wine-reviews-analysis.ipynb

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   "source": [
    "# 数据挖掘作业一\n",
    "## 姓名：余晓 学号：3220200998\n",
    "## 数据集：wine-reviews"
   ]
  },
  {
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   "source": [
    "#导入必要的包\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from collections import Counter\n",
    "from math import isnan\n",
    "import math\n"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "## 查看数据集并对数据集进行了解"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/wine-reviews/winemag-data_first150k.csv\n",
      "/kaggle/input/wine-reviews/winemag-data-130k-v2.json\n",
      "/kaggle/input/wine-reviews/winemag-data-130k-v2.csv\n"
     ]
    }
   ],
   "source": [
    "#查看当前文件夹下有哪些数据集以及数据集所处的路径\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "#数据集文件解释，这里我们拿winemag-data_first150k.csv做数据分析        \n",
    "#winemag-data-130k-v2.csv包含10列和13万行葡萄酒评论。\n",
    "#winemag-data_first150k.csv包含10列和15万行葡萄酒评论。\n",
    "#winemag-data-130k-v2.json包含6919个葡萄酒评论节点。"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
       "      <td>Carodorum Selección Especial Reserva</td>\n",
       "      <td>96</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  country                                        description  \\\n",
       "0      US  This tremendous 100% varietal wine hails from ...   \n",
       "1   Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2      US  Mac Watson honors the memory of a wine once ma...   \n",
       "3      US  This spent 20 months in 30% new French oak, an...   \n",
       "4  France  This is the top wine from La Bégude, named aft...   \n",
       "\n",
       "                            designation  points  price        province  \\\n",
       "0                     Martha's Vineyard      96  235.0      California   \n",
       "1  Carodorum Selección Especial Reserva      96  110.0  Northern Spain   \n",
       "2         Special Selected Late Harvest      96   90.0      California   \n",
       "3                               Reserve      96   65.0          Oregon   \n",
       "4                            La Brûlade      95   66.0        Provence   \n",
       "\n",
       "            region_1           region_2             variety  \\\n",
       "0        Napa Valley               Napa  Cabernet Sauvignon   \n",
       "1               Toro                NaN       Tinta de Toro   \n",
       "2     Knights Valley             Sonoma     Sauvignon Blanc   \n",
       "3  Willamette Valley  Willamette Valley          Pinot Noir   \n",
       "4             Bandol                NaN  Provence red blend   \n",
       "\n",
       "                    winery  \n",
       "0                    Heitz  \n",
       "1  Bodega Carmen Rodríguez  \n",
       "2                 Macauley  \n",
       "3                    Ponzi  \n",
       "4     Domaine de la Bégude  "
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   "source": [
    "#读取数据集\n",
    "path = '/kaggle/input/wine-reviews/'\n",
    "data = pd.read_csv(path+'winemag-data_first150k.csv',index_col=0)\n",
    "data.head()#默认展示前五行数据\n",
    "\n",
    "# 数据集的含义\n",
    "# 列名----------含义\n",
    "# country     葡萄酒来自的国家\n",
    "# description 描述葡萄酒的味道、气味、外观、感觉等\n",
    "# designation 酿酒厂内的葡萄园，酿造葡萄酒的葡萄来自葡萄园\n",
    "# points      Wine Enthusiast 对葡萄酒的评分为1-100\n",
    "# price       一瓶葡萄酒的成本\n",
    "# province    葡萄酒来自的产地\n",
    "# region_1    葡萄酒来自的产地\n",
    "# region_2    葡萄酒来自的产地\n",
    "# variety     用于酿造葡萄酒的葡萄种类\n",
    "# winery      生产葡萄酒的酿酒厂"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "country         object\n",
       "description     object\n",
       "designation     object\n",
       "points           int64\n",
       "price          float64\n",
       "province        object\n",
       "region_1        object\n",
       "region_2        object\n",
       "variety         object\n",
       "winery          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes #每列数据的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150930, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape #数据集的大小"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "# 数据分析要求"
   ]
  },
  {
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   },
   "source": [
    "## 数据可视化和摘要\n",
    "\n"
   ]
  },
  {
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   "source": [
    "### 数据摘要\n",
    "（1）标称属性，给出每个可能聚会的频数"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "US                        62397\n",
       "Italy                     23478\n",
       "France                    21098\n",
       "Spain                      8268\n",
       "Chile                      5816\n",
       "Argentina                  5631\n",
       "Portugal                   5322\n",
       "Australia                  4957\n",
       "New Zealand                3320\n",
       "Austria                    3057\n",
       "Germany                    2452\n",
       "South Africa               2258\n",
       "Greece                      884\n",
       "Israel                      630\n",
       "Hungary                     231\n",
       "Canada                      196\n",
