TASK4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     aardvark\n",
       "1    artichoke\n",
       "2          NaN\n",
       "3      avocado\n",
       "dtype: object"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "string_data = pd.Series(['aardvark','artichoke',np.nan,'avocado'])\n",
    "string_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在pandas中，缺失值表示为NA（not available）。在统计学中，NA的数据可能是不存在的数据或者虽然存在，但是没有观察到。当进行数据清洗以进行分析时，最好直接对缺失数据进行分析，以判断数据采集的问题或缺失数据可能导致的偏差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data[0] = None\n",
    "string_data.isnull()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 滤除缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    过滤掉缺失数据的方法有多种。你可以通过pandas.isnull或布尔索引的手工方法，但fropna可能会更加使用一些，对于一个Series，dropna返回一个仅含非空数据和索引值的Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import nan as NA\n",
    "data = pd.Series([1,NA,3.5,NA,7])\n",
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  6.5  3.0\n",
       "1  1.0  NaN  NaN\n",
       "2  NaN  NaN  NaN\n",
       "3  NaN  6.5  3.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame([[1.,6.5,3.],[1,NA,NA],\n",
    "                    [NA,NA,NA],[NA,6.5,3.]])\n",
    "cleaned = data.dropna()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  6.5  3.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "传入how='all'将只丢弃全为NA的那一行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  6.5  3.0\n",
       "1  1.0  NaN  NaN\n",
       "3  NaN  6.5  3.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用这种方式丢弃列，传入axis=1即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>NaN</td>\n",
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       "     0    1    2   4\n",
       "0  1.0  6.5  3.0 NaN\n",
       "1  1.0  NaN  NaN NaN\n",
       "2  NaN  NaN  NaN NaN\n",
       "3  NaN  6.5  3.0 NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[4]=NA\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "     0    1    2\n",
       "0  1.0  6.5  3.0\n",
       "1  1.0  NaN  NaN\n",
       "2  NaN  NaN  NaN\n",
       "3  NaN  6.5  3.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(how='all',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一个滤除DataFrame行的问题涉及时间序列数据。假设你只想留下一部分观测数据，可以用thresh参数来实现此目的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>0.566737</td>\n",
       "      <td>-0.562793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.491586</td>\n",
       "      <td>-1.744311</td>\n",
       "      <td>0.264377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.645945</td>\n",
       "      <td>-0.076212</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.180504</td>\n",
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       "      <td>1.672286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.304258</td>\n",
       "      <td>0.596032</td>\n",
       "      <td>-0.533851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.464614</td>\n",
       "      <td>-0.896882</td>\n",
       "      <td>0.402701</td>\n",
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       "</table>\n",
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       "          0         1         2\n",
       "0  0.856176  0.202939  0.322232\n",
       "1 -0.294206  0.566737 -0.562793\n",
       "2  0.491586 -1.744311  0.264377\n",
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       "5  0.304258  0.596032 -0.533851\n",
       "6  0.464614 -0.896882  0.402701"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(7,3))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "          0         1         2\n",
       "0  0.856176       NaN  0.322232\n",
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       "5  0.304258  0.596032 -0.533851\n",
       "6  0.464614 -0.896882  0.402701"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:4,1] = NA\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.533851</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.464614</td>\n",
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      "text/plain": [
       "          0         1         2\n",
       "0  0.856176       NaN       NaN\n",
       "1 -0.294206       NaN       NaN\n",
       "2  0.491586       NaN  0.264377\n",
       "3  0.645945       NaN  1.886442\n",
       "4 -1.180504 -1.442841  1.672286\n",
       "5  0.304258  0.596032 -0.533851\n",
       "6  0.464614 -0.896882  0.402701"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:2,2] = NA\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0.596032</td>\n",
       "      <td>-0.533851</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.464614</td>\n",
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       "      <td>0.402701</td>\n",
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      ],
      "text/plain": [
       "          0         1         2\n",
       "4 -1.180504 -1.442841  1.672286\n",
       "5  0.304258  0.596032 -0.533851\n",
       "6  0.464614 -0.896882  0.402701"
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.491586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.264377</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.645945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.886442</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.180504</td>\n",
       "      <td>-1.442841</td>\n",
       "      <td>1.672286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.304258</td>\n",
       "      <td>0.596032</td>\n",
       "      <td>-0.533851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.464614</td>\n",
       "      <td>-0.896882</td>\n",
       "      <td>0.402701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "2  0.491586       NaN  0.264377\n",
       "3  0.645945       NaN  1.886442\n",
       "4 -1.180504 -1.442841  1.672286\n",
       "5  0.304258  0.596032 -0.533851\n",
       "6  0.464614 -0.896882  0.402701"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(thresh=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 填充缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.856176</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.294206</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.491586</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.264377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.645945</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.886442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.180504</td>\n",
       "      <td>-1.442841</td>\n",
       "      <td>1.672286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.304258</td>\n",
       "      <td>0.596032</td>\n",
       "      <td>-0.533851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.464614</td>\n",
