corona_overtime.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython import display\n",
    "#from ipywidgets import Button, Layout\n",
    "#import tkinter as tk\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "#from functools import partial\n",
    "import pandas as pd\n",
    "import xlrd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Cook</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17031.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     county       state     fips  cases  deaths\n",
       "0  2020-01-21  Snohomish  Washington  53061.0      1       0\n",
       "1  2020-01-22  Snohomish  Washington  53061.0      1       0\n",
       "2  2020-01-23  Snohomish  Washington  53061.0      1       0\n",
       "3  2020-01-24       Cook    Illinois  17031.0      1       0\n",
       "4  2020-01-24  Snohomish  Washington  53061.0      1       0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df = pd.read_csv(r'/Users/eileenyang/JupyterPython/Corona/covid_may25.csv')\n",
    "covid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>totcases</th>\n",
       "      <th>totdeaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>70092.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>76151.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date     fips  cases  deaths  totcases  totdeaths\n",
       "0 2020-01-21  53061.0      1       0         1          0\n",
       "1 2020-01-22  53061.0      1       0         1          0\n",
       "2 2020-01-23  53061.0      1       0         1          0\n",
       "3 2020-01-24  70092.0      2       0         2          0\n",
       "4 2020-01-25  76151.0      3       0         3          0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidus_df = covid_df.groupby(['date']).sum()\n",
    "\n",
    "covidus_df = covidus_df.reset_index()\n",
    "\n",
    "covidus_df['date'] =  pd.to_datetime(covidus_df['date'])\n",
    "\n",
    "covidus_df['totcases'] = covidus_df['cases']\n",
    "covidus_df['totdeaths'] = covidus_df['deaths']\n",
    "\n",
    "covidus_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17031.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date       state     fips  cases  deaths\n",
       "0 2020-01-21  Washington  53061.0      1       0\n",
       "1 2020-01-22  Washington  53061.0      1       0\n",
       "2 2020-01-23  Washington  53061.0      1       0\n",
       "3 2020-01-24    Illinois  17031.0      1       0\n",
       "4 2020-01-24  Washington  53061.0      1       0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidstate_df = covid_df.groupby(['date','state']).sum()\n",
    "\n",
    "covidstate_df = covidstate_df.reset_index()\n",
    "\n",
    "covidstate_df['date'] =  pd.to_datetime(covidstate_df['date'])\n",
    "\n",
    "covidstate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'US COVID-19 Cases Over Time')"
      ]
     },
     "execution_count": 14,
     "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": [
    "#graphing total cases in the US\n",
    "covidus_df = covidus_df.sort_values('date', ascending=True)\n",
    "plt.plot(covidus_df['date'], covidus_df['cases'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"US COVID-19 Cases Over Time\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "def state_compare(state1, state2):\n",
    "    plt.plot(covidstate_df[covidstate_df['state']==state1]['date'],covidstate_df[covidstate_df['state']==state1]['cases'], label=state1)\n",
    "    plt.plot(covidstate_df[covidstate_df['state']==state2]['date'],covidstate_df[covidstate_df['state']==state2]['cases'], label=state2)\n",
    "    plt.xticks(rotation='vertical')\n",
    "    plt.title(\"COVID-19 Cases Over Time in \" +state1+ \" and \"+ state2)\n",
    "    plt.legend(loc=\"upper left\")\n",
    "state_compare(\"Washington\", \"North Carolina\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>totcases</th>\n",
       "      <th>totdeaths</th>\n",
       "      <th>state</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  totcases  totdeaths       state  cases  deaths\n",
       "0 2020-01-21         1          0  Washington      1       0\n",
       "1 2020-01-22         1          0  Washington      1       0\n",
       "2 2020-01-23         1          0  Washington      1       0\n",
       "3 2020-01-24         2          0    Illinois      1       0\n",
       "4 2020-01-24         2          0  Washington      1       0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidus = covidus_df.drop(columns=['fips','cases','deaths'])\n",
    "#print(covidus)\n",
    "covidstate = covidstate_df.drop(columns=['fips'])\n",
    "#print(covidstate)\n",
    "\n",
    "covidall = covidus.merge(covidstate, on = ['date'])\n",
    "covidall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#a function to compare two states as well as the US total, if desired\n",
    "def state_compare2(state1, state2, us=None):\n",
