merge_with_four_square.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import four_square_api\n",
      "import time\n",
      "four_square = four_square_api.four_square_api()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yelp = pd.read_csv('yelp.csv')\n",
      "cats = pd.read_csv('categories.csv')\n",
      "print yelp.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Index([u'Unnamed: 0', u'address', u'categories', u'city', u'display_address', u'id', u'image_url', u'name', u'phone', u'postal_code', u'rating', u'rating_img_url_large', u'review_count', u'url', u'complete_lookup_address', u'latitude', u'longitude', u'latlng_confidence'], dtype='object')\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print four_square.find_matching_business('Town', 94070, 6505953003)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "49f78793f964a520b16c1fe3\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#choosing businessed to match\n",
      "yelp_to_match = yelp.sort('review_count', ascending = True)[['name','id','postal_code','phone']]\n",
      "#must have name, postal code and phone\n",
      "for column in ['name','postal_code','phone']:\n",
      "    yelp_to_match = yelp_to_match[yelp_to_match[column].notnull()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#initializing the empty columns\n",
      "def empty_column():\n",
      "    if not 'four_square_id' in yelp_to_match.columns:\n",
      "        yelp_to_match['four_square_id'] = pd.np.NaN\n",
      "    print yelp_to_match.head(2)\n",
      "empty_column()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                        name                              id  postal_code  \\\n",
        "19690  Dublin Lockout & Keys  dublin-lockout-and-keys-dublin        94568   \n",
        "33747     Consolidated Parts  consolidated-parts-santa-clara        95050   \n",
        "\n",
        "            phone  four_square_id  \n",
        "19690  9252351124             NaN  \n",
        "33747  4087640200             NaN  \n",
        "\n",
        "[2 rows x 5 columns]\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "done_rows = 0\n",
      "finished_rows = 54891\n",
      "for index, row in yelp_to_match.head(60000).iterrows():\n",
      "    done_rows += 1\n",
      "    if done_rows < finished_rows:\n",
      "        continue\n",
      "    try:\n",
      "#         rand_sleep = pd.np.random.random() / 5.\n",
      "#         time.sleep(rand_sleep)\n",
      "        name, zip_code, phone = row['name'], int(row.postal_code), int(row.phone)\n",
      "        matching_id = four_square.find_matching_business(name, int(zip_code), int(phone))\n",
      "        print done_rows, name, matching_id\n",
      "        if matching_id is not None:\n",
      "            yelp_to_match.loc[index, 'four_square_id'] = matching_id\n",
      "    except Exception as e:\n",
      "        print e\n",
      "        print row"
     ],
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     "collapsed": false,
     "input": [
      "# yelp_to_match.head(5000)\n",
      "print yelp.head(3)\n",
      "print yelp_to_match.head(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   Unnamed: 0                               address  \\\n",
        "0           0               [u'3201 California St']   \n",
        "1           1               [u'565 Buckingham Way']   \n",
        "2           2  [u'1860 El Camino Real', u'Ste 100']   \n",
        "\n",
        "                                          categories           city  \\\n",
        "0  [[u'Banks & Credit Unions', u'banks'], [u'Inve...  San Francisco   \n",
        "1             [[u'Banks & Credit Unions', u'banks']]  San Francisco   \n",
        "2             [[u'Banks & Credit Unions', u'banks']]     Burlingame   \n",
        "\n",
        "                                     display_address  \\\n",
        "0  3201 California St, Laurel Heights, San Franci...   \n",
        "1  565 Buckingham Way, Stonestown, San Francisco,...   \n",
        "2  1860 El Camino Real, Ste 100, Burlingame, CA 9...   \n",
        "\n",
        "                                        id  \\\n",
        "0       sf-fire-credit-union-san-francisco   \n",
        "1     sf-fire-credit-union-san-francisco-2   \n",
        "2  united-health-credit-union-burlingame-2   \n",