       "      <td>-0.896882</td>\n",
       "      <td>0.402701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.856176  0.000000  0.000000\n",
       "1 -0.294206  0.000000  0.000000\n",
       "2  0.491586  0.000000  0.264377\n",
       "3  0.645945  0.000000  1.886442\n",
       "4 -1.180504 -1.442841  1.672286\n",
       "5  0.304258  0.596032 -0.533851\n",
       "6  0.464614 -0.896882  0.402701"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "若是通过一个字典调用fillna，就可以实现不同的列填充不同的值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.294206</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.491586</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.264377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.645945</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.886442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.180504</td>\n",
       "      <td>-1.442841</td>\n",
       "      <td>1.672286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.304258</td>\n",
       "      <td>0.596032</td>\n",
       "      <td>-0.533851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.464614</td>\n",
       "      <td>-0.896882</td>\n",
       "      <td>0.402701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.856176  0.500000  0.000000\n",
       "1 -0.294206  0.500000  0.000000\n",
       "2  0.491586  0.500000  0.264377\n",
       "3  0.645945  0.500000  1.886442\n",
       "4 -1.180504 -1.442841  1.672286\n",
       "5  0.304258  0.596032 -0.533851\n",
       "6  0.464614 -0.896882  0.402701"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({1:0.5,2:0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fillna默认会返回新对象，但也可以对现有对象进行就地修改："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.491586</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.264377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.645945</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.886442</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.180504</td>\n",
       "      <td>-1.442841</td>\n",
       "      <td>1.672286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.304258</td>\n",
       "      <td>0.596032</td>\n",
       "      <td>-0.533851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.464614</td>\n",
       "      <td>-0.896882</td>\n",
       "      <td>0.402701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.856176  0.000000  0.000000\n",
       "1 -0.294206  0.000000  0.000000\n",
       "2  0.491586  0.000000  0.264377\n",
       "3  0.645945  0.000000  1.886442\n",
       "4 -1.180504 -1.442841  1.672286\n",
       "5  0.304258  0.596032 -0.533851\n",
       "6  0.464614 -0.896882  0.402701"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ = df.fillna(0,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对reindexing有效的那些插值方法也可用于fillna："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "      <th>2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.277560</td>\n",
       "      <td>0.589702</td>\n",
       "      <td>0.938571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.058454</td>\n",
       "      <td>0.884085</td>\n",
       "      <td>-2.064114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.441315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.121430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.798948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.204778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.076700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.018290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.277560  0.589702  0.938571\n",
       "1 -0.058454  0.884085 -2.064114\n",
       "2  0.441315       NaN -0.121430\n",
       "3  0.798948       NaN -1.204778\n",
       "4  0.076700       NaN       NaN\n",
       "5  1.018290       NaN       NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,3))\n",
    "df.iloc[2:,1] = NA\n",
    "df.iloc[4:,2] = NA\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.018290</td>\n",
       "      <td>0.884085</td>\n",
       "      <td>-1.204778</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.277560  0.589702  0.938571\n",
       "1 -0.058454  0.884085 -2.064114\n",
       "2  0.441315  0.884085 -0.121430\n",
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       "4  0.076700  0.884085 -1.204778\n",
       "5  1.018290  0.884085 -1.204778"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>0.884085</td>\n",
       "      <td>-2.064114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.441315</td>\n",
       "      <td>0.884085</td>\n",
       "      <td>-0.121430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.798948</td>\n",
       "      <td>0.884085</td>\n",
       "      <td>-1.204778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.076700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.204778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.018290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.204778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.277560  0.589702  0.938571\n",
       "1 -0.058454  0.884085 -2.064114\n",
       "2  0.441315  0.884085 -0.121430\n",
       "3  0.798948  0.884085 -1.204778\n",
       "4  0.076700       NaN -1.204778\n",
       "5  1.018290       NaN -1.204778"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill',limit=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.000000\n",
       "1    3.833333\n",
       "2    3.500000\n",
       "3    3.833333\n",
       "4    7.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1.,NA,3.5,NA,7])\n",
    "data.fillna(data.mean())#平均值"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fillna的参数\n",
    "![image.png](attachment:image.png)\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据转换"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 移除重复数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     k1  k2\n",
       "0  one1   1\n",
       "1   two   1\n",
       "2  one1   2\n",
       "3   two   3\n",
       "4  one1   3\n",
       "5   two   4\n",
       "6   two   4"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({'k1':['one1','two']*3+['two'],\n",
    "                    'k2':[1,1,2,3,3,4,4]})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    DataFrame的duplicated方法返回一个布尔型的Series，表示各行是否重复行（前面出现的行）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "还有一个drop_duplicates方法，返回一个DataFrame，重复的数组会标False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     k1  k2\n",
       "0  one1   1\n",
       "1   two   1\n",
       "2  one1   2\n",
       "3   two   3\n",
       "4  one1   3\n",
       "5   two   4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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