电商葡萄酒数据.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "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>ageable</th>\n",
       "      <th>alcohol_%</th>\n",
       "      <th>avg_rating_%</th>\n",
       "      <th>color</th>\n",
       "      <th>description</th>\n",
       "      <th>drink_now</th>\n",
       "      <th>keep_2_3_years</th>\n",
       "      <th>n_votes</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>tags</th>\n",
       "      <th>url</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>8.5</td>\n",
       "      <td></td>\n",
       "      <td>Λευκός</td>\n",
       "      <td>Κάποια από τα Riesling του Rheingau της Γερμαν...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>Riesling Spatlese 2013</td>\n",
       "      <td>33.7</td>\n",
       "      <td>[Riesling, Ήπιος, Ημίγλυκος]</td>\n",
       "      <td>http://www.houseofwine.gr/how/riesling-spatles...</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>13.5</td>\n",
       "      <td></td>\n",
       "      <td>Λευκός</td>\n",
       "      <td>Τυπικό Νέο-Ζηλανδέζικο Sauvignon Blanc από ένα...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>Spy Valley - Sauvignon Blanc 2016</td>\n",
       "      <td>18.3</td>\n",
       "      <td>[Ήπιος, Sauvignon Blanc, Ξηρός]</td>\n",
       "      <td>http://www.houseofwine.gr/how/spy-valley-sauvi...</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>13.0</td>\n",
       "      <td></td>\n",
       "      <td>Ερυθρός</td>\n",
       "      <td>Η κορωνίδα του Κτήματος Χρυσοχόου και ταυτόχρο...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>Cava Χρυσοχόου 2008</td>\n",
       "      <td>25.0</td>\n",
       "      <td>[Ήπιος, Ξηρός, Ξινόμαυρο, Merlot]</td>\n",
       "      <td>http://www.houseofwine.gr/how/cava-hrysohoou.html</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>12.5</td>\n",
       "      <td>90</td>\n",
       "      <td>Λευκός</td>\n",
       "      <td>Ένα από τα δημοφιλέστερα λευκά κρασιά στην Ελλ...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>27</td>\n",
       "      <td>Κτήμα Γεροβασιλείου - Λευκός 2016</td>\n",
       "      <td>12.1</td>\n",
       "      <td>[Ήπιος, Ξηρός, Ασύρτικο, Μαλαγουζιά]</td>\n",
       "      <td>http://www.houseofwine.gr/how/ktima-gerovasile...</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>13.5</td>\n",
       "      <td></td>\n",
       "      <td>Ερυθρός</td>\n",
       "      <td>Εισαγωγή στα κρασιά από τη γηγενή ποικιλία της...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>Landskroon - Pinotage 2013</td>\n",
       "      <td>13.1</td>\n",
       "      <td>[Ήπιος, Ξηρός, Pinotage]</td>\n",
       "      <td>http://www.houseofwine.gr/how/landskroon-pinot...</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ageable  alcohol_% avg_rating_%    color  \\\n",
       "0    False        8.5                Λευκός   \n",
       "1    False       13.5                Λευκός   \n",
       "2     True       13.0               Ερυθρός   \n",
       "3    False       12.5           90   Λευκός   \n",
       "4    False       13.5               Ερυθρός   \n",
       "\n",
       "                                         description  drink_now  \\\n",
       "0  Κάποια από τα Riesling του Rheingau της Γερμαν...      False   \n",
       "1  Τυπικό Νέο-Ζηλανδέζικο Sauvignon Blanc από ένα...       True   \n",
       "2  Η κορωνίδα του Κτήματος Χρυσοχόου και ταυτόχρο...      False   \n",
       "3  Ένα από τα δημοφιλέστερα λευκά κρασιά στην Ελλ...       True   \n",
       "4  Εισαγωγή στα κρασιά από τη γηγενή ποικιλία της...      False   \n",
       "\n",
       "   keep_2_3_years n_votes                               name  price  \\\n",
       "0            True                     Riesling Spatlese 2013   33.7   \n",
       "1           False          Spy Valley - Sauvignon Blanc 2016   18.3   \n",
       "2           False                        Cava Χρυσοχόου 2008   25.0   \n",
       "3           False      27  Κτήμα Γεροβασιλείου - Λευκός 2016   12.1   \n",
       "4            True                 Landskroon - Pinotage 2013   13.1   \n",
       "\n",
       "                                   tags  \\\n",
       "0          [Riesling, Ήπιος, Ημίγλυκος]   \n",
       "1       [Ήπιος, Sauvignon Blanc, Ξηρός]   \n",
       "2     [Ήπιος, Ξηρός, Ξινόμαυρο, Merlot]   \n",
       "3  [Ήπιος, Ξηρός, Ασύρτικο, Μαλαγουζιά]   \n",
       "4              [Ήπιος, Ξηρός, Pinotage]   \n",
       "\n",
       "                                                 url    year  \n",
       "0  http://www.houseofwine.gr/how/riesling-spatles...  2013.0  \n",
       "1  http://www.houseofwine.gr/how/spy-valley-sauvi...  2016.0  \n",
       "2  http://www.houseofwine.gr/how/cava-hrysohoou.html  2008.0  \n",
       "3  http://www.houseofwine.gr/how/ktima-gerovasile...  2016.0  \n",
       "4  http://www.houseofwine.gr/how/landskroon-pinot...  2013.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "file = r'D:\\Programing\\python_projects\\data_analysis\\data_set\\houseofwine.gr-wines.json'\n",