        "\n",
        "                                           image_url  \\\n",
        "0  http://s3-media2.ak.yelpcdn.com/bphoto/t31F2Pk...   \n",
        "1  http://s3-media3.ak.yelpcdn.com/bphoto/pskjEfI...   \n",
        "2  http://s3-media2.ak.yelpcdn.com/bphoto/DMHNTcQ...   \n",
        "\n",
        "                         name       phone  postal_code  rating  \\\n",
        "0        SF Fire Credit Union  4156744800        94118     4.5   \n",
        "1        SF Fire Credit Union  4156744800        94132     4.5   \n",
        "2  United Health Credit Union  6502311300        94010     5.0   \n",
        "\n",
        "                                rating_img_url_large  review_count  \\\n",
        "0  http://s3-media4.ak.yelpcdn.com/assets/2/www/i...           184   \n",
        "1  http://s3-media4.ak.yelpcdn.com/assets/2/www/i...            31   \n",
        "2  http://s3-media3.ak.yelpcdn.com/assets/2/www/i...             8   \n",
        "\n",
        "                                                 url  \\\n",
        "0  http://www.yelp.com/biz/sf-fire-credit-union-s...   \n",
        "1  http://www.yelp.com/biz/sf-fire-credit-union-s...   \n",
        "2  http://www.yelp.com/biz/united-health-credit-u...   \n",
        "\n",
        "      complete_lookup_address   latitude   longitude latlng_confidence  \n",
        "0   3201 California St, 94118  37.787171 -122.447225             P1AAA  \n",
        "1   565 Buckingham Way, 94132  37.730150 -122.476210             L1AAA  \n",
        "2  1860 El Camino Real, 94010  37.596147 -122.384672             P1AAA  \n",
        "\n",
        "[3 rows x 18 columns]\n",
        "                        name                              id  postal_code  \\\n",
        "19690  Dublin Lockout & Keys  dublin-lockout-and-keys-dublin        94568   \n",
        "33747     Consolidated Parts  consolidated-parts-santa-clara        95050   \n",
        "51038           Boost Mobile          boost-mobile-hayward-5        94541   \n",
        "\n",
        "            phone            four_square_id  \n",
        "19690  9252351124                       NaN  \n",
        "33747  4087640200  4dd693a21fc7d8d8663d5523  \n",
        "51038  5102005181                       NaN  \n",
        "\n",
        "[3 rows x 5 columns]\n"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# yelp_to_match.to_csv('yelp_to_match.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'yelp_to_match' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-2-78989de3499e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myelp_to_match\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'yelp_to_match.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mNameError\u001b[0m: name 'yelp_to_match' is not defined"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# matched = yelp_to_match[yelp_to_match.four_square_id.notnull()]\n",
      "# matched.to_csv('matched.csv')\n",
      "matched = pd.read_csv('matched.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_went_next = []\n",
      "already_finished = 0\n",
      "done_count = 0\n",
      "total_count = 0\n",
      "for four_square_from in matched.four_square_id:\n",
      "    done_count+=1\n",
      "    rand_duration = pd.np.random.random() * 1.45\n",
      "    time.sleep(rand_duration)\n",
      "    if done_count < already_finished:\n",
      "        continue\n",
      "    try:\n",
      "        went_next = four_square.find_next_venues(four_square_from)\n",
      "        all_went_next.extend(went_next)\n",
      "        if done_count % 10 == 0:\n",
      "            print done_count, total_count#, four_square_from, len(went_next)\n",
      "        total_count += len(went_next)\n",
      "    except Exception as e:\n",
      "        print e    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 5\n",
        "20"
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      },
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        " 10\n",
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      },
      {
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       "stream": "stdout",
       "text": [
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       "stream": "stdout",
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       ]
      },
