create tables.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#start of artist table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "artist_csv = open('U:/Notebook/artist.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artistcolumns = ['id', 'name_last', 'name_first']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "artist_dwriter = csv.DictWriter(artist_csv, fieldnames = artistcolumns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "artist_dwriter.writeheader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "artist_loop = csv.DictReader(open('U:/Notebook/values.csv', 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "artist_dict_list = []\n",
    "i = 1\n",
    "for row in artist_loop:\n",
    "    artist_dict_list.append({'id': i, 'name_last': row['artist_last_name'], 'name_first': row['artist_first_name']})\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artist_dwriter.writerows(artist_dict_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artist_csv.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artist_table = open('U:/Notebook/artist.csv', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "artist_dreader = csv.DictReader(artist_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name_first': 'George', 'id': '1', 'name_last': 'Tsutakawa'}\n",
      "{'name_first': 'Doris', 'id': '2', 'name_last': 'Chase'}\n",
      "{'name_first': 'Mark', 'id': '3', 'name_last': 'Lere'}\n",
      "{'name_first': 'Kurt', 'id': '4', 'name_last': 'Kiefer'}\n",
      "{'name_first': 'Edgar ', 'id': '5', 'name_last': 'Hachivi'}\n",
      "{'name_first': 'Val', 'id': '6', 'name_last': 'Laigo'}\n",
      "{'name_first': 'Bartoldi', 'id': '7', 'name_last': 'After'}\n",
      "{'name_first': 'Mandy', 'id': '8', 'name_last': 'Greer'}\n",
      "{'name_first': 'Gary', 'id': '9', 'name_last': 'Hill'}\n",
      "{'name_first': 'James', 'id': '10', 'name_last': 'Schoppert'}\n"
     ]
    }
   ],
   "source": [
    "for i in range (0, 10):\n",
    "    print artist_dreader.next()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artist_table.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#End of art table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Start of Location table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "location_csv = open('U:/Notebook/location.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "locationcolumns = ['id', 'location', 'address', 'latitude', 'longitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "location_dwriter = csv.DictWriter(location_csv, fieldnames = locationcolumns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "location_dwriter.writeheader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "location_loop = csv.DictReader(open('U:/Notebook/Public_Art_Data.csv', 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "location_dict_list = []\n",
    "i = 1\n",
    "for row in location_loop:\n",
    "    location_dict_list.append({'id': i , 'location': row['location'], 'address': row['address'], 'latitude': row['latitude'], 'longitude': row['longitude']})\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "location_dwriter.writerows(location_dict_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "location_csv.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "location_table = open('U:/Notebook/location.csv', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "location_dreader = csv.DictReader(location_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'latitude': '47.6065', 'address': '1000 4th Ave', 'id': '1', 'longitude': '-122.33312', 'location': 'Central Library, Spring St Plaza'}\n",
      "{'latitude': '47.620033', 'address': '305 Harrison St', 'id': '2', 'longitude': '-122.348917', 'location': 'Space Needle Sculpture Garden'}\n",
      "{'latitude': '47.632467', 'address': ' 23rd Ave W & Gilman Ave W', 'id': '3', 'longitude': '-122.37565', 'location': 'West Gilman Bridge'}\n",
      "{'latitude': '47.601833', 'address': '2300 E Yesler St', 'id': '4', 'longitude': '-122.301767', 'location': 'Douglas Truth Branch Library '}\n",
      "{'latitude': '47.61805', 'address': '2nd Ave & Denny Wy', 'id': '5', 'longitude': '-122.35205', 'location': '2nd Ave & Denny Wy'}\n",
      "{'latitude': '47.615617', 'address': '4th Ave & Bell St', 'id': '6', 'longitude': '-122.344583', 'location': 'Belltown'}\n",
      "{'latitude': '47.601983', 'address': '1st Ave & Yesler Wy', 'id': '7', 'longitude': '-122.33395', 'location': 'Pioneer Square'}\n",
      "{'latitude': '47.59205', 'address': '1008 12th Ave S', 'id': '8', 'longitude': '-122.317417', 'location': 'Dr. Jose P. Rizal Park'}\n",
      "{'latitude': '47.579383', 'address': '61st Ave SW & Alki Ave SW', 'id': '9', 'longitude': '-122.410633', 'location': 'Alki Beach'}\n",
      "{'latitude': '47.6065', 'address': '1000 4th Ave', 'id': '10', 'longitude': '-122.333117', 'location': \"Central Library, Faye G. Allen Children's Center\"}\n"
     ]
    }
   ],
   "source": [
    "for i in range (0, 10):\n",
    "    print location_dreader.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "location_table.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#End location table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#start class table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class_csv = open('U:/Notebook/classification.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classcolumns = ['id', 'classification']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class_dwriter = csv.DictWriter(class_csv, fieldnames = classcolumns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class_dwriter.writeheader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_loop = csv.DictReader(open('U:/Notebook/Public_Art_Data_New_1.csv', 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_dict_list = []\n",
    "i = 1\n",
    "for row in class_loop:\n",