       "Romania                     139\n",
       "Slovenia                     94\n",
       "Uruguay                      92\n",
       "Croatia                      89\n",
       "Bulgaria                     77\n",
       "Moldova                      71\n",
       "Mexico                       63\n",
       "Turkey                       52\n",
       "Georgia                      43\n",
       "Lebanon                      37\n",
       "Cyprus                       31\n",
       "Brazil                       25\n",
       "Macedonia                    16\n",
       "Serbia                       14\n",
       "Morocco                      12\n",
       "England                       9\n",
       "Luxembourg                    9\n",
       "Lithuania                     8\n",
       "India                         8\n",
       "Czech Republic                6\n",
       "Ukraine                       5\n",
       "Bosnia and Herzegovina        4\n",
       "South Korea                   4\n",
       "Switzerland                   4\n",
       "China                         3\n",
       "Slovakia                      3\n",
       "Egypt                         3\n",
       "Japan                         2\n",
       "Albania                       2\n",
       "Montenegro                    2\n",
       "Tunisia                       2\n",
       "US-France                     1\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#由上面对数据集各列进行分析得知，该数据集的标称属性有'country','designation','province','region_1','region_2',\n",
    "#'variety','winery'七个标称属性\n",
    "#下面给出每个属性取值的频数\n",
    "#(1)country\n",
    "pd.value_counts(data['country'])"
   ]
  },
  {
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reserve                                   2752\n",
       "Reserva                                   1810\n",
       "Estate                                    1571\n",
       "Barrel sample                             1326\n",
       "Riserva                                    754\n",
       "                                          ... \n",
       "Bourriquot                                   1\n",
       "Shiraz-Mourvèdre-Viognier                    1\n",
       "Sierra Legend Estate                         1\n",
       "Grenache-Shiraz                              1\n",
       "Brauneberger Juffer Auslese Goldkapsel       1\n",
       "Name: designation, Length: 30621, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(2) designation\n",
    "pd.value_counts(data['designation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "California                                44508\n",
       "Washington                                 9750\n",
       "Tuscany                                    7281\n",
       "Bordeaux                                   6111\n",
       "Northern Spain                             4892\n",
       "                                          ...  \n",
       "Langenlois                                    1\n",
       "Vino da Tavola della Svizzera Italiana        1\n",
       "San Clemente                                  1\n",
       "Martinborough Terrace                         1\n",
       "Dolenjska                                     1\n",
       "Name: province, Length: 455, dtype: int64"
      ]
     },
     "execution_count": 8,
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    }
   ],
   "source": [
    "#(3) province\n",
    "pd.value_counts(data['province'])"
   ]
  },
  {
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   "execution_count": 9,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Napa Valley                                  6209\n",
       "Columbia Valley (WA)                         4975\n",
       "Mendoza                                      3586\n",
       "Russian River Valley                         3571\n",
       "California                                   3462\n",
       "                                             ... \n",
       "Catalanesca del Monte Somma                     1\n",
       "Mazoyeres-Chambertin                            1\n",
       "Mâcon-Mancey                                    1\n",
       "Monterey County-Napa County-Sonoma County       1\n",
       "Arlanza                                         1\n",
       "Name: region_1, Length: 1236, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(4)region_1\n",
    "pd.value_counts(data['region_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:12.865767Z",
     "iopub.status.busy": "2021-03-28T11:35:12.862855Z",
     "iopub.status.idle": "2021-03-28T11:35:12.870262Z",
     "shell.execute_reply": "2021-03-28T11:35:12.870775Z"
    },
    "papermill": {
     "duration": 0.065535,
     "end_time": "2021-03-28T11:35:12.870967",
     "exception": false,
     "start_time": "2021-03-28T11:35:12.805432",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Central Coast              13057\n",
       "Sonoma                     11258\n",
       "Columbia Valley             9157\n",
       "Napa                        8801\n",
       "California Other            3516\n",
       "Willamette Valley           3181\n",