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       "stream": "stdout",
       "text": [
        " 31965\n",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 32014\n",
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       ]
      },
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       "stream": "stdout",
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        " 32062\n",
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       "stream": "stdout",
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        " 32209\n",
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      },
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       ]
      },
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        " 32985\n",
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       ]
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        " 33035\n",
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       ]
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        " 33085\n",
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       ]
      },
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       ]
      },
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       ]
      },
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        " 33235\n",
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       ]
      },
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       ]
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       "stream": "stdout",
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       ]
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       "stream": "stdout",
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       ]
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       ]
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       ]
      },
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       "stream": "stdout",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 33580\n",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 33627\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 33676\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 33726\n",
        "21360"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 33771\n",
        "21370"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 33819\n",
        "21380"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 33869\n",
        "21390"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 33919\n",
        "21400"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 33969\n",
        "21410"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34019\n",
        "21420"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34069\n",
        "21430"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34116\n",
        "21440"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34166\n",
        "21450"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34216\n",
        "21460"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34264\n",
        "21470"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34314\n",
        "21480"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34364\n",
        "21490"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34414\n",
        "21500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34462\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "in_table = 0\n",
      "for form,to,count in all_went_next:\n",
      "    in_table += to in (matched.four_square_id).sum()\n",
      "    print in_table\n",
      "print in_table"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "6"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "7"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "8"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "9"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "9"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "11"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "12"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "12"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "12"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "14"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "14"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "15"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-38-476fe383cd37>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