    "    class_dict_list.append({'id': i, 'classification': row['classification'], })\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_dwriter.writerows(class_dict_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_csv.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_table = open('U:/Notebook/classification.csv', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class_reader = csv.DictReader(class_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class_dup_list = []\n",
    "for row in class_reader:\n",
    "    if row['classification'] not in class_dup_list:\n",
    "        class_dup_list.append(row['classification'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sculpture', 'Structures', 'IN PUBLIC: SEATTLE 1991', '2-Dimensional', 'VIDEO Installation', '', 'Bronze', 'Environments', 'Mixed Media', 'Installation', 'DRAWINGS', 'Sculptures', 'Print/PHOTO TRANSFER', 'Photographs', 'Structures/Sculpture', 'VIDEO', 'Infrastructure', 'Paintings', 'Integrated light Installation', 'Sculpture/FOUNTAIN', 'Structure', 'MOSAIC']\n"
     ]
    }
   ],
   "source": [
    "print class_dup_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class_table.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_table = open('U:/Notebook/classification.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_writer = csv.DictWriter(class_table, fieldnames = classcolumns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_writer.writeheader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_list = []\n",
    "i = 1\n",
    "for thing in class_dup_list:\n",
    "    class_list.append({'id':i, 'classification': thing})\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_writer.writerows(class_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_table.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classification = open('U:/Notebook/classification.csv', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class_reader = csv.DictReader(classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id': '1', 'classification': 'Sculpture'}\n",
      "{'id': '2', 'classification': 'Structures'}\n",
      "{'id': '3', 'classification': 'IN PUBLIC: SEATTLE 1991'}\n",
      "{'id': '4', 'classification': '2-Dimensional'}\n",
      "{'id': '5', 'classification': 'VIDEO Installation'}\n",
      "{'id': '6', 'classification': ''}\n",
      "{'id': '7', 'classification': 'Bronze'}\n",
      "{'id': '8', 'classification': 'Environments'}\n",
      "{'id': '9', 'classification': 'Mixed Media'}\n",
      "{'id': '10', 'classification': 'Installation'}\n",
      "{'id': '11', 'classification': 'DRAWINGS'}\n",
      "{'id': '12', 'classification': 'Sculptures'}\n",
      "{'id': '13', 'classification': 'Print/PHOTO TRANSFER'}\n",
      "{'id': '14', 'classification': 'Photographs'}\n",
      "{'id': '15', 'classification': 'Structures/Sculpture'}\n",
      "{'id': '16', 'classification': 'VIDEO'}\n",
      "{'id': '17', 'classification': 'Infrastructure'}\n",
      "{'id': '18', 'classification': 'Paintings'}\n",
      "{'id': '19', 'classification': 'Integrated light Installation'}\n",
      "{'id': '20', 'classification': 'Sculpture/FOUNTAIN'}\n",
      "{'id': '21', 'classification': 'Structure'}\n",
      "{'id': '22', 'classification': 'MOSAIC'}\n"
     ]
    }
   ],
   "source": [
    "for row in class_reader:\n",
    "    print row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classification.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# end class table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# start material table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "material_csv = open('U:/Notebook/material.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "materialcolumns = ['id', 'type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "material_dwriter = csv.DictWriter(material_csv, fieldnames = materialcolumns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "material_dwriter.writeheader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "material_loop = csv.DictReader(open('U:/Notebook/values.csv', 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "material_dict_list = []\n",
    "i = 1\n",
    "for row in material_loop:\n",
    "    material_dict_list.append({'id': i, 'type': row['Media'], })\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "material_dwriter.writerows(material_dict_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "material_csv.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "material_table = open('U:/Notebook/material.csv', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "material_dreader = csv.DictReader(material_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'type': '1/2 stainless steel screed', 'id': '1'}\n",
      "{'type': '2 projection lamps with steel gobos, timer and light sensor switch', 'id': '2'}\n",
      "{'type': 'Acrylic', 'id': '3'}\n",
      "{'type': 'Acrylic enamel on concrete', 'id': '4'}\n",
      "{'type': 'Acrylic, collage and varnish on recycled tin trays', 'id': '5'}\n",
      "{'type': 'Acrylic/plexiglas', 'id': '6'}\n",
      "{'type': 'African black granite', 'id': '7'}\n",
      "{'type': 'Aggregate', 'id': '8'}\n",
      "{'type': 'Aluminum ', 'id': '9'}\n",
      "{'type': 'Aluminum deck plating', 'id': '10'}\n"
     ]
    }
   ],
   "source": [
    "for i in range (0, 10):\n",
    "    print material_dreader.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "material_table.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#end material table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#start art table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_csv = open('U:/Notebook/art.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artcolumns = ['id', 'title', 'description', 'location_id', 'measurements', 'date', 'project']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_dwriter = csv.DictWriter(art_csv, fieldnames = artcolumns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "art_dwriter.writeheader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_loop = csv.DictReader(open('U:/Notebook/Public_Art_Data.csv', 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "art_dict_list = []\n",
