Pandas_Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas_Introduction\n",
    "\n",
    "This is a quick overview of using pandas as a tool for data and statistical analaysis. Covid-19 data is drawn from the state of Tennessee to use for demonstration. \n",
    "This is based upon the [pandas_fun](https://github.com/chrisiacovella/pandas_fun) repository.  For a more detailed tutorial on pandas see [this tutorial](https://github.com/stefmolin/pandas-workshop).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting a dictionary to a Pandas dataframe\n",
    "Let us define some data as a simple dictionary and then convert it to a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = { \n",
    "\t'run1': [360, 0.91, 20.1],\n",
    "\t'run2': [305, 0.98, 22.0]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dictionary can be easily converted  to a pandas dataframe.  We can also pass labels via \"index\", to define what each of the entries in the array correspond to, which will also allow us to more easily search for specific data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame = pd.DataFrame(data_dict, index=['T', 'S2', 'angle'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas makes it easy to output this as a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>run1</th>\n",
       "      <th>run2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>360.00</td>\n",
       "      <td>305.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>angle</th>\n",
       "      <td>20.10</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         run1    run2\n",
       "T      360.00  305.00\n",
       "S2       0.91    0.98\n",
       "angle   20.10   22.00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we use the print function the output is basically the same, just not quite as nicely formatted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         run1    run2\n",
      "T      360.00  305.00\n",
      "S2       0.91    0.98\n",
      "angle   20.10   22.00\n"
     ]
    }
   ],
   "source": [
    "print(data_frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The display function will output the nicely formatted table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>run1</th>\n",
       "      <th>run2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>360.00</td>\n",
       "      <td>305.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>angle</th>\n",
       "      <td>20.10</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         run1    run2\n",
       "T      360.00  305.00\n",
       "S2       0.91    0.98\n",
       "angle   20.10   22.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting information\n",
    "Let's now examine how to extract information from the dataframe.   \n",
    "\n",
    "To get info for a single entry that was define in our original dict, we can just use the name of that key as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T        360.00\n",
       "S2         0.91\n",
       "angle     20.10\n",
       "Name: run1, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame['run1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the Temperature info from each of the runs, we access the data a little bit differently, using the \"loc\" command.  Here, we will take advantage of the fact that when we created the pandas dataframe from the dictionary, we  defined index labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "run1    360.0\n",
       "run2    305.0\n",
       "Name: T, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.loc['T']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two ways of access data can be combined, e.g., to print out the temperature of run1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame['run1'].loc['T']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, to access the temperature of run1, we can think of this as a 2d array, passing the index label, or, since  T is the first entry, using an integer index of 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame['run1'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame['run1']['T']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read/writing CSV files\n",
    "Pandas includes functions that make it trivial to write to common file formats.  For example, to write a dataframe to CSV file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame.to_csv(r'test_data.csv', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also read directly from a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame_from_csv = pd.read_csv('test_data.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we print the dataframe we just loaded, we will see it is identical to what we wrote. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>run1</th>\n",