0min_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mform\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcount\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mall_went_next\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0min_table\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mto\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmatched\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfour_square_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0min_table\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0min_table\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mstat_func\u001b[0;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m   3248\u001b[0m                                               skipna=skipna)\n\u001b[1;32m   3249\u001b[0m                 return self._reduce(f, axis=axis,\n\u001b[0;32m-> 3250\u001b[0;31m                                     skipna=skipna, numeric_only=numeric_only)\n\u001b[0m\u001b[1;32m   3251\u001b[0m             \u001b[0mstat_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3252\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mstat_func\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m_reduce\u001b[0;34m(self, op, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m   2032\u001b[0m                 filter_type=None, **kwds):\n\u001b[1;32m   2033\u001b[0m         \u001b[0;34m\"\"\" perform a reduction operation \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2034\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2036\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_reindex_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/nanops.pyc\u001b[0m in \u001b[0;36m_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     41\u001b[0m                                 'this dtype'.format(f.__name__.replace('nan',\n\u001b[1;32m     42\u001b[0m                                                                        '')))\n\u001b[0;32m---> 43\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/nanops.pyc\u001b[0m in \u001b[0;36mf\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m     84\u001b[0m                         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/nanops.pyc\u001b[0m in \u001b[0;36mnansum\u001b[0;34m(values, axis, skipna)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnansum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m     \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m     \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m     \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_maybe_null_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_sum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mthe_sum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/_methods.pyc\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     return um.add.reduce(a, axis=axis, dtype=dtype,\n\u001b[0;32m---> 25\u001b[0;31m                             out=out, keepdims=keepdims)\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(matched.four_square_id == '4af70aa2f964a5201d0522e3').sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(all_went_next)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "34477\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "next_records = pd.DataFrame.from_records(all_went_next, columns = ['from_id','to_id','total_checkins'])\n",
      "next_records.to_csv('next_records.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print next_records.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                    from_id                     to_id  total_checkins\n",
        "0  4a57edf0f964a52018b71fe3  4a873d60f964a5206a0320e3            1317\n",
        "1  4a57edf0f964a52018b71fe3  4a11b8aaf964a52037771fe3            4107\n",
        "2  4a57edf0f964a52018b71fe3  4af1f21cf964a520a0e421e3            1445\n",
        "3  4a57edf0f964a52018b71fe3  44ef7327f964a520ed371fe3           10792\n",
        "4  4a57edf0f964a52018b71fe3  4af70aa2f964a5201d0522e3             771\n",
        "\n",
        "[5 rows x 3 columns]\n"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print next_records.to_id.unique().shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(6895,)\n"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_went_next "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "[('4a57edf0f964a52018b71fe3', u'4a873d60f964a5206a0320e3', 1317),\n",
