customer_data_analyse.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "\n",
    "person_data = pd.read_csv(r'F:\\datasets\\customer&fixed-position/trainperson-ccf_first_round_user_shop_behavior.csv')\n",
    "shop_data = pd.read_csv(r'F:\\datasets\\customer&fixed-position/trainshop-ccf_first_round_shop_info.csv')\n",
    "test_data = pd.read_csv(r'F:\\datasets\\customer&fixed-position/ABtest-evaluation_public.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The elements of person data Index(['user_id', 'shop_id', 'time_stamp', 'longitude', 'latitude',\n",
      "       'wifi_infos'],\n",
      "      dtype='object')\n",
      "The elements of shop data Index(['shop_id', 'category_id', 'longitude', 'latitude', 'price', 'mall_id'], dtype='object')\n",
      "The elements of test_data Index(['row_id', 'user_id', 'mall_id', 'time_stamp', 'longitude', 'latitude',\n",
      "       'wifi_infos'],\n",
      "      dtype='object')\n",
      "  user_id    shop_id        time_stamp   longitude   latitude  \\\n",
      "0   u_376  s_2871718  2017-08-06 21:20  122.308291  32.088040   \n",
      "1   u_376  s_2871718  2017-08-06 21:20  122.308162  32.087970   \n",
      "2  u_1041   s_181637  2017-08-02 13:10  117.365255  40.638214   \n",
      "3  u_1158   s_609470  2017-08-13 12:30  121.134451  31.197416   \n",
      "\n",
      "                                          wifi_infos  \n",
      "0  b_6396480|-67|false;b_41124514|-86|false;b_287...  \n",
      "1  b_6396480|-67|false;b_56328155|-73|false;b_411...  \n",
      "2  b_8006367|-78|false;b_2485110|-52|false;b_3005...  \n",
      "3  b_26250579|-73|false;b_26250580|-64|false;b_26...  \n",
      "  shop_id category_id   longitude   latitude  price mall_id\n",
      "0    s_26         c_4  122.346736  31.833507     57   m_690\n",
      "1   s_133         c_6  121.134362  31.197511     58  m_6587\n",
      "2   s_251        c_38  121.000505  30.907667     34  m_5892\n",
      "3   s_372        c_30  119.864982  26.659876     44   m_625\n",
      "4   s_456        c_26  122.594243  31.581499     44  m_3839\n"
     ]
    }
   ],
   "source": [
    "print('The elements of person data',person_data.columns)\n",
    "print('The elements of shop data',shop_data.columns)\n",
    "print('The elements of test_data',test_data.columns)\n",
    "print(person_data.head(4))\n",
    "print(shop_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' So the label is so big ahhhhhh'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' To find out if there are limited numbers of location'''\n",
    "\n",
    "# The main purpose is to find the location_shop to get the label\n",
    "location_shop = list(zip(list(shop_data['latitude']),list(shop_data['longitude'])))\n",
    "location_person = list(zip(list(person_data['latitude']),list(person_data['longitude'])))\n",
    "#print(location_shop[:4])\n",
    "#print('The number of shops data,and the number of shop',len(location_shop),len(set(location_shop)))\n",
    "\n",
    "''' So the label is so big ahhhhhh'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' So how about the mall \\n    Oh shit I forget the shop_id and mall _id'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' So how about the mall \n",
    "    Oh shit I forget the shop_id and mall _id'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['s_26' 'c_4' 122.346736 31.833507 57 'm_690']\n",
      "['s_133' 'c_6' 121.13436200000001 31.197511 58 'm_6587']\n",
      "['s_251' 'c_38' 121.000505 30.907667 34 'm_5892']\n",
      "['s_372' 'c_30' 119.86498200000001 26.659876 44 'm_625']\n",
      "['s_456' 'c_26' 122.59424299999999 31.581498999999997 44 'm_3839']\n"
     ]
    }
   ],
   "source": [
    "# Show the data\n",
    "\n",
    "for data in shop_data.values[:5]:\n",
    "    print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shop_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>price</th>\n",
       "      <th>mall_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s_26</td>\n",
       "      <td>c_4</td>\n",
       "      <td>122.346736</td>\n",
       "      <td>31.833507</td>\n",
       "      <td>57</td>\n",
       "      <td>m_690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s_133</td>\n",
       "      <td>c_6</td>\n",
       "      <td>121.134362</td>\n",
       "      <td>31.197511</td>\n",
       "      <td>58</td>\n",
