BuildBaseData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "from pandas_gbq import to_gbq\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (8, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate all data into single Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "path_train = 'ignored_assets/public_data/training_data/order_data'\n",
    "trainfiles = [f for f in listdir(path_train) if isfile(join(path_train, f)) and f != '.DS_Store']\n",
    "\n",
    "path_test1 = 'ignored_assets/public_data/test_set_1/order_data'\n",
    "testfiles1 = [f for f in listdir(path_test1) if isfile(join(path_test1, f)) and f != '.DS_Store']\n",
    "\n",
    "path_test2 = 'ignored_assets/public_data/test_set_2/order_data'\n",
    "testfiles2 = [f for f in listdir(path_test2) if isfile(join(path_test2, f)) and f != '.DS_Store']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orderdf = []\n",
    "for f in trainfiles:\n",
    "    orderdf.append(pd.read_csv(join(path_train, f), delimiter='\\t', header = None))\n",
    "    \n",
    "for f in testfiles1:\n",
    "    orderdf.append(pd.read_csv(join(path_test1, f), delimiter='\\t', header = None))\n",
    "    \n",
    "for f in testfiles2:\n",
    "    orderdf.append(pd.read_csv(join(path_test2, f), delimiter='\\t', header = None))\n",
    "\n",
    "\n",
    "orderdf = pd.concat(orderdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orderdf.columns = ['order_id', 'driver_id', 'passenger_id', 'start_district_hash', 'dest_district_hash', 'price', 'timestamp']\n",
    "orderdf['timestamp'] = pd.to_datetime(orderdf['timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtain driver clusters and label drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orderdf['hour'] = orderdf['timestamp'].dt.hour\n",
    "orderdf['dayofweek'] = orderdf['timestamp'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bigtable = pd.pivot_table(orderdf, index = ['hour'], columns=['driver_id'], values = ['order_id'],aggfunc=len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample = bigtable.fillna(0).as_matrix().T\n",
    "nclusters = 5\n",
    "kmeans = KMeans(n_clusters=nclusters, random_state=0).fit(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1dadb20d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4+/uzcOFCIiNFzlvPnj254YYb2Llzp9Egb968meeff54ffvgBb28Tux91AR8f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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x206cbba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(nclusters):\n",
    "    plt.plot(kmeans.cluster_centers_[i], label=str(i) + ', ' + str(np.sum(kmeans.labels_ == i)) + ' of drivers')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "match = {did[1]: kmeans.labels_[i] for i,did in enumerate(bigtable.columns)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def assign_driver_label(dids):\n",
    "    labels = np.zeros(len(dids), dtype=object)\n",
    "    for i,did in enumerate(dids):\n",
    "        if pd.isnull(did):\n",
    "            labels[i] = np.nan\n",
    "        else:\n",
    "            labels[i] = match[did]\n",
    "    return labels\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orderdf['driver_cluster'] = assign_driver_label(orderdf['driver_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtain travel time for each ride"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# [time, distance, cost]\n",
    "\n",
    "D = np.array([\n",
    "    [24., 15.2, 68.57],\n",
    "    [14., 11.7, 53.75],\n",
    "    [20., 18.2, 75.16],\n",
    "    [50., 40.5, 152.44],\n",
    "    [49., 48.8, 174.97],\n",
    "    [8, 2.7, 25.42],\n",
    "    [13, 9.4, 47.03]\n",
    "], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "slope, intercept, r_value, p_value, std_err = stats.linregress(D[:,0],(D[:,2] - 14)*0.6666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orderdf['expected_travel_time'] = orderdf['price'] / slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.077747978871562"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orderdf['expected_travel_time'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Push the new dataset to BigQuery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# first to csv\n",
    "orderdf.to_csv('ignored_assets/orders.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Streaming Insert is 0% Complete\n",
      "Streaming Insert is 0% Complete\n",
      "Streaming Insert is 0% Complete\n",
      "Streaming Insert is 0% Complete\n",
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      "Streaming Insert is 16% Complete\n",
      "Streaming Insert is 16% Complete\n",
      "Streaming Insert is 17% Complete\n",
      "Streaming Insert is 17% Complete\n",
      "Streaming Insert is 17% Complete\n",
      "Streaming Insert is 17% Complete\n",
      "Streaming Insert is 17% Complete\n",
      "Streaming Insert is 17% Complete\n",
      "Streaming Insert is 17% Complete\n",
      "Streaming Insert is 17% Complete\n",
