Prophet_Code_Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Importing plotly failed. Interactive plots will not work.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from fbprophet import Prophet\n",
    "from fbprophet.plot import plot_plotly, plot_components_plotly\n",
    "from fbprophet.diagnostics import cross_validation\n",
    "from fbprophet.diagnostics import performance_metrics\n",
    "from fbprophet.plot import plot_cross_validation_metric\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-976439e19f7f>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cleaned_stay_at_home_df['cases_per_tests_ratio'] = cleaned_stay_at_home_df['total_cases']/cleaned_stay_at_home_df['total_tests']\n"
     ]
    }
   ],
   "source": [
    "stay_at_home_df = pd.read_csv(\"https://covid.ourworldindata.org/data/owid-covid-data.csv\")\n",
    "stay_at_home_df.head()\n",
    "stay_at_home_df.columns\n",
    "cleaned_stay_at_home_df = stay_at_home_df[['location','date','total_cases','total_tests','stringency_index','positive_rate','total_deaths']]\n",
    "cleaned_stay_at_home_df\n",
    "cleaned_stay_at_home_df['cases_per_tests_ratio'] = cleaned_stay_at_home_df['total_cases']/cleaned_stay_at_home_df['total_tests']\n",
    "cleaned_stay_at_home_df\n",
    "cleaned_stay_at_home_df.dtypes\n",
    "groupby_location_df = cleaned_stay_at_home_df.groupby('location',as_index=False)\n",
    "separated_location_df = dict(iter(groupby_location_df))\n",
    "us_covid_df = separated_location_df['United States']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>location</th>\n",
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       "      <th>stringency_index</th>\n",
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       "      <th>47619</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-01-01</td>\n",
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       "      <th>47620</th>\n",
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       "      <td>2020-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>47621</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>47622</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            location        date  total_cases  total_tests  stringency_index  \\\n",
       "47618  United States  2019-12-31          NaN          NaN               NaN   \n",
       "47619  United States  2020-01-01          NaN          NaN               0.0   \n",
       "47620  United States  2020-01-02          NaN          NaN               0.0   \n",
       "47621  United States  2020-01-03          NaN          NaN               0.0   \n",
       "47622  United States  2020-01-04          NaN          NaN               0.0   \n",
       "\n",
       "       positive_rate  total_deaths  cases_per_tests_ratio  \n",
       "47618            NaN           NaN                    NaN  \n",
       "47619            NaN           NaN                    NaN  \n",
       "47620            NaN           NaN                    NaN  \n",
       "47621            NaN           NaN                    NaN  \n",
       "47622            NaN           NaN                    NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_covid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_tests</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>positive_rate</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>cases_per_tests_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44033</th>\n",
       "      <td>United States</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>44034</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>44035</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44036</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>44037</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>44274</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>5867785.0</td>\n",
       "      <td>87644326.0</td>\n",
       "      <td>67.13</td>\n",
       "      <td>0.050</td>\n",
       "      <td>180824.0</td>\n",
       "      <td>0.066950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44275</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>5917439.0</td>\n",
       "      <td>88506633.0</td>\n",
       "      <td>67.13</td>\n",
       "      <td>0.049</td>\n",
       "      <td>181773.0</td>\n",
       "      <td>0.066859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44276</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>5961582.0</td>\n",
       "      <td>89126574.0</td>\n",
       "      <td>67.13</td>\n",
       "      <td>0.049</td>\n",
       "      <td>182779.0</td>\n",
