Denver's Real Estate Market Final Version .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vacancy_Rates19 = 'Quarterly Rental Vacancy Rates 1900s.csv'\n",
    "Vacancy_Rates20 = 'Quarterly Rental Vacancy Rates 2000s.csv'\n",
    "Interest_Rates = 'fed funds rate historical data.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>Vacancy Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/56</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/1/56</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7/1/56</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10/1/56</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/57</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DATE  Vacancy Rate\n",
       "0   1/1/56           6.2\n",
       "1   4/1/56           5.9\n",
       "2   7/1/56           6.3\n",
       "3  10/1/56           5.8\n",
       "4   1/1/57           5.3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vacancy19 = pd.read_csv(Vacancy_Rates19)\n",
    "vacancy19.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE             object\n",
       "Vacancy Rate    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vacancy19.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>Vacancy Rate</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/56</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1956</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/1/56</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7/1/56</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10/1/56</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/57</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DATE  Vacancy Rate  year\n",
       "0   1/1/56           6.2  1956\n",
       "1   4/1/56           5.9  1956\n",
       "2   7/1/56           6.3  1956\n",
       "3  10/1/56           5.8  1956\n",
       "4   1/1/57           5.3  1957"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = []\n",
    "for date in vacancy19['DATE']:\n",
    "    split_date = date.split('/')\n",
    "    year = '19' + str(split_date[2])\n",
    "    years.append(int(year))\n",
    "\n",
    "vacancy19['year'] = years\n",
    "vacancy19.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>Vacancy Rate</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/00</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/1/00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7/1/00</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10/1/00</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/01</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      DATE  Vacancy Rate  year\n",
       "0   1/1/00           7.9  2000\n",
       "1   4/1/00           8.0  2000\n",
       "2   7/1/00           8.2  2000\n",
       "3  10/1/00           7.8  2000\n",
       "4   1/1/01           8.2  2001"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "vacancy20 = pd.read_csv(Vacancy_Rates20)\n",
    "\n",
    "years = []\n",
    "for date in vacancy20['DATE']:\n",
    "    split_date = date.split('/')\n",
    "    year = '20' + str(split_date[2])\n",
    "    years.append(int(year))\n",
    "\n",
    "vacancy20['year'] = years\n",
    "vacancy20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>Vacancy Rate</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/56</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/1/56</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1956</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7/1/56</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10/1/56</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/57</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1957</td>\n",
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      ],
      "text/plain": [
       "      DATE  Vacancy Rate  year\n",
       "0   1/1/56           6.2  1956\n",
       "1   4/1/56           5.9  1956\n",
       "2   7/1/56           6.3  1956\n",
       "3  10/1/56           5.8  1956\n",
       "4   1/1/57           5.3  1957"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "merged_vacancy = pd.concat([vacancy19,vacancy20])\n",
    "merged_vacancy.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>Vacancy Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1956</td>\n",
       "      <td>6.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1957</td>\n",