      "Streaming Insert is 17% Complete\n",
      "Streaming Insert is 18% Complete\n",
      "Streaming Insert is 18% Complete\n",
      "Streaming Insert is 18% Complete\n",
      "Streaming Insert is 18% Complete\n",
      "Streaming Insert is 18% Complete\n",
      "Streaming Insert is 18% Complete\n",
      "Streaming Insert is 18% Complete\n",
      "Streaming Insert is 18% Complete\n",
      "Streaming Insert is 18% Complete\n",
      "Streaming Insert is 18% Complete\n",
      "Streaming Insert is 19% Complete\n",
      "Streaming Insert is 19% Complete\n",
      "Streaming Insert is 19% Complete\n",
      "Streaming Insert is 19% Complete\n",
      "Streaming Insert is 19% Complete\n",
      "Streaming Insert is 19% Complete\n",
      "Streaming Insert is 19% Complete\n",
      "Streaming Insert is 19% Complete\n",
      "Streaming Insert is 19% Complete\n",
      "Streaming Insert is 19% Complete\n",
      "Streaming Insert is 20% Complete\n",
      "Streaming Insert is 20% Complete\n",
      "Streaming Insert is 20% Complete\n",
      "Streaming Insert is 20% Complete\n",
      "Streaming Insert is 20% Complete\n",
      "Streaming Insert is 20% Complete\n",
      "Streaming Insert is 20% Complete\n",
      "Streaming Insert is 20% Complete\n",
      "Streaming Insert is 20% Complete\n",
      "Streaming Insert is 21% Complete\n",
      "Streaming Insert is 21% Complete\n",
      "Streaming Insert is 21% Complete\n",
      "Streaming Insert is 21% Complete\n",
      "Streaming Insert is 21% Complete\n",
      "Streaming Insert is 21% Complete\n",
      "Streaming Insert is 21% Complete\n",
      "Streaming Insert is 21% Complete\n",
      "Streaming Insert is 21% Complete\n",
      "Streaming Insert is 21% Complete\n",
      "Streaming Insert is 22% Complete\n",
      "Streaming Insert is 22% Complete\n",
      "Streaming Insert is 22% Complete\n",
      "Streaming Insert is 22% Complete\n",
      "Streaming Insert is 22% Complete\n",
      "Streaming Insert is 22% Complete\n",
      "Streaming Insert is 22% Complete\n",
      "Streaming Insert is 22% Complete\n",
      "Streaming Insert is 22% Complete\n",
      "Streaming Insert is 22% Complete\n",
      "Streaming Insert is 23% Complete\n",
      "Streaming Insert is 23% Complete\n",
      "Streaming Insert is 23% Complete\n",
      "Streaming Insert is 23% Complete\n",
      "Streaming Insert is 23% Complete\n",
      "Streaming Insert is 23% Complete\n",
      "Streaming Insert is 23% Complete\n",
      "Streaming Insert is 23% Complete\n",
      "Streaming Insert is 23% Complete\n",
      "Streaming Insert is 24% Complete\n",
      "Streaming Insert is 24% Complete\n",
      "Streaming Insert is 24% Complete\n",
      "Streaming Insert is 24% Complete\n",
      "Streaming Insert is 24% Complete\n",
      "Streaming Insert is 24% Complete\n",
      "Streaming Insert is 24% Complete\n",
      "Streaming Insert is 24% Complete\n",
      "Streaming Insert is 24% Complete\n",
      "Streaming Insert is 24% Complete\n",
      "Streaming Insert is 25% Complete\n",
      "Streaming Insert is 25% Complete\n",
      "Streaming Insert is 25% Complete\n",
      "Streaming Insert is 25% Complete\n",
      "Streaming Insert is 25% Complete\n",
      "Streaming Insert is 25% Complete\n",
      "Streaming Insert is 25% Complete\n",
      "Streaming Insert is 25% Complete\n",
      "Streaming Insert is 25% Complete\n",
      "Streaming Insert is 26% Complete\n",
      "Streaming Insert is 26% Complete\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Streaming Insert is 26% Complete\n",
      "Streaming Insert is 26% Complete\n",
      "Streaming Insert is 26% Complete\n",
      "Streaming Insert is 26% Complete\n",
      "Streaming Insert is 26% Complete\n",
      "Streaming Insert is 26% Complete\n",
      "Streaming Insert is 26% Complete\n",
      "Streaming Insert is 26% Complete\n",
      "Streaming Insert is 27% Complete\n",
      "Streaming Insert is 27% Complete\n",
      "Streaming Insert is 27% Complete\n",
      "Streaming Insert is 27% Complete\n",
      "Streaming Insert is 27% Complete\n",
      "Streaming Insert is 27% Complete\n",
      "Streaming Insert is 27% Complete\n",
      "Streaming Insert is 27% Complete\n",
      "Streaming Insert is 27% Complete\n",
      "Streaming Insert is 27% Complete\n",
      "Streaming Insert is 28% Complete\n",
      "Streaming Insert is 28% Complete\n",
      "Streaming Insert is 28% Complete\n",
      "Streaming Insert is 28% Complete\n",
      "Streaming Insert is 28% Complete\n",
      "Streaming Insert is 28% Complete\n",
      "Streaming Insert is 28% Complete\n",
      "Streaming Insert is 28% Complete\n",
      "Streaming Insert is 28% Complete\n",
      "Streaming Insert is 29% Complete\n",
      "Streaming Insert is 29% Complete\n",
      "Streaming Insert is 29% Complete\n",
      "Streaming Insert is 29% Complete\n",
      "Streaming Insert is 29% Complete\n",
      "Streaming Insert is 29% Complete\n",