       "Mendocino/Lake Counties     2389\n",
       "Sierra Foothills            1660\n",
       "Napa-Sonoma                 1645\n",
       "Finger Lakes                1510\n",
       "Central Valley              1115\n",
       "Long Island                  771\n",
       "Southern Oregon              662\n",
       "Oregon Other                 661\n",
       "North Coast                  632\n",
       "Washington Other             593\n",
       "South Coast                  198\n",
       "New York Other               147\n",
       "Name: region_2, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(5)region_2\n",
    "pd.value_counts(data['region_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:12.975655Z",
     "iopub.status.busy": "2021-03-28T11:35:12.972283Z",
     "iopub.status.idle": "2021-03-28T11:35:12.981729Z",
     "shell.execute_reply": "2021-03-28T11:35:12.980947Z"
    },
    "papermill": {
     "duration": 0.080821,
     "end_time": "2021-03-28T11:35:12.981877",
     "exception": false,
     "start_time": "2021-03-28T11:35:12.901056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chardonnay                  14482\n",
       "Pinot Noir                  14291\n",
       "Cabernet Sauvignon          12800\n",
       "Red Blend                   10062\n",
       "Bordeaux-style Red Blend     7347\n",
       "                            ...  \n",
       "Grenache Gris                   1\n",
       "Malvoisie                       1\n",
       "Syrah-Carignan                  1\n",
       "Mandilaria                      1\n",
       "Karasakiz                       1\n",
       "Name: variety, Length: 632, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(6)variety\n",
    "pd.value_counts(data['variety'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:13.095950Z",
     "iopub.status.busy": "2021-03-28T11:35:13.094792Z",
     "iopub.status.idle": "2021-03-28T11:35:13.106003Z",
     "shell.execute_reply": "2021-03-28T11:35:13.105487Z"
    },
    "papermill": {
     "duration": 0.092302,
     "end_time": "2021-03-28T11:35:13.106156",
     "exception": false,
     "start_time": "2021-03-28T11:35:13.013854",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Williams Selyem          374\n",
       "Testarossa               274\n",
       "DFJ Vinhos               258\n",
       "Chateau Ste. Michelle    225\n",
       "Columbia Crest           217\n",
       "                        ... \n",
       "Tre Anelli                 1\n",
       "Viña del Sopié             1\n",
       "Château le Chêne           1\n",
       "Neckenmarkt                1\n",
       "Patrice Moreux             1\n",
       "Name: winery, Length: 14810, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(7)winery\n",
    "pd.value_counts(data['winery'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.031323,
     "end_time": "2021-03-28T11:35:13.169608",
     "exception": false,
     "start_time": "2021-03-28T11:35:13.138285",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "(2)数值属性，给出5数概括及缺失值的个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:13.243613Z",
     "iopub.status.busy": "2021-03-28T11:35:13.242958Z",
     "iopub.status.idle": "2021-03-28T11:35:13.267680Z",
     "shell.execute_reply": "2021-03-28T11:35:13.268154Z"
    },
    "papermill": {
     "duration": 0.067434,
     "end_time": "2021-03-28T11:35:13.268337",
     "exception": false,
     "start_time": "2021-03-28T11:35:13.200903",
     "status": "completed"
    },
    "tags": []
   },
   "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>points</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150930.000000</td>\n",
       "      <td>137235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>87.888418</td>\n",
       "      <td>33.131482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.222392</td>\n",
       "      <td>36.322536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              points          price\n",
       "count  150930.000000  137235.000000\n",
       "mean       87.888418      33.131482\n",
       "std         3.222392      36.322536\n",
       "min        80.000000       4.000000\n",
       "25%        86.000000      16.000000\n",
       "50%        88.000000      24.000000\n",
       "75%        90.000000      40.000000\n",
       "max       100.000000    2300.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#这里的数值属性包括points和price\n",
    "#用describe函数对数据的5数进行概括\n",
    "digital_data = ['points','price']\n",
    "data[digital_data].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:13.335884Z",
     "iopub.status.busy": "2021-03-28T11:35:13.334908Z",
     "iopub.status.idle": "2021-03-28T11:35:13.342543Z",
     "shell.execute_reply": "2021-03-28T11:35:13.341995Z"
    },
    "papermill": {
     "duration": 0.042641,
     "end_time": "2021-03-28T11:35:13.342683",
     "exception": false,
     "start_time": "2021-03-28T11:35:13.300042",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Null num of 'points' is: 0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#给出points和price缺失值个数\n",
    "print(\"The Null num of 'points' is:\",data['points'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:13.415705Z",
