notebooks/pandas-working-with-missing-data.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "None==None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    False\n",
       " 1    False\n",
       " 2    False\n",
       " dtype: bool,\n",
       " 0     True\n",
       " 1    False\n",
       " 2     True\n",
       " dtype: bool)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([np.nan,1,np.nan])\n",
    "s == np.nan, s.isna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__np.nan is a float__ and forces int array ot be float type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dtype('float64'), dtype('int64'))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2, np.nan]).dtype, pd.Series([1,2]).dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use Int dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int32Dtype()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2,np.nan], dtype=pd.Int32Dtype()).dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filling missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. fill with scallar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1      1\n",
       "2      2\n",
       "3    NaT\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([np.nan, 1,2, pd.NaT])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    missing\n",
       "1          1\n",
       "2          2\n",
       "3    missing\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna('missing')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Pad/ffill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    NaN\n",
       " 1    1.0\n",
       " 2    2.0\n",
       " 3    2.0\n",
       " dtype: float64,\n",
       " 0    NaN\n",
       " 1    1.0\n",
       " 2    2.0\n",
       " 3    2.0\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(method='pad'), s.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Bfill/backfill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0      1\n",
       " 1      1\n",
       " 2      2\n",
       " 3    NaT\n",
       " dtype: object,\n",
       " 0      1\n",
       " 1      1\n",
       " 2      2\n",
       " 3    NaT\n",
       " dtype: object,\n",
       " 0      1\n",
       " 1      1\n",
       " 2      2\n",
       " 3    NaT\n",