      "Streaming Insert is 29% Complete\n",
      "Streaming Insert is 29% Complete\n",
      "Streaming Insert is 29% Complete\n",
      "Streaming Insert is 29% Complete\n",
      "Streaming Insert is 30% Complete\n",
      "Streaming Insert is 30% Complete\n",
      "Streaming Insert is 30% Complete\n",
      "Streaming Insert is 30% Complete\n",
      "Streaming Insert is 30% Complete\n",
      "Streaming Insert is 30% Complete\n",
      "Streaming Insert is 30% Complete\n",
      "Streaming Insert is 30% Complete\n",
      "Streaming Insert is 30% Complete\n",
      "Streaming Insert is 30% Complete\n",
      "Streaming Insert is 31% Complete\n",
      "Streaming Insert is 31% Complete\n",
      "Streaming Insert is 31% Complete\n",
      "Streaming Insert is 31% Complete\n",
      "Streaming Insert is 31% Complete\n",
      "Streaming Insert is 31% Complete\n",
      "Streaming Insert is 31% Complete\n",
      "Streaming Insert is 31% Complete\n",
      "Streaming Insert is 31% Complete\n",
      "Streaming Insert is 32% Complete\n",
      "Streaming Insert is 32% Complete\n",
      "Streaming Insert is 32% Complete\n",
      "Streaming Insert is 32% Complete\n",
      "Streaming Insert is 32% Complete\n",
      "Streaming Insert is 32% Complete\n",
      "Streaming Insert is 32% Complete\n",
      "Streaming Insert is 32% Complete\n",
      "Streaming Insert is 32% Complete\n",
      "Streaming Insert is 32% Complete\n",
      "Streaming Insert is 33% Complete\n",
      "Streaming Insert is 33% Complete\n",
      "Streaming Insert is 33% Complete\n",
      "Streaming Insert is 33% Complete\n",
      "Streaming Insert is 33% Complete\n",
      "Streaming Insert is 33% Complete\n",
      "Streaming Insert is 33% Complete\n",
      "Streaming Insert is 33% Complete\n",
      "Streaming Insert is 33% Complete\n",
      "Streaming Insert is 33% Complete\n",
      "Streaming Insert is 34% Complete\n",
      "Streaming Insert is 34% Complete\n",
      "Streaming Insert is 34% Complete\n",
      "Streaming Insert is 34% Complete\n",
      "Streaming Insert is 34% Complete\n",
      "Streaming Insert is 34% Complete\n",
      "Streaming Insert is 34% Complete\n",
      "Streaming Insert is 34% Complete\n",
      "Streaming Insert is 34% Complete\n",
      "Streaming Insert is 35% Complete\n",
      "Streaming Insert is 35% Complete\n",
      "Streaming Insert is 35% Complete\n",
      "Streaming Insert is 35% Complete\n",
      "Streaming Insert is 35% Complete\n",
      "Streaming Insert is 35% Complete\n",
      "Streaming Insert is 35% Complete\n",
      "Streaming Insert is 35% Complete\n",
      "Streaming Insert is 35% Complete\n",
      "Streaming Insert is 35% Complete\n",
      "Streaming Insert is 36% Complete\n",
      "Streaming Insert is 36% Complete\n",
      "Streaming Insert is 36% Complete\n",
      "Streaming Insert is 36% Complete\n",
      "Streaming Insert is 36% Complete\n",
      "Streaming Insert is 36% Complete\n",
      "Streaming Insert is 36% Complete\n",
      "Streaming Insert is 36% Complete\n",
      "Streaming Insert is 36% Complete\n",
      "Streaming Insert is 36% Complete\n",
      "Streaming Insert is 37% Complete\n",
      "Streaming Insert is 37% Complete\n",
      "Streaming Insert is 37% Complete\n",
      "Streaming Insert is 37% Complete\n",
      "Streaming Insert is 37% Complete\n",
      "Streaming Insert is 37% Complete\n",
      "Streaming Insert is 37% Complete\n",
      "Streaming Insert is 37% Complete\n",
      "Streaming Insert is 37% Complete\n",
      "Streaming Insert is 38% Complete\n",
      "Streaming Insert is 38% Complete\n",
      "Streaming Insert is 38% Complete\n",
      "Streaming Insert is 38% Complete\n",
      "Streaming Insert is 38% Complete\n",
      "Streaming Insert is 38% Complete\n",
      "Streaming Insert is 38% Complete\n",
      "Streaming Insert is 38% Complete\n",
      "Streaming Insert is 38% Complete\n",
      "Streaming Insert is 38% Complete\n",
      "Streaming Insert is 39% Complete\n",
      "Streaming Insert is 39% Complete\n",
      "Streaming Insert is 39% Complete\n",
      "Streaming Insert is 39% Complete\n",
      "Streaming Insert is 39% Complete\n",
      "Streaming Insert is 39% Complete\n",
      "Streaming Insert is 39% Complete\n",
      "Streaming Insert is 39% Complete\n",
      "Streaming Insert is 39% Complete\n",
      "Streaming Insert is 39% Complete\n",
      "Streaming Insert is 40% Complete\n",
      "Streaming Insert is 40% Complete\n",
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      "Streaming Insert is 40% Complete\n",
      "Streaming Insert is 40% Complete\n",
      "Streaming Insert is 40% Complete\n",
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      "Streaming Insert is 40% Complete\n",
      "Streaming Insert is 40% Complete\n",
      "Streaming Insert is 41% Complete\n",
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      "Streaming Insert is 41% Complete\n",