       "      <td>5.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>6.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959</td>\n",
       "      <td>7.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960</td>\n",
       "      <td>8.175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   year Vacancy Rate\n",
       "                mean\n",
       "0  1956        6.050\n",
       "1  1957        5.525\n",
       "2  1958        6.425\n",
       "3  1959        7.075\n",
       "4  1960        8.175"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Store the Average Vacancy Rate Data Grouped by Year\n",
    "#avg_yearly_vacancy = merged_vacancy.groupby('Year')\n",
    "#df.resample('D').mean()\n",
    "#avg_yearly_vacancy.resample('A').mean()\n",
    "\n",
    "avg_yearly_vacancy = merged_vacancy.groupby('year').agg({'Vacancy Rate': ['mean']})\n",
    "\n",
    "\n",
    "# Convert to DataFrame\n",
    "avg_yearly_vacancy = pd.DataFrame(avg_yearly_vacancy)\n",
    "\n",
    "\n",
    "# Flatten the column headings\n",
    "avg_yearly_vacancy = avg_yearly_vacancy.reset_index()\n",
    "\n",
    "# Preview DataFrame\n",
    "avg_yearly_vacancy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the Average Yearly Vacancy rate (line graph) \n",
    "\n",
    "plt.plot(avg_yearly_vacancy[\"year\"], avg_yearly_vacancy[\"Vacancy Rate\"], color=\"m\", linestyle=\"-\", linewidth=1.50)\n",
    "\n",
    "plt.title(\"Average Federal Vacancy Rate\")\n",
    "plt.ylabel(\"Vacancy Rate (%)\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.grid(axis='y')\n",
    "plt.legend(['Average Vacancy'],loc=\"best\", fontsize=\"small\", fancybox=True)\n",
    "\n",
    "# Save the Figure\n",
    "plt.savefig(\"vacancy.png\")\n",
    "\n",
    "# Show the Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>interest rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7/1/54</td>\n",
       "      <td>1954</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7/2/54</td>\n",
       "      <td>1954</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7/3/54</td>\n",
       "      <td>1954</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7/4/54</td>\n",
       "      <td>1954</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7/5/54</td>\n",
       "      <td>1954</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     date  year  interest rate\n",
       "0  7/1/54  1954           1.13\n",
       "1  7/2/54  1954           1.25\n",
       "2  7/3/54  1954           1.25\n",
       "3  7/4/54  1954           1.25\n",
       "4  7/5/54  1954           0.88"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interest = pd.read_csv(Interest_Rates)\n",
    "interest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>interest rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1954</td>\n",
       "      <td>1.006066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1955</td>\n",
       "      <td>1.785699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1956</td>\n",
       "      <td>2.728224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1957</td>\n",
       "      <td>3.105342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>1.574055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year interest rate\n",
       "                 mean\n",
       "0  1954      1.006066\n",
       "1  1955      1.785699\n",
       "2  1956      2.728224\n",
       "3  1957      3.105342\n",
       "4  1958      1.574055"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Store the Average Interest Rate Grouped by Year \n",
    "avg_yearly_interest = interest.groupby('year').agg({'interest rate': ['mean']})\n",
    "\n",
    "# Convert to DataFrame\n",
    "avg_yearly_interest = pd.DataFrame(avg_yearly_interest)\n",
    "\n",
    "# Flatten the column headings\n",
    "avg_yearly_interest = avg_yearly_interest.reset_index()\n",
    "\n",
    "# Preview DataFrame\n",
    "avg_yearly_interest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the Average Yearly Interest rate (line graph) \n",
    "\n",
    "plt.plot(avg_yearly_interest[\"year\"], avg_yearly_interest[\"interest rate\"], color=\"c\", linestyle=\"-\", linewidth=1.50)\n",
    "\n",
    "plt.title(\"Average Federal Interest Rate\")\n",
    "plt.ylabel(\"Interest Rate (%)\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.grid(axis='y')\n",
    "plt.legend(['Average Interest'],loc=\"best\", fontsize=\"small\", fancybox=True)\n",