       "      <th>run2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>360.00</td>\n",
       "      <td>305.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>angle</th>\n",
       "      <td>20.10</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         run1    run2\n",
       "T      360.00  305.00\n",
       "S2       0.91    0.98\n",
       "angle   20.10   22.00"
      ]
     },
     "execution_count": 29,
     "metadata": {},
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   ],
   "source": [
    "data_frame_from_csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with data in dataframes\n",
    "\n",
    "Let's create some totally fictitious data for T and PE for two different runs (data that most likely would be read in from a simulation energy file or the result of analysis by a code and wouldn't be defined by hand).  We will then make a dataframe for each run and then put these in a dictionary.   \n",
    "\n",
    "By using a dictionary we can again, hone in on specific pieces of information, like e.g., only run2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "run1_data = { \n",
    "\t'T': [300, 305, 310, 315, 310, 315, 320, 325, 320, 315, 310, 315],\n",
    "\t'PE': [1489, 1523, 1649, 1554, 1634, 1780, 1900, 1843, 1724, 1652, 1400, 1323]\n",
    "}\n",
    "\n",
    "run2_data = { \n",
    "\t'T': [300, 305, 305, 310, 315, 320, 325, 320, 325, 320, 315, 320],\n",
    "\t'PE': [1482, 1512, 1432, 1623, 1723, 1849, 1948, 2200, 2129, 2003, 1802, 1938]\n",
    "}\n",
    "\n",
    "r1_pd = pd.DataFrame(run1_data)\n",
    "r2_pd = pd.DataFrame(run2_data)\n",
    "\n",
    "\n",
    "sim_data_dict = {'run1': r1_pd,  'run2': r2_pd}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>PE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>300</td>\n",
       "      <td>1482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>305</td>\n",
       "      <td>1512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>305</td>\n",
       "      <td>1432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>310</td>\n",
       "      <td>1623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>315</td>\n",
       "      <td>1723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>320</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>325</td>\n",
       "      <td>1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>320</td>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>325</td>\n",
       "      <td>2129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>320</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>315</td>\n",
       "      <td>1802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>320</td>\n",
       "      <td>1938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      T    PE\n",
       "0   300  1482\n",
       "1   305  1512\n",
       "2   305  1432\n",
       "3   310  1623\n",
       "4   315  1723\n",
       "5   320  1849\n",
       "6   325  1948\n",
       "7   320  2200\n",
       "8   325  2129\n",
       "9   320  2003\n",
       "10  315  1802\n",
       "11  320  1938"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_data_dict['run2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot our data of course, using the key value in the dictionary to label each line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14601b190>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "for sim in sim_data_dict:\n",
    "    plt.plot(sim_data_dict[sim]['T'], label=sim)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is of course not the only way to define a dataspace.  For example, instead of having a dictionary be the top level container with the entires being pandas dataframes, we could put the run1_data and run2_data dictionaries into a dictionary, then convert that to a pandas dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_data_dict2 = {'run1': run1_data,  'run2': run2_data}\n",
    "\n",
    "sim_df = pd.DataFrame(sim_data_dict2)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The one drawback of this is if we wish to look at just a single entry, e.g., run1, we lose the automatic formatting we had in the approach above, but we wold essentially interact with the the dataframe in the same way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[300, 305, 310, 315, 310, 315, 320, 325, 320, 315, 310, 315]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_df['run1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": [
    "for sim in sim_df:\n",