       "      <td>m_6587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s_251</td>\n",
       "      <td>c_38</td>\n",
       "      <td>121.000505</td>\n",
       "      <td>30.907667</td>\n",
       "      <td>34</td>\n",
       "      <td>m_5892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s_372</td>\n",
       "      <td>c_30</td>\n",
       "      <td>119.864982</td>\n",
       "      <td>26.659876</td>\n",
       "      <td>44</td>\n",
       "      <td>m_625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s_456</td>\n",
       "      <td>c_26</td>\n",
       "      <td>122.594243</td>\n",
       "      <td>31.581499</td>\n",
       "      <td>44</td>\n",
       "      <td>m_3839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  shop_id category_id   longitude   latitude  price mall_id\n",
       "0    s_26         c_4  122.346736  31.833507     57   m_690\n",
       "1   s_133         c_6  121.134362  31.197511     58  m_6587\n",
       "2   s_251        c_38  121.000505  30.907667     34  m_5892\n",
       "3   s_372        c_30  119.864982  26.659876     44   m_625\n",
       "4   s_456        c_26  122.594243  31.581499     44  m_3839"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shop_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of the mall 97\n"
     ]
    },
    {
     "data": {
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HxFMlxmhmZgWVuUTr48Dj6f3nJS0BdiRZpxtJAj4ITCkrBjMzGzoNGcOQNIFk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e0a1007c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' So the first we need to locate the mall\n",
    "    I'll plot a graph to show if the shop will be a cluster'''\n",
    "\n",
    "\n",
    "# Get a dict consist of the mall_id and the location corresponding\n",
    "\n",
    "#data[5] means shop_id ; data[2] means latitude; data[3] means longitude\n",
    "location_shop = {}\n",
    "for data in shop_data.values:\n",
    "    if data[5] not in location_shop.keys():\n",
    "        location_shop[data[5]] = [(data[2],data[3])]\n",
    "    else: location_shop[data[5]].append((data[2],data[3]))\n",
    "        \n",
    "print('The number of the mall',len(location_shop.keys()))\n",
    "\n",
    "for mall_id in location_shop.keys():\n",
    "    buff = np.asarray(location_shop[mall_id])\n",
    "    plt.scatter(buff[:,0],buff[:,1])\n",
    "\n",
    "plt.title('The SHOP GRAPH')\n",
    "plt.xlabel('latitude');plt.ylabel('longtitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(person_data['wifi_infos'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b_6396480|-67|false;b_41124514|-86|false;b_28723327|-90|false;b_6396479|-55|false;b_8764723|-90|false;b_32053319|-74|false;b_5857370|-68|false;b_56326644|-89|false;b_56328155|-77|false;b_5857369|-55|false'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person_data['wifi_infos'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m_1021    [(107.516848, 29.738175), (107.516225, 29.7378...\n",
      "m_1085    [(123.801051, 42.367171), (123.80129699999999,...\n",
      "m_1089    [(118.20491100000001, 24.782885), (118.20382, ...\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "shop_mall = pd.Series(location_shop)\n",
    "print(shop_mall.head(3))\n",
    "shop_mall.to_csv(r'F:\\datasets\\customer&fixed-position\\data/shop2mall.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a csv to give a digit_id for shop_id, mall_id \n",
    "shop_id = shop_data['shop_id']\n",
    "mall_id = shop_data['mall_id']\n",
    "\n",
    "shop_dic = {}; mall_dic = {}\n",
    "for index,ids in enumerate(shop_id):\n",
    "    shop_dic[ids] = index\n",
    "\n",
    "for i in range(mall_id.shape[0]):\n",
    "    mall_dic[mall_id[i]] = i\n",
    "    \n",
    "df0 = pd.DataFrame({'shop_id':list(shop_dic.keys()),\n",
    "                    'shop_digit_id':list(shop_dic.values())})                 \n",
    "df0.to_csv(r'F:\\datasets\\customer&fixed-position\\data/shop_id.csv')\n",
    "\n",
    "df1 = pd.DataFrame({'mall_id':list(mall_dic.keys()),\n",
    "                   'mall_digit_id':list(mall_dic.values())})\n",
    "df1.to_csv(r'F:\\datasets\\customer&fixed-position\\data/mall_id.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8476"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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