3.4 Detecting Missing Value.ipynb

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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>frank</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tom</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ted</td>\n",
       "      <td>M</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>jean</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lisa</td>\n",
       "      <td>F</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "    name gender   age\n",
       "0  frank      M   NaN\n",
       "1   mary    NaN   NaN\n",
       "2    tom      M  35.0\n",
       "3    ted      M  33.0\n",
       "4   jean    NaN  21.0\n",
       "5   lisa      F  20.0"
      ]
     },
     "execution_count": 7,
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   "source": [
    "import pandas as pd\n",
    "import numpy  as np\n",
    "df = pd.DataFrame([\\\n",
    "                   ['frank', 'M',    np.nan], \\\n",
    "                   ['mary' , np.nan, np.nan], \\\n",
    "                   ['tom'  , 'M',    35], \\\n",
    "                   ['ted'  , 'M',    33], \\\n",
    "                   ['jean' , np.nan, 21], \\\n",
    "                   ['lisa' , 'F',    20]])\n",
    "df.columns = ['name', 'gender', 'age']\n",
    "df"
   ]
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   "execution_count": 31,
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      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3     True\n",
       "4    False\n",
       "5     True\n",
       "Name: gender, dtype: bool"
      ]
     },
     "execution_count": 31,
     "metadata": {},
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   "source": [
    "df['gender'].notnull()"
   ]
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   "cell_type": "code",
   "execution_count": 35,
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     "data": {
      "text/plain": [
       "True"
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     "execution_count": 35,
     "metadata": {},
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   "source": [
    "df['age'].isnull().values.any()"
   ]
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   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "True"
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     "execution_count": 37,
     "metadata": {},
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   "source": [
    "df.isnull().values.any()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "2"
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     "execution_count": 40,
     "metadata": {},
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   "source": [
    "df['age'].isnull().sum()"
   ]
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   "cell_type": "code",
   "execution_count": 43,
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   "outputs": [
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     "data": {
      "text/plain": [
       "4"
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     "execution_count": 43,
     "metadata": {},
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   ],
   "source": [
    "df.isnull().sum().sum()"
   ]
  }
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