       " dtype: object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(method='bfill'), s.fillna(method='backfill'), s.bfill()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Limit number of filled value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    missing\n",
       "1          1\n",
       "2          2\n",
       "3        NaT\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna('missing', limit=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop axis based on missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
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      "text/plain": [
       "   a    b\n",
       "0  1  NaN\n",
       "1  2  2.0\n",
       "2  3  3.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'a': [1 ,2, 3], 'b': [np.nan, 2,3]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
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      ],
      "text/plain": [
       "   a    b\n",
       "1  2  2.0\n",
       "2  3  3.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\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>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "   a\n",
       "0  1\n",
       "1  2\n",
       "2  3"
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     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2.0\n",
       "2    3.0\n",
       "Name: b, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.b.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series([1,2,3,1,2,2, np.nan, np.nan, 4,3,2], index=pd.date_range('2020/1/1', '2020/1/11'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f97c1c0da60>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.interpolate().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f97c1f09ca0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jVfzrezsYNbgXv/+p/w/3ttLsixLpHhrM/GW+V9W3p6IfAKwUkR3AJpw9+s9E5AkRudJ1zMuAQ0SygEeAxzwTrv94ZW0u5TUN/MtPhnFeQgzrs3/0DyClOqSmvpF7l6QTHCw8e/MYwkL0Nhl36h0Zxi8vTODznUfYe6Tc7nA6pD2rbnYYY8YYY0YaY0YYY55wPf+4MeYT1+MaY8x1xphkY8wEY4z/jWhxo7Lqel5ek8tPzunHiLho0hIdZBdV+dW2qMp6//HJbvYeKWfeDaMDari3lX51YSJR4SE8vSzD7lA6RH/l2+CltTlU1DYwZ4ZzPmdaknOB0vocrepV57y3pYC3NuVz/9Qkpg7ra3c4fiu6eyh3XZTAV7uPsetQmd3htJsmeoudqKrjlbW5XH7ugO/vUjxnYDRRESFs0ESvOmHf0XL+8NFOJibGMHdGYA73ttIvL0ygZ4RvVfWa6C22aE0O1fWNPDwj5fvngoOE87VPrzqhsraB+5akExURyoIAH+5tlZ4RocyenMiyvYVszy+1O5