       "      <td>0.066889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44277</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>5997163.0</td>\n",
       "      <td>89825565.0</td>\n",
       "      <td>67.13</td>\n",
       "      <td>0.049</td>\n",
       "      <td>183069.0</td>\n",
       "      <td>0.066765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44278</th>\n",
       "      <td>United States</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>6031013.0</td>\n",
       "      <td>90694206.0</td>\n",
       "      <td>67.13</td>\n",
       "      <td>0.048</td>\n",
       "      <td>183598.0</td>\n",
       "      <td>0.066498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            location        date  total_cases  total_tests  stringency_index  \\\n",
       "44033  United States  2019-12-31          0.0          NaN               NaN   \n",
       "44034  United States  2020-01-01          0.0          NaN              0.00   \n",
       "44035  United States  2020-01-02          0.0          NaN              0.00   \n",
       "44036  United States  2020-01-03          0.0          NaN              0.00   \n",
       "44037  United States  2020-01-04          0.0          NaN              0.00   \n",
       "...              ...         ...          ...          ...               ...   \n",
       "44274  United States  2020-08-28    5867785.0   87644326.0             67.13   \n",
       "44275  United States  2020-08-29    5917439.0   88506633.0             67.13   \n",
       "44276  United States  2020-08-30    5961582.0   89126574.0             67.13   \n",
       "44277  United States  2020-08-31    5997163.0   89825565.0             67.13   \n",
       "44278  United States  2020-09-01    6031013.0   90694206.0             67.13   \n",
       "\n",
       "       positive_rate  total_deaths  cases_per_tests_ratio  \n",
       "44033            NaN           0.0                    NaN  \n",
       "44034            NaN           0.0                    NaN  \n",
       "44035            NaN           0.0                    NaN  \n",
       "44036            NaN           0.0                    NaN  \n",
       "44037            NaN           0.0                    NaN  \n",
       "...              ...           ...                    ...  \n",
       "44274          0.050      180824.0               0.066950  \n",
       "44275          0.049      181773.0               0.066859  \n",
       "44276          0.049      182779.0               0.066889  \n",
       "44277          0.049      183069.0               0.066765  \n",
       "44278          0.048      183598.0               0.066498  \n",
       "\n",
       "[246 rows x 8 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_prediction = us_covid_df[:-30]\n",
    "us_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyClass:\n",
    "    def __init__(self, series, fig):\n",
    "        self.series = series\n",
    "        self.figure = fig\n",
    "        self.cid = self.figure.canvas.mpl_connect('button_press_event', self)\n",
    "\n",
    "    def __call__(self, event):\n",
    "        self.series.plot([us_prediction['date']], [us_prediction['total_cases']])  \n",
    "        self.figure.canvas.draw()\n",
    "\n",
    "def plot_metrics(forecast_cv,location, imgname):\n",
    "    mse_plot = plot_cross_validation_metric(forecast_cv, metric = 'mse')\n",
    "    mse_plot.savefig(f'./Seasonality_Comparison/{location}_mse_{imgname}')\n",
    "                      \n",
    "    rmse_plot =plot_cross_validation_metric(forecast_cv, metric = 'rmse')\n",
    "    rmse_plot.savefig(f'./Seasonality_Comparison/{location}_rmse_{imgname}')\n",
    "                      \n",
    "    mae_plot = plot_cross_validation_metric(forecast_cv, metric = 'mae')\n",
    "    mae_plot.savefig(f'./Seasonality_Comparison/{location}_mae_{imgname}')\n",
    "                      \n",
    "    mape_plot = plot_cross_validation_metric(forecast_cv, metric = 'mape')\n",
    "    mae_plot.savefig(f'./Seasonality_Comparison/{location}_mape_{imgname}')\n",
    "                      \n",
    "                      \n",
    "def plot_forecast(forecast,m,location,img_name):\n",
    "    forecastplot = m.plot(forecast)\n",
    "    forecastplot.savefig(f'./Seasonality_Comparison/{location}_forecast_{img_name}')\n",
    "    \n",
    "    plot_components = m.plot_components(forecast)\n",
    "    plot_components.savefig(f'./Seasonality_Comparison/{location}_components_{img_name}')\n",
    "                             \n",
    "                             \n",
    "\n",