    "    plt.plot(sim_df[sim]['T'], label=sim)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some built in functions that make things easy to get information quickly out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313.3333333333333 +/- 6.853444168423418\n"
     ]
    }
   ],
   "source": [
    "print(sim_data_dict['run1']['T'].mean(), '+/-', sim_data_dict['run1']['T'].std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily export to a numpy array as well.  A quick note, pandas uses the Bessel's correction in the standard deviation formulat. That is using N-1, rather than N.  So this will give a slightly different value than numpy.std(). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313.3333333333333 +/- 6.5616732283431745\n"
     ]
    }
   ],
   "source": [
    "T_array = sim_data_dict['run1']['T'].to_numpy()\n",
    "print(T_array.mean(), '+/-', T_array.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: working with real data\n",
    "\n",
    "Let's now read in some real data as a CSV file.  This file includes information on COVID-19 cases from 3/12/20 to 5/27/21 in the state of Tennessee per day from tn.gov [Daily Case Information](https://www.tn.gov/health/cedep/ncov/data/downloadable-datasets.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "tenn_data = pd.read_csv('datasets/Tenn_Pandemic_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>TOTAL_CASES</th>\n",
       "      <th>NEW_CASES</th>\n",
       "      <th>TOTAL_CONFIRMED</th>\n",
       "      <th>NEW_CONFIRMED</th>\n",
       "      <th>TOTAL_PROBABLE</th>\n",
       "      <th>NEW_PROBABLE</th>\n",
       "      <th>POS_TESTS</th>\n",
       "      <th>NEG_TESTS</th>\n",
       "      <th>TOTAL_TESTS</th>\n",
       "      <th>...</th>\n",
       "      <th>NEW_DEATHS</th>\n",
       "      <th>TOTAL_DEATHS</th>\n",
       "      <th>NEW_ACTIVE</th>\n",
       "      <th>TOTAL_ACTIVE</th>\n",
       "      <th>NEW_INACTIVE_RECOVERED</th>\n",
       "      <th>TOTAL_INACTIVE_RECOVERED</th>\n",
       "      <th>NEW_HOSP</th>\n",
       "      <th>TOTAL_HOSP</th>\n",
       "      <th>TOTAL_DEATHS_BY_DOD</th>\n",
       "      <th>NEW_DEATHS_BY_DOD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5/27/21</td>\n",
       "      <td>862401</td>\n",
       "      <td>405</td>\n",
       "      <td>710379</td>\n",
       "      <td>262</td>\n",
       "      <td>152022</td>\n",
       "      <td>143</td>\n",
       "      <td>818866</td>\n",
       "      <td>7142537</td>\n",
       "      <td>7961403</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>12441</td>\n",
       "      <td>-236</td>\n",
       "      <td>4898</td>\n",
       "      <td>628</td>\n",
       "      <td>845062</td>\n",
       "      <td>3</td>\n",
       "      <td>21918</td>\n",
       "      <td>12433</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5/26/21</td>\n",
       "      <td>861996</td>\n",
       "      <td>516</td>\n",
       "      <td>710117</td>\n",
       "      <td>366</td>\n",
       "      <td>151879</td>\n",
       "      <td>150</td>\n",
       "      <td>818539</td>\n",
       "      <td>7132186</td>\n",
       "      <td>7950725</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>12428</td>\n",
       "      <td>-102</td>\n",
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       "      <td>844434</td>\n",
       "      <td>13</td>\n",
       "      <td>21915</td>\n",
       "      <td>12432</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5/25/21</td>\n",
       "      <td>861480</td>\n",
       "      <td>437</td>\n",
       "      <td>709751</td>\n",
       "      <td>282</td>\n",
       "      <td>151729</td>\n",
       "      <td>155</td>\n",
       "      <td>818109</td>\n",
       "      <td>7120354</td>\n",
       "      <td>7938463</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>12406</td>\n",
       "      <td>-184</td>\n",
       "      <td>5236</td>\n",
       "      <td>615</td>\n",
       "      <td>843838</td>\n",
       "      <td>9</td>\n",
       "      <td>21902</td>\n",
       "      <td>12426</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5/24/21</td>\n",
       "      <td>861043</td>\n",
       "      <td>464</td>\n",
       "      <td>709469</td>\n",
       "      <td>317</td>\n",
       "      <td>151574</td>\n",
       "      <td>147</td>\n",
       "      <td>817798</td>\n",
       "      <td>7111391</td>\n",
       "      <td>7929189</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>12400</td>\n",
       "      <td>-319</td>\n",
       "      <td>5420</td>\n",
       "      <td>776</td>\n",
       "      <td>843223</td>\n",
       "      <td>13</td>\n",
       "      <td>21893</td>\n",
       "      <td>12418</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/23/21</td>\n",
       "      <td>860579</td>\n",