x20Z8KCx2vrGXxujx+NnIgqf2ifvDaxEQHecXVfjPoQHmeMYbH3t9B3vEqFtw4hr5RgT3c20p3XJBA7+6hzPORql4TvYUWrc6hpr6Rh6an/Oi17/v0WtWrdvrHhgN8tuMIj14y7PufH2WNHuEh3H1xEqv2F7HlwAm7w2mTJnqLFFbU8Nr6PK4eHUdy3x/fqXh2/5706h6qiV61y/b8Uv7rMx3ubafb0obiiAzziV69JnqLvLAqh/pGw4MtVPMAQc19ev1AVrWhtLqO+5ak0zcqQod726h7WAj3TkliTeZxvsv17jvbNdFb4Fh5Da9vPMAvxsSR0Cey1ePSEh0UnDjp80MOlOc0NRkefWc7hRU1PDtrrA73ttms84cSGxXOvKXeXdVrorfAcyuzaGoyPDit5Wq+WVpSH0DX06vWvbA6m+X7CvnD5cMZPbiX3eEEvG5hwdw3JYn1OcWsy/bebUw00XvYodKTvPldPteNH8QQx5nvVkzt1wNHZBgbtE+vWrAhp5j/99V+Lh85gNvShtodjnK5acIQ+vV0VvXeusWXJnoPe3ZlFgbDA21U8wAiwsREB+tzir32B0bZo7Cihgff3Eq8I5Inrxmpw729SERoMA9MTWZT3gnWZnlnVa+J3oPyS6p5Z1M+N543hLhe3dr1nrQkB0fKajhQrH165dTYZHj4zW1U1NTz3C1j6REeYndI6jTXnzeYgdERPOWlVb0meg9auCKLoCDhvqntX/6m+96o081bmsH6nGL+66oRnNVfh3t7o/CQYB6YlsLWg6WsyvC+oUqa6D3kQHEV76UXcPOEIQyIbl81D5DYJ5K+UeG6nl4BsHJ/IQtXZnH9+EFcp8O9vdq14wYxqHc3r+zVa6L3kAXLswgJEu6b0rGbWUSEtCTt0yvnB/lz397GWf11uLcvCAsJ4qFpKewoKGP53kK7w/kBTfQekFNUyYdbC7h14lD69uz4/iNpiQ6KKmp9dhCx6rq6hibuX5JOQ6Ph+VvGERGqw719wS/GxjHU0d3revWa6D1gwfJMwkOCubuTt6brvjfqT1/sZVt+KX+5duQZb7JT3iUkOIiHp6ew50g5X+0+Znc439NE72ZZhRV8vP0wt01y3jHXGUNiujMwOkI/kA1Qn+84wqvr8rjzgnguO1eHe/uaK0cNJDE2kqeXZdDU5B1VvSZ6N3t6WSbdQ4O5e3LnN5oSESYmOdiQU+I1PyjKGjlFlfz2/R2MGdKL312mw719UXNVv+9oBV/uOmp3OIAmerfad7Scz3ce4Y4L4onp4h4kaYkOSqrqyCiscFN0ytvV1Ddy35J0QoKFhTeP1eHePuyKkQNJ6duDp5dl0OgFxZr+JLnR/GWZ9AgL4dcXJXb5XNqnDzyPf7yLfUcrmHfD6HbfYKe8U3CQMGdGKpmFlXy247Dd4Wiid5fdh8v4ctdRfnlhAr26d31HwUG9uzM4ppsm+gDx7uZ83tlcwANTk3W4t5+4bER/zuofxfxlmTQ0NtkaiyZ6N5m3NJOeESH88sIEt50zLdHBxlzt0/u7fUfL+bePd5GW6GDuTB3u7S+CXFV9zvEqPt5mb1Wvid4NdhSUsmzvMX59USLR3ULddt60JAdlJ+vZc6TcbedU3qWipp77XncO955/02iCdYiIX/nJOf04Z2BPFqzIpN7Gqr7NRC8ig0VkpYjsEZHdIvJwC8dMEZEyEdnm+nrcM+F6p3lLM+jVPZQ7Loh363nTEp3702/QZZZ+yRjDYx/s5EBJNQtv0uHe/khEmDsjlQPF1XyYfsi2ONpT0TcAjxpjhgMTgftFZHgLx60xxox2fT3h1ii9WPrBE6zcX8TsyYlERbivmgfoHx1BQp9I7dP7qcXr8vh8xxF+c8kwzk/U4d7+avrZfRk5KJoFKzKpa7Cnqm8z0Rtjjhhj0l2PK4C9QJynA/MV85ZmEBMZxu1p8R45/8REB9/lltj+YY5yr60HT/DHL/Yy/ay+3D2566u0lPcSEebOTKXgxEne21JgSwwd6tGLSDwwBtjYwstpIrJdRL4UkXPcEJvX25RXwprM49xzcSKRHtojPC3JQUVtA7sPa5/eX5yoquOBN7bSNyqCv14/Sod7B4ApqbGMHdKLhSsyqW1otPz67U70ItIDeB+YY4w5PeukA0ONMaOAZ4CPWjnHbBHZLCKbi4q8b8/mjpq3NIM+PcK5dWK8x64xMTEG0P3p/UVTk+GRd7ZRVFHLc7PGumUprvJ+IsIjM4dxuKyGdzblW379diV6EQnFmeSXGGM+OP11Y0y5MabS9fgLIFRE+rRw3CJjzHhjzPjY2Nguhm