    "def prophet_predicts(location_df,location, yearly_seasonality, weekly_seasonality):\n",
    "    print(location_df.columns)\n",
    "    num_rows = str(location_df.shape[0] - 31) + ' days'\n",
    "    prophet_df = location_df[['date','total_cases']]\n",
    "    prophet_df = prophet_df.rename(columns={'date':'ds','total_cases': 'y'})\n",
    "    m = Prophet(interval_width = 0.95, yearly_seasonality = yearly_seasonality, weekly_seasonality = weekly_seasonality)\n",
    "    m.fit(prophet_df)\n",
    "    future = m.make_future_dataframe(periods=30)\n",
    "    forecast = m.predict(future)\n",
    "    forecast_cv = cross_validation(m, initial = num_rows, period = '30 days', horizon = '30 days' ) # of rows - 31 days\n",
    "    forecast_df = pd.DataFrame(forecast_cv)\n",
    "    file_name = 'daily'\n",
    "    if(yearly_seasonality):\n",
    "        file_name = 'yearly_' + file_name\n",
    "        \n",
    "    if(weekly_seasonality):\n",
    "        file_name = 'weekly_' + file_name\n",
    "    \n",
    "    \n",
    "    forecast_df.to_csv(f'predicted_{file_name}.csv')\n",
    "    \n",
    "\n",
    "#     plot_forecast(forecast,m, location, img_name)\n",
    "#     plot_metrics(forecast_cv, location, img_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['location', 'date', 'total_cases', 'total_tests', 'stringency_index',\n",
      "       'positive_rate', 'total_deaths', 'cases_per_tests_ratio'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Making 1 forecasts with cutoffs between 2020-09-01 00:00:00 and 2020-09-01 00:00:00\n",
      "WARNING:fbprophet:Seasonality has period of 365.25 days which is larger than initial window. Consider increasing initial.\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Index(['location', 'date', 'total_cases', 'total_tests', 'stringency_index',\n",
      "       'positive_rate', 'total_deaths', 'cases_per_tests_ratio'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Making 1 forecasts with cutoffs between 2020-09-01 00:00:00 and 2020-09-01 00:00:00\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Index(['location', 'date', 'total_cases', 'total_tests', 'stringency_index',\n",
      "       'positive_rate', 'total_deaths', 'cases_per_tests_ratio'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Making 1 forecasts with cutoffs between 2020-09-01 00:00:00 and 2020-09-01 00:00:00\n"
     ]
    },
    {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Index(['location', 'date', 'total_cases', 'total_tests', 'stringency_index',\n",
      "       'positive_rate', 'total_deaths', 'cases_per_tests_ratio'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Making 1 forecasts with cutoffs between 2020-09-01 00:00:00 and 2020-09-01 00:00:00\n",
      "WARNING:fbprophet:Seasonality has period of 365.25 days which is larger than initial window. Consider increasing initial.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1b65e11f98f48de87319a0c45d151a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "prophet_predicts(us_prediction, 'United_States', yearly_seasonality = True, weekly_seasonality = False) \n",
    "\n",
    "prophet_predicts(us_prediction, 'United_States', yearly_seasonality = False, weekly_seasonality = False)\n",
    "\n",
    "prophet_predicts(us_prediction, 'United_States', yearly_seasonality = False, weekly_seasonality = True) \n",
    "\n",
    "prophet_predicts(us_prediction, 'United_States', yearly_seasonality = True,  weekly_seasonality = True)\n",
    "# forecast_dfs[0].to_csv('predicted_yearly_weekly.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(215, 8)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_join = us_prediction[:-30]\n",
    "us_join.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>y</th>\n",
       "      <th>cutoff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-08-02</td>\n",
       "      <td>4.484069e+06</td>\n",
       "      <td>4.404893e+06</td>\n",
       "      <td>4.556888e+06</td>\n",
       "      <td>4620444.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08-03</td>\n",
       "      <td>4.536675e+06</td>\n",
       "      <td>4.461733e+06</td>\n",
       "      <td>4.614419e+06</td>\n",
       "      <td>4667955.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-08-04</td>\n",
       "      <td>4.589636e+06</td>\n",
       "      <td>4.511375e+06</td>\n",
       "      <td>4.670260e+06</td>\n",
       "      <td>4713562.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>4.644685e+06</td>\n",
       "      <td>4.563468e+06</td>\n",
       "      <td>4.725630e+06</td>\n",
       "      <td>4771087.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>4.700427e+06</td>\n",