       "      <td>258</td>\n",
       "      <td>709152</td>\n",
       "      <td>190</td>\n",
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       "      <td>7103890</td>\n",
       "      <td>7921314</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>12393</td>\n",
       "      <td>-225</td>\n",
       "      <td>5739</td>\n",
       "      <td>477</td>\n",
       "      <td>842447</td>\n",
       "      <td>9</td>\n",
       "      <td>21880</td>\n",
       "      <td>12416</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>3/16/20</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>588</td>\n",
       "      <td>612</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>3/15/20</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>431</td>\n",
       "      <td>452</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>3/14/20</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>403</td>\n",
       "      <td>422</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>3/13/20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>87</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>3/12/20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  TOTAL_CASES  NEW_CASES  TOTAL_CONFIRMED  NEW_CONFIRMED  \\\n",
       "0    5/27/21       862401        405           710379            262   \n",
       "1    5/26/21       861996        516           710117            366   \n",
       "2    5/25/21       861480        437           709751            282   \n",
       "3    5/24/21       861043        464           709469            317   \n",
       "4    5/23/21       860579        258           709152            190   \n",
       "..       ...          ...        ...              ...            ...   \n",
       "437  3/16/20           14          4               14              4   \n",
       "438  3/15/20           10          4               10              4   \n",
       "439  3/14/20            6          5                6              5   \n",
       "440  3/13/20            1          0                1              0   \n",
       "441  3/12/20            1          1                1              1   \n",
       "\n",
       "     TOTAL_PROBABLE  NEW_PROBABLE  POS_TESTS  NEG_TESTS  TOTAL_TESTS  ...  \\\n",
       "0            152022           143     818866    7142537      7961403  ...   \n",
       "1            151879           150     818539    7132186      7950725  ...   \n",
       "2            151729           155     818109    7120354      7938463  ...   \n",
       "3            151574           147     817798    7111391      7929189  ...   \n",
       "4            151427            68     817424    7103890      7921314  ...   \n",
       "..              ...           ...        ...        ...          ...  ...   \n",
       "437               0             0         24        588          612  ...   \n",
       "438               0             0         21        431          452  ...   \n",
       "439               0             0         19        403          422  ...   \n",
       "440               0             0          2         85           87  ...   \n",
       "441               0             0          2         43           45  ...   \n",
       "\n",
       "     NEW_DEATHS  TOTAL_DEATHS  NEW_ACTIVE  TOTAL_ACTIVE  \\\n",
       "0            13         12441        -236          4898   \n",
       "1            22         12428        -102          5134   \n",
       "2             6         12406        -184          5236   \n",
       "3             7         12400        -319          5420   \n",
       "4             6         12393        -225          5739   \n",
       "..          ...           ...         ...           ...   \n",
       "437           0             0           4            14   \n",
       "438           0             0           4            10   \n",
       "439           0             0           5             6   \n",
       "440           0             0           0             1   \n",
       "441           0             0           1             1   \n",
       "\n",
       "     NEW_INACTIVE_RECOVERED  TOTAL_INACTIVE_RECOVERED  NEW_HOSP  TOTAL_HOSP  \\\n",
       "0                       628                    845062         3       21918   \n",
       "1                       596                    844434        13       21915   \n",
       "2                       615                    843838         9       21902   \n",
       "3                       776                    843223        13       21893   \n",
       "4                       477                    842447         9       21880   \n",