6v9dnFrMsu5t4pSXQL89zOgn2jIkju20P79H7i+W+yWbm/iD9ccTajdLh3QLkg2cGE+BgWrsyipt7aqr49q24EeBnYa4x5qpVj+ruOQ0QmuM7rt5nJGMO8ZRn0jQpn1vlDPH69tEQHm/JKbF2epbpufXYxf/16Pz8bNZBbJ+pw70DT3Ks/Vl7Lm98dtPTa7anoLwBuBaadsnzypyJyj4jc4zrmWmCXiGwHFgA3Gm/ajNnN1mUX811uCfdPTbZkn/C0JAfVdY3sKCjz+LWUZ3w/3LtPJP/9i3N1uHeASktykJbo4LlV2Zyss66qb/MTRGPMWuCMP5XGmIXAQncF5c2MMTy1NIMB0RHcOMGa0W4TXUvvNuQUM25ob0uuqdynobGJh97cSmVtPUt+db4O9w5wc2emcv3f1rNk4wF+5YZ9sdpD74ztoNWZx9ly4AQPTEsmPMSaqT8xkWGc1T9K+/Q+at6yDDbklPDHq89lWP8ou8NRNpuQEMNFKX14flU2VbUNllxTE30HGGN46uv9xPXqxnXjrB3UPDHRweYDJbYszVKdt2LfMZ5dmc2N5w3mmnGD7A5HeYk5M1IprqrjtfUHLLmeJvoOWLGvkO0FZTw0PdnyvcLTkhzU1DexPV/79L6i4EQ1c9/ezvABPfmPKwPi1hLVTuOG9mbKsFj+tjqbipp6j19PE307Nffmh8R05xdjra/MJiY4ENH96X1FXUMT97+xlaYmw3Ozxupwb/Ujc2ekUlpdz+J1eR6/lib6dvp6zzF2Hy7noekphAZb/8cW3T2U4QN6sj7nuOXXVh33py/2sj2/lL9cN5J4He6tWjBqcC9mnN2XRatzKPdwVa+Jvh2amgzzlmaQ0CeSq0cPtC2OSUkO0g+WWn6zheqY5uHev7wggUtH6HBv1bo5M1Ipr2nglbW5Hr2OJvp2+Ofuo+w7WsHD01MIsaGab5aW5KCuoYn0gydsi0Gd2anDvR+77Cy7w1FebkRcND85px8vr8mlrNpzVb0m+jY0uqr5pNhIfjbKvmoe4Lz4GIKDhA3ap/dKJ+ucw71Dg4Vndbi3aqe5M1OpqG3gpbU5HruG/iS24fOdR8gsrGTOjFTbp/9ERYQyIi5aNzjzUo9/vIv9x5zDvQfqcG/VTmf178nlIwfwytpcTlTVeeQamujPoLHJ8PSyDIb1i+Lyc72j15qW6GBbfinVddbcaKHa553N+by7pYAHpyYzRYd7qw6aMz2F6vpGFq3xTFWvif4MPtl+iJyiKubOTPGaPcPTkhzUNxo252mf3lvsPVLOv320iwuSHTw8Q4d7q45L6RfFlaMGsnhdHscra91+fk30rWhobGL+skyGD+jJJcP72x3O98YP7U1IkGj7xktU1NRz35J0oruF8vQNY2xv7ynf9dD0FGrqG1m02v1VvSb6Vny49RB5xdXMnZnqNdU8QGR4CKMG99Ibp7yAMYbfvr+DgyXVLLx5LLFR4XaHpHxYUmwPrh4Tx2vr8yisqHHruTXRt6C+sYkFKzI5Ny6aGWd7X781LdHBzkNlVFq0IZJq2avr8vhi51H+5SfDmJAQY3c4yg88NC2F+kbD86uy3XpeTfQteG9LAfklJ3lkZqpX7hueluSgscmwKbfE7lAC1taDJ/jTF3uZcXZfZlu01azyf/F9IrlmbBxLNh7kaJn7qnpN9KepbWhk4YosRg/uxZRh3jnucNzQ3oQFB2mf3iYnquq4f0k6/XpG8NfrRntVa0/5vgenpTj3SFqV5bZzaqI/zTubCzhU6r3VPEBEaDCjh2if3g5NTYa572zjeGUdz80aS3T3ULtDUn5mcEx3rhs/mLe+y+dQ6Um3nFMT/Slq6ht5dkUW44f25qKUH8029yppiQ52Hy6j7KTntzhV/+v5b7JZtb+If7vibEYO0uHeyjMemJaMwfDsSvdU9ZroT/HWdwc5Wl7j1dV8s7QkB00GvtM+vWXWZR/nr1/v58pRA7lFh3srD4rr1Y0bzxvCO5vyyS+p7vL5NNG71NQ38uyqbM5PiCEtyWF3OG0aM6QX4SFB2r6xSGF5DQ+9uY0EHe6tLHL/1GSCgoSFK7pe1Wuid3l9wwGKKmp9opoHCA8JZtzQ3vqBrAUaGpt48M2tVNU28Pwt44jU4d7KAv2jI5h1/hDeSy/gQHFVl86liR6ormvghW+yuTC5D+cnen813ywt0cHeI+Ue2whJOT21NIONuSX88ecjSO2nw72Vde6dkkRosLBgedeqek30wD/WH+B4ZR1zZ6bYHUqHNLeYNuZqVe8py/ce47lV2dw0YbAtIyRVYOsbFcGtE4fy4dYCcooqO32egE/0lbXOav7i1FjGDfWtuxtHDupFt9Bg7dN7SH5JNY+84xzu/e8/0+Heyh53X5xEeEgwC5ZndvocbSZ6ERksIitFZI+I7BaRh1s4RkRkgYhkicgOERnb6YgstnhdHieq65k70/d2HQwLCWJ8vPbpPaG2oZEH3kinqcnw/C063FvZp0+PcG6fFM/H2w+TeayiU+doT0XfADxqjBkOTATuF5Hhpx1zGZDi+poNPN+paCxWUVPPotU5TD+rL6MH++aa6LQkBxnHKj2ytWkg+9Pne9leUMZfrhvJUIcO91b2mj05ke6hwTzdyaq+zURvjDlijEl3Pa4A9gJxpx12FfCacdoA9BIR75jUcQZ//zaPspO+Wc03S3N9eLxBq3q3+XT7YRavP8CvLtTh3so7xESGcecFCXy+40in3t+hdWIiEg+MATae9lIckH/K9wWu5zoXlQXKqut5cU0Olwzvx4i4aLvD6bRz46LpER7C+uxirhhp70zbrnpmeSYvf5trdxhU1DQwbmhvfqvDvZUX+dVFCSxel9ep97Y70YtID+B9YI4xprwzFxOR2ThbOwwZMqQzp3Cbl9fmUFHTwBwfnwgUEhzEeX7Qpz9WXsMzK7M4Z2BPRtr8izciLJi7LkwgNDjg1yooL9KrexiPXpLKnU90/L3tSvQiEoozyS8xxnzQwiGHgMGnfD/I9dwPGGMWAYsAxo8fbzocrZucqKrjlW/z+Om5/Rk+sKddYbhNWpKDlfuLOFZeQ7+eEXaH0ynPrcyiqckw/4YxDHF0tzscpbzSHRckcGcn3teeVTcCvAzsNcY81cphnwC3uVbfTATKjDFe27Z5cU0OVXUNPDzdt6v5ZmmJzg3YfLVPf7j0JG9+l8914wdpklfKA9pT0V8A3ArsFJFtrud+DwwBMMa8AHwB/BTIAqqhU790LFFcWcur6+2L80EAABOmSURBVPK4YuRAhvX3j7schw/sSc8IZ5/+qtGnf07u/Z5dmYXBcP/UZLtDUcovtZnojTFrgTNu/mKMMcD97grKkxatzqGmvpGHp/vWXbBnEhwknJ/o8Mk+fX5JNe9szueG8wYzqLdW80p5QkB92lRUUcvi9XlcPTqO5L497A7HrdISHRworuawmwYVWOXZlVkIotW8Uh4UUIn+hW+yqW80POhH1Xyz5n1vfGk7hAPFVby7pYCbzx/CgOhudoejlN8KmER/rLyG1zcc4Bdj4kjo4393Og7rF0Xv7qGs86FE/8yKLEKChHunJNkdilJ+LWAS/fOrsmlsMjw4zf+qeYCgIGFiooMNOcU4PzLxbrnHq/ggvYBbJg712SWhSvmKgEj0R8pO8sbGg36/fC8tycGh0pPkl3h/n37B8kzCQ4K552Kt5pXytIBI9IGyfK9535v1OcdtjuTMsgor+HjbIW6bNJTYqHC7w1HK7/l9oi84Uc3bmwJj+V5y3x706RHu9R/IPr0sk26hwdw9Wat5pazg94l+4YrAWb4nIkxMjGG9F/fp9x0t5/OdR7jjgnhiIsPsDkepgODXiT4Ql++lJTk4Vl5L7vGuDRP2lPnLMokMC+HXFyXaHYpSAcOvE30gLt/73z6997Vvdh8u48tdR/nlhQn06q7VvFJW8dtEH6jL9xL6RNKvp3f26Z9elklURAh3XZhgdyhKBRS/TfQLlmcSFhIUcMv3RIS0RAcbckq8qk+/s6CMpXuO8euLEonuFmp3OEoFFL9M9M3L925Piw/I5XtpSQ6OV9aSVVhpdyjfm7csg+huodx5QbzdoSgVcPwy0c9fnkVEaDCzJwfmB37N+9N7S59+68ETrNhXyOzJiURFaDWvlNX8LtHvP1rBZzsOc+cF8Th6BF41DzA4phtxvbp5TZ9+3rJMYiLDuH1SvN2hKBWQ/C7Rz1+eEfDL95zr6Z373jQ12dun35xXwuqMIu6enEiP8A7NoldKuYlfJfo9h8v5Yqcu3wNnn/5EdT37j1XYGse8ZRn06RHObWnxtsahVCDzq0T/9LIMXb7n4g3702/IKebbrGLunZJEt7Bg2+JQKtD5TaLfWVDG17p873txvboxJKa7bR/IGmN4amkGfaPCmXX+EFtiUEo5+U2i1+V7P5aW6GBjTjGNNvTp12UX811uCfdPTSYiVKt5pezkF4lel++1LC3JQXlNA3uPlFt63eZqfkB0BDecN9jSayulfswvEr0u32uZXX361ZnH2XLghFbzSnkJn0/0unyvdf16RpDYJ9LSPn1zNR/XqxvXj9dqXilv4POJ3rl8L4xb04baHYpXmpjk4LvcEhoamyy53sr9hWzPL+XBacmEhfj8j5dSfqHNv4ki8oqIFIrIrlZenyIiZSKyzfX1uPvDbFnz8r17Lk6ie5hW8y1JS3RQWdvArsOe79M3V/ODY7pxzbhBHr+eUqp92lNyvQpc2sYxa4wxo11fT3Q9rLY1J5XYqHBumajVfGsmJlrXp1+65xi7DpXz0LQUQoO1mlfKW7T5t9EYsxoosSCWDlnfvHxvSpJ+4HcGsVHhpPbr4fE+fVOTYd6yTBL6RPLzMXEevZZSqmPcVXalich2EflSRM5x0zlbderyvRsn6M04bUlLdLA5r4R6D/bpv9p9lL1Hynl4egohWs0r5VXc8TcyHRhqjBkFPAN81NqBIjJbRDaLyOaioqJOX3BN5nE26/K9dktLclBd18iOglKPnN9ZzWeQFBvJz0YN9Mg1lFKd1+VEb4wpN8ZUuh5/AYSKSJ9Wjl1kjBlvjBkfGxvb2evp8r0OOj/BgQisy/JM++bznUfIOFbJnBmpBAeJR66hlOq8Lid6EekvIuJ6PMF1To81hFftL2KbLt/rkN6RYZzVv6dH+vSNTYanl2WQ2q8Hl587wO3nV0p1XZtrEkXkTWAK0EdECoB/B0IBjDEvANcC94pIA3ASuNF4aFipLt/rvLREB0s2HqC2oZHwEPe1uz7ZfojsoiqenzWWIK3mlfJKbSZ6Y8xNbby+EFjotojOYOmeY+w8VMZfrh2py/c6KC3JwSvf5rL1YOn3Sy67qqGxifnLMjl7QE9+ck5/t5xTKeV+PpMtm5fvxTu66/K9TpiQEEOQuHc9/YdbD5FXXM3cGSlazSvlxXwm0X+/fG+GLt/rjOhuoZwzMNptffr6xiYWrMhkRFxPZg7v55ZzKqU8wycyZvPyvcTYSK4cpdV8Z6UlOdh2sJSa+sYun+v9LQXkl5zkkZmpuD6LV0p5KZ9I9Lp8zz3SEh3UNTax5cCJLp2nrqGJZ1ZkMWpwL6YO6+um6JRSnuL1iV6X77nPeQkxBAdJl/v072zO51CpVvNK+QqvT/Sfbj9MdlGVVvNu0CM8hHPjutanr6lv5NmVWYwb2pvJKS3eF6eU8jJenegbGpuYvzyTs/pHcaku33OLtCQH2/NLqapt6NT7396Uz5GyGh7Val4pn+HVif6jbYfJPV7F3JmpunzPTdISHTQ0GTZ3ok/fXM2fnxDz/ZhCpZT389pEX9/YxILlzuV7l+jyPbcZH9+b0ODO9emXbDxIYUUtc7WaV8qneG2i/yC9gIMl1fqBn5t1Dwth1KBeHe7TV9c18PyqLC5IdrjtzlqllDW8MtHXNTSxYLku3/OUtCQHuw6VUVFT3+73/GP9AY5X1jF3RqoHI1NKeYJXJvp3t+jyPU9KS3TQ2GTYlNe+wWGVtQ288E02k1NjGR8f4+HolFLu5nWJvqa+kYUrdPmeJ40d2puw4KB29+kXr8vjRHU9c2ekeDgypZQneF2ib16+p9W850SEBjNmSPv69BU19SxancO0s/oyZkhvC6JTSrmbVyX65uV7ExJimKTL9zwqLcnB7sPllFWfuU//92/zKDtZr715pXyYVyX65uV7Ws17XlqiA2NgY27rVX3ZyXpeXJPDzOH9OHdQtIXRKaXcyWsSffPyvUlJunzPCqOH9CI8JOiM7ZuX1+ZSUdPAHO3NK+XTvCbRv77BtXxvprYIrBAeEsz4+N6tfiBbWl3HK2tzuWxEf84ZqNW8Ur7MKxJ9VW0DL3yTw0UpfThPl+9ZZlJSH/YdraCkqu5Hr724JoequgbmaG9eKZ/nFYl+8fo8Sqq0mrdac4ts42ntm5KqOv7+bR5XjBzIsP5RdoSmlHIj2xN98/K9qcNiGavL9yw1clA03cOCf9Sn/9vqbGrqG3l4uvbmlfIHtif6V7/No7S6Xqt5G4QGB3FefMwP+vRFFbW8tu4AV42OI7lvDxujU0q5i62J/tTleyMH9bIzlICVluQgs7CSwooaAF74Jpu6xiYe0mpeKb9ha6J/ZW0u5bp8z1Zprj79hpwSjpXX8PqGA/x8TBwJfSJtjkwp5S5tJnoReUVECkVkVyuvi4gsEJEsEdkhImPbc+HGJqPL97zAOQN7EhUewvrsYp5flU1Dk+GhafqLVyl/EtKOY14FFgKvtfL6ZUCK6+t84HnXf8+oqLKWUF2+Z7uQ4CAmJMSwfO8xSqvruW7cIIY4utsdllLKjdqs6I0xq4Ez7Wd7FfCacdoA9BKRAW2dt7iyjsvPHaDL97xAWpKDwopaDIb7pybbHY5Sys3c0aOPA/JP+b7A9dwZNRmjvXkv0Tz/9frxgxkco9W8Uv6mPa0btxGR2cBsAEdcAsl