      "Streaming Insert is 41% Complete\n",
      "Streaming Insert is 41% Complete\n",
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      "Streaming Insert is 41% Complete\n",
      "Streaming Insert is 41% Complete\n",
      "Streaming Insert is 41% Complete\n",
      "Streaming Insert is 41% Complete\n",
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      "Streaming Insert is 42% Complete\n",
      "Streaming Insert is 42% Complete\n",
      "Streaming Insert is 42% Complete\n",
      "Streaming Insert is 42% Complete\n",
      "Streaming Insert is 42% Complete\n",
      "Streaming Insert is 42% Complete\n",
      "Streaming Insert is 42% Complete\n",
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      "Streaming Insert is 42% Complete\n",
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      "Streaming Insert is 43% Complete\n",
      "Streaming Insert is 43% Complete\n",
      "Streaming Insert is 43% Complete\n",
      "Streaming Insert is 43% Complete\n",
      "Streaming Insert is 43% Complete\n",
      "Streaming Insert is 43% Complete\n",
      "Streaming Insert is 43% Complete\n",
      "Streaming Insert is 43% Complete\n",
      "Streaming Insert is 44% Complete\n",
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      "Streaming Insert is 44% Complete\n",
      "Streaming Insert is 44% Complete\n",
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      "Streaming Insert is 44% Complete\n",
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      "Streaming Insert is 45% Complete\n",
      "Streaming Insert is 45% Complete\n",
      "Streaming Insert is 45% Complete\n",
      "Streaming Insert is 45% Complete\n",
      "Streaming Insert is 45% Complete\n",
      "Streaming Insert is 45% Complete\n",
      "Streaming Insert is 45% Complete\n",
      "Streaming Insert is 45% Complete\n",
      "Streaming Insert is 45% Complete\n",
      "Streaming Insert is 46% Complete\n",
      "Streaming Insert is 46% Complete\n",
      "Streaming Insert is 46% Complete\n",
      "Streaming Insert is 46% Complete\n",
      "Streaming Insert is 46% Complete\n",
      "Streaming Insert is 46% Complete\n",
      "Streaming Insert is 46% Complete\n",
      "Streaming Insert is 46% Complete\n",
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      "Streaming Insert is 47% Complete\n",
      "Streaming Insert is 47% Complete\n",
      "Streaming Insert is 47% Complete\n",
      "Streaming Insert is 47% Complete\n",
      "Streaming Insert is 47% Complete\n",
      "Streaming Insert is 47% Complete\n",
      "Streaming Insert is 47% Complete\n",
      "Streaming Insert is 47% Complete\n",
      "Streaming Insert is 47% Complete\n",
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      "Streaming Insert is 48% Complete\n",
      "Streaming Insert is 48% Complete\n",
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      "Streaming Insert is 48% Complete\n",
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      "Streaming Insert is 48% Complete\n",
      "Streaming Insert is 48% Complete\n",
      "Streaming Insert is 48% Complete\n",
      "Streaming Insert is 49% Complete\n",
      "Streaming Insert is 49% Complete\n",
      "Streaming Insert is 49% Complete\n",
      "Streaming Insert is 49% Complete\n",
      "Streaming Insert is 49% Complete\n",
      "Streaming Insert is 49% Complete\n",
      "Streaming Insert is 49% Complete\n",
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      "Streaming Insert is 49% Complete\n",
      "Streaming Insert is 50% Complete\n",
      "Streaming Insert is 50% Complete\n",
      "Streaming Insert is 50% Complete\n",
      "Streaming Insert is 50% Complete\n",
      "Streaming Insert is 50% Complete\n",
      "Streaming Insert is 50% Complete\n",
      "Streaming Insert is 50% Complete\n",
      "Streaming Insert is 50% Complete\n",
      "Streaming Insert is 50% Complete\n",
      "Streaming Insert is 50% Complete\n",
      "Streaming Insert is 51% Complete\n",
      "Streaming Insert is 51% Complete\n",
      "Streaming Insert is 51% Complete\n",
      "Streaming Insert is 51% Complete\n",
      "Streaming Insert is 51% Complete\n",
      "Streaming Insert is 51% Complete\n",
      "Streaming Insert is 51% Complete\n",
      "Streaming Insert is 51% Complete\n",
      "Streaming Insert is 51% Complete\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Streaming Insert is 52% Complete\n",
      "Streaming Insert is 52% Complete\n",
      "Streaming Insert is 52% Complete\n",
      "Streaming Insert is 52% Complete\n",
      "Streaming Insert is 52% Complete\n",
      "Streaming Insert is 52% Complete\n",
      "Streaming Insert is 52% Complete\n",
      "Streaming Insert is 52% Complete\n",
      "Streaming Insert is 52% Complete\n",
      "Streaming Insert is 52% Complete\n",
      "Streaming Insert is 53% Complete\n",
      "Streaming Insert is 53% Complete\n",
      "Streaming Insert is 53% Complete\n",
      "Streaming Insert is 53% Complete\n",