    "\n",
    "# Save the Figure\n",
    "plt.savefig(\"interest.png\")\n",
    "\n",
    "# Show the Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"dataset\": {\n",
      "        \"collapse\": null,\n",
      "        \"column_index\": null,\n",
      "        \"column_names\": [\n",
      "            \"Date\",\n",
      "            \"Value\"\n",
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      "                335000.0\n",
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      "                329000.0\n",
      "            ],\n",
      "            [\n",
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      "                322400.0\n",
      "            ],\n",
      "            [\n",
      "                \"2014-01-31\",\n",
      "                310000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-12-31\",\n",
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      "            ],\n",
      "            [\n",
      "                \"2013-11-30\",\n",
      "                300000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-10-31\",\n",
      "                300000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-09-30\",\n",
      "                300000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-08-31\",\n",
      "                299000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-07-31\",\n",
      "                299900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-06-30\",\n",
      "                292700.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-05-31\",\n",
      "                289900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-04-30\",\n",
      "                280000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-03-31\",\n",
      "                274900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-02-28\",\n",
      "                269725.0\n",
      "            ],\n",
      "            [\n",
      "                \"2013-01-31\",\n",
      "                260000.0\n",
      "            ],\n",
      "            [\n",
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      "                260000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-11-30\",\n",
      "                259950.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-10-31\",\n",
      "                260000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-09-30\",\n",
      "                260000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-08-31\",\n",
      "                260000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-07-31\",\n",
      "                263500.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-06-30\",\n",
      "                265000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-05-31\",\n",
      "                262800.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-04-30\",\n",
      "                258000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-03-31\",\n",
      "                251950.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-02-29\",\n",
      "                245000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2012-01-31\",\n",
      "                239999.5\n",
      "            ],\n",
      "            [\n",
      "                \"2011-12-31\",\n",
      "                240000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-11-30\",\n",
      "                245000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-10-31\",\n",
      "                249000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-09-30\",\n",
      "                249900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-08-31\",\n",
      "                249900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-07-31\",\n",
      "                249900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-06-30\",\n",
      "                249900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-05-31\",\n",
      "                249900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-04-30\",\n",
      "                240000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-03-31\",\n",
      "                239900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-02-28\",\n",
      "                239000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2011-01-31\",\n",