     "iopub.status.busy": "2021-03-28T11:35:13.414859Z",
     "iopub.status.idle": "2021-03-28T11:35:13.420005Z",
     "shell.execute_reply": "2021-03-28T11:35:13.419339Z"
    },
    "papermill": {
     "duration": 0.042879,
     "end_time": "2021-03-28T11:35:13.420151",
     "exception": false,
     "start_time": "2021-03-28T11:35:13.377272",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Null num of 'price' is: 13695\n"
     ]
    }
   ],
   "source": [
    "print(\"The Null num of 'price' is:\",data['price'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032473,
     "end_time": "2021-03-28T11:35:13.486520",
     "exception": false,
     "start_time": "2021-03-28T11:35:13.454047",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 数据可视化\n",
    "（1）绘制points的直方图、盒图、qq图（此处只针对数值类型的数据）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:13.556297Z",
     "iopub.status.busy": "2021-03-28T11:35:13.555567Z",
     "iopub.status.idle": "2021-03-28T11:35:14.368399Z",
     "shell.execute_reply": "2021-03-28T11:35:14.367847Z"
    },
    "papermill": {
     "duration": 0.849017,
     "end_time": "2021-03-28T11:35:14.368549",
     "exception": false,
     "start_time": "2021-03-28T11:35:13.519532",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding=utf-8\n",
    "plt.figure(figsize = (10,10))\n",
    "\n",
    "#直方图\n",
    "plt.subplot(2,2,1)\n",
    "plt.title(\"points hist\")\n",
    "data['points'].hist(alpha=0.5,bins=15) #alpha透明度，bins竖条数\n",
    "\n",
    "#盒图\n",
    "plt.subplot(2,2,2)\n",
    "plt.title(\"points box\")\n",
    "data['points'].plot(kind='box',notch=True,grid=True)\n",
    "\n",
    "#q-q图\n",
    "plt.subplot(2,2,3)\n",
    "stats.probplot(data['points'],dist=\"norm\",plot=plt)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:14.545390Z",
     "iopub.status.busy": "2021-03-28T11:35:14.455350Z",
     "iopub.status.idle": "2021-03-28T11:35:15.641207Z",
     "shell.execute_reply": "2021-03-28T11:35:15.640614Z"
    },
    "papermill": {
     "duration": 1.228583,
     "end_time": "2021-03-28T11:35:15.641344",
     "exception": false,
     "start_time": "2021-03-28T11:35:14.412761",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制price的直方图、盒图、qq图\n",
    "plt.figure(figsize = (10,10))\n",
    "\n",
    "#直方图\n",
    "plt.subplot(2,2,1)\n",
    "plt.title(\"price hist\")\n",
    "data['price'].hist(alpha=0.5,bins=15) #alpha透明度，bins竖条数\n",
    "\n",
    "#盒图\n",
    "plt.subplot(2,2,2)\n",
    "plt.title(\"price box\")\n",
    "data['price'].plot(kind='box',notch=True,grid=True)\n",
    "\n",
    "#q-q图\n",
    "plt.subplot(2,2,3)\n",
    "stats.probplot(data['price'],dist=\"norm\",plot=plt)\n",
    "\n",
    "#去除缺失值再绘制q-q图\n",
    "plt.subplot(2,2,4)\n",
    "pricewithooutnulldata = pd.DataFrame(data['price']) \n",
    "pricewithooutnulldata = pricewithooutnulldata.dropna()  \n",
    "stats.probplot(data['price'],dist=\"norm\",plot=plt)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.035243,
     "end_time": "2021-03-28T11:35:15.712270",
     "exception": false,
     "start_time": "2021-03-28T11:35:15.677027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 由上图可以得出结论：\n",
    "1.points属性分布符合正态分布，但不是完全正态分布\n",
    "\n",
    "2.price属性分布不符合正态分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.03488,
     "end_time": "2021-03-28T11:35:15.782661",
     "exception": false,
     "start_time": "2021-03-28T11:35:15.747781",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 数据缺失处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:15.859525Z",
     "iopub.status.busy": "2021-03-28T11:35:15.858821Z",
     "iopub.status.idle": "2021-03-28T11:35:15.862731Z",
     "shell.execute_reply": "2021-03-28T11:35:15.862153Z"
    },
    "papermill": {
     "duration": 0.045212,
     "end_time": "2021-03-28T11:35:15.863070",
     "exception": false,
     "start_time": "2021-03-28T11:35:15.817858",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#绘制表格查看数据缺失值并检验四种方案填充后是否还有缺失值\n",
    "def missing_data(datatodel):\n",
    "    missing_num = datatodel.isnull().sum()\n",
    "    missing_percent = missing_num/datatodel.shape[0]*100\n",
    "    concat_data = pd.concat([missing_num,missing_percent],axis=1,keys=['missing_num','missing_percent'])\n",
    "    concat_data['Types'] = datatodel.dtypes\n",
    "    return concat_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.035005,
     "end_time": "2021-03-28T11:35:15.933658",
     "exception": false,
     "start_time": "2021-03-28T11:35:15.898653",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "由上表可以看出，数值型数据price存在缺失值\n",
    "\n",
    "标称型数据country，designation,province,region_1,region_2存在缺失值\n",
    "\n",
    "这里缺失的原因可能是由于未完全记录、遗漏或无法获取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.036138,
     "end_time": "2021-03-28T11:35:16.004888",
     "exception": false,
     "start_time": "2021-03-28T11:35:15.968750",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 方案一 缺失值剔除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:16.221594Z",
     "iopub.status.busy": "2021-03-28T11:35:16.102020Z",
     "iopub.status.idle": "2021-03-28T11:35:16.243423Z",