      "Streaming Insert is 53% Complete\n",
      "Streaming Insert is 53% Complete\n",
      "Streaming Insert is 53% Complete\n",
      "Streaming Insert is 53% Complete\n",
      "Streaming Insert is 53% Complete\n",
      "Streaming Insert is 53% Complete\n",
      "Streaming Insert is 54% Complete\n",
      "Streaming Insert is 54% Complete\n",
      "Streaming Insert is 54% Complete\n",
      "Streaming Insert is 54% Complete\n",
      "Streaming Insert is 54% Complete\n",
      "Streaming Insert is 54% Complete\n",
      "Streaming Insert is 54% Complete\n",
      "Streaming Insert is 54% Complete\n",
      "Streaming Insert is 54% Complete\n",
      "Streaming Insert is 55% Complete\n",
      "Streaming Insert is 55% Complete\n",
      "Streaming Insert is 55% Complete\n",
      "Streaming Insert is 55% Complete\n",
      "Streaming Insert is 55% Complete\n",
      "Streaming Insert is 55% Complete\n",
      "Streaming Insert is 55% Complete\n",
      "Streaming Insert is 55% Complete\n",
      "Streaming Insert is 55% Complete\n",
      "Streaming Insert is 55% Complete\n",
      "Streaming Insert is 56% Complete\n",
      "Streaming Insert is 56% Complete\n",
      "Streaming Insert is 56% Complete\n",
      "Streaming Insert is 56% Complete\n",
      "Streaming Insert is 56% Complete\n",
      "Streaming Insert is 56% Complete\n",
      "Streaming Insert is 56% Complete\n",
      "Streaming Insert is 56% Complete\n",
      "Streaming Insert is 56% Complete\n",
      "Streaming Insert is 56% Complete\n",
      "Streaming Insert is 57% Complete\n",
      "Streaming Insert is 57% Complete\n",
      "Streaming Insert is 57% Complete\n",
      "Streaming Insert is 57% Complete\n",
      "Streaming Insert is 57% Complete\n",
      "Streaming Insert is 57% Complete\n",
      "Streaming Insert is 57% Complete\n",
      "Streaming Insert is 57% Complete\n",
      "Streaming Insert is 57% Complete\n",
      "Streaming Insert is 58% Complete\n",
      "Streaming Insert is 58% Complete\n",
      "Streaming Insert is 58% Complete\n",
      "Streaming Insert is 58% Complete\n",
      "Streaming Insert is 58% Complete\n",
      "Streaming Insert is 58% Complete\n",
      "Streaming Insert is 58% Complete\n",
      "Streaming Insert is 58% Complete\n",
      "Streaming Insert is 58% Complete\n",
      "Streaming Insert is 58% Complete\n",
      "Streaming Insert is 59% Complete\n",
      "Streaming Insert is 59% Complete\n",
      "Streaming Insert is 59% Complete\n",
      "Streaming Insert is 59% Complete\n",
      "Streaming Insert is 59% Complete\n",
      "Streaming Insert is 59% Complete\n",
      "Streaming Insert is 59% Complete\n",
      "Streaming Insert is 59% Complete\n",
      "Streaming Insert is 59% Complete\n",
      "Streaming Insert is 59% Complete\n",
      "Streaming Insert is 60% Complete\n",
      "Streaming Insert is 60% Complete\n",
      "Streaming Insert is 60% Complete\n",
      "Streaming Insert is 60% Complete\n",
      "Streaming Insert is 60% Complete\n",
      "Streaming Insert is 60% Complete\n",
      "Streaming Insert is 60% Complete\n",
      "Streaming Insert is 60% Complete\n",
      "Streaming Insert is 60% Complete\n",
      "Streaming Insert is 61% Complete\n",
      "Streaming Insert is 61% Complete\n",
      "Streaming Insert is 61% Complete\n",
      "Streaming Insert is 61% Complete\n",
      "Streaming Insert is 61% Complete\n",
      "Streaming Insert is 61% Complete\n",
      "Streaming Insert is 61% Complete\n",
      "Streaming Insert is 61% Complete\n",
      "Streaming Insert is 61% Complete\n",
      "Streaming Insert is 61% Complete\n",
      "Streaming Insert is 62% Complete\n",
      "Streaming Insert is 62% Complete\n",
      "Streaming Insert is 62% Complete\n",
      "Streaming Insert is 62% Complete\n",
      "Streaming Insert is 62% Complete\n",
      "Streaming Insert is 62% Complete\n",
      "Streaming Insert is 62% Complete\n",
      "Streaming Insert is 62% Complete\n",
      "Streaming Insert is 62% Complete\n",
      "Streaming Insert is 62% Complete\n",
      "Streaming Insert is 63% Complete\n",
      "Streaming Insert is 63% Complete\n",
      "Streaming Insert is 63% Complete\n",
      "Streaming Insert is 63% Complete\n",
      "Streaming Insert is 63% Complete\n",
      "Streaming Insert is 63% Complete\n",
      "Streaming Insert is 63% Complete\n",
      "Streaming Insert is 63% Complete\n",
      "Streaming Insert is 63% Complete\n",
      "Streaming Insert is 64% Complete\n",
      "Streaming Insert is 64% Complete\n",
      "Streaming Insert is 64% Complete\n",
      "Streaming Insert is 64% Complete\n",
      "Streaming Insert is 64% Complete\n",
      "Streaming Insert is 64% Complete\n",
      "Streaming Insert is 64% Complete\n",
      "Streaming Insert is 64% Complete\n",
      "Streaming Insert is 64% Complete\n",
      "Streaming Insert is 64% Complete\n",
      "Streaming Insert is 65% Complete\n",
      "Streaming Insert is 65% Complete\n",