       "..                      ...                       ...       ...         ...   \n",
       "437                       0                         0         2           3   \n",
       "438                       0                         0         0           1   \n",
       "439                       0                         0         1           1   \n",
       "440                       0                         0         0           0   \n",
       "441                       0                         0         0           0   \n",
       "\n",
       "     TOTAL_DEATHS_BY_DOD  NEW_DEATHS_BY_DOD  \n",
       "0                  12433                  1  \n",
       "1                  12432                  6  \n",
       "2                  12426                  8  \n",
       "3                  12418                  2  \n",
       "4                  12416                  7  \n",
       "..                   ...                ...  \n",
       "437                    0                  0  \n",
       "438                    0                  0  \n",
       "439                    0                  0  \n",
       "440                    0                  0  \n",
       "441                    0                  0  \n",
       "\n",
       "[442 rows x 21 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tenn_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see by the way this has been imported, the dictionary keys are 'DATE', 'TOTAL_CASES', 'NEW_CASES', and etc., so we can use these to extract out specific information.  For example, let's plot  'DATE' and 'TOTAL_CASES'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14612fb80>]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(tenn_data['DATE'], tenn_data['TOTAL_CASES'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's rather difficult to read that x-axis, let's do some quick formatting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'date')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#first let's make a copy of the data that gives the date/time of update\n",
    "formatted_date = tenn_data['DATE'][:].copy()\n",
    "#next, let's only keep the date, discarding the time, this is just the first\n",
    "#10 characters of the string\n",
    "formatted_date = [date.replace(date[10:], '') for date in formatted_date]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "#to be able to read the labels, we'll use a built-in function to tilt them\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# and then define the number of tick markers to show a more manageable set\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "\n",
    "ax.plot(formatted_date, tenn_data['TOTAL_CASES'])\n",
    "ax.invert_xaxis()\n",
    "\n",
    "plt.ylabel('number of positive cases')\n",
    "plt.xlabel('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's rather easy to just restrict the data range we plot, e.g., let's just pick out data from the \"beginning\" where the number of cases was most rapidly growing (the end of this window roughly corresponds  with the end of the first wave, around 07-31-20).  Note, the data is formatted with the latest data coming first and oldest data last: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zf88DwDRgiH9M9uVXAhWqOhi4G7jzEOM1xpiQPDNvPS8vKmHaiYPolZF4t0E+EKFUDI4IfuMHWo492A2KSAZwIvAogKrWqep2XNfn6X6x6cBU/3oK8Iyq1qrqGlxvtnEi0gfIUNU5qqrAE83WCXzXTODUwNWNMcaEg6py/9tF/Pj5RZx0eA5Xn3xYtEOKun0mGF8tVQUcKSI7/KMK2AK8eAjbHISb2+yvIrJQRB4RkTSgl6qWAPjnwC0B+gEbgtYv9mX9/Ovm5Xuto6oNQCWQ1cLPOE1ECkWksKys7BB+JGNMe/fUvPX89rUVTBndl0cuK6Bzx+T9r5TgWmuD+Y2qpgO/a9b+kqWqNx3CNjsARwMPqOoYoBpfHbYPLV15aCvlra2zd4Hqw6paoKoFOTk5rUdtjDH7sHH7Lm5/eRknDMnm7gtGt9teY821dgUzzL/8u4gc3fxxCNssBopVda5/PxOXcLb4ai/8c2nQ8v2D1s8FNvny3BbK91rHV+l1A8oPIWZjjGmRqvLTFxahwO3njiIpyWrjA1obB/N9XAP671v4TIEvHMwGVXWziGwQkaGqugI4FVjqH5cBd/jnQDXcS8BTIvIHoC+uMX+eqjaKSJWITADmApcC9wWtcxkwBzf7wJu+ncYYY9rUCws38vaKMm7+0nD6Z6ZGO5yYss8Eo6rT/PMpYdjudcCTIpICrAYux11NPSciVwLrgfP99peIyHO4BNQAXKuqjf57rgYex82P9qp/gOtAMENEinBXLjZY1BjT5jaU13Dzi0soyOvBpcfmRzucmCP7O7EXkfNxXYqrRORnuOqs21R1YSQCjJSCggItLCyMdhjGmDjR0NjEhQ9/wIrNVbxywwnt9upFROarakFLn4XSEvVzn1yOBybhuv8+2JYBGmNMvPnzW6soXFfBbVNHttvksj+hJJhAddRZuJ5fLwIp4QvJGGNi27sry7hn9qdMHd2XqWP67X+FdiqUBLNRRB4CLgBeEZFOIa5njDEJZ+P2XVz/9EKG9OzK7V8eFe1wYlooieICYBYw2Y+4zwR+GM6gjDEmFjU2Kdc9tYD6RuXBi8eSmmJ3kG9NKLdMrgFWAZNE5DtAT1X9T9gjM8aYGPPshxtYsH47t00dwaCcrtEOJ+aFMtnlDcCTuKlbegJ/E5Hrwh2YMcbEkorqOn47aznjB2YydbS1u4QilOu7K4HxqloNICJ34gYw3tfqWsYYk0Du+s8KqnY3cOuUkdjcuaEJJcEIn/Ukw7+2vWuMaRdq6hq4/ZVlPDl3PVdMHMjQ3unRDiluhJJg/grMFZEXcIllCn6qfWOMSWTrtlVz+V8/ZM22aq46fiA/nDw02iHFlf0mGFX9g4i8DRzviy5PtFH8xhjT3Mbtu7joL3OpqWvgyavGc9xh2dEOKe4cSB87AZqw6jFjTIIrrdrNxY/MZcfuep7+5gRG9usW7ZDiUii9yH6Bmx6mB5CNu1HYz8IdmDHGREN1bQNXPP