9tZr3BsMH9GTBTWOYMizW7lCUUh7gjor+EDD4lO8HuZ77EWPMImPMeGPM+Pj+utWBtxARrhw1kJ4RoXaHopTyAHck+k+A21yrbyYCZcaYI244r1JKKTdos3UjIm8CU4A+IlIA/DsQCmCMeQH4AvgpkAVUA3d6KlillFId12aiN8bc1MbrBrjfbREppZRyK9v3ulFKKeVZmuiVUsrPaaJXSik/p4leKaX8nCZ6pZTyc+JcNGPDhUUqgP22XPyHooEyjQHwjji8IQbwjji8IQbwjji8IQbwjjiGGWM6tK2ApVsgnGa/MWa8jdcHQEQWGWNmB3oM3hKHN8TgLXF4QwzeEoc3xOAtcYjI5o6+R1s38KndAeAdMYB3xOENMYB3xOENMYB3xOENMYD3xNEhdrZuNntDRa+UUr6kM7nTzop+kY3XVkopX9Xh3GlbojfGWJ7oReRSEdnvGnv4mOu5Ja7ndrnGJnp8C8dW4nhZRLa7xjG+JyI9rI7hlNcWiEilJ6/fWgwi8qqI5IrINtfXaJviEBH5o4hkiMheEXnIhhjWnPLncFhEPvJkDGeIY7qIpLviWCsiHp1O00oM01wx7BKRxSLi0c8XWxqhKiIxIrJURDJd/+3tyRha06ncaYwJiC8gGMgGEoEwYDswHOeGbOL6ehO416Y4ep5yzFPAY1bH4HptPPAPoNKmP4dXgWu94OfiTpzjM4Ncx/W14//HKce8D9xm059FBnC265j7gFdtiCEfSHUd8wRwl4f/LCYDY4Fdpzz3P81/L4HHgCet+jnt6pdlFX0rv6UfcH1vRKSPh0OYAGQZY3KMMXXAW8BVxpgvjAvwHc799O2IoxyclSTQDfDkhyctxiAiwcBfgH/14LXPGIMF121vHPcCTxhjmgCMMYU2xACAiPQEpgGeruhbi8MAPV3HRAOHLY7hGqDOGJPhOmap6zmPMS2PUL0KWOx6vBi42pMxQKv/srhORHaLSJOItKtXb0midyWQZ3EOEh8O3CQiw4FvgRnAAQvCOOPIQ1fL5lbgn3bFISJ/B44CZwHP2BDDA8Anxpp5Amf6//FHVwtrnoiE2xRHEnCDiGwWkS9FxJNzL9sax3k1sLy5GLAhjl8BX7i2Kb8V+LPFMfQHQk5Jatfyw2FHVul3yt+No0A/C675KnDpac/tAn4BrG7vSayq6FurYrcaY/IsiqEtzwGrjTFr7ArAGHMnMBDYC9xg8eW7A9fh2V8w7fE7nL/ozgNigN/aFEc4UGOcqxteBF6xKQ6Am3C2Fe0yF/ipMWYQ8HecrUUrGeBGYJ6IfAdUAI0Wx/DDgJwdAI8vWWzpXxbGmL3GmA7dbGpVou/UAHE3a3XkoYj8OxALPGJnHADGmEb+95+rVsaQDSQDWSKSB3QXkSyLYzhkjDni6qTV4kwqEzwYQ6tx4PwZ/cD13IfASBtiwNXSnAB87sHrnymOY8AoY8xG13NvA5MsjuGQMWa9MeYiY8wEnJVsRovv9qxjIjIAwPVfT7bz3CqQbpjaBKSISIKIhOGsED4RkV8BPwFuau7H2hRHMnzfo78S2GdxDB8ZY/obY+KNMfFAtTHGk6srWvtzaP6LJDhbFrvOcA6PxYGzHz7VdczFeDaxtBYDONsUnxljajx4/bbiiBaRVNcxM3H+i9PSGESkL4Crlfdb4AUPxtCaT4DbXY9vBz62IYZOsWoLhHYPEPcUY0yDiDwAfIXzk/1XjDG7RWQ7zs8I1jtzCx8YY56wMg6cf3HWuD50E5wrDe61MgZjzG5PXa8jMYjIChGJxfnnsA24x6Y4/gwsEZG5QCXOPrWlMbhevhHP9sTbimO7iPwaeF9EmoATwC8tjmG3iPxFRK7AWZw+b4xZ4akYoNURqn8G3hGRu3DmjOs9GYM7WXJnrGvNawYwHWeC3wTc3PzD7GoVjDfGHPd4MEop5UNEJB7nv+pGnPb8KuA3xpg2976xpHVjjGnAuaLjK5zV6zuu39IPuX5bDgJ2iMhLVsSjlFK+wPUvi/XAMBEpEJG7ROTnrryZBnwuIl+1eR4rKnqllFL2CaQPY5VSKiBpoldKKT/n8UQvFmyOpZRSqnVa0SullJ+zaq+bHiKy3LXN6E4Rucr1fLw4t4B90bVJz9ci0s2KmJRSKlB4fNWNq3XTC+hujCl33dK9AUgBhgJZONfQbxORd3BuqvW6R4NSSqkAYtWdsQL8SUQmA00497lp3vkt1xizzfV4CxBvUUxKKRUQrEr0s3BuGjbOGFPvuhM2wvVa7SnHNeLci10ppZSbWPVhbDRQ6EryU3G2bJRSSlnA03MXQ3BW7EuAT0VkJ7AZz+7MqJRS6hQe/TBWREYBL7r2kFZKKWUDj7VuROQenFNx/uCpayillGqbbmqmlFJ+Tu+MVUopP+e2RC8ig0VkpYjscd3l+rDr+RgRWSoima7/9nY9P0tEdrjulF3n6uc3n+tSEdkvIlki8pi7YlRKqUDkttaNa9bnAGNMuohE4bz56WrgDqDEGPNnV9LubYz5rYhMAvYaY06IyGXAfxhjzheRYJzTqGbiHNC8Cec81z1uCVQppQKM2yp6Y8wRY0y663EFzklSccBVwGLXYYtxJn+MMeuMMSdcz2/AOWUKnBPvs4wxOcaYOuAt1zmUUkp1gkd69K4Zh2OAjUA/Y8wR10tH+d+tD051F/Cl63EckH/KawWu55RSSnWC22+YEpEewPvAHNcmZt+/ZowxImJOO34qzkR/obtjUUop5eaKXkRCcSb5JcaYD1xPH3P175v7+IWnHD8SeAm4yhhT7Hr6EDD4lNMOcj2nlFKqE9y56kaAl3F+wPrUKS99Atzuenw78LHr+CHAB8CtxpiMU47fBKSISIKIhAE3us6hlFKqE9y56uZCYA2wE+dWxAC/x9mnfwcYAhwArjfGlIjIS8A1rucAGowx413n+inwNBAMvGKM+aNbglRKqQCkd8YqpZSf0ztjlVLKz2miV0opP6eJXiml/JwmeqWU8nOa6JVSys9poldKKT+niV4ppfycJnqllPJz/x9CnDUo5rW/YgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.interpolate().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f97c1f05100>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.interpolate(method='pad').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f97c26aaa30>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.interpolate(method='polynomial', order=2).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    missing\n",
       "1          1\n",
       "2          2\n",
       "3    missing\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.replace(np.nan, 'missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        NaN\n",
       "1    invalid\n",
       "2          2\n",
       "3        NaT\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.replace(1,'invalid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               NaN\n",
       "1                -1\n",
       "2    over bountries\n",
       "3               NaT\n",
       "dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.replace({1:-1, 2: 'over bountries'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pd.NA \n",
    "was introduced to consistently indicate missing data across all types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, <NA>, False)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.NA is pd.NA, pd.NA == pd.NA, np.nan == np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<NA>, True, True)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.NA * 1, pd.isna(pd.NA), pd.isnull(pd.NA)"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}