     "shell.execute_reply": "2021-03-28T11:35:16.242838Z"
    },
    "papermill": {
     "duration": 0.201964,
     "end_time": "2021-03-28T11:35:16.243573",
     "exception": false,
     "start_time": "2021-03-28T11:35:16.041609",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "del_null_data = data.copy(deep=True)\n",
    "del_null_data = del_null_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:16.354195Z",
     "iopub.status.busy": "2021-03-28T11:35:16.353517Z",
     "iopub.status.idle": "2021-03-28T11:35:16.367698Z",
     "shell.execute_reply": "2021-03-28T11:35:16.368211Z"
    },
    "papermill": {
     "duration": 0.088997,
     "end_time": "2021-03-28T11:35:16.368397",
     "exception": false,
     "start_time": "2021-03-28T11:35:16.279400",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>missing_num</th>\n",
       "      <th>missing_percent</th>\n",
       "      <th>Types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>description</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>designation</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>points</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>province</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variety</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winery</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             missing_num  missing_percent    Types\n",
       "country                0              0.0   object\n",
       "description            0              0.0   object\n",
       "designation            0              0.0   object\n",
       "points                 0              0.0    int64\n",
       "price                  0              0.0  float64\n",
       "province               0              0.0   object\n",
       "region_1               0              0.0   object\n",
       "region_2               0              0.0   object\n",
       "variety                0              0.0   object\n",
       "winery                 0              0.0   object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data(del_null_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:16.466745Z",
     "iopub.status.busy": "2021-03-28T11:35:16.466074Z",
     "iopub.status.idle": "2021-03-28T11:35:17.540820Z",
     "shell.execute_reply": "2021-03-28T11:35:17.541338Z"
    },
    "papermill": {
     "duration": 1.136797,
     "end_time": "2021-03-28T11:35:17.541512",
     "exception": false,
     "start_time": "2021-03-28T11:35:16.404715",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化对比新旧数据\n",
    "\n",
    "plt.figure(figsize = (10,10))\n",
    "\n",
    "#直方图\n",
    "plt.subplot(3,2,1)\n",
    "plt.title(\"price hist\")\n",
    "data['price'].hist(alpha=0.5,bins=15) #alpha透明度，bins竖条数\n",
    "\n",
    "#直方图\n",
    "plt.subplot(3,2,2)\n",
    "plt.title(\"new price hist\")\n",
    "del_null_data['price'].hist(alpha=0.5,bins=15) #alpha透明度，bins竖条数\n",
    "\n",
    "\n",
    "#盒图\n",
    "plt.subplot(3,2,3)\n",
    "plt.title(\"price box\")\n",
    "data['price'].plot(kind='box',notch=True,grid=True)\n",
    "\n",
    "#盒图\n",
    "plt.subplot(3,2,4)\n",
    "plt.title(\"new price box\")\n",
    "del_null_data['price'].plot(kind='box',notch=True,grid=True)\n",
    "\n",
    "#q-q图\n",
    "plt.subplot(3,2,5)\n",
    "stats.probplot(data['price'],dist=\"norm\",plot=plt)\n",
    "\n",
    "plt.subplot(3,2,6)\n",
    "stats.probplot(del_null_data['price'],dist=\"norm\",plot=plt)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:17.620771Z",
     "iopub.status.busy": "2021-03-28T11:35:17.620006Z",
     "iopub.status.idle": "2021-03-28T11:35:17.637190Z",
     "shell.execute_reply": "2021-03-28T11:35:17.637671Z"
    },
    "papermill": {
     "duration": 0.058417,
     "end_time": "2021-03-28T11:35:17.637858",
     "exception": false,
     "start_time": "2021-03-28T11:35:17.579441",
     "status": "completed"
    },
    "tags": []
   },
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39241.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.546316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.716547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price\n",
       "count  39241.000000\n",
       "mean      37.546316\n",
       "std       26.716547\n",
       "min        4.000000\n",
       "25%       22.000000\n",
       "50%       32.000000\n",
       "75%       45.000000\n",
       "max     2013.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del_null_data[['price']].describe()  #缺失部分剔除后数据的5数概况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.038351,
     "end_time": "2021-03-28T11:35:17.714545",
     "exception": false,
     "start_time": "2021-03-28T11:35:17.676194",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 用最高频率值来填补缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:17.797080Z",
     "iopub.status.busy": "2021-03-28T11:35:17.796401Z",
     "iopub.status.idle": "2021-03-28T11:35:21.439383Z",
     "shell.execute_reply": "2021-03-28T11:35:21.438803Z"
    },
    "papermill": {
     "duration": 3.686529,
     "end_time": "2021-03-28T11:35:21.439523",
     "exception": false,
