Rental Market.ipynb

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   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standard imports\n",
    "import quandl\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Plotly for better graphs\n",
    "import plotly.offline as plotly\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "plotly.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read Data from Indicators CSV\n",
    "indicators = pd.read_csv('indicators.csv', sep=\"|\")\n",
    "indicators.set_index('CODE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "# Set your Quandl API Key\n",
    "quandl.ApiConfig.api_key = {YOUR API KEY}\n",
    "zip_code = 94608\n",
    "start_date = '2014-01-01'\n",
    "end_date = '2018-07-01'\n",
    "code_prefix = f'ZILLOW/Z{zip_code}_'\n",
    "\n",
    "# Enter in the cities you want to see data for\n",
    "cities = ['Berkeley, CA','Oakland, CA', 'Emeryville, CA', 'Dublin, CA', 'San Ramon, CA', 'Fremont, CA']\n",
    "\n",
    "codes = dict(MSPAH=indicators.loc['MSPAH']['INDICATOR'],\n",
    "             MLPAH=indicators.loc['MLPAH']['INDICATOR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "areas_city = pd.read_csv('areas_city.csv', sep='|')\n",
    "areas_city.index = areas_city['AREA']\n",
    "areas_city['CODE'] = 'C' + areas_city['CODE'].astype(str)\n",
    "cities_dict = areas_city.loc[cities]['CODE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Translate DataFrame into Plotly Scatter Plot\n",
    "def plot_df(dataframe, title): \n",
    "    data = [go.Scatter(x=dataframe.index,\n",
    "                                  y=dataframe[column],\n",
    "                                  name=column) for column in dataframe.columns]\n",
    "    layout = dict(title=title)\n",
    "    fig = dict(data=data, layout=layout)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Multiple Indicators for One Location\n",
    "def get_indicator_data(location, indicators):\n",
    "    df_array = []\n",
    "    for code, name in indicators.items():\n",
    "        data = quandl.get(f'ZILLOW/{location}_{code}', start_date=start_date, end_date=end_date)\n",
    "        data.rename(index=str, columns={'Value': name}, inplace=True)\n",
    "        data.index = pd.to_datetime(data.index, format='%Y/%m/%d')\n",
    "        df_array.append(data)\n",
    "    ret = pd.concat(df_array, axis=1)\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get One Indicator for Multiple Cities\n",
    "def get_city_data(cities, indicator):\n",
    "    df_array = []\n",
    "    for city, code in cities.items():\n",
    "        quandl_db_code = f'ZILLOW/{code}_{indicator}'\n",
    "        try:\n",
    "            data = quandl.get(quandl_db_code, start_date=start_date, end_date=end_date)\n",
    "            data.rename(index=str, columns={'Value': city}, inplace=True)\n",
    "            data.index = pd.to_datetime(data.index, format='%Y/%m/%d')\n",
    "            df_array.append(data)\n",
    "        except:\n",
    "            print('Could not fetch for:', city)\n",
    "    ret = pd.concat(df_array, axis=1)\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = get_city_data(cities_dict, 'MRP2B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_df(dataset, 'Median Rental Price 2 Bedroom')\n",
    "plotly.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotly.plot(fig, include_plotlyjs=False, output_type='div')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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\"2017-05-31\", \"2017-06-30\", \"2017-07-31\", \"2017-08-31\", \"2017-09-30\", \"2017-10-31\", \"2017-11-30\", \"2017-12-31\", \"2018-01-31\", \"2018-02-28\", \"2018-03-31\", \"2018-04-30\", \"2018-05-31\", \"2018-06-30\"], \"y\": [2395.0, 2325.0, 2395.0, 2475.0, 2500.0, 2495.0, 2600.0, 2600.0, 2687.5, 2600.0, 2600.0, 2700.0, 2475.0, 2525.0, 2595.0, 2750.0, 2825.0, 2950.0, 2995.0, 3000.0, 3000.0, 2999.5, 3000.0, 2900.0, 2895.0, 2977.5, 2900.0, 2875.0, 2995.0, 3100.0, 3100.0, 2990.0, 3100.0, 2999.5, 2950.0, 2950.0, 2995.0, 3100.0, 3050.0, 3150.0, 3200.0, 3200.0, 3200.0, 3200.0, 3195.0, 3100.0, 3000.0, 3050.0, 3000.0, 3200.0, 3200.0, 3300.0, 3395.0, 3400.0], \"type\": \"scatter\", \"uid\": \"febbf610-99e2-11e8-8b76-0242ac130002\"}], {\"title\": \"Median Rental Price All Homes\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = get_city_data(cities_dict, 'MRPAH')\n",
    "fig = plot_df(dataset, 'Median Rental Price All Homes')\n",
    "plotly.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotly.plot(fig, include_plotlyjs=False, output_type='div')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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{\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = get_city_data(cities_dict, 'MRPFAH')\n",
    "fig = plot_df(dataset, 'Median Rental $/SqFt All Homes')\n",
    "plotly.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotly.plot(fig, include_plotlyjs=False, output_type='div')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = get_city_data(cities_dict, 'MRPF2B')\n",
    "fig = plot_df(dataset, 'Median Rental $/SqFt 2 Bedroom')\n",
    "plotly.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotly.plot(fig, include_plotlyjs=False, output_type='div')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = get_city_data(cities_dict, 'PRRAH')\n",
    "fig = plot_df(dataset, 'Price to Rent Ratio All Homes')\n",
    "plotly.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotly.plot(fig, include_plotlyjs=False, output_type='div')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = get_city_data(cities_dict, 'ZRIAH')\n",
    "fig = plot_df(dataset, 'Zillow Rental Index All Homes')\n",
    "plotly.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotly.plot(fig, include_plotlyjs=False, output_type='div')"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
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