4hmyt38/jlx1hyOQShXMF8DRijqrsBROQOYAHwq3AGZowxkVbf2MQ1Ty5gWUkVj1xawNi8zGiHFNdCGcm/Fugc9L4TbuClMcYkjKYm5abnF/HfT8v41dSRnDKs5/5XMq0K5QqmFlgiIq/jbjR2OvCeiPwRQFWvD2N8xhgTdqrKL/+1hJnzi/nuaUP42rgB0Q4pIYSSYF7wj4C3wxOKMcZEx91vrGT6nHV884SB3HDqkGiHkzBC6aY8PRKBGGNMNBSuLee+N1fylaNz+cmZR9go/TZk0+4bY9qtXXWN/HDmJ/Tt1oVfThlhyaWN2VzTxph263ezVrBmazVPXTWerp3sz2Fb2+cVjIjM8M83RC4cY4yJjOcKN/DY/9Zw6bF5HDfYRumHQ2tVZGNFJA+4QkR6iEhm8CNSARpjTFubvWwLNz2/iBOGZPOzs4ZHO5yE1do14YPAa8AgYD57TxGjvtwYY+LK0k07uPapBYzom8GDF48lpYM1RYfLPvesqv5RVY8AHlPVQao6MOhhycUYE3cqd9Vz9ZPz6dalI49edgxp1u4SVqF0U75aRI4CTvBF76jqJ+ENyxhj2lZTk/KD5z5iY8Uunv3WBHLSO0U7pIQXymSX17P3LZOfbItbJotIsogsFJF/+/eZIvK6iKz0zz2Clr1JRIpEZIWITAoqHysii/xnfxTfx1BEOonIs758rojkH2q8xpj49uh7a3hjWSk/O+sIm2MsQkKpfLwKd8vkX6jqL4AJwDfbYNs3AMuC3v8YmK2qQ4DZ/j0iMhy4EBgBTAbuF5Fkv84DwDRgiH9M9uVXAhWqOhi4G7izDeI1xsSpRcWV/HbWciaN6MVlx+VHO5x2I5QE0+a3TBaRXOAs4JGg4im42wLgn6cGlT+jqrWqugYoAsaJSB8gQ1XnqKoCTzRbJ/BdM4FTA1c3xpj2pbq2gRueWUhWWifu+PKRNpgygg70lsng/ogf6i2T7wF+BATf3LqXqpYAqGqJiASmMu0HfBC0XLEvq/evm5cH1tngv6tBRCqBLGDrIcZtjIkTtQ2NPL9gI396s4hNlbt46qoJ9EhLiXZY7cqB3jJZOMRbJovI2UCpqs4XkZNDWaWlsFopb22d5rFMw1WxMWCAzZ5qTLxraGzi4XdXM2vxZpaVVFHX2MTo/t353XlHcuxhWdEOr90JqY+eqi7A3WSsLUwEzhGRM3H3mckQkb8BW0Skj7966QOU+uWLgf5B6+cCm3x5bgvlwesUi0gHoBtQ3sLP9TDwMEBBQcHnEpAxJn6U7tjN9c8s5IPV5RyT34PLJ+YzcXA2JwzJtmqxKIl4J3BVvQm4CcBfwfyfql4sIr8DLgPu8M8v+lVeAp4SkT8AfXGN+fNUtVFEqkRkAjAXuBS4L2idy4A5wHnAm76dxhiTgD7esJ2rniikanc9d51/FOeNzd3/SibsYmmU0R3AcyJyJbAeOB9AVZeIyHPAUqABuFZVA50OrgYeB7oAr/oHuDaiGSJShLtyuTBSP4QxJrJe/qSE7z/3ETnpnZhx7USG9c6IdkjGk9ZO7H134FmqelrkQoqOgoICLSwsjHYYxpgQ7dhdz+0vL+OZDzdQkNeDhy4ZS1ZXGzwZaSIyX1ULWvqs1SsYXw1VIyLdVLUyPOEZY8yB+WD1Nr77zEeUVu3mWycN4vunH06nDsn7X9FEVChVZLuBRSLyOlAdKFTV68MWlTHGtEBVefS9Nfzm1eXkZabywjUTOap/92iHZfYhlATzsn8YY0xUqCr//bSM+99axby15ZwxvBe/v+Ao0jt3jHZophWhjIOZLiJdgAGquiICMRljDACNTcprizdz/9tFLNm0gz7dOnPblBF8fXweSUnW9TjW7TfBiMiXgLuAFGCgiIwGblXVc8IcmzGmnVq4voJ/fVzCrCWb2bh9FwOz0/jtV45k6ph+dv+WOBJKFdktwDjgbQBV/UhEBoYxJmNMO7V+Ww2/fmUps5ZsISU5ieOHZPOTM49g8sjeJNsVS9wJJcE0qGpls5GwNmjRGNOm/rlwIz/6xyd0SBJ+OGkolx6bZ20scS6UBLNYRC4CkkVkCHA98H54wzLGtBdNTcq9s1dy7+yVTBiUyT1fHUPvbp2jHZZpA6EkmOuAnwK1wNPALOC2cAZljEl8H2/YzrOFG/jPki1s3VnLeWNzuf3cUdbGkkBC6UVWA/xURO50b7Uq/GEZYxLVh2vLufeNlbxXtJXUlGROGdaTs0f1YfLI3jYpZYIJpRfZMcBj+Hu3+HurXKGq88McmzEmgRSV7uTO15bz+tIt5KR34idnDuOi8Xl07RRLUyKathTK/+yjwDWq+i6AiByPuwnZkeEMzBgT/1SVeWvK+cu7a5i9fAtpKR344aShXDFxIF1SbGqXRBdKgqkKJBcAVX1PRKyazBizT3UNTby6uIRH3l3Doo2V9EjtyHWnDObS4/LJtgkp2419JhgROdq/nCciD+Ea+BX4Kn5MjDHGBNu0fRdPz1vP0/M2sHVnLYNy0vj1uSP58phcu2Jph1q7gvl9s/c3B722cTDGGMB1M/7fqq3MmLOON5ZtQYEvDO3JxRPyOOnwHJvSpR3bZ4JR1VMiGYgxJr5U1tTz9/kbeHLuetZsrSYzLYVvnXQYF40bQP/M1GiHZ2JAKL3IuuNuR5wfvLxN129M+7SouJIZH6zlpY83sbu+ibF5Pbjh1CF8cVRvuyeL2UsojfyvAB8Ai4Cm8IZjjIlFZVW1vPzJJl5YuJGPiyvp0jGZc8fkcvGEAYzo2y3a4ZkYFUqC6ayq3w97JMaYmLKztoFXF5Xw0seb+F/RVpoUhvVO55YvDefLY3PJsHnCzH6EkmBmiMg3gX/jposBQFXLwxaVMSbiVJVNlbtZsXkHry/dwosfbaKmrpEBmalcc/Jgzhndl8N7pUc7TBNHQkkwdcDvcPORBXqPKTAoXEEZYyKjobGJWUu28MayLby7soytO+sA6NIxmS8d1YevHjOAowd0tylczEEJJcF8HxisqlvDHYwxJjLqGpr458KN/PntItZtqyEzLYUThmRTkJ/J8D7pDOudQZpN4WIOUShH0BKgJtyBGGPCb3d9IzPnF/PA26vYuH0XI/tl8NAlYzn9iF42XsW0uVASTCPwkYi8xd5tMNZN2Zg4sWN3Pc/O28Bf3l1NaVUtYwZ051dTR3Ly0Byr/jJhE0qC+ad/GGPiRG1DIyu37GRpyQ7eXlHKG8tKqWto4rjDsvjDBaOZODjLEosJu1DuBzO9LTcoIv2BJ4DeuHE1D6vqvSKSCTyLG9C5FrhAVSv8OjcBV+Kupq5X1Vm+fCzwONAFN17nBlVVEenktzEW2AZ8VVXXtuXPYUws2razlunvr2X6nHVU7qoHICsthYvGDeArR+cyKtfGrJjICWUk/xpamHtMVQ+2F1kD8ANVXSAi6cB8EXkd+AYwW1XvEJEfAz8GbhSR4cCFwAigL/CGiByuqo3AA8A03EDQV4DJwKu4ZFShqoNF5ELgTtwkncYklKLSKv776VbmrNrK8s1VFFfsAuCM4b2YOqYfw3qnk5eVRrK1r5goCKWKrCDodWfgfCDzYDeoqiVAiX9dJSLLgH7AFOBkv9h03IzNN/ryZ1S1FlgjIkXAOBFZC2So6hwAEXkCmIpLMFOAW/x3zQT+JCKiqjZJp4l7TU3KWytKeeDtVRSuqwBgUHYaYwb04KsF/fniqN4M7mnjVUz0hVJFtq1Z0T0i8h7wi0PduIjkA2OAuUAvn3xQ1RIR6ekX64e7Qgko9mX1/nXz8sA6G/x3Nfi7cGYB1tXaxKWyqlpmL9vCOyvL+GB1OeXVdfTr3oWfnz2cSSN6kdvDJpc0sSeUKrKjg94m4a5oDvn0SES6Av8AvquqO1ppcGzpA22lvLV1mscwDVfFxoABA/YXsjERtXZrNa8v3cKsJZuZv74CVejTrTOnDO3JKcNymDSiNx2Tk6IdpjH7FEoVWfB9YRrwDfCHslER6YhLLk+q6vO+eIuI9PFXL32AUl9eDPQPWj0X2OTLc1soD16nWEQ6AN2Az01to6oPAw8DFBQUWPWZiZrANC2LirezYP12Zi/bwqqyagCG98ngu6cezhkjejGsd7r1/jJxI5Qqsja9L4y4345HgWWq+oegj14CLgPu8M8vBpU/JSJ/wDXyDwHmqWqjiFSJyARcFdulwH3NvmsOcB7wprW/mFhTVlXL+6u28r+irfyvaBsbt7sG+o7JwviBWVw8IY/Tjuhl91YxcSuUKrJOwFf4/P1gbj3IbU4ELgEWichHvuwnuMTynIhcCazHdSZAVZeIyHPAUtwV1LW+BxnA1XzWTflV/wCXwGb4DgHluF5oxkRd82nvATI6d+C4w7KZduIgjurfnWG90+nc0e6rYuKf7O/EXkReAyqB+bhxKACoavNbKse1goICLSwsjHYYJkGoKqvKqvl4w3ZWb93JqtJqFm+q3NONeETfDM4c1YcThmQzom8360Zs4paIzFfVgpY+C6UNJldVJ7dxTMYknNVlO3l18WYWrKtg/voKtte4gY4dkoQBmamM7t+dSybkcfLQngztbd2ITeILJcG8LyKjVHVR2KMxJg5t2bGbe2ev5NkPN9DYpByWk8YZw3tRkJfJmAHdyc9Os95epl0KJcEcD3zDj+ivxXUBVlU9MqyRGRPDauoaWFRcybMfbuBfn7jOi5dMyOOakw+jZ0bnKEdnTGwIJcF8MexRGBOjVJV122