     "start_time": "2021-03-28T11:35:17.752994",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#用最高频率来填补缺失值--此处使用深拷贝，否则会改变原值\n",
    "fill_data_with_most_frequency = data.copy(deep=True)\n",
    "#对price进行最高频率值填补缺失值\n",
    "word_counts = Counter(fill_data_with_most_frequency['price'])\n",
    "top = word_counts.most_common(1)[0][0]\n",
    "fill_data_with_most_frequency['price'] = fill_data_with_most_frequency['price'].fillna(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:21.519741Z",
     "iopub.status.busy": "2021-03-28T11:35:21.519109Z",
     "iopub.status.idle": "2021-03-28T11:35:21.653932Z",
     "shell.execute_reply": "2021-03-28T11:35:21.654454Z"
    },
    "papermill": {
     "duration": 0.176928,
     "end_time": "2021-03-28T11:35:21.654624",
     "exception": false,
     "start_time": "2021-03-28T11:35:21.477696",
     "status": "completed"
    },
    "tags": []
   },
   "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>missing_num</th>\n",
       "      <th>missing_percent</th>\n",
       "      <th>Types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>5</td>\n",
       "      <td>0.003313</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>description</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>designation</th>\n",
       "      <td>45735</td>\n",
       "      <td>30.302127</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>points</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>province</th>\n",
       "      <td>5</td>\n",
       "      <td>0.003313</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_1</th>\n",
       "      <td>25060</td>\n",
       "      <td>16.603724</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_2</th>\n",
       "      <td>89977</td>\n",
       "      <td>59.615053</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variety</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winery</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             missing_num  missing_percent    Types\n",
       "country                5         0.003313   object\n",
       "description            0         0.000000   object\n",
       "designation        45735        30.302127   object\n",
       "points                 0         0.000000    int64\n",
       "price                  0         0.000000  float64\n",
       "province               5         0.003313   object\n",
       "region_1           25060        16.603724   object\n",
       "region_2           89977        59.615053   object\n",
       "variety                0         0.000000   object\n",
       "winery                 0         0.000000   object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data(fill_data_with_most_frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:21.736593Z",
     "iopub.status.busy": "2021-03-28T11:35:21.735789Z",
     "iopub.status.idle": "2021-03-28T11:35:23.109596Z",
     "shell.execute_reply": "2021-03-28T11:35:23.109054Z"
    },
    "papermill": {
     "duration": 1.415674,
     "end_time": "2021-03-28T11:35:23.109753",
     "exception": false,
     "start_time": "2021-03-28T11:35:21.694079",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化对比新旧数据\n",
    "\n",
    "plt.figure(figsize = (10,10))\n",
    "\n",
    "#直方图\n",
    "plt.subplot(3,2,1)\n",
    "plt.title(\"price hist\")\n",
    "data['price'].hist(alpha=0.5,bins=15) #alpha透明度，bins竖条数\n",
    "\n",
    "#直方图\n",
    "plt.subplot(3,2,2)\n",
    "plt.title(\"new price hist\")\n",
    "fill_data_with_most_frequency['price'].hist(alpha=0.5,bins=15) #alpha透明度，bins竖条数\n",
    "\n",
    "\n",
    "#盒图\n",
    "plt.subplot(3,2,3)\n",
    "plt.title(\"price box\")\n",
    "data['price'].plot(kind='box',notch=True,grid=True)\n",
    "\n",
    "#盒图\n",
    "plt.subplot(3,2,4)\n",
    "plt.title(\"new price box\")\n",
    "fill_data_with_most_frequency['price'].plot(kind='box',notch=True,grid=True)\n",
    "\n",
    "#q-q图\n",
    "plt.subplot(3,2,5)\n",
    "stats.probplot(data['price'],dist=\"norm\",plot=plt)\n",
    "\n",
    "plt.subplot(3,2,6)\n",
    "stats.probplot(fill_data_with_most_frequency['price'],dist=\"norm\",plot=plt)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:23.200470Z",
     "iopub.status.busy": "2021-03-28T11:35:23.199782Z",
     "iopub.status.idle": "2021-03-28T11:35:23.215668Z",
     "shell.execute_reply": "2021-03-28T11:35:23.216197Z"
    },
    "papermill": {
     "duration": 0.065691,
     "end_time": "2021-03-28T11:35:23.216377",
     "exception": false,
     "start_time": "2021-03-28T11:35:23.150686",
     "status": "completed"
    },
    "tags": []
   },
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150930.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.939966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34.840211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price\n",
       "count  150930.000000\n",
       "mean       31.939966\n",
       "std        34.840211\n",
       "min         4.000000\n",
       "25%        16.000000\n",
       "50%        22.000000\n",
       "75%        38.000000\n",
       "max      2300.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对填充后的新数据进行描述\n",
