Temporal_Diversity_I.ipynb

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   "source": [
    "import pandas as pd\n",
    "from geopy.distance import vincenty\n",
    "import geohash\n",
    "import numpy as np\n",
    "import csv\n",
    "import operator\n",
    "import collections\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "df = pd.read_csv('masterfile.csv')\n",
    "df['ts'] = pd.to_datetime(df.time)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 24,
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      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df.sort(columns=['ID'], inplace=True)\n",
    "names=df['ID'].unique().tolist()\n",
    "\n",
    "people = {}\n",
    "for n in names:\n",
    "    select = df.loc[df['ID']==n]\n",
    "    people[n] = len(select['Day'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
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   "source": [
    "def round_to_5min(t):\n",
    "    delta = pd.datetools.timedelta(minutes=t.minute%5, \n",
    "                               seconds=t.second, \n",
    "                               microseconds=t.microsecond)\n",
    "    t -= delta\n",
    "    if delta >= pd.datetools.timedelta(minutes=2.5):\n",
    "        t += pd.datetools.timedelta(minutes=5)\n",
    "    return t"
   ]
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  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
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   "source": [
    "df['ts10'] = df['ts'].apply(lambda x: round_to_5min(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "drop_ids = [p for p in people if people[p] <= 5]\n",
    "df = df.loc[~(df['ID'].isin(drop_ids))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trial = df[['ts10','ID','lat','lon','accuracy']].drop_duplicates(subset=['ts10','ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trial.to_csv('pivot_table.csv',index = False)"
   ]
  }
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