        " ('4a57edf0f964a52018b71fe3', u'4a11b8aaf964a52037771fe3', 4107),\n",
        " ('4a57edf0f964a52018b71fe3', u'4af1f21cf964a520a0e421e3', 1445),\n",
        " ('4a57edf0f964a52018b71fe3', u'44ef7327f964a520ed371fe3', 10792),\n",
        " ('4a57edf0f964a52018b71fe3', u'4af70aa2f964a5201d0522e3', 771),\n",
        " ('4af78597f964a520b10922e3', u'4b2084abf964a520dd3224e3', 957),\n",
        " ('4af78597f964a520b10922e3', u'4bba0152cf2fc9b65cffa002', 869),\n",
        " ('4af78597f964a520b10922e3', u'4b637392f964a520d87a2ae3', 1257),\n",
        " ('4af78597f964a520b10922e3', u'4af0e45af964a520f0df21e3', 618),\n",
        " ('4af78597f964a520b10922e3', u'4bf58b67493c2d7fced54785', 853),\n",
        " ('4a498253f964a52065ab1fe3', u'455ad6b4f964a520573d1fe3', 13386),\n",
        " ('4b886b28f964a5207df631e3', u'4a510702f964a52072b01fe3', 15327),\n",
        " ('4b886b28f964a5207df631e3', u'44e34b8bf964a5203b371fe3', 7572),\n",
        " ('4b410042f964a52023bf25e3', u'50abe3bce4b0de093ecafb5b', 318),\n",
        " ('4b410042f964a52023bf25e3', u'4b539920f964a5200ba427e3', 1535),\n",
        " ('4c634904edd320a1dc2faf29', u'45606719f964a5209b3d1fe3', 25891),\n",
        " ('4b42334bf964a520c7ce25e3', u'4b7c964bf964a5207c9c2fe3', 2092),\n",
        " ('4b42334bf964a520c7ce25e3', u'4af78bc4f964a520cb0922e3', 8245),\n",
        " ('4b42334bf964a520c7ce25e3', u'4ada3de7f964a5206e2021e3', 7648),\n",
        " ('4b42334bf964a520c7ce25e3', u'4b0ec6f8f964a520e35a23e3', 1994),\n",
        " ('4b42334bf964a520c7ce25e3', u'4788a5ddf964a520384d1fe3', 3963),\n",
        " ('4a998859f964a520ea2e20e3', u'4b1b1e5ef964a52058f823e3', 3417),\n",
        " ('4a998859f964a520ea2e20e3', u'49fca052f964a520c16e1fe3', 2389),\n",
        " ('4a998859f964a520ea2e20e3', u'4e7f455ed3e3b8285efe60fb', 2535),\n",
        " ('4a998859f964a520ea2e20e3', u'4aea628df964a52054bb21e3', 2346),\n",
        " ('4a43b1e5f964a520b1a61fe3', u'51507428e4b06e0e5323c594', 194),\n",
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        " ('4b4a66dff964a520878626e3', u'4dd1580eb3adb047f5024231', 16115),\n",
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        " ('4a4e5dcaf964a5205dae1fe3', u'4aff687ff964a520483822e3', 1463),\n",
        " ('4a4e5dcaf964a5205dae1fe3', u'4a0f514ef964a5204b761fe3', 3635),\n",
        " ('4b4147e8f964a5208fc425e3', u'4b464b35f964a520c61c26e3', 197),\n",
        " ('4b4621a7f964a5207a1726e3', u'4c0533a8187ec9280a10b87b', 1901),\n",
        " ('4b671bb3f964a5201a3b2be3', u'4a43d42df964a520d9a61fe3', 4648),\n",
        " ('4b8ebd3cf964a520283433e3', u'4b2537ddf964a520366e24e3', 2584),\n",
        " ('4b8ebd3cf964a520283433e3', u'4ee424d5b634710b8ab3afa4', 183),\n",
        " ('4b4617e3f964a520201626e3', u'4b590db6f964a520507a28e3', 5777),\n",
        " ('4c79737b81bca093d57cfd14', u'43e73ce3f964a5200a2f1fe3', 5281),\n",
        " ('4c79737b81bca093d57cfd14', u'4c29567f9fb5d13aa2139b57', 23784),\n",
        " ('4c79737b81bca093d57cfd14', u'4b0d94b7f964a520824b23e3', 1042),\n",
        " ('4c79737b81bca093d57cfd14', u'4c547b97a724e21eed4979f6', 845),\n",
        " ('4c79737b81bca093d57cfd14', u'435c2400f964a52008291fe3', 36158),\n",
        " ('4bb25bf242959c748cba202c', u'49bc2d06f964a5201d541fe3', 8801),\n",
        " ('4bb25bf242959c748cba202c', u'4a9eedbcf964a520ce3b20e3', 2672),\n",
        " ('4bb25bf242959c748cba202c', u'49b71814f964a5201a531fe3', 30273),\n",
        " ('4bb25bf242959c748cba202c', u'4a8c50ddf964a520a30d20e3', 1389),\n",
        " ('4ad0f592f964a5207bdb20e3', u'4a79c77bf964a520a5e71fe3', 6744),\n",
        " ('4ad0f592f964a5207bdb20e3', u'4a283a2cf964a520fe941fe3', 1513),\n",
        " ('4ad0f592f964a5207bdb20e3', u'4b045053f964a520375322e3', 3990),\n",
        " ('4ad0f592f964a5207bdb20e3', u'4a6dcaaef964a520fed21fe3', 6798),\n",
        " ('4ad0f592f964a5207bdb20e3', u'4a7a36a9f964a520e1e81fe3', 4857),\n",
        " ('4bc3d2ec461576b052427f32', u'4a34225bf964a520c89b1fe3', 21098),\n",
        " ('4bc3d2ec461576b052427f32', u'4bd4c0426f649521760e6eec', 1737),\n",
        " ('4bc3d2ec461576b052427f32', u'4c311432ed37a59308db6903', 5111),\n",
        " ('4bc3d2ec461576b052427f32', u'4cc9b9320f7bef3bc7aa7fdd', 2540),\n",
        " ('4bc3d2ec461576b052427f32', u'43a457b4f964a520482c1fe3', 28858),\n",