      "                235000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-12-31\",\n",
      "                238900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-11-30\",\n",
      "                249900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-10-31\",\n",
      "                250000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-09-30\",\n",
      "                254900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-08-31\",\n",
      "                260000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-07-31\",\n",
      "                256500.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-06-30\",\n",
      "                259900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-05-31\",\n",
      "                259900.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-04-30\",\n",
      "                255000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-03-31\",\n",
      "                255000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-02-28\",\n",
      "                259000.0\n",
      "            ],\n",
      "            [\n",
      "                \"2010-01-31\",\n",
      "                259900.0\n",
      "            ]\n",
      "        ],\n",
      "        \"database_code\": \"ZILLOW\",\n",
      "        \"database_id\": 15274,\n",
      "        \"dataset_code\": \"M20_MLPAH\",\n",
      "        \"description\": \"The Zillow Home Value Index is Zillow's estimate of the median market value of median listing price - all homes within the metro of Denver, CO. This data is calculated by Zillow Real Estate Research (www.zillow.com/research) using their database of 110 million homes.\",\n",
      "        \"end_date\": \"2019-09-30\",\n",
      "        \"frequency\": \"monthly\",\n",
      "        \"id\": 41537773,\n",
      "        \"limit\": null,\n",
      "        \"name\": \"Zillow Home Value Index (Metro): Median Listing Price - All Homes - Denver, CO\",\n",
      "        \"newest_available_date\": \"2019-09-30\",\n",
      "        \"oldest_available_date\": \"2010-01-31\",\n",
      "        \"order\": null,\n",
      "        \"premium\": false,\n",
      "        \"refreshed_at\": \"2019-10-23T05:37:13.979Z\",\n",
      "        \"start_date\": \"2010-01-31\",\n",
      "        \"transform\": null,\n",
      "        \"type\": \"Time Series\"\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Quandl API \n",
    "import requests\n",
    "import json\n",
    "\n",
    "URL = \"https://www.quandl.com/api/v3/datasets/ZILLOW/M20_MLPAH.json?api_key=Y_qydkhKhvyzFTQ3Cit-\"\n",
    "\n",
    "denver_data = requests.get(URL).json()\n",
    "print(json.dumps(denver_data, indent=4, sort_keys=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([['Date', 'Value']],\n",
       " [[['2019-09-30', 442000.0],\n",
       "   ['2019-08-31', 445000.0],\n",
       "   ['2019-07-31', 450000.0],\n",
       "   ['2019-06-30', 450000.0],\n",
       "   ['2019-05-31', 455000.0],\n",
       "   ['2019-04-30', 450990.0],\n",
       "   ['2019-03-31', 450000.0],\n",
       "   ['2019-02-28', 446900.0],\n",
       "   ['2019-01-31', 444925.0],\n",
       "   ['2018-12-31', 439900.0],\n",
       "   ['2018-11-30', 439000.0],\n",
       "   ['2018-10-31', 439900.0],\n",
       "   ['2018-09-30', 439950.0],\n",
       "   ['2018-08-31', 439900.0],\n",
       "   ['2018-07-31', 445000.0],\n",
       "   ['2018-06-30', 449500.0],\n",
       "   ['2018-05-31', 450000.0],\n",
       "   ['2018-04-30', 450000.0],\n",
       "   ['2018-03-31', 450000.0],\n",
       "   ['2018-02-28', 449900.0],\n",
       "   ['2018-01-31', 439900.0],\n",
       "   ['2017-12-31', 434945.0],\n",
       "   ['2017-11-30', 425000.0],\n",
       "   ['2017-10-31', 437695.0],\n",
       "   ['2017-09-30', 442900.0],\n",
       "   ['2017-08-31', 440000.0],\n",
       "   ['2017-07-31', 440000.0],\n",
       "   ['2017-06-30', 439900.0],\n",
       "   ['2017-05-31', 439600.0],\n",
       "   ['2017-04-30', 449000.0],\n",
       "   ['2017-03-31', 440000.0],\n",
       "   ['2017-02-28', 428000.0],\n",
       "   ['2017-01-31', 419000.0],\n",
       "   ['2016-12-31', 415000.0],\n",
       "   ['2016-11-30', 410000.0],\n",
       "   ['2016-10-31', 410000.0],\n",
       "   ['2016-09-30', 415000.0],\n",
       "   ['2016-08-31', 410995.0],\n",
