71, 0.51,  nan,  nan],\n",
    "        [ nan,  nan,  nan, 0.04, 0.76,  nan,  nan, 0.67, 0.76, 0.16],\n",
    "        [ nan,  nan, 0.5 ,  nan, 0.31, 0.4 ,  nan,  nan, 0.24, 0.01],\n",
    "        [0.49,  nan,  nan, 0.62, 0.73, 0.26, 0.85,  nan,  nan,  nan],\n",
    "        [ nan,  nan, 0.41,  nan, 0.05,  nan, 0.61,  nan, 0.48, 0.68]]\n",
    "columns = list('abcdefghij')\n",
    "df = pd.DataFrame(data, columns=columns)\n",
    "df = (df.isnull().cumsum(axis=1) == 3).idxmax(axis=1)\n",
    "print(df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grps\n",
      "a    345\n",
      "b     57\n",
      "c    235\n",
      "Name: vals, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 27. 다음 객체에서 grps 에서 a, b, c 별로 가장 큰 값을 찾으시오.\n",
    "# 출력 결과는\n",
    "#  grps\n",
    "#   a    409\n",
    "#   b    156\n",
    "#   c    345\n",
    "# 이렇게 나옵니다.\n",
    "df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'),\n",
    "                   'vals': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})\n",
    "# print(type(df.groupby('grps'))) DataFrameGroupBy\n",
    "# print(type(df.groupby('grps')['vals'])) SeriesGroupBy\n",
    "\n",
    "df = df.groupby('grps')['vals'].max()\n",
    "print(df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 4], [2, 5], [3, 6]]\n"
     ]
    }
   ],
   "source": [
    "# 28 다음 객체를 list 로 변환하시오\n",
    "df = pd.DataFrame({'A':[1,2,3], 'B':[4,5,6]})\n",
    "arr = df.values\n",
    "# print(type(arr)) <class 'numpy.ndarray'>\n",
    "ls = arr.tolist()\n",
    "# print(ls)\n",
    "print(df.values.tolist())\n",
    "# 메소드 체인으로 한번에 하기"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': {0: 1, 1: 2, 2: 3}, 'B': {0: 4, 1: 5, 2: 6}}\n"
     ]
    }
   ],
   "source": [
    "# 29 다음 객체를 dictionary 로 변환하시오\n",
    "df = pd.DataFrame({'A':[1,2,3], 'B':[4,5,6]})\n",
    "print(df.to_dict())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "product_code   com  phone    tv\ncustomer_id                    \njang           NaN    NaN  10.0\nkang           NaN    NaN  30.0\nkim           30.0    NaN   NaN\nlee            NaN   10.0   NaN\npark           NaN    NaN   0.0\nryu            NaN    0.0   NaN\nsong          40.0    NaN   NaN\ntak            0.0    NaN   NaN\nyoon           NaN   15.0   NaN",
      "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>product_code</th>\n      <th>com</th>\n      <th>phone</th>\n      <th>tv</th>\n    </tr>\n    <tr>\n      <th>customer_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>jang</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>kang</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>kim</th>\n      <td>30.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>lee</th>\n      <td>NaN</td>\n      <td>10.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>park</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>ryu</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>song</th>\n      <td>40.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>tak</th>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>yoon</th>\n      <td>NaN</td>\n      <td>15.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 30 다음 객체를 customer_id 를 인덱스로하고 product_code 를 컬럼으로, purchare_amount 를 값으로, 재구성하시오\n",
    "df = pd.DataFrame({\"customer_id\":['kim','lee','park','song','yoon','kang','tak','ryu','jang'],\n",
    "               \"product_code\":['com','phone','tv','com','phone','tv','com','phone','tv'],\n",
    "               \"grade\":['A','A','A','A','A','A','B','B','B'],\n",
    "               \"purchase_amount\":[30,10,0,40,15,30,0,0,10]})\n",
    "df = df.pivot(index='customer_id', columns='product_code', values='purchase_amount')\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "product_code        com  phone    tv\ncustomer_id grade                   \njang        B       NaN    NaN  10.0\nkang        A       NaN    NaN  30.0\nkim         A      30.0    NaN   NaN\nlee         A       NaN   10.0   NaN\npark        A       NaN    NaN   0.0\nryu         B       NaN    0.0   NaN\nsong        A      40.0    NaN   NaN\ntak         B       0.0    NaN   NaN\nyoon        A       NaN   15.0   NaN",
      "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>product_code</th>\n      <th>com</th>\n      <th>phone</th>\n      <th>tv</th>\n    </tr>\n    <tr>\n      <th>customer_id</th>\n      <th>grade</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>jang</th>\n      <th>B</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>kang</th>\n      <th>A</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>kim</th>\n      <th>A</th>\n      <td>30.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>lee</th>\n      <th>A</th>\n      <td>NaN</td>\n      <td>10.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>park</th>\n      <th>A</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>ryu</th>\n      <th>B</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>song</th>\n      <th>A</th>\n      <td>40.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>tak</th>\n      <th>B</th>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>yoon</th>\n      <th>A</th>\n      <td>NaN</td>\n      <td>15.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 31 30번 객체를\n",
    "#    customer_id 와 grade 를 인덱스로하고 product_code 를 컬럼으로,\n",
    "#    purchare_amount 를 값으로, 재구성하시오\n",
    "df = pd.DataFrame({\"customer_id\":['kim','lee','park','song','yoon','kang','tak','ryu','jang'],\n",
    "               \"product_code\":['com','phone','tv','com','phone','tv','com','phone','tv'],\n",
    "               \"grade\":['A','A','A','A','A','A','B','B','B'],\n",
    "               \"purchase_amount\":[30,10,0,40,15,30,0,0,10]})\n",
    "df = df.pivot(index=['customer_id','grade'], columns='product_code', values='purchase_amount')\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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