{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NaN - Not a Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = np.nan\n",
    "type(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 任何数值（整数、浮点数、0）与 NaN 作运算结果都是 NaN\n",
    "m = 1\n",
    "m + n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NaN in Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.0\n",
       "B    2.0\n",
       "C    NaN\n",
       "D    3.0\n",
       "E    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1, 2, np.nan, 3, 4], index=['A', 'B', 'C', 'D', 'E'])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    False\n",
       "B    False\n",
       "C     True\n",
       "D    False\n",
       "E    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 判断 Series 中的值是否是 NaN\n",
    "s1.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     True\n",
       "B     True\n",
       "C    False\n",
       "D     True\n",
       "E     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 判断 Series 中的值是否不是 NaN\n",
    "s1.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.0\n",
       "B    2.0\n",
       "D    3.0\n",
       "E    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # 删除 Series 中的值是 NaN 的数据\n",
    "s1.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nan in DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>NaN</td>\n",
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       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "     0    1    2\n",
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       "3  NaN  NaN  NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame([[1, 2, 3], [np.nan, 5, 6], [7, np.nan, 9], [np.nan, np.nan, np.nan]])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "df1.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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     "metadata": {},
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   "source": [
    "df1.notnull()"
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "df1.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "Empty DataFrame\n",
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     "execution_count": 12,
     "metadata": {},
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   "source": [
    "df1.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "     0    1    2\n",
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    "df1.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "     0    1    2\n",
       "0  NaN  5.0  6.0\n",
       "1  7.0  NaN  NaN\n",
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     "metadata": {},
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   ],
   "source": [
    "df2 = pd.DataFrame([[np.nan, 5, 6], [7, np.nan, np.nan], [np.nan, np.nan, np.nan]])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    0    1    2\n",
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     "execution_count": 15,
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   ],
   "source": [
    "# thresh 表示范围 删除少于 3 个非 NaN 值的行或列\n",
    "df2.dropna(thresh=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 16,
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   "source": [
    "# 替换值为 NaN 的值\n",
    "df2.fillna(value=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 17,
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   "source": [
    "# 按列替换值为 NaN 的值\n",
    "df2.fillna(value={0:0, 1:1, 2:2})"
   ]
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   "outputs": [],
   "source": []
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