      "Streaming Insert is 65% Complete\n",
      "Streaming Insert is 65% Complete\n",
      "Streaming Insert is 65% Complete\n",
      "Streaming Insert is 65% Complete\n",
      "Streaming Insert is 65% Complete\n",
      "Streaming Insert is 65% Complete\n",
      "Streaming Insert is 65% Complete\n",
      "Streaming Insert is 65% Complete\n",
      "Streaming Insert is 66% Complete\n",
      "Streaming Insert is 66% Complete\n",
      "Streaming Insert is 66% Complete\n",
      "Streaming Insert is 66% Complete\n",
      "Streaming Insert is 66% Complete\n",
      "Streaming Insert is 66% Complete\n",
      "Streaming Insert is 66% Complete\n",
      "Streaming Insert is 66% Complete\n",
      "Streaming Insert is 66% Complete\n",
      "Streaming Insert is 67% Complete\n",
      "Streaming Insert is 67% Complete\n",
      "Streaming Insert is 67% Complete\n",
      "Streaming Insert is 67% Complete\n",
      "Streaming Insert is 67% Complete\n",
      "Streaming Insert is 67% Complete\n",
      "Streaming Insert is 67% Complete\n",
      "Streaming Insert is 67% Complete\n",
      "Streaming Insert is 67% Complete\n",
      "Streaming Insert is 67% Complete\n",
      "Streaming Insert is 68% Complete\n",
      "Streaming Insert is 68% Complete\n",
      "Streaming Insert is 68% Complete\n",
      "Streaming Insert is 68% Complete\n",
      "Streaming Insert is 68% Complete\n",
      "Streaming Insert is 68% Complete\n",
      "Streaming Insert is 68% Complete\n",
      "Streaming Insert is 68% Complete\n",
      "Streaming Insert is 68% Complete\n",
      "Streaming Insert is 68% Complete\n",
      "Streaming Insert is 69% Complete\n",
      "Streaming Insert is 69% Complete\n",
      "Streaming Insert is 69% Complete\n",
      "Streaming Insert is 69% Complete\n",
      "Streaming Insert is 69% Complete\n",
      "Streaming Insert is 69% Complete\n",
      "Streaming Insert is 69% Complete\n",
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      "Streaming Insert is 70% Complete\n",
      "Streaming Insert is 70% Complete\n",
      "Streaming Insert is 70% Complete\n",
      "Streaming Insert is 70% Complete\n",
      "Streaming Insert is 70% Complete\n",
      "Streaming Insert is 70% Complete\n",
      "Streaming Insert is 70% Complete\n",
      "Streaming Insert is 70% Complete\n",
      "Streaming Insert is 70% Complete\n",
      "Streaming Insert is 70% Complete\n",
      "Streaming Insert is 71% Complete\n",
      "Streaming Insert is 71% Complete\n",
      "Streaming Insert is 71% Complete\n",
      "Streaming Insert is 71% Complete\n",
      "Streaming Insert is 71% Complete\n",
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      "Streaming Insert is 71% Complete\n",
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      "Streaming Insert is 72% Complete\n",
      "Streaming Insert is 72% Complete\n",
      "Streaming Insert is 72% Complete\n",
      "Streaming Insert is 72% Complete\n",
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      "Streaming Insert is 72% Complete\n",
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      "Streaming Insert is 72% Complete\n",
      "Streaming Insert is 72% Complete\n",
      "Streaming Insert is 73% Complete\n",
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      "Streaming Insert is 73% Complete\n",
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      "Streaming Insert is 73% Complete\n",
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      "Streaming Insert is 74% Complete\n",
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      "Streaming Insert is 74% Complete\n",
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      "Streaming Insert is 74% Complete\n",
      "Streaming Insert is 74% Complete\n",
      "Streaming Insert is 74% Complete\n",
      "Streaming Insert is 74% Complete\n",
      "Streaming Insert is 74% Complete\n",
      "Streaming Insert is 74% Complete\n",
      "Streaming Insert is 75% Complete\n",
      "Streaming Insert is 75% Complete\n",
      "Streaming Insert is 75% Complete\n",
      "Streaming Insert is 75% Complete\n",
      "Streaming Insert is 75% Complete\n",
      "Streaming Insert is 75% Complete\n",
      "Streaming Insert is 75% Complete\n",
      "Streaming Insert is 75% Complete\n",
      "Streaming Insert is 75% Complete\n",
      "Streaming Insert is 76% Complete\n",
      "Streaming Insert is 76% Complete\n",
      "Streaming Insert is 76% Complete\n",
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      "Streaming Insert is 76% Complete\n",
      "Streaming Insert is 76% Complete\n",
      "Streaming Insert is 76% Complete\n",
      "Streaming Insert is 76% Complete\n",
      "Streaming Insert is 76% Complete\n",
      "Streaming Insert is 76% Complete\n",
      "Streaming Insert is 77% Complete\n",
      "Streaming Insert is 77% Complete\n",
      "Streaming Insert is 77% Complete\n",
      "Streaming Insert is 77% Complete\n",
      "Streaming Insert is 77% Complete\n",