       "      <td>4.624459e+06</td>\n",
       "      <td>4.778472e+06</td>\n",
       "      <td>4823891.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-08-07</td>\n",
       "      <td>4.757801e+06</td>\n",
       "      <td>4.679140e+06</td>\n",
       "      <td>4.842249e+06</td>\n",
       "      <td>4883646.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>4.815148e+06</td>\n",
       "      <td>4.738716e+06</td>\n",
       "      <td>4.895842e+06</td>\n",
       "      <td>4941796.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-08-09</td>\n",
       "      <td>4.867407e+06</td>\n",
       "      <td>4.783746e+06</td>\n",
       "      <td>4.952940e+06</td>\n",
       "      <td>4998017.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-08-10</td>\n",
       "      <td>4.920013e+06</td>\n",
       "      <td>4.833570e+06</td>\n",
       "      <td>5.014387e+06</td>\n",
       "      <td>5044864.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>4.972974e+06</td>\n",
       "      <td>4.885735e+06</td>\n",
       "      <td>5.067232e+06</td>\n",
       "      <td>5094394.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-08-12</td>\n",
       "      <td>5.028023e+06</td>\n",
       "      <td>4.932994e+06</td>\n",
       "      <td>5.128886e+06</td>\n",
       "      <td>5141207.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-08-13</td>\n",
       "      <td>5.083766e+06</td>\n",
       "      <td>4.979247e+06</td>\n",
       "      <td>5.183582e+06</td>\n",
       "      <td>5197148.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-08-14</td>\n",
       "      <td>5.141139e+06</td>\n",
       "      <td>5.038105e+06</td>\n",
       "      <td>5.250555e+06</td>\n",
       "      <td>5248242.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-08-15</td>\n",
       "      <td>5.198486e+06</td>\n",
       "      <td>5.081485e+06</td>\n",
       "      <td>5.308028e+06</td>\n",
       "      <td>5313080.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-08-16</td>\n",
       "      <td>5.250745e+06</td>\n",
       "      <td>5.135254e+06</td>\n",
       "      <td>5.375754e+06</td>\n",
       "      <td>5361165.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>5.303351e+06</td>\n",
       "      <td>5.171800e+06</td>\n",
       "      <td>5.439181e+06</td>\n",
       "      <td>5403269.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-08-18</td>\n",
       "      <td>5.356312e+06</td>\n",
       "      <td>5.214915e+06</td>\n",
       "      <td>5.512359e+06</td>\n",
       "      <td>5438325.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-08-19</td>\n",
       "      <td>5.411361e+06</td>\n",
       "      <td>5.253616e+06</td>\n",
       "      <td>5.567062e+06</td>\n",
       "      <td>5482416.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-08-20</td>\n",
       "      <td>5.467104e+06</td>\n",
       "      <td>5.315679e+06</td>\n",
       "      <td>5.634107e+06</td>\n",
       "      <td>5529842.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>5.524477e+06</td>\n",
       "      <td>5.368754e+06</td>\n",
       "      <td>5.708258e+06</td>\n",
       "      <td>5573847.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-08-22</td>\n",
       "      <td>5.581824e+06</td>\n",
       "      <td>5.406135e+06</td>\n",
       "      <td>5.778899e+06</td>\n",
       "      <td>5623727.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-08-23</td>\n",
       "      <td>5.634083e+06</td>\n",
       "      <td>5.438855e+06</td>\n",
       "      <td>5.830926e+06</td>\n",
       "      <td>5668105.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-08-24</td>\n",
       "      <td>5.686689e+06</td>\n",
       "      <td>5.487131e+06</td>\n",
       "      <td>5.902403e+06</td>\n",
       "      <td>5702611.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-08-25</td>\n",
       "      <td>5.739650e+06</td>\n",
       "      <td>5.525729e+06</td>\n",
       "      <td>5.965786e+06</td>\n",
       "      <td>5740909.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-08-26</td>\n",
       "      <td>5.794699e+06</td>\n",
       "      <td>5.573582e+06</td>\n",
       "      <td>6.050682e+06</td>\n",
       "      <td>5779028.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>5.850442e+06</td>\n",
       "      <td>5.611302e+06</td>\n",
       "      <td>6.100082e+06</td>\n",
       "      <td>5821876.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>5.907815e+06</td>\n",
       "      <td>5.657785e+06</td>\n",
       "      <td>6.188213e+06</td>\n",