       "   ['2016-07-31', 415450.0],\n",
       "   ['2016-06-30', 419950.0],\n",
       "   ['2016-05-31', 420105.0],\n",
       "   ['2016-04-30', 424900.0],\n",
       "   ['2016-03-31', 419900.0],\n",
       "   ['2016-02-29', 414900.0],\n",
       "   ['2016-01-31', 414400.0],\n",
       "   ['2015-12-31', 399950.0],\n",
       "   ['2015-11-30', 395000.0],\n",
       "   ['2015-10-31', 395000.0],\n",
       "   ['2015-09-30', 395000.0],\n",
       "   ['2015-08-31', 389900.0],\n",
       "   ['2015-07-31', 391407.5],\n",
       "   ['2015-06-30', 389900.0],\n",
       "   ['2015-05-31', 390000.0],\n",
       "   ['2015-04-30', 389950.0],\n",
       "   ['2015-03-31', 380950.0],\n",
       "   ['2015-02-28', 379950.0],\n",
       "   ['2015-01-31', 380000.0],\n",
       "   ['2014-12-31', 372995.0],\n",
       "   ['2014-11-30', 359999.0],\n",
       "   ['2014-10-31', 355950.0],\n",
       "   ['2014-09-30', 350000.0],\n",
       "   ['2014-08-31', 345950.0],\n",
       "   ['2014-07-31', 345000.0],\n",
       "   ['2014-06-30', 345950.0],\n",
       "   ['2014-05-31', 339000.0],\n",
       "   ['2014-04-30', 335000.0],\n",
       "   ['2014-03-31', 329000.0],\n",
       "   ['2014-02-28', 322400.0],\n",
       "   ['2014-01-31', 310000.0],\n",
       "   ['2013-12-31', 309900.0],\n",
       "   ['2013-11-30', 300000.0],\n",
       "   ['2013-10-31', 300000.0],\n",
       "   ['2013-09-30', 300000.0],\n",
       "   ['2013-08-31', 299000.0],\n",
       "   ['2013-07-31', 299900.0],\n",
       "   ['2013-06-30', 292700.0],\n",
       "   ['2013-05-31', 289900.0],\n",
       "   ['2013-04-30', 280000.0],\n",
       "   ['2013-03-31', 274900.0],\n",
       "   ['2013-02-28', 269725.0],\n",
       "   ['2013-01-31', 260000.0],\n",
       "   ['2012-12-31', 260000.0],\n",
       "   ['2012-11-30', 259950.0],\n",
       "   ['2012-10-31', 260000.0],\n",
       "   ['2012-09-30', 260000.0],\n",
       "   ['2012-08-31', 260000.0],\n",
       "   ['2012-07-31', 263500.0],\n",
       "   ['2012-06-30', 265000.0],\n",
       "   ['2012-05-31', 262800.0],\n",
       "   ['2012-04-30', 258000.0],\n",
       "   ['2012-03-31', 251950.0],\n",
       "   ['2012-02-29', 245000.0],\n",
       "   ['2012-01-31', 239999.5],\n",
       "   ['2011-12-31', 240000.0],\n",
       "   ['2011-11-30', 245000.0],\n",
       "   ['2011-10-31', 249000.0],\n",
       "   ['2011-09-30', 249900.0],\n",
       "   ['2011-08-31', 249900.0],\n",
       "   ['2011-07-31', 249900.0],\n",
       "   ['2011-06-30', 249900.0],\n",
       "   ['2011-05-31', 249900.0],\n",
       "   ['2011-04-30', 240000.0],\n",
       "   ['2011-03-31', 239900.0],\n",
       "   ['2011-02-28', 239000.0],\n",
       "   ['2011-01-31', 235000.0],\n",
       "   ['2010-12-31', 238900.0],\n",
       "   ['2010-11-30', 249900.0],\n",
       "   ['2010-10-31', 250000.0],\n",
       "   ['2010-09-30', 254900.0],\n",
       "   ['2010-08-31', 260000.0],\n",
       "   ['2010-07-31', 256500.0],\n",
       "   ['2010-06-30', 259900.0],\n",
       "   ['2010-05-31', 259900.0],\n",
       "   ['2010-04-30', 255000.0],\n",
       "   ['2010-03-31', 255000.0],\n",
       "   ['2010-02-28', 259000.0],\n",
       "   ['2010-01-31', 259900.0]]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_value = denver_data[\"dataset\"][\"column_names\"]\n",
    "date_value = denver_data[\"dataset\"][\"data\"]\n",
    "\n",
    "city_data = ([column_value], [date_value])\n",
    "city_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>445000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>450000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>450000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>455000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>450000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date     Value\n",
       "0  2019-08-31  445000.0\n",
       "1  2019-07-31  450000.0\n",
       "2  2019-06-30  450000.0\n",
       "3  2019-05-31  455000.0\n",
       "4  2019-04-30  450000.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = ([['Date', 'Value']],\n",
    " [[['2019-08-31', 445000.0],\n",
    "   ['2019-07-31', 450000.0],\n",
    "   ['2019-06-30', 450000.0],\n",
    "   ['2019-05-31', 455000.0],\n",
    "   ['2019-04-30', 450000.0],\n",
    "   ['2019-03-31', 450000.0],\n",
    "   ['2019-02-28', 445000.0],\n",
    "   ['2019-01-31', 444000.0],\n",