      "Streaming Insert is 77% Complete\n",
      "Streaming Insert is 77% Complete\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Streaming Insert is 77% Complete\n",
      "Streaming Insert is 77% Complete\n",
      "Streaming Insert is 78% Complete\n",
      "Streaming Insert is 78% Complete\n",
      "Streaming Insert is 78% Complete\n",
      "Streaming Insert is 78% Complete\n",
      "Streaming Insert is 78% Complete\n",
      "Streaming Insert is 78% Complete\n",
      "Streaming Insert is 78% Complete\n",
      "Streaming Insert is 78% Complete\n",
      "Streaming Insert is 78% Complete\n",
      "Streaming Insert is 78% Complete\n",
      "Streaming Insert is 79% Complete\n",
      "Streaming Insert is 79% Complete\n",
      "Streaming Insert is 79% Complete\n",
      "Streaming Insert is 79% Complete\n",
      "Streaming Insert is 79% Complete\n",
      "Streaming Insert is 79% Complete\n",
      "Streaming Insert is 79% Complete\n",
      "Streaming Insert is 79% Complete\n",
      "Streaming Insert is 79% Complete\n",
      "Streaming Insert is 79% Complete\n",
      "Streaming Insert is 80% Complete\n",
      "Streaming Insert is 80% Complete\n",
      "Streaming Insert is 80% Complete\n",
      "Streaming Insert is 80% Complete\n",
      "Streaming Insert is 80% Complete\n",
      "Streaming Insert is 80% Complete\n",
      "Streaming Insert is 80% Complete\n",
      "Streaming Insert is 80% Complete\n",
      "Streaming Insert is 80% Complete\n",
      "Streaming Insert is 81% Complete\n",
      "Streaming Insert is 81% Complete\n",
      "Streaming Insert is 81% Complete\n",
      "Streaming Insert is 81% Complete\n",
      "Streaming Insert is 81% Complete\n",
      "Streaming Insert is 81% Complete\n",
      "Streaming Insert is 81% Complete\n",
      "Streaming Insert is 81% Complete\n",
      "Streaming Insert is 81% Complete\n",
      "Streaming Insert is 81% Complete\n",
      "Streaming Insert is 82% Complete\n",
      "Streaming Insert is 82% Complete\n",
      "Streaming Insert is 82% Complete\n",
      "Streaming Insert is 82% Complete\n",
      "Streaming Insert is 82% Complete\n",
      "Streaming Insert is 82% Complete\n",
      "Streaming Insert is 82% Complete\n",
      "Streaming Insert is 82% Complete\n",
      "Streaming Insert is 82% Complete\n",
      "Streaming Insert is 82% Complete\n",
      "Streaming Insert is 83% Complete\n",
      "Streaming Insert is 83% Complete\n",
      "Streaming Insert is 83% Complete\n",
      "Streaming Insert is 83% Complete\n",
      "Streaming Insert is 83% Complete\n",
      "Streaming Insert is 83% Complete\n",
      "Streaming Insert is 83% Complete\n",
      "Streaming Insert is 83% Complete\n",
      "Streaming Insert is 83% Complete\n",
      "Streaming Insert is 84% Complete\n",
      "Streaming Insert is 84% Complete\n",
      "Streaming Insert is 84% Complete\n",
      "Streaming Insert is 84% Complete\n",
      "Streaming Insert is 84% Complete\n",
      "Streaming Insert is 84% Complete\n",
      "Streaming Insert is 84% Complete\n",
      "Streaming Insert is 84% Complete\n",
      "Streaming Insert is 84% Complete\n",
      "Streaming Insert is 84% Complete\n",
      "Streaming Insert is 85% Complete\n",
      "Streaming Insert is 85% Complete\n",
      "Streaming Insert is 85% Complete\n",
      "Streaming Insert is 85% Complete\n",
      "Streaming Insert is 85% Complete\n",
      "Streaming Insert is 85% Complete\n",
      "Streaming Insert is 85% Complete\n",
      "Streaming Insert is 85% Complete\n",
      "Streaming Insert is 85% Complete\n",
      "Streaming Insert is 85% Complete\n",
      "Streaming Insert is 86% Complete\n",
      "Streaming Insert is 86% Complete\n",
      "Streaming Insert is 86% Complete\n",
      "Streaming Insert is 86% Complete\n",
      "Streaming Insert is 86% Complete\n",
      "Streaming Insert is 86% Complete\n",
      "Streaming Insert is 86% Complete\n",
      "Streaming Insert is 86% Complete\n",
      "Streaming Insert is 86% Complete\n",
      "Streaming Insert is 87% Complete\n",
      "Streaming Insert is 87% Complete\n",
      "Streaming Insert is 87% Complete\n",
      "Streaming Insert is 87% Complete\n",
      "Streaming Insert is 87% Complete\n",
      "Streaming Insert is 87% Complete\n",
      "Streaming Insert is 87% Complete\n",
      "Streaming Insert is 87% Complete\n",
      "Streaming Insert is 87% Complete\n",
      "Streaming Insert is 87% Complete\n",
      "Streaming Insert is 88% Complete\n",
      "Streaming Insert is 88% Complete\n",
      "Streaming Insert is 88% Complete\n",
      "Streaming Insert is 88% Complete\n",
      "Streaming Insert is 88% Complete\n",
      "Streaming Insert is 88% Complete\n",
      "Streaming Insert is 88% Complete\n",
      "Streaming Insert is 88% Complete\n",
      "Streaming Insert is 88% Complete\n",
      "Streaming Insert is 88% Complete\n",
      "Streaming Insert is 89% Complete\n",
      "Streaming Insert is 89% Complete\n",