pYvnkHy0qqWL55Bys2V7GuvAZVSEtJ5qJxA7ji+IHkZaVFO1xjYkoo3ZTXRSIQY2JB8HiU94q28sbSUjbv2A2ACAzMSuOIPhmcOyaXob3TOW5wlt2b3ph9sFvWmXZLVVm9tZoP15RTuK6CotKdrNlavWcW4tSUZE4cksNJQ3MY0TeDIT3T6ZJi3YeNCZUlGNNuNDUpS0t2MHdNuU8q5XvuQZ+VlsKwPul86ag+DO2Vzqjc7hzRJ51OHSyhGHOwLMGYdmHB+gp+8eJiFm/cAUD/zC6ceHgO4/IzOWZgJoOy02wKFmPamCUYk9B21zfy65eXMeODdfTO6MwdXx7FSUNz6NOtS7RDMybhWYIxCWvj9l1c/bf5fFJcyZXHD+T7px9OWic75I2JFPttMwnFTdGyk+nvr2Pm/GI6JAkPXTKWSSN6Rzs0Y9odSzAmru2ub2TJpkrmr6tgwbrtzF9fQVlVLSnJSZwzui/XfWGwjU8xJkoswZi4UllTz9w12yhcV0Hh2nIWb9xBXWMTAAMyUzl+cDZHD+jO5JF9yEnvFOVojWnfLMGYmFff2MTbK8p4fkExs5eVUtfYREpyEqNyu3H5xHyOzuvB0QN6WEIxJsZYgjExSVVZvHEH/1hQzEsfb6K8uo6stBQunpDH5JG9OTK3m90zxZgYZwnGxISmJjeq/uMN2/m4eDvvr9pGUelOUpKTOH14L758dD9OPDzHZiU2Jo5YgjERV7W7nrVba1hXXs2ykh18tGE7n2yopKq2AXATSB7VvzuXT8zn7FF96ZZqc30ZE48swZiw27JjN/PWlPPh2nLmrSlnxZYqAjdS7ZAkDOuTzjmj+zK6f3dG9+/OoJyudodHYxKAJRjTZqprG1hdVs2qsp2sLtvJ8s1VfFJcuWc24tSUZI4e0IMvjuzD0N7p5GWlMjA7zdpSjElQlmDMQdm2s5YF67ezYH0Fy0vcPVI2Ve7e83mSQH5WGuMHZXJkbncK8nowom8GHawNxZh2wxKMaVVdQxPrtlWztGQHC9dvZ/HGSlZvraa82s1C3DFZGNwznXEDMxnSK53DctI4LKcrA7JSbSZiY9o5SzBmj7qGJlZv3cnykioWrK9g3ppyVpbupLHJNZh06ZjMiL4ZTBrRi8NyunJU/+6M6mfdhY0xLbME0041NinrtlWzYnMVS0t2MG9NOQs3bKeuwY2KT01JZmxeD049oieDe3bl8F7pDO2VblVcxpiQWYJpB9wEkNXMX1fORxsqWbKpkk+3VLG73iWTJIGR/bpxyYQ8jsztxtDe6QzO6WrJxBhzSCzBJKDArYAL15ZTuLaC/xVt3dMAn9G5A6Nyu/H18XkM7Z3OsN7pditgY0xYJHSCEZHJwL1AMvCIqt4R5ZDaXG1DIxvKd1FcUUNFTR1rt9bwyqISVpbuBKB7akcmDMziO1/IYcKgTAbanRuNMRGSsAlGRJKBPwOnA8XAhyLykqoujW5kB253fSPry2tYu7WaddtqWLOtmnXbqlm7tYZNlbv2DFoEEIFj8jO5dcoIJg7OtlsBG2OiJmETDDAOKFLV1QAi8gwwBYh6glFVqmobKKuqpayqlq07a/e8Drwvr6mnvLqWiup6dvopVAK6p3YkPyuNY/J7kJeVS352Krk9UslKSyE7vRMZnW1qFWNM9CVygukHbAh6XwyMb+uNbK+p44KH5qAKikseChD0vklBUVTdpI7bquuo9b21gnVIErK6ppDdtROZaSkMzEqlR1oKWWkp9M9MJT8rjbysVLqnprT1j2GMMW0ukRNMS/VCutcCItOAaQADBgw4qI0kJwmDsrsi4qqnBMH/Q0RICnotQFKSkJmWQk7XTuSku0e2f929S0eSbA4uY0yCSOQEUwz0D3qfC2wKXkBVHwYeBigoKNgr+YQqvXNHHrxk7MHGaIwxCSuRBzp8CAwRkYEikgJcCLwU5ZiMMabdSNgrGFVtEJHvALNw3ZQfU9UlUQ7LGGPajYRNMACq+grwSrTjMMaY9iiRq8iMMcZEkSUYY4wxYWEJxhhjTFhYgjHGGBMWonpQwz8SjoiUAesO4Suyga1tFE64xVOsEF/xxlOsEF/xxlOsEF/xHkqseaqa09IHlmDaiIgUqmpBtOMIRTzFCvEVbzzFCvEVbzzFCvEVb7hitSoyY4wxYWEJxhhjTFhYgmk7D0c7gAMQT7FCfMUbT7FCfMUbT7FCfMUbllitDcYYY0xY2BWMMcaYsLAEY4wxJiwswYRARApEZEDQ+5i+K5iI9BKRZP861mONt30bb/HuORZiXTzt23iKFaIXr7XBtEJEegDTcTcrKwLmAH9W1bqoBrYPIlIAPAF8CpQA16lqQ3Sjalkc7tt4i9eOhTCIp1gh+vHaFUzrBgI7VfVo4C7gCOBmABGJqX0nIp2AbwC/UdWpQG/gRhEZ4j+PtTOsuNm3XtzEu49j4UeBYyEGxc2+Jb5ihSjHG4s7JKpEJCtoxx8DBDL9QuBuYIqIDFPVplj6o62qtcA4oNQX/RLoDpwuIqIxcKkab/s23uIN8MfCeD47Fm4FegCnRy2oZuJp38ZTrBBb8VqC8UTkRBH5H/CgfwD8AzhTRPJVtV5VlwHPAT8AiOYfbRG5UERmi8iPRGSSL/4nMNrH9hGwCMjHnbVETRzu23iLN3As3Bh0LDzPZ8fCQvyxICLDoxQmEF/7Np5ihdiMt90nGBFJFZETgQeAe4BLgAkicrGqbsXVX/7GL9sBeB1QEcmKUrxZIvJH4BrgPqAGuFlEOgNLgF4iMtEv/g4wFIhK3Xsc7tt4i7f5sVAN3OKPhaW0fCw0RinWuNm38RRrrMfbrhOMiPTEZfg6YIqq/l1Vd/PZfxLAr4BjROTcoEbSDqq6LcKxJonIZOAUYImqnqiq/wSeBFYCw4B5QCUwSUQyVHUtLrkcFclYfbxxs2/jLd79HAuf0vqxcGQkY/XxxtO+jZtY4yHedptgfN1jLfABUK+qRUH1kZ2BdwFUtRL4IXC1P1v8C7DB/5JHrL5VVZuAc4AqVX3In4kEHA5sVtUS4F9AL+AxEbkRGAJ8Eqk4If72bbzFG0/Hghc3+zbOYo35eDvsf5HEIa4XTaOqrlZVFTc+oDewyy/SEXcmMBB3VQCAqr4gIguBScBMVX0nQvGm43qABOpJZwHXAbOCzkS6ABVAlY91gYh8B/gmrkrkAlVdEYFY84E6Vd3k920Ssb1v4y3eJN8o28H/38fysZCqqjUi0lFV6wEhRvdtPMUaj/Giqgn/ADJxjV4LgJnAVUBX/9ktwD3Nln8XGACk4X45uzX7XPBjiMIU73Dc2eczwINB5Sm4uvZRQWWnAC/718OA0f51UtAySWGMtQfwV1yVzCzgq0C6/+yXMbhv4y3eUcBjwEWB7fnnjjF4LHTzv1+fBJUl++eY+j2Lp1jjMd7AI+GryMSNCZiOq044Gvef9AXcLyjAI0BXX5eJiPTDNZZeA7yPOxPYEfR9ol4YYu0gIhN8jK8B04BT/FkouLvOVbB3Q+3RwHIR+SkuIeXDnmqUQLxNbR2r/+5U3B+/SlUdBzwFnAj084v8hRjZt/EWr4gki0gf4FncILkTxPUECmyrB7F1LCQD3wU2+k3d6D8KzCIQS79ncRNrPMa7l3BnsGg92PusLTvo9TTcH/B8XBbvhxsnkOs/Hwo0AX8D8iMYbzbwJ1zd+oig8rOB14LePwB8O+j9DFxj7l34q7IIxJrOZ2dPE4EM/7ojsBg42r8fghvUFe19G3wsjI+DeHOA+3FnoMP943bgh82Wi7VjYaR/Ho1LfqlByw2O9r5tdhwMjeVY/XazgBT/ekSsx9vSI+GuYERklIg8BlwYKFPXVQ8RuR7X/3stLql8R1U3AiOBvn7xWmCMql6sqmslzKNdRSRwJdUdNzBuOa4nUEA/3BiGgEeAySKS4t+/D4xX1f9T1Z3hjFdEhovIv3Bn+n8GUNX/qeoOEemkrk54FVDvV1mDO6vu499Het9+7lgA5sVwvH18o2tnYBtumMJSVV0KFAJD/BVuwKPE1rGw2D9/hOsK+6BfVnC/c1HZt/v4m7AiFmP1MQTv20d8nEtiNd5WRTO7tXG2T/Y7eCnwH9zZXb7/rIN/7ha0/GRct85U3LQaj7bwneGq/03CnflNB37pyzriuhsWBN775xuBG4LW7YU7Wzmihe8MV7wdgAl+314LZAArcAkaPjuDzQHeA9KC1r0Y+Guk9m0rx8LAwH4KWi5W4h0AvOKPx2wf/8zAseCX6Qf8FPhtUFkf4OcxdiwEzri7466mhgetexHweKT27X7+JiQFjoVYiDVo3x7j9+f1vqwY+Ebg81iKN5RHQlzBiEg2rsGzI3Aerr6yAjgfQD/rZbMjaLVioBOwGzeFQpk0G3ik/n+oramrB++Au9w9UUSOU3c2/QZwtV8mcFb9RV+OiFwEKFCAG2C513eGI16/b+8BegLnq+qfVXUH7krwLL/tQDvACbgrhGoR+baIfB13RbY5Uvu2lWPhPL/d4DaIWIg3FXc2OkdVv66qW/3+3HMs+O1vxF2hqIicJyKX4E5SxhFbx0Kd7+G0HXci9JCIfENEfoTr1VQSiX0rIjm4Kud9/U1oUtcrL+qxBsV7L+73e4yq/tF/9Aqwzm+7IVbiDVVCJBjcL1o5rVQpBBq2/OvTcAffav8HZxtwl0Z2oNQg3MymM3BnguB6jm31jbuBX+gm3GC5D4DJqlqKu/Ip/fxXtp