    "   ['2018-12-31', 439900.0],\n",
    "   ['2018-11-30', 439000.0],\n",
    "   ['2018-10-31', 439500.0],\n",
    "   ['2018-09-30', 439900.0],\n",
    "   ['2018-08-31', 439900.0],\n",
    "   ['2018-07-31', 444999.0],\n",
    "   ['2018-06-30', 449500.0],\n",
    "   ['2018-05-31', 450000.0],\n",
    "   ['2018-04-30', 450000.0],\n",
    "   ['2018-03-31', 450000.0],\n",
    "   ['2018-02-28', 449900.0],\n",
    "   ['2018-01-31', 439900.0],\n",
    "   ['2017-12-31', 435000.0],\n",
    "   ['2017-11-30', 425000.0],\n",
    "   ['2017-10-31', 437797.5],\n",
    "   ['2017-09-30', 442950.0],\n",
    "   ['2017-08-31', 440000.0],\n",
    "   ['2017-07-31', 440000.0],\n",
    "   ['2017-06-30', 439900.0],\n",
    "   ['2017-05-31', 439500.0],\n",
    "   ['2017-04-30', 449000.0],\n",
    "   ['2017-03-31', 440000.0],\n",
    "   ['2017-02-28', 427500.0],\n",
    "   ['2017-01-31', 419000.0],\n",
    "   ['2016-12-31', 415000.0],\n",
    "   ['2016-11-30', 409925.0],\n",
    "   ['2016-10-31', 410000.0],\n",
    "   ['2016-09-30', 415000.0],\n",
    "   ['2016-08-31', 410950.0],\n",
    "   ['2016-07-31', 415165.0],\n",
    "   ['2016-06-30', 419950.0],\n",
    "   ['2016-05-31', 420000.0],\n",
    "   ['2016-04-30', 424900.0],\n",
    "   ['2016-03-31', 419900.0],\n",
    "   ['2016-02-29', 414900.0],\n",
    "   ['2016-01-31', 414495.0],\n",
    "   ['2015-12-31', 399950.0],\n",
    "   ['2015-11-30', 395000.0],\n",
    "   ['2015-10-31', 395000.0],\n",
    "   ['2015-09-30', 395000.0],\n",
    "   ['2015-08-31', 389900.0],\n",
    "   ['2015-07-31', 391325.0],\n",
    "   ['2015-06-30', 389900.0],\n",
    "   ['2015-05-31', 390000.0],\n",
    "   ['2015-04-30', 389990.0],\n",
    "   ['2015-03-31', 380950.0],\n",
    "   ['2015-02-28', 379995.0],\n",
    "   ['2015-01-31', 380000.0],\n",
    "   ['2014-12-31', 373000.0],\n",
    "   ['2014-11-30', 360000.0],\n",
    "   ['2014-10-31', 356163.0],\n",
    "   ['2014-09-30', 350000.0],\n",
    "   ['2014-08-31', 345950.0],\n",
    "   ['2014-07-31', 345000.0],\n",
    "   ['2014-06-30', 345925.0],\n",
    "   ['2014-05-31', 339000.0],\n",
    "   ['2014-04-30', 335000.0],\n",
    "   ['2014-03-31', 329000.0],\n",
    "   ['2014-02-28', 322200.0],\n",
    "   ['2014-01-31', 310000.0],\n",
    "   ['2013-12-31', 309900.0],\n",
    "   ['2013-11-30', 300000.0],\n",
    "   ['2013-10-31', 300000.0],\n",
    "   ['2013-09-30', 300000.0],\n",
    "   ['2013-08-31', 299000.0],\n",
    "   ['2013-07-31', 299900.0],\n",
    "   ['2013-06-30', 292925.0],\n",
    "   ['2013-05-31', 289900.0],\n",
    "   ['2013-04-30', 280000.0],\n",
    "   ['2013-03-31', 274900.0],\n",
    "   ['2013-02-28', 269500.0],\n",
    "   ['2013-01-31', 260000.0],\n",
    "   ['2012-12-31', 260000.0],\n",
    "   ['2012-11-30', 259925.0],\n",
    "   ['2012-10-31', 260000.0],\n",
    "   ['2012-09-30', 260000.0],\n",
    "   ['2012-08-31', 260000.0],\n",
    "   ['2012-07-31', 263500.0],\n",
    "   ['2012-06-30', 265000.0],\n",
    "   ['2012-05-31', 262800.0],\n",
    "   ['2012-04-30', 258000.0],\n",
    "   ['2012-03-31', 251950.0],\n",
    "   ['2012-02-29', 245000.0],\n",
    "   ['2012-01-31', 239999.0],\n",
    "   ['2011-12-31', 240000.0],\n",
    "   ['2011-11-30', 245000.0],\n",
    "   ['2011-10-31', 249000.0],\n",
    "   ['2011-09-30', 249900.0],\n",
    "   ['2011-08-31', 249900.0],\n",
    "   ['2011-07-31', 249900.0],\n",
    "   ['2011-06-30', 249900.0],\n",
    "   ['2011-05-31', 249900.0],\n",
    "   ['2011-04-30', 240000.0],\n",
    "   ['2011-03-31', 239900.0],\n",
    "   ['2011-02-28', 239000.0],\n",
    "   ['2011-01-31', 235000.0],\n",
    "   ['2010-12-31', 238900.0],\n",
    "   ['2010-11-30', 249900.0],\n",
    "   ['2010-10-31', 250000.0],\n",
    "   ['2010-09-30', 254900.0],\n",
    "   ['2010-08-31', 260000.0],\n",
    "   ['2010-07-31', 256900.0],\n",
    "   ['2010-06-30', 259900.0],\n",
    "   ['2010-05-31', 259900.0],\n",
    "   ['2010-04-30', 255000.0],\n",
    "   ['2010-03-31', 255000.0],\n",
    "   ['2010-02-28', 259000.0],\n",
    "   ['2010-01-31', 259900.0]]])\n",
    "\n",
    "denver_df = pd.DataFrame(d[1][0], columns=d[0][0])\n",
    "denver_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>445000.0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>455000.0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Value  year\n",
       "0  445000.0  2019\n",