        " ('4ae371ebf964a5202f9521e3', u'4c1f9a12b306c92885ad68b7', 2292),\n",
        " ('4b5e6f56f964a520998e29e3', u'4a33bce5f964a5203a9b1fe3', 8415),\n",
        " ('4b5e6f56f964a520998e29e3', u'4c65c9357abde21e49c46168', 2947),\n",
        " ('4b5e6f56f964a520998e29e3', u'49dfad1ef964a520f9601fe3', 24586),\n",
        " ('4b5e6f56f964a520998e29e3', u'4afdc6eef964a520c22a22e3', 11863),\n",
        " ('4d86751661676dcbcf9ba0e4', u'4a7f6580f964a520e4f31fe3', 2054),\n",
        " ('4d86751661676dcbcf9ba0e4', u'43d3901ef964a5201f2e1fe3', 18472),\n",
        " ('4a9709a0f964a5201b2820e3', u'43f703c9f964a520b52f1fe3', 9707),\n",
        " ('4a9709a0f964a5201b2820e3', u'49fa1dc1f964a520c16d1fe3', 17608),\n",
        " ('4a9709a0f964a5201b2820e3', u'42911d00f964a5200f241fe3', 8107),\n",
        " ('4b62645cf964a520a7452ae3', u'49f50f7af964a5208c6b1fe3', 31140),\n",
        " ('4b62645cf964a520a7452ae3', u'4bafcfc0f964a520bc223ce3', 637),\n",
        " ('4b62645cf964a520a7452ae3', u'45059d74f964a5200b391fe3', 17206),\n",
        " ('4b62645cf964a520a7452ae3', u'4d700dbcedca2c0fb7ff58ea', 7089),\n",
        " ('4b62645cf964a520a7452ae3', u'4a834e63f964a520b6fa1fe3', 4378),\n",
        " ('4a5f5853f964a5206bbf1fe3', u'4b29521cf964a520d99c24e3', 2347),\n",
        " ('4a5f5853f964a5206bbf1fe3', u'4a747b6af964a52044de1fe3', 1131),\n",
        " ('4a5f5853f964a5206bbf1fe3', u'4a7e134cf964a520dff01fe3', 386),\n",
        " ('4a5f5853f964a5206bbf1fe3', u'4b4fc498f964a520b11427e3', 400),\n",
        " ('4a5f5853f964a5206bbf1fe3', u'4e3b0e0fb0fb875af8421e80', 210),\n",
        " ('4b7332a7f964a52055a12de3', u'409d7480f964a520f2f21ee3', 71653),\n",
        " ('4b7332a7f964a52055a12de3', u'4c279d763703d13a5a4aa736', 42936),\n",
        " ('4b7332a7f964a52055a12de3', u'4ba7f1b9f964a520b8c039e3', 25231),\n",
        " ('4b7332a7f964a52055a12de3', u'49f5e047f964a520c86b1fe3', 4351),\n",
        " ('4b7332a7f964a52055a12de3', u'4bcbdc60511f95217b46b2c7', 1374),\n",
        " ('4bac73c6f964a520b9f53ae3', u'4bf0483a3a002d7fe5ed85a4', 2374),\n",
        " ('4bac73c6f964a520b9f53ae3', u'4b0ed506f964a520ae5b23e3', 4311),\n",
        " ('4bac73c6f964a520b9f53ae3', u'4a23661cf964a520d47d1fe3', 5444),\n",
        " ('4bac73c6f964a520b9f53ae3', u'468d6e84f964a52075481fe3', 7233),\n",
        " ('4fb5337d7beb65c72e11d762', u'4b85e394f964a520147831e3', 1097),\n",
        " ('4b91684cf964a520f6b933e3', u'4af78bc4f964a520cb0922e3', 8245),\n",
        " ('4bad090ff964a520e1263be3', u'455ad71cf964a520593d1fe3', 19921),\n",
        " ('4bad090ff964a520e1263be3', u'4f3d9affe4b03dd02358bb0a', 2916),\n",
        " ('4bb11513f964a520d8783ce3', u'4ecd6afa6da162f1bb1829b1', 1249),\n",
        " ('4bb11513f964a520d8783ce3', u'4a6ce9dbf964a5200cd21fe3', 2936),\n",
        " ('4bb11513f964a520d8783ce3', u'4b00e2c4f964a5206c4122e3', 3456),\n",
        " ('4bb11513f964a520d8783ce3', u'4a4f8b77f964a5206aaf1fe3', 5460),\n",
        " ('4c2130edaa8e9c74d72df4d4', u'442fe898f964a520e5311fe3', 39419),\n",
        " ...]"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print matched.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   Unnamed: 0                          name  \\\n",
        "0       33747            Consolidated Parts   \n",
        "1       19204             Serrano Electric.   \n",
        "2       38206           Art Beat Foundation   \n",
        "3       19221  Sherwin-Williams Paint Store   \n",
        "4       19184                   PostmanMojo   \n",
        "\n",
        "                                       id  postal_code       phone  \\\n",
        "0          consolidated-parts-santa-clara        95050  4087640200   \n",
        "1            serrano-electric-santa-clara        95054  4089861570   \n",
        "2             art-beat-foundation-oakland        94612  5107100034   \n",
        "3  sherwin-williams-paint-store-clayton-2        94517  9256735367   \n",
        "4                     postmanmojo-fremont        94538  5102302555   \n",
        "\n",
        "             four_square_id  \n",
        "0  4dd693a21fc7d8d8663d5523  \n",
        "1  502ac68fe4b0928ebe632d88  \n",
        "2  50f60b53f0e42ccaf5ae33c6  \n",
        "3  51432c4df0e4066e7ad3c951  \n",
        "4  526f2877498ee1d570588cd9  \n",
        "\n",
        "[5 rows x 6 columns]\n"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}