       "      <td>5867785.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>5.965162e+06</td>\n",
       "      <td>5.698940e+06</td>\n",
       "      <td>6.251827e+06</td>\n",
       "      <td>5917439.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>6.017421e+06</td>\n",
       "      <td>5.753705e+06</td>\n",
       "      <td>6.319244e+06</td>\n",
       "      <td>5961582.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>6.070027e+06</td>\n",
       "      <td>5.778783e+06</td>\n",
       "      <td>6.396157e+06</td>\n",
       "      <td>5997163.0</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds          yhat    yhat_lower    yhat_upper          y     cutoff\n",
       "0  2020-08-02  4.484069e+06  4.404893e+06  4.556888e+06  4620444.0 2020-08-01\n",
       "1  2020-08-03  4.536675e+06  4.461733e+06  4.614419e+06  4667955.0 2020-08-01\n",
       "2  2020-08-04  4.589636e+06  4.511375e+06  4.670260e+06  4713562.0 2020-08-01\n",
       "3  2020-08-05  4.644685e+06  4.563468e+06  4.725630e+06  4771087.0 2020-08-01\n",
       "4  2020-08-06  4.700427e+06  4.624459e+06  4.778472e+06  4823891.0 2020-08-01\n",
       "5  2020-08-07  4.757801e+06  4.679140e+06  4.842249e+06  4883646.0 2020-08-01\n",
       "6  2020-08-08  4.815148e+06  4.738716e+06  4.895842e+06  4941796.0 2020-08-01\n",
       "7  2020-08-09  4.867407e+06  4.783746e+06  4.952940e+06  4998017.0 2020-08-01\n",
       "8  2020-08-10  4.920013e+06  4.833570e+06  5.014387e+06  5044864.0 2020-08-01\n",
       "9  2020-08-11  4.972974e+06  4.885735e+06  5.067232e+06  5094394.0 2020-08-01\n",
       "10 2020-08-12  5.028023e+06  4.932994e+06  5.128886e+06  5141207.0 2020-08-01\n",
       "11 2020-08-13  5.083766e+06  4.979247e+06  5.183582e+06  5197148.0 2020-08-01\n",
       "12 2020-08-14  5.141139e+06  5.038105e+06  5.250555e+06  5248242.0 2020-08-01\n",
       "13 2020-08-15  5.198486e+06  5.081485e+06  5.308028e+06  5313080.0 2020-08-01\n",
       "14 2020-08-16  5.250745e+06  5.135254e+06  5.375754e+06  5361165.0 2020-08-01\n",
       "15 2020-08-17  5.303351e+06  5.171800e+06  5.439181e+06  5403269.0 2020-08-01\n",
       "16 2020-08-18  5.356312e+06  5.214915e+06  5.512359e+06  5438325.0 2020-08-01\n",
       "17 2020-08-19  5.411361e+06  5.253616e+06  5.567062e+06  5482416.0 2020-08-01\n",
       "18 2020-08-20  5.467104e+06  5.315679e+06  5.634107e+06  5529842.0 2020-08-01\n",
       "19 2020-08-21  5.524477e+06  5.368754e+06  5.708258e+06  5573847.0 2020-08-01\n",
       "20 2020-08-22  5.581824e+06  5.406135e+06  5.778899e+06  5623727.0 2020-08-01\n",
       "21 2020-08-23  5.634083e+06  5.438855e+06  5.830926e+06  5668105.0 2020-08-01\n",
       "22 2020-08-24  5.686689e+06  5.487131e+06  5.902403e+06  5702611.0 2020-08-01\n",
       "23 2020-08-25  5.739650e+06  5.525729e+06  5.965786e+06  5740909.0 2020-08-01\n",
       "24 2020-08-26  5.794699e+06  5.573582e+06  6.050682e+06  5779028.0 2020-08-01\n",
       "25 2020-08-27  5.850442e+06  5.611302e+06  6.100082e+06  5821876.0 2020-08-01\n",
       "26 2020-08-28  5.907815e+06  5.657785e+06  6.188213e+06  5867785.0 2020-08-01\n",
       "27 2020-08-29  5.965162e+06  5.698940e+06  6.251827e+06  5917439.0 2020-08-01\n",
       "28 2020-08-30  6.017421e+06  5.753705e+06  6.319244e+06  5961582.0 2020-08-01\n",
       "29 2020-08-31  6.070027e+06  5.778783e+06  6.396157e+06  5997163.0 2020-08-01"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_dfs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30,)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_dfs[0]['ds'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "('date', 'total_cases')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2646\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ('date', 'total_cases')",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-141-8c80e66a5331>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mus_join\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'total_cases'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2798\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2799\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2800\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2801\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2646\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2648\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ('date', 'total_cases')"
     ]
    }
   ],
   "source": [
    "us_join['date', 'total_cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cdbcd67d30>]"
      ]
     },
     "execution_count": 101,
     "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(forecast_dfs[0]['ds'],forecast_dfs[0]['y'])\n",