    "df = pd.read_json(file, encoding='utf-8')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1125 entries, 0 to 1124\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   ageable         1125 non-null   bool   \n",
      " 1   alcohol_%       950 non-null    float64\n",
      " 2   avg_rating_%    1125 non-null   object \n",
      " 3   color           1115 non-null   object \n",
      " 4   description     1125 non-null   object \n",
      " 5   drink_now       1125 non-null   bool   \n",
      " 6   keep_2_3_years  1125 non-null   bool   \n",
      " 7   n_votes         1125 non-null   object \n",
      " 8   name            1125 non-null   object \n",
      " 9   price           1120 non-null   float64\n",
      " 10  tags            1125 non-null   object \n",
      " 11  url             1125 non-null   object \n",
      " 12  year            995 non-null    float64\n",
      "dtypes: bool(3), float64(3), object(7)\n",
      "memory usage: 91.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 清洗数据\n",
    "df = df.replace('', np.nan, regex=True)\n",
    "df = df.rename(columns={\n",
    "    'alcohol_%': 'alcohol',\n",
    "    'avg_rating_%': 'avg_rating'\n",
    "},\n",
    "               inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 特征类型转换\n",
    "df['alcohol'] = df['alcohol'].astype(float)\n",
    "df['n_votes'] = df['n_votes'].astype(int, errors='ignore')\n",
    "df['price'] = df['price'].astype(float)\n",
    "df['year'] = df['year'].astype(int, errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# color特征中希腊文转成英文\n",
    "df['color'] = df.color.replace({\n",
    "    \"Λευκός\": 'White',\n",
    "    \"Ερυθρός\": 'Red',\n",
    "    \"Ροζέ\": 'Rose'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Wines'), Text(0.5, 1.0, 'Wine Color Frequency')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 统计不同颜色的葡萄酒的数量\n",
    "ax = df['color'].value_counts().plot(kind='bar')\n",
    "ax.set(ylabel='Wines', title='Wine Color Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Average Rating')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 38144 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21806 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 37327 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 38144 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21806 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 37327 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 37202 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 31934 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 27987 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24230 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20998 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24067 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 37202 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 31934 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 27987 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24230 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20998 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24067 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对整体统计\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2, figsize=(16, 12))\n",
    "\n",
    "# 特征year，每个样本的生产日期\n",
    "# 删除缺失值，转化为整数，对样本计数，绘制柱形图\n",
    "df.year.dropna().astype(int).value_counts().plot(kind='bar', ax=ax1)\n",
    "ax1.set(title='年销售量', xlabel='Year')\n",
    "\n",
    "# 特征alcohol，酒精浓度，直方图\n",
    "sns.distplot(df[df.alcohol < 100].alcohol.dropna(), ax=ax2)\n",
    "ax2.set(title='酒精浓度分布图', xlabel='Alcohol %')\n",
    "\n",
    "# 特征price，价格（欧元）\n",
    "sns.distplot(df[df.price < 100].price.dropna(), ax=ax3)\n",
    "ax3.set(xlabel='Price < 100')\n",
    "\n",
    "# 样本平均分，酒评分等级\n",
    "sns.distplot(df.avg_rating.dropna(), ax=ax4)\n",
    "ax4.set(xlabel='Average Rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Château', 'Lafite', 'Rothschild', '2005']]\n"
     ]
    }
   ],
   "source": [
    "# 寻找1982年的拉菲\n",
    "wine_name = df['name'].str.split()\n",
    "laf_name = []\n",
    "for name in wine_name:\n",
    "    try:\n",
    "        if 'Lafite' in name:\n",
    "            laf_name.append(name)\n",
    "    except:\n",
    "        pass\n",
    "print(laf_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>name</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>color</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Château Lafite Rothschild 2005</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name   price    year color  avg_rating  alcohol\n",
       "16  Château Lafite Rothschild 2005  3100.0  2005.0   Red         NaN     12.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lafname = ' '.join(laf_name[0])\n",
    "laf = df[df['name'] == lafname]\n",
    "laf[['name', 'price', 'year', 'color', 'avg_rating', 'alcohol']]"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}