    "fill_data_with_most_frequency[['price']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.040478,
     "end_time": "2021-03-28T11:35:23.297845",
     "exception": false,
     "start_time": "2021-03-28T11:35:23.257367",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 通过属性的相关关系来填补缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:23.383178Z",
     "iopub.status.busy": "2021-03-28T11:35:23.382386Z",
     "iopub.status.idle": "2021-03-28T11:35:23.398257Z",
     "shell.execute_reply": "2021-03-28T11:35:23.398831Z"
    },
    "papermill": {
     "duration": 0.060374,
     "end_time": "2021-03-28T11:35:23.399018",
     "exception": false,
     "start_time": "2021-03-28T11:35:23.338644",
     "status": "completed"
    },
    "tags": []
   },
   "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>points</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>points</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.459863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.459863</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          points     price\n",
       "points  1.000000  0.459863\n",
       "price   0.459863  1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看相关的属性关系\n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:23.486339Z",
     "iopub.status.busy": "2021-03-28T11:35:23.485543Z",
     "iopub.status.idle": "2021-03-28T11:35:24.130330Z",
     "shell.execute_reply": "2021-03-28T11:35:24.130814Z"
    },
    "papermill": {
     "duration": 0.690092,
     "end_time": "2021-03-28T11:35:24.131045",
     "exception": false,
     "start_time": "2021-03-28T11:35:23.440953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#通过属性的相关关系来填补缺失值\n",
    "target_data = data['price'].copy(deep=True)\n",
    "source_data = data['points'].copy(deep=True)\n",
    "\n",
    "flag1 = target_data.isnull().values\n",
    "flag2 = source_data.isnull().values\n",
    "\n",
    "i=0\n",
    "for _,value in target_data.iteritems():\n",
    "    if(flag1[i]==True) and (flag2[i]==False):\n",
    "        target_data[i] = 104 - source_data[i]\n",
    "    i=i+1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:24.237827Z",
     "iopub.status.busy": "2021-03-28T11:35:24.223209Z",
     "iopub.status.idle": "2021-03-28T11:35:25.606599Z",
     "shell.execute_reply": "2021-03-28T11:35:25.607194Z"
    },
    "papermill": {
     "duration": 1.4347,
     "end_time": "2021-03-28T11:35:25.607389",
     "exception": false,
     "start_time": "2021-03-28T11:35:24.172689",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化对比新旧数据\n",
    "\n",
    "plt.figure(figsize = (10,10))\n",
    "\n",
    "#直方图\n",
    "plt.subplot(3,2,1)\n",
    "plt.title(\"price hist\")\n",
    "data['price'].hist(alpha=0.5,bins=15) #alpha透明度，bins竖条数\n",
    "\n",
    "#直方图\n",
    "plt.subplot(3,2,2)\n",
    "plt.title(\"new price hist\")\n",
    "target_data.hist(alpha=0.5,bins=15) #alpha透明度，bins竖条数\n",
    "\n",
    "\n",
    "#盒图\n",
    "plt.subplot(3,2,3)\n",
    "plt.title(\"price box\")\n",
    "data['price'].plot(kind='box',notch=True,grid=True)\n",
    "\n",
    "#盒图\n",
    "plt.subplot(3,2,4)\n",
    "plt.title(\"new price box\")\n",
    "target_data.plot(kind='box',notch=True,grid=True)\n",
    "\n",
    "#q-q图\n",
    "plt.subplot(3,2,5)\n",
    "stats.probplot(data['price'],dist=\"norm\",plot=plt)\n",
    "\n",
    "plt.subplot(3,2,6)\n",
    "stats.probplot(target_data,dist=\"norm\",plot=plt)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:25.698408Z",
     "iopub.status.busy": "2021-03-28T11:35:25.697703Z",
     "iopub.status.idle": "2021-03-28T11:35:25.712749Z",
     "shell.execute_reply": "2021-03-28T11:35:25.711971Z"
    },
    "papermill": {
     "duration": 0.061382,
     "end_time": "2021-03-28T11:35:25.712900",
     "exception": false,
     "start_time": "2021-03-28T11:35:25.651518",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150930.00000\n",
       "mean         31.49576\n",
       "std          35.03242\n",
       "min           4.00000\n",
       "25%          15.00000\n",
       "50%          22.00000\n",
       "75%          38.00000\n",
       "max        2300.00000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.043072,
     "end_time": "2021-03-28T11:35:25.799828",
     "exception": false,
     "start_time": "2021-03-28T11:35:25.756756",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 通过对象的相似性填补缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:26.034321Z",
     "iopub.status.busy": "2021-03-28T11:35:26.033609Z",
     "iopub.status.idle": "2021-03-28T11:35:41.541957Z",
     "shell.execute_reply": "2021-03-28T11:35:41.541261Z"
    },
    "papermill": {
     "duration": 15.698736,
     "end_time": "2021-03-28T11:35:41.542120",
     "exception": false,
     "start_time": "2021-03-28T11:35:25.843384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "空数据数量为: 13695\n"
     ]
    }
   ],
   "source": [
    "numeric_attr = ['price','points']\n",
    "#查找两个对象间的相似性\n",
    "#如果通过暴力法求解耗时耗力\n",
    "#所以选择通过二分法查找的方法进行相似性选择\n",
    "\n",
    "def find_dis_value(dataset, pos, numeric_attr):\n",
    "    def dis_objs(tar_obj_index, sou_obj_index):\n",
    "        tar_obj = dataset.iloc[tar_obj_index]\n",