       "1  450000.0  2019\n",
       "2  450000.0  2019\n",
       "3  455000.0  2019\n",
       "4  450000.0  2019"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pull Year out of Home value df  \n",
    "\n",
    "years = []\n",
    "for date in denver_df['Date']:\n",
    "    split_date = date.split('-')\n",
    "    year = str(split_date[0])\n",
    "    years.append(int(year))\n",
    "\n",
    "denver_df['year'] = years\n",
    "\n",
    "new_denver_df = denver_df[['Value', 'year']].copy()\n",
    "\n",
    "new_denver_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>254941.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>244783.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>257181.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>289668.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>342603.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year          Value\n",
       "                  mean\n",
       "0  2010  254941.666667\n",
       "1  2011  244783.333333\n",
       "2  2012  257181.166667\n",
       "3  2013  289668.750000\n",
       "4  2014  342603.166667"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the average home value per year \n",
    "avg_yearly_value = new_denver_df.groupby('year').agg({'Value': ['mean']})\n",
    "\n",
    "avg_yearly_value = pd.DataFrame(avg_yearly_value)\n",
    "\n",
    "avg_yearly_value = avg_yearly_value.reset_index()\n",
    "\n",
    "# Preview DataFrame\n",
    "avg_yearly_value.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(avg_yearly_value[\"year\"], avg_yearly_value[\"Value\"], color=\"c\", linestyle=\"-\", linewidth=1.0)\n",
    "\n",
    "plt.title(\"Denver Median Home Value (2010 - 2019)\")\n",
    "plt.ylabel(\"Price ($)\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.grid(axis='y')\n",
    "plt.legend(['Median Value'],loc=\"best\", fontsize=\"small\", fancybox=True)\n",
    "\n",
    "plt.xticks(avg_yearly_value.year)\n",
    "\n",
    "# Save the Figure\n",
    "plt.savefig(\"home_value.png\")\n",
    "\n",
    "# Show the Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>Vacancy Rate</th>\n",
       "      <th>interest rate</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>10.225</td>\n",
       "      <td>0.176027</td>\n",
       "      <td>254941.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>9.525</td>\n",
       "      <td>0.101753</td>\n",
       "      <td>244783.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>8.675</td>\n",
       "      <td>0.140437</td>\n",
       "      <td>257181.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>8.325</td>\n",
       "      <td>0.107397</td>\n",
       "      <td>289668.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>7.550</td>\n",
       "      <td>0.088521</td>\n",
       "      <td>342603.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year Vacancy Rate interest rate          Value\n",
       "                mean          mean           mean\n",
       "0  2010       10.225      0.176027  254941.666667\n",
       "1  2011        9.525      0.101753  244783.333333\n",
       "2  2012        8.675      0.140437  257181.166667\n",
       "3  2013        8.325      0.107397  289668.750000\n",
       "4  2014        7.550      0.088521  342603.166667"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MERGE ALL THE DATAFRAMES TO CREATE ONE GRAPH  \n",
    "from functools import reduce\n",
    "\n",
    "dfs = [avg_yearly_vacancy, avg_yearly_interest, avg_yearly_value]\n",
    "\n",
    "df_final = reduce(lambda left,right: pd.merge(left,right,on='year'), dfs)\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 6]})\n",
    ">>> df.rename(columns={\"A\": \"a\", \"B\": \"c\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "('year', 'interest rate')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2657\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'interest rate'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.BaseMultiIndexCodesEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\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   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'interest rate'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-71a60486b743>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m#plt.plot(years, gdp_nz, label='New