Chapter-07/7.1 Handling Missing Data（处理缺失数据）.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# CHAPTER 7 Data Cleaning and Preparation\n",
    "\n",
    "其实数据分析中80%的时间都是在数据清理部分，loading, clearning, transforming, rearranging。而pandas非常适合用来执行这些任务。\n",
    "\n",
    "# 7.1 Handling Missing Data\n",
    "\n",
    "在pandas中，missing data呈现的方式有些缺点的，但对大部分用户能起到足够的效果。对于数值型数据，pandas用浮点值Nan(Not a Number)来表示缺失值。我们称之为识别符（sentinel value)，这种值能被轻易检测到："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     aardvark\n",
       "1    artichoke\n",
       "2          NaN\n",
       "3      avocado\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data = pd.Series(['aardvark', 'artichoke', np.nan, 'avocado'])\n",
    "string_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在pandas中，我们使用了R语言中的一些传统，把缺失值表示为NA（not available）。在统计应用里，NA数据别是要么是数据不存在，要么是存在但不能被检测到。做数据清理的时候，对缺失值做分析是很重要的，我们要确定是否是数据收集的问题，或者缺失值是否会带来潜在的偏见。\n",
    "\n",
    "内建的Python None值也被当做NA："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "string_data[0] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里有一些用来处理缺失值的函数：\n",
    "\n",
    "![](http://oydgk2hgw.bkt.clouddn.com/pydata-book/zq0q8.png)\n",
    "\n",
    "# 1 Filtering Out Missing Data（过滤缺失值）\n",
    "\n",
    "有一些方法来过滤缺失值。可以使用pandas.isnull和boolean indexing, 配合使用dropna。对于series，只会返回non-null数据和index values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import nan as NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.Series([1, NA, 3.5, NA, 7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的等同于："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于DataFrame，会复杂一些。你可能想要删除包含有NA的row和column。dropna默认会删除包含有缺失值的row："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "     0    1    2\n",
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     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "data = pd.DataFrame([[1., 6.5, 3.], [1., NA, NA],\n",
    "                     [NA, NA, NA], [NA, 6.5, 3.]])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "cleaned = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     "execution_count": 18,
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    "cleaned"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设定`how=all`只会删除那些全是NA的行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "data.dropna(how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除列也一样，设置axis=1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     "execution_count": 20,
     "metadata": {},
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   "source": [
    "data[4] = NA\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "data.dropna(axis=1, how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一种删除DataFrame row的相关应用是是time series data。假设你想要保留有特定数字的观测结果，可以使用thresh参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(7, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
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    "collapsed": false
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     "execution_count": 23,
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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     "execution_count": 25,
     "metadata": {},
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   ],
   "source": [
    "df.iloc[:4, 1] = NA\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
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    "collapsed": false
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.777182</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.327522</td>\n",
       "      <td>0.781662</td>\n",
       "      <td>-0.651949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.454611</td>\n",
       "      <td>-0.170581</td>\n",
       "      <td>-1.740959</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.711897</td>\n",
       "      <td>0.074983</td>\n",
       "      <td>1.343807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.986575       NaN       NaN\n",
       "1  2.008704       NaN       NaN\n",
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       "3  0.777182       NaN -0.220044\n",
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       "5  1.454611 -0.170581 -1.740959\n",
       "6 -0.711897  0.074983  1.343807"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:2, 2] = NA\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ]
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     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>-1.740959</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.711897</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "2  2.240856       NaN  0.273062\n",
       "3  0.777182       NaN -0.220044\n",
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       "6 -0.711897  0.074983  1.343807"
      ]
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     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(thresh=2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Filling In Missing Data（填补缺失值）\n",
    "\n",
    "不是删除缺失值，而是用一些数字填补。对于大部分目的，fillna是可以用的。调用fillna的时候设置好一个常用用来替换缺失值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.454611</td>\n",
       "      <td>-0.170581</td>\n",
       "      <td>-1.740959</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.711897</td>\n",
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       "</div>"
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       "          0         1         2\n",
       "0 -0.986575  0.000000  0.000000\n",
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "给fillna传入一个dict，可以给不同列替换不同的值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.777182</td>\n",
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       "      <td>-0.220044</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.327522</td>\n",
       "      <td>0.781662</td>\n",
       "      <td>-0.651949</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.454611</td>\n",
       "      <td>-0.170581</td>\n",
       "      <td>-1.740959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.711897</td>\n",
       "      <td>0.074983</td>\n",
       "      <td>1.343807</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.986575  0.500000  0.000000\n",
       "1  2.008704  0.500000  0.000000\n",
       "2  2.240856  0.500000  0.273062\n",
       "3  0.777182  0.500000 -0.220044\n",
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       "6 -0.711897  0.074983  1.343807"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({1: 0.5, 2: 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fillna返回一个新对象，但你可以使用in-place来直接更改原有的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
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       "      <th>5</th>\n",
       "      <td>1.454611</td>\n",
       "      <td>-0.170581</td>\n",
       "      <td>-1.740959</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.711897</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.986575  0.000000  0.000000\n",
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      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ = df.fillna(0, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在使用fillna的时候，这种插入法同样能用于reindexing："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <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>-1.151508</td>\n",
       "      <td>1.185176</td>\n",
       "      <td>-1.766933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.544729</td>\n",
       "      <td>-0.807814</td>\n",
       "      <td>0.696087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.461950</td>\n",
       "      <td>0.448852</td>\n",
       "      <td>0.189045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.559766</td>\n",
       "      <td>0.341335</td>\n",
       "      <td>1.469807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.362789</td>\n",
       "      <td>1.117338</td>\n",
       "      <td>-0.383870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.452329</td>\n",
       "      <td>-0.282040</td>\n",
       "      <td>-0.541759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -1.151508  1.185176 -1.766933\n",
       "1  0.544729 -0.807814  0.696087\n",
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       "4 -0.362789  1.117338 -0.383870\n",
       "5 -0.452329 -0.282040 -0.541759"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6, 3))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:, 1] = NA\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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    "df.iloc[4:, 2] = NA\n",
    "df"
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  {
   "cell_type": "code",
   "execution_count": 38,
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    "collapsed": false
   },
   "outputs": [
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       "          0         1         2\n",
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     "execution_count": 38,
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   "source": [
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -1.151508  1.185176 -1.766933\n",
       "1  0.544729 -0.807814  0.696087\n",
       "2 -1.461950 -0.807814  0.189045\n",
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       "4 -0.362789       NaN  1.469807\n",
       "5 -0.452329       NaN  1.469807"
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     "execution_count": 39,
     "metadata": {},
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   ],
   "source": [
    "df.fillna(method='ffill', limit=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用fillna可以我们做一些颇有创造力的事情。比如，可以传入一个series的平均值或中位数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1., NA, 3.5, NA, 7])\n",
    "data.fillna(data.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面是fillna的一些参数：\n",
    "\n",
    "![](http://oydgk2hgw.bkt.clouddn.com/pydata-book/vtzrf.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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