      "Streaming Insert is 89% Complete\n",
      "Streaming Insert is 89% Complete\n",
      "Streaming Insert is 89% Complete\n",
      "Streaming Insert is 89% Complete\n",
      "Streaming Insert is 89% Complete\n",
      "Streaming Insert is 89% Complete\n",
      "Streaming Insert is 89% Complete\n",
      "Streaming Insert is 90% Complete\n",
      "Streaming Insert is 90% Complete\n",
      "Streaming Insert is 90% Complete\n",
      "Streaming Insert is 90% Complete\n",
      "Streaming Insert is 90% Complete\n",
      "Streaming Insert is 90% Complete\n",
      "Streaming Insert is 90% Complete\n",
      "Streaming Insert is 90% Complete\n",
      "Streaming Insert is 90% Complete\n",
      "Streaming Insert is 90% Complete\n",
      "Streaming Insert is 91% Complete\n",
      "Streaming Insert is 91% Complete\n",
      "Streaming Insert is 91% Complete\n",
      "Streaming Insert is 91% Complete\n",
      "Streaming Insert is 91% Complete\n",
      "Streaming Insert is 91% Complete\n",
      "Streaming Insert is 91% Complete\n",
      "Streaming Insert is 91% Complete\n",
      "Streaming Insert is 91% Complete\n",
      "Streaming Insert is 91% Complete\n",
      "Streaming Insert is 92% Complete\n",
      "Streaming Insert is 92% Complete\n",
      "Streaming Insert is 92% Complete\n",
      "Streaming Insert is 92% Complete\n",
      "Streaming Insert is 92% Complete\n",
      "Streaming Insert is 92% Complete\n",
      "Streaming Insert is 92% Complete\n",
      "Streaming Insert is 92% Complete\n",
      "Streaming Insert is 92% Complete\n",
      "Streaming Insert is 93% Complete\n",
      "Streaming Insert is 93% Complete\n",
      "Streaming Insert is 93% Complete\n",
      "Streaming Insert is 93% Complete\n",
      "Streaming Insert is 93% Complete\n",
      "Streaming Insert is 93% Complete\n",
      "Streaming Insert is 93% Complete\n",
      "Streaming Insert is 93% Complete\n",
      "Streaming Insert is 93% Complete\n",
      "Streaming Insert is 93% Complete\n",
      "Streaming Insert is 94% Complete\n",
      "Streaming Insert is 94% Complete\n",
      "Streaming Insert is 94% Complete\n",
      "Streaming Insert is 94% Complete\n",
      "Streaming Insert is 94% Complete\n",
      "Streaming Insert is 94% Complete\n",
      "Streaming Insert is 94% Complete\n",
      "Streaming Insert is 94% Complete\n",
      "Streaming Insert is 94% Complete\n",
      "Streaming Insert is 94% Complete\n",
      "Streaming Insert is 95% Complete\n",
      "Streaming Insert is 95% Complete\n",
      "Streaming Insert is 95% Complete\n",
      "Streaming Insert is 95% Complete\n",
      "Streaming Insert is 95% Complete\n",
      "Streaming Insert is 95% Complete\n",
      "Streaming Insert is 95% Complete\n",
      "Streaming Insert is 95% Complete\n",
      "Streaming Insert is 95% Complete\n",
      "Streaming Insert is 96% Complete\n",
      "Streaming Insert is 96% Complete\n",
      "Streaming Insert is 96% Complete\n",
      "Streaming Insert is 96% Complete\n",
      "Streaming Insert is 96% Complete\n",
      "Streaming Insert is 96% Complete\n",
      "Streaming Insert is 96% Complete\n",
      "Streaming Insert is 96% Complete\n",
      "Streaming Insert is 96% Complete\n",
      "Streaming Insert is 96% Complete\n",
      "Streaming Insert is 97% Complete\n",
      "Streaming Insert is 97% Complete\n",
      "Streaming Insert is 97% Complete\n",
      "Streaming Insert is 97% Complete\n",
      "Streaming Insert is 97% Complete\n",
      "Streaming Insert is 97% Complete\n",
      "Streaming Insert is 97% Complete\n",
      "Streaming Insert is 97% Complete\n",
      "Streaming Insert is 97% Complete\n",
      "Streaming Insert is 97% Complete\n",
      "Streaming Insert is 98% Complete\n",
      "Streaming Insert is 98% Complete\n",
      "Streaming Insert is 98% Complete\n",
      "Streaming Insert is 98% Complete\n",
      "Streaming Insert is 98% Complete\n",
      "Streaming Insert is 98% Complete\n",
      "Streaming Insert is 98% Complete\n",
      "Streaming Insert is 98% Complete\n",
      "Streaming Insert is 98% Complete\n",
      "Streaming Insert is 99% Complete\n",
      "Streaming Insert is 99% Complete\n",
      "Streaming Insert is 99% Complete\n",
      "Streaming Insert is 99% Complete\n",
      "Streaming Insert is 99% Complete\n",
      "Streaming Insert is 99% Complete\n",
      "Streaming Insert is 99% Complete\n",
      "Streaming Insert is 99% Complete\n",
      "Streaming Insert is 99% Complete\n",
      "Streaming Insert is 99% Complete\n",
      "Streaming Insert is 100% Complete\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "to_gbq(orderdf, 'didi.orders', 'flawless-augury-163721', chunksize=10000, if_exists= 'replace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(orderdf) / 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 2
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 },
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