Zealand')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_final\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'interest rate'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Interest'\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     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m#plt.plot(df_final[\"year\"], df_final[\"interest rate\"], df_final[\"Vacancy Rate\"], df_final[\"Value\"], color='m', linestyle=\"-\", linewidth=1.50)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2924\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_single_key\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2925\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2926\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2927\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_multilevel\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2971\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2972\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2973\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2974\u001b[0m             \u001b[0mnew_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method)\u001b[0m\n\u001b[1;32m   2404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2405\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkeylen\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2406\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2407\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2408\u001b[0m         \u001b[0;31m# -- partial selection or non-unique index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.BaseMultiIndexCodesEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: ('year', 'interest rate')"
     ]
    }
   ],
   "source": [
    "## Plot the Average Yearly Interest rate (line graph) \n",
    "# may need this for ticks --  [::10]\n",
    "\n",
    "#df_final[-15:].plot(x='year', y=['interest rate', 'Vacancy Rate', \"Value\"], figsize=(10,5), grid=True)\n",
    "#wide_form.melt('year', var_name='interest rate', value_name='Value')\n",
    "\n",
    "#plt.plot(years, gdp_australia, label='Australia')\n",
    "#plt.plot(years, gdp_nz, label='New Zealand')\n",
    "\n",
    "plt.plot(df_final['year', 'interest rate'], label='Interest')\n",
    "\n",
    "#plt.plot(df_final[\"year\"], df_final[\"interest rate\"], df_final[\"Vacancy Rate\"], df_final[\"Value\"], color='m', linestyle=\"-\", linewidth=1.50)\n",
    "\n",
    "plt.title(\"Denver Real Estate Trends\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.grid(axis='y')\n",
    "#plt.legend(['Median Home Value'], ['Federal Interest'], ['Federal Vacancy'], loc=\"best\", fontsize=\"small\", fancybox=True)\n",
    "\n",
    "plt.xticks(avg_yearly_value.year)\n",
    "\n",
    "# Save the Figure\n",
    "plt.savefig(\"Denver_Trends.png\")\n",
    "\n",
    "# Show the Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "can't assign to literal (<ipython-input-36-94c09dabe490>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-36-94c09dabe490>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    'Vacancy Rate' = df_final.loc['Australia']\u001b[0m\n\u001b[0m                                              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to literal\n"
     ]
    }
   ],
   "source": [
    "# Select two countries' worth of data.\n",
    "'Vacancy Rate' = df_final.loc['Australia']\n",
    "'interest rate' = df_final.loc['New Zealand']\n",
    "\n",
    "# Plot with differently-colored markers.\n",
    "plt.plot(years, gdp_australia, 'b-', label='Australia')\n",
    "plt.plot(years, gdp_nz, 'g-', label='New Zealand')\n",
    "\n",
    "# Create legend.\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('GDP per capita ($)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'randn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-2358bacba802>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m df = df_final(randn(1000, 4), index=ts.index,\n\u001b[0m\u001b[1;32m      2\u001b[0m               columns=['A', 'B', 'C', 'D'])\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'randn' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "df = df_final(randn(1000, 4), index=ts.index,\n",
    "              columns=['A', 'B', 'C', 'D'])\n",
    "\n",
    "\n",
    "df = df.cumsum()\n",
    "\n",
    "plt.figure(); df.plot(); plt.legend(loc='best')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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