    "        sou_obj = dataset.iloc[sou_obj_index]\n",
    "        dis_value = 0\n",
    "        for column in tar_obj.index:\n",
    "            if column == 'points':\n",
    "                if (not math.isnan(tar_obj[column])) and (not math.isnan(sou_obj[column])):                 \n",
    "                    dis_value += sou_obj[column] - tar_obj[column]                \n",
    "                else:\n",
    "                    dis_value += 9998\n",
    "        return dis_value\n",
    "\n",
    "    \n",
    "    mindis = 9999\n",
    "    result_pos = -1\n",
    "    leftindex = 0;\n",
    "    rightindex = dataset.shape[0]-1\n",
    "    #二分查找返回最近距离的一个result_pos\n",
    "    while leftindex<=rightindex:\n",
    "        midindex = int((leftindex+rightindex)/2)\n",
    "        tmpdis = dis_objs(pos,midindex)        \n",
    "        if(tmpdis>0):\n",
    "            rightindex = midindex-1            \n",
    "        elif(tmpdis == 0):\n",
    "            result_pos = midindex\n",
    "            break;\n",
    "        else:\n",
    "            leftindex = midindex+1        \n",
    "        if(tmpdis<mindis):\n",
    "            result_pos = midindex  \n",
    "    return result_pos\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# 通过数据对象之间的相似性来填补缺失值\n",
    "numical_datasets = pd.DataFrame(data[numeric_attr].copy(deep=True))\n",
    "\n",
    "#对numical_datasets排序\n",
    "numical_datasets.sort_values(\"points\",inplace=True)\n",
    "data_price = numical_datasets['price'].copy(deep=True)\n",
    "\n",
    "print('空数据数量为:',data_price.isnull().sum())\n",
    "length = numical_datasets.shape[0]\n",
    "count=1;\n",
    "for i in range(length):\n",
    "    if math.isnan(numical_datasets['price'].iloc[i]):       \n",
    "#         print('当前处理第'+str(count)+\"个\")\n",
    "#         print(i,numical_datasets.iloc[i])  \n",
    "        result_pos = find_dis_value(numical_datasets, i, numeric_attr)\n",
    "#         print(result_pos,numical_datasets.iloc[result_pos])\n",
    "        data_price.iloc[i] = data_price.iloc[result_pos]\n",
    "#         print(i,data_price.iloc[i])\n",
    "        count+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:41.636596Z",
     "iopub.status.busy": "2021-03-28T11:35:41.635631Z",
     "iopub.status.idle": "2021-03-28T11:35:41.641556Z",
     "shell.execute_reply": "2021-03-28T11:35:41.642277Z"
    },
    "papermill": {
     "duration": 0.055397,
     "end_time": "2021-03-28T11:35:41.642502",
     "exception": false,
     "start_time": "2021-03-28T11:35:41.587105",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(data_price.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:41.758611Z",
     "iopub.status.busy": "2021-03-28T11:35:41.756910Z",
     "iopub.status.idle": "2021-03-28T11:35:43.055964Z",
     "shell.execute_reply": "2021-03-28T11:35:43.055417Z"
    },
    "papermill": {
     "duration": 1.368928,
     "end_time": "2021-03-28T11:35:43.056139",
     "exception": false,
     "start_time": "2021-03-28T11:35:41.687211",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化对比新旧数据\n",
    "\n",
    "plt.figure(figsize = (10,10))\n",
    "\n",
    "#直方图\n",
    "plt.subplot(3,2,1)\n",
    "plt.title(\"price hist\")\n",
    "data['price'].hist(alpha=0.5,bins=15) #alpha透明度，bins竖条数\n",
    "\n",
    "#直方图\n",
    "plt.subplot(3,2,2)\n",
    "plt.title(\"new price hist\")\n",
    "data_price.hist(alpha=0.5,bins=15) #alpha透明度，bins竖条数\n",
    "\n",
    "\n",
    "#盒图\n",
    "plt.subplot(3,2,3)\n",
    "plt.title(\"price box\")\n",
    "data['price'].plot(kind='box',notch=True,grid=True)\n",
    "\n",
    "#盒图\n",
    "plt.subplot(3,2,4)\n",
    "plt.title(\"new price box\")\n",
    "data_price.plot(kind='box',notch=True,grid=True)\n",
    "\n",
    "#q-q图\n",
    "plt.subplot(3,2,5)\n",
    "stats.probplot(data['price'],dist=\"norm\",plot=plt)\n",
    "\n",
    "plt.subplot(3,2,6)\n",
    "stats.probplot(data_price,dist=\"norm\",plot=plt)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-28T11:35:43.155474Z",
     "iopub.status.busy": "2021-03-28T11:35:43.154794Z",
     "iopub.status.idle": "2021-03-28T11:35:43.168120Z",
     "shell.execute_reply": "2021-03-28T11:35:43.167561Z"
    },
    "papermill": {
     "duration": 0.064641,
     "end_time": "2021-03-28T11:35:43.168269",
     "exception": false,
     "start_time": "2021-03-28T11:35:43.103628",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150930.000000\n",
       "mean         34.107434\n",
       "std          36.281767\n",
       "min           4.000000\n",
       "25%          16.000000\n",
       "50%          25.000000\n",
       "75%          42.000000\n",
       "max        2300.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_price.describe()  # #通过数据对象之间的相似性来填补后数据的5数概况"
   ]
  }
 ],
 "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": "3.7.6"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 40.842103,
   "end_time": "2021-03-28T11:35:43.926073",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-03-28T11:35:03.083970",
   "version": "2.2.2"
  }
 },
 "nbformat": 4,
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