correlation.ipynb

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   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import hvplot.pandas\n",
    "import plotly.express as px\n",
    "from pathlib import Path\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8095b6",
   "metadata": {},
   "source": [
    "Is There a Correlation Between List Price of Homes and Inventory?"
   ]
  },
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   "execution_count": 38,
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   "source": [
    "#read in data for inventory\n",
    "path =r'C:/Users/kd_84/Working/Project 1/UMN_Project1/correlation_files'\n",
    "filenames = glob.glob(path + \"/*Inventory.csv\")\n",
    "\n",
    "inv_dfs = []\n",
    "for filename in filenames:\n",
    "    inv_dfs.append(pd.read_csv(filename))\n",
    "    \n",
    "#concat\n",
    "inventory = pd.concat(inv_dfs)\n",
    "inventory = inventory.sort_values(by='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0afbba71",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in data for prices\n",
    "path =r'C:/Users/kd_84/Working/Project 1/UMN_Project1/correlation_files'\n",
    "filenames = glob.glob(path + \"/*Price.csv\")\n",
    "\n",
    "price_dfs = []\n",
    "for filename in filenames:\n",
    "    price_dfs.append(pd.read_csv(filename))\n",
    "\n",
    "#concat\n",
    "prices = pd.concat(price_dfs)\n",
    "prices = prices.sort_values(by='date')\n",
    "prices = prices.rename(columns={'value':'Price'})"
   ]
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   "cell_type": "code",
   "execution_count": 40,
   "id": "f760d1d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot correlation between inventory and listing price of each metro area\n",
    "inventory_plot = inventory.hvplot.line(x='date', y='value', groupby='metro_area', width=800, dynamic=False, rot=75)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 41,
   "id": "02c39d18",
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_plot = prices.hvplot.line(x='date', y='Price', groupby='metro_area', width=800, dynamic=False, rot=75).opts(yformatter=\"%.0f\")"
   ]
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      ],
      "text/plain": [
       ":Layout\n",
       "   .HoloMap.I  :HoloMap   [metro_area]\n",
       "      :Curve   [date]   (value)\n",
       "   .HoloMap.II :HoloMap   [metro_area]\n",
       "      :Curve   [date]   (Price)"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "4241"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inventory_plot + prices_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a55e9d42",
   "metadata": {},
   "source": [
    "There is a correlation with inventory of homes and the listing price. In every metro area when inventory is low the listing prices are high as well as when inventory is high the listing prices lower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5c62bda",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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