    "plt.plot(us_prediction['date'][-30:],us_prediction['total_cases'][-30:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on list object:\n",
      "\n",
      "class list(object)\n",
      " |  list(iterable=(), /)\n",
      " |  \n",
      " |  Built-in mutable sequence.\n",
      " |  \n",
      " |  If no argument is given, the constructor creates a new empty list.\n",
      " |  The argument must be an iterable if specified.\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __add__(self, value, /)\n",
      " |      Return self+value.\n",
      " |  \n",
      " |  __contains__(self, key, /)\n",
      " |      Return key in self.\n",
      " |  \n",
      " |  __delitem__(self, key, /)\n",
      " |      Delete self[key].\n",
      " |  \n",
      " |  __eq__(self, value, /)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __ge__(self, value, /)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getattribute__(self, name, /)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __getitem__(...)\n",
      " |      x.__getitem__(y) <==> x[y]\n",
      " |  \n",
      " |  __gt__(self, value, /)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __iadd__(self, value, /)\n",
      " |      Implement self+=value.\n",
      " |  \n",
      " |  __imul__(self, value, /)\n",
      " |      Implement self*=value.\n",
      " |  \n",
      " |  __init__(self, /, *args, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __iter__(self, /)\n",
      " |      Implement iter(self).\n",
      " |  \n",
      " |  __le__(self, value, /)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __len__(self, /)\n",
      " |      Return len(self).\n",
      " |  \n",
      " |  __lt__(self, value, /)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __mul__(self, value, /)\n",
      " |      Return self*value.\n",
      " |  \n",
      " |  __ne__(self, value, /)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __reversed__(self, /)\n",
      " |      Return a reverse iterator over the list.\n",
      " |  \n",
      " |  __rmul__(self, value, /)\n",
      " |      Return value*self.\n",
      " |  \n",
      " |  __setitem__(self, key, value, /)\n",
      " |      Set self[key] to value.\n",
      " |  \n",
      " |  __sizeof__(self, /)\n",
      " |      Return the size of the list in memory, in bytes.\n",
      " |  \n",
      " |  append(self, object, /)\n",
      " |      Append object to the end of the list.\n",
      " |  \n",
      " |  clear(self, /)\n",
      " |      Remove all items from list.\n",
      " |  \n",
      " |  copy(self, /)\n",
      " |      Return a shallow copy of the list.\n",
      " |  \n",
      " |  count(self, value, /)\n",
      " |      Return number of occurrences of value.\n",
      " |  \n",
      " |  extend(self, iterable, /)\n",
      " |      Extend list by appending elements from the iterable.\n",
      " |  \n",
      " |  index(self, value, start=0, stop=9223372036854775807, /)\n",
      " |      Return first index of value.\n",
      " |      \n",
      " |      Raises ValueError if the value is not present.\n",
      " |  \n",
      " |  insert(self, index, object, /)\n",
      " |      Insert object before index.\n",
      " |  \n",
      " |  pop(self, index=-1, /)\n",
      " |      Remove and return item at index (default last).\n",
      " |      \n",
      " |      Raises IndexError if list is empty or index is out of range.\n",
      " |  \n",
      " |  remove(self, value, /)\n",
      " |      Remove first occurrence of value.\n",
      " |      \n",
      " |      Raises ValueError if the value is not present.\n",
      " |  \n",
      " |  reverse(self, /)\n",
      " |      Reverse *IN PLACE*.\n",
      " |  \n",
      " |  sort(self, /, *, key=None, reverse=False)\n",
      " |      Sort the list in ascending order and return None.\n",
      " |      \n",
      " |      The sort is in-place (i.e. the list itself is modified) and stable (i.e. the\n",
      " |      order of two equal elements is maintained).\n",
      " |      \n",
      " |      If a key function is given, apply it once to each list item and sort them,\n",
      " |      ascending or descending, according to their function values.\n",
      " |      \n",
      " |      The reverse flag can be set to sort in descending order.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __hash__ = None\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jjft/6LgP2VTYfgQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "help(plt.plot())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}