    "for row in art_loop:\n",
    "    art_dict_list.append({'id': row['sac_id'], 'title': row['title'], 'description':row['description'] , 'location_id':'' , 'measurements':row['measurements'], 'date':row['date'], 'project':row['project'] })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "art_dwriter.writerows(art_dict_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_csv.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_table = open('U:/Notebook/art.csv', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_dreader = csv.DictReader(art_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'description': \"''\", 'title': 'Fountain of Wisdom', 'measurements': '', 'project': 'CENTRAL LIBRARY', 'date': '1957-60', 'location_id': '', 'id': 'ESD00.074.02'}\n",
      "{'description': \"''\", 'title': 'Moon Gates', 'measurements': '', 'project': 'SEATTLE CENTER SCULPTURE GARDEN', 'date': '6/23/99', 'location_id': '', 'id': 'CL77.025'}\n",
      "{'description': \"''\", 'title': 'Seattle Scatter Piece', 'measurements': '', 'project': 'WEST GILMAN BRIDGE', 'date': '1991', 'location_id': '', 'id': 'CL99.053'}\n",
      "{'description': \"''\", 'title': 'Renouveau', 'measurements': '', 'project': 'DOUGLAS-TRUTH BRANCH LIBRARY', 'date': '1979', 'location_id': '', 'id': 'PR99.077'}\n",
      "{'description': \"''\", 'title': 'Second Avenue Additions', 'measurements': '', 'project': 'SECOND AVENUE PROJECT', 'date': '2/15/00', 'location_id': '', 'id': 'CL77.026'}\n",
      "{'description': \"''\", 'title': 'Untitled (Kiosk)', 'measurements': '108x45x22\"', 'project': 'SEATTLE ARTISTS 1990', 'date': 'MARCH- JUNE 1994', 'location_id': '', 'id': 'CL77.023'}\n",
      "{'description': \"'Lutshootseed and English texts painted on panels  installed on either side of a historical bust of Chief Sealth dedicated to the many transient intertribal native people who live on the streets of Seattle.'\", 'title': 'Day/Night', 'measurements': '42\" X 84\" EACH', 'project': 'IN PUBLIC: SEATTLE 1991', 'date': 'JUNE 1991-JANUARY 1992', 'location_id': '', 'id': 'CL91.015a,b'}\n",
      "{'description': \"'Wall mural of ceramic and glass tile depicting Filipino history.'\", 'title': 'East is West', 'measurements': '12\\'x14\\'7x3\\'2.5\"', 'project': 'JOSE RIZAL PARK', 'date': 'FEBRUARY 1978- JUNE 1981', 'location_id': '', 'id': 'PR81.014a-c'}\n",
      "{'description': \"'Replica of the Statue of Liberty'\", 'title': 'Little Liberty', 'measurements': '6\\'10\"x3\\'6\"x2\\'11\"', 'project': 'ALKI BEACH', 'date': 'ca. 1952', 'location_id': '', 'id': 'GCK74.074'}\n",
      "{'description': \"'The three artworks for the Faye G. Allen Children's Center are based on a series of folk tale themes found in the folklore section of the Central Library.  '\", 'title': 'Babe, The Phoenix Fairy, and The Magic Grove', 'measurements': \"Babe - .01: 4'h x 8'w x 3.5'd; Phoenix Fairy - .02:  5'h x 6.5'w x 4'd; Magic Grove - .03  4'h x 8'w x 10'd\", 'project': 'LIBRARIES FOR ALL - CENTRAL LIBRARY/LIBRARY UNBOUND', 'date': '2005. 2005, 2006', 'location_id': '', 'id': 'LIB06.011.01-.03'}\n"
     ]
    }
   ],
   "source": [
    "for i in range (0, 10):\n",
    "    print art_dreader.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_table.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#end art table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#start artist_art"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artist_art_csv = open('U:/Notebook/artist_art.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artist_art_columns = ['art_id', 'artist_id' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "artist_art_dwriter = csv.DictWriter(artist_art_csv, fieldnames = artist_art_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artist_art_dwriter.writeheader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artist_art_dwriter.writerow({'art_id': 1 , 'artist_id': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "artist_art_csv.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#end art table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#start art_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_class_csv = open('U:/Notebook/art_class.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_class_columns = ['art_id', 'class_id' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_class_dwriter = csv.DictWriter(art_class_csv, fieldnames = art_class_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "art_class_dwriter.writeheader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "art_class_dwriter.writerow({'art_id': 1 , 'class_id': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_class_csv.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#end art_class table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#start art_material table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_material_csv = open('U:/Notebook/art_material.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_material_columns = ['art_id', 'material_id' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_material_dwriter = csv.DictWriter(art_material_csv, fieldnames = art_material_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_material_dwriter.writeheader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "art_material_dwriter.writerow({'art_id': 1 , 'material_id': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "art_material_csv.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#end art_material table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}