0Qq+4W+2UDjY05wHEi8howFZiNu3z/fbj3rbjR6+DOqFutXgr62aIWb5BcYI2q3uZjG+3LZwHFvsEWAFV9CzfO5W/AV1T1U+Bxwn8sBKruUtnPseDjDJwcfYxro7sS11lhFWHetyIyVNwMwh3Y/3GQFM1Y9xHvFnXdkHNE5N/AqcCVIvJ7EUmLdrwHKi4TjIgMFJE7ROQqcWNF1uO6ZfYKWmwuLvN/GdxRJiKdROQG4PfAvar6I/9Zsfp2mghaj/vj/RGAiHwBF38t7qoK3C/0ccAZwDWqeqmPd6aq7qKNiRt4lS4i04Gf+eJ1uJkAMlS1vlniKfbxBK5gjgfycMl6sqpuBraFc9+KSJ6I/BPXVRPcfj2C1o+FwC/pcbjOHhGLtwXJwBdE5HAReQG4W0TuxnVGqSXo6kREHsB1RDhe3SzJqOo/wnEs+O0NEJFXcJ0GsnC9mFo9FoLWnYi7srlQVU9Q1XVARbj2rT8O/g68iethV8b+j4NA77rjIhlrK/GOCIq3Dtep4zDgO7gr1fF+3eOB70cy3oMW7jq4tn4Ad+CqtH6Ku3y8w5d/i2btKLhxAXfiLpGvwF3pdG+2TFTqKHFVXz/3r7+HO/Afx525nufLBwMnBcca7nhxB/vHwFvAcb7s6hb27dt81rPlcv/cM5L7FrgNN7bpR83K93csXAV0jXS8rfwcD+LGLZyLq9L5Kq49bjEwKWi54N6QYWtn8d+f6n+/ft6s/Nv7ORa+EYgvEvvW/078DvgQ94d4ETC1leM2+Di40h8HEYn1IOMNjHv6M/C9SO7btnjE4xXMVtycO7/GHSg5vvxVYEugegk+V6VwNm46he3gztb9MtGqo1wD9BWR3wHXA9txv6jX4C6LUdUiVf0v7Lmc1wjEezBVdyeJSIq66rs9VWYRiPUk3Ej23/rt9vTlr+HaUfZ1LJwJNEQh3n2ZiTtD3azu6updXBXUw8CUoOW2wWej+sMc7/6q7vr68ubHwsm+V1ugOlogrPs2E9e2co6q/gl3k7WT/WevAqUi0juwcLPj4CzccbBnnFCYYz2YeNW3t43HnfQR4XgPSdxNFaOqd8GeS/DHgaUicgWu7ryJz6ZMaF6lUNjse8Iy4OwA1OFGaRfjEkoObsbTJlxjXaaqlgcWjmC8wVV3p/qqu0parrqrw1XdLQj+Av2syizcvgs8ISIf4q5atonIJ7izQiX0YyEqMwwHbf8NEbkP12NwKq7zwQTcmW5G4FgI/CGJ0LGwp+oOdyLXXUQ+wh0DVXy2b/d3LIT1j5+6NofbYc8f3EYfH7j2uE7su5qx+XEQ9j/UBxKviIzB1dR0w+3bjyId7yGL9iXUwT6AC3BVHfm4XkM/AP4LfClomYhVKRzkzzAgOFZc75wMYFAUY4rJqrtW4n0YdyvYqbg67Ed83G/H07Hg47oPd2b9Aa6HYXdgcBTjaa3qbnIsHQt81lV+MrAqqPzf+CqoWDoOQokXN8fY2OB9G61j4WAfgfq9uCYiX8VVl7wBnKqq1/pyUXUTGWr0r1ha5M9i9kzhEdzbLUrxDANuAHbi6ql34O6C9y7wf6p6dbPlo7pvxU0FlKWqm/z7wLHwOnBaPB0LACKSpb4nUAwcC6cBLwMnq+ocXy12HVACDFPVa5otH/V9K67b97PA3ar6poicCXxRVa/zn8fUcbCPeM9U1e80Wy4m4j1Q8dgG05JU3M9ShWuHyYTPLiFj+T9Gnabg99GMh8+q7vrjqu6mAZfiukSWBPZtQLT3rarWBpKL19U/7yTOjgXYU4Wyp80tyrG8gbuqCsx9Fai6KwI2+e61wcvHwr7NwA1Z2Onf1+HaD2P1OGgp3rJY+z07WHF7BeMz/xjcgKNUXI+QclwXylXRjC3eicgAVV3vX2fjkvduXPXC6qgG1wJx90MZi7uxUmfcsbAdN0uyHQuHyLcP9cBVh30b196ZrapF0YxrX0RkLjBDVf8kbhxRp1g8bgPiLd4DEc8JJhk3PUIXVX3Yl0W1SiGRxFrV3f6IyPm4qrI9czTFcrzxJpaq7vYlqPqrp/oegrEs3uI9GHGbYJqL1zpK0/bsWAifeNi3QX+4YzIRNhdv8R6IhEkwxhhjYkuiNPIbY4yJMZZgjDHGhIUlGGOMMWFhCcYYY0xYWIIxxhgTFpZgjDHGhIUlGGOMMWHx//vhM5EMc/8vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "#to be able to read the labels, we'll use a built in function to tilt them\n",
    "fig.autofmt_xdate()\n",
    "# and then define the number of tick markers to show\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "\n",
    "ax.plot(formatted_date[300:-1], tenn_data['TOTAL_CASES'][300:-1])\n",
    "plt.ylabel('number of positive cases')\n",
    "ax.invert_xaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us  use the scipy optimize routine to fit this region, so we can interpolate the initial exponential growth. For more information of scipy, follow this [link.](https://docs.scipy.org/doc/scipy/reference/index.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exp_func(x, a, b, c):\n",
    "    return a * np.exp(-b * x) + c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an array of 'N' points from 1 to 0 corresponding to the number of entrees\n",
    "# This is necessary since the x-axis is dates rather than floats, and is in a reversed order\n",
    "\n",
    "xdata = np.linspace(1, 0, len(tenn_data['TOTAL_CASES']))\n",
    "\n",
    "#fit the curve, but limit to that middle data above\n",
    "popt, pcov = curve_fit(exp_func, xdata[300:-1], tenn_data['TOTAL_CASES'][300:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Curve fit doesn't give you an R-squared value by default, so we just need to do a few quick calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9945568860185705\n"
     ]
    }
   ],
   "source": [
    "residuals = tenn_data['TOTAL_CASES'][300:-1]- exp_func(xdata[300:-1], *popt)\n",
    "#calculate residual sum of squares\n",
    "ss_res = np.sum(residuals**2)\n",
    "#get the total sum of squares\n",
    "ss_tot = np.sum((tenn_data['TOTAL_CASES'][300:-1]-np.mean(tenn_data['TOTAL_CASES'][300:-1]))**2)\n",
    "#get the r-squared value\n",
    "r_squared = 1 - (ss_res / ss_tot)\n",
    "print(r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "#to be able to read the labels, we'll use a built in function to tilt them\n",
    "fig.autofmt_xdate()\n",
    "# and then define the number of tick markers to show\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "\n",
    "ax.plot(formatted_date[300:-1], tenn_data['TOTAL_CASES'][300:-1])\n",
    "ax.plot(formatted_date[300:-1],  exp_func(xdata[300:-1], *popt), 'r--')\n",
    "\n",
    "\n",
    "plt.ylabel('number of positive cases')\n",
    "ax.invert_xaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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