9.1 Handling Missing Data.ipynb

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    "![title](img/cover4.png)"
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   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
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   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
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   "source": [
    "## 9.1 Handling Missing Data"
   ]
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   "source": [
    "First we import numpy and pandas libraries:"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
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   "source": [
    "Here we create a series that contains missing values:"
   ]
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   "execution_count": 2,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23.0\n",
       "1    54.0\n",
       "2     NaN\n",
       "3     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
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   "source": [
    "ser1 = pd.Series([23, 54, np.nan, None])\n",
    "ser1"
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   "source": [
    "We can check for missing values using the function **isnull()**: "
   ]
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   "execution_count": 3,
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      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3     True\n",
       "dtype: bool"
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     "execution_count": 3,
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    "ser1.isnull()"
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   "source": [
    "We can aslo check for missing values using the function **isna()**: "
   ]
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   "cell_type": "code",
   "execution_count": 4,
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    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3     True\n",
       "dtype: bool"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "ser1.isna()"
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   "source": [
    "The same functions can also be applied to **string objects**, like this example:"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    green\n",
       "1    black\n",
       "2    white\n",
       "3     None\n",
       "4      red\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "ser2 = pd.Series(['green','black', 'white', None ,'red'])\n",
    "ser2"
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
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    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "ser2.isnull()"
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  },
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   "cell_type": "markdown",
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   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
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