Data Preprocessing.ipynb

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   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import time\n",
    "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    \n",
    "%matplotlib inline"
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   "source": [
    "path = os.getcwd().split('TianChi_IJCAI-17_footfall_prediction_code')[0]\n",
    "\n",
    "#shop_info = pd.read_table(path+'dataset/shop_info.txt',names=['shop_id','city_name','location_id','per_pay','score','comment_cnt','shop_level','cate_1_name','cate_2_name','cate_3_name'],header=None,delimiter=',',encoding='utf8',delim_whitespace=False,index_col=False)\n",
    "shop_info = pd.read_table(path+'dataset/shop info with count.csv',delimiter=',',encoding='utf8',delim_whitespace=False,index_col=False)\n",
    "\n",
    "shop_info.index = shop_info['shop_id'].tolist()\n"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "def dateRange(start, end):\n",
    "    days = (datetime.datetime.strptime(end, \"%Y-%m-%d\") - datetime.datetime.strptime(start, \"%Y-%m-%d\")).days + 1\n",
    "    return [datetime.datetime.strftime(datetime.datetime.strptime(start, \"%Y-%m-%d\") + datetime.timedelta(i), \"%Y-%m-%d\") for i in xrange(days)]\n",
    "#len(dateRange('2015-07-01','2016-10-31'))"
   ]
  }
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