    "\n",
    "   # if state2=None:\n",
    "   #     plt.plot(covidall['date'],covidall['totcases'])\n",
    "   #     plt.plot(covidall[covidall['state']==state1]['date'],covidall[covidall['state']==state1]['cases'])\n",
    "\n",
    "    if us is None:\n",
    "        plt.plot(covidall[covidall['state']==state1]['date'],covidall[covidall['state']==state1]['cases'], label=state1)\n",
    "        plt.plot(covidall[covidall['state']==state2]['date'],covidall[covidall['state']==state2]['cases'], label=state2)\n",
    "        plt.xticks(rotation='vertical')\n",
    "        plt.title(\"COVID-19 Cases Over Time in \" +state1+ \" and \"+ state2)\n",
    "        plt.legend(loc=\"upper left\")\n",
    "        plt.figure()\n",
    "\n",
    "    else:\n",
    "        plt.plot(covidall['date'],covidall['totcases'], label=\"US\")\n",
    "        plt.plot(covidall[covidall['state']==state1]['date'],covidall[covidall['state']==state1]['cases'], label=state1)\n",
    "        plt.plot(covidall[covidall['state']==state2]['date'],covidall[covidall['state']==state2]['cases'], label=state2)\n",
    "        plt.xticks(rotation='vertical')\n",
    "        plt.title(\"COVID-19 Cases Over Time in \" +state1+ \", \"+ state2+\" and the US\")\n",
    "        plt.legend(loc=\"upper left\")\n",
    "        plt.figure()\n",
    "    \n",
    "    \n",
    "#now we call the function\n",
    "state_compare2(\"New York\", \"Washington\", \"us\")\n",
    "state_compare2(\"New York\", \"Washington\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'District of Columbia', 'Florida', 'Georgia', 'Guam', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Northern Mariana Islands', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Puerto Rico', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virgin Islands', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'update' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-424a94efd36e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0mcontinuous_update\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m )\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mint_slider\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobserve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'value'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0mint_slider\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'update' is not defined"
     ]
    }
   ],
   "source": [
    "states = covidall['state'].unique()\n",
    "states = sorted(states)\n",
    "print(states)\n",
    "\n",
    "\n",
    "dropdown1 = widgets.Dropdown(\n",
    "    value='Alabama', \n",
    "    options=sorted(covidall['state'].unique()), \n",
    "    description='State 1'\n",
    ")\n",
    "dropdown2 = widgets.Dropdown(\n",
    "    value='Alabama', \n",
    "    options=sorted(covidall['state'].unique()), \n",
    "    description='State 2'\n",
    ")\n",
    "checkbox = widgets.Checkbox(\n",
    "    description='Include US Overall',\n",
    "    value=True\n",
    ")\n",
    "\n",
    "widgets.VBox([dropdown1,dropdown2,checkbox])\n",
    "\n",
    "def update(change):\n",
    "    \n",
    "\n",
    "#def show_change(change):\n",
    " #   display(change)\n",
    "\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0ebda2f5679427ca0b96e705902d723",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=10, description='a', max=30, min=-10), IntSlider(value=20, description='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "\n",
    "def f(a, b):\n",
    "    display(a + b)\n",
    "    return a+b\n",
    "\n",
    "w = interactive(f, a=10, b=20)\n",
    "display(w)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ebf50c147284257b155a711c8bba230",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='state1', options=('Alabama', 'Alaska', 'Arizona', 'Arkansas', 'Cal…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def state_compare(state1, state2):\n",
    "    plt.plot(covidstate_df[covidstate_df['state']==state1]['date'],covidstate_df[covidstate_df['state']==state1]['cases'], label=state1)\n",
    "    plt.plot(covidstate_df[covidstate_df['state']==state2]['date'],covidstate_df[covidstate_df['state']==state2]['cases'], label=state2)\n",
    "    plt.xticks(rotation='vertical')\n",
    "    plt.title(\"COVID-19 Cases Over Time in \" +state1+ \" and \"+ state2)\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    \n",
    "w=interactive(state_compare, state1=sorted(covidall['state'].unique()), state2=sorted(covidall['state'].unique()))\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae108d46988546b89b336a5f5bd13fc9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='State1', options=('Alabama', 'Alaska', 'Arizona', 'Arkansas', 'Cal…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def state_compare2(State1, State2, US=True):\n",
    "\n",
    "   # if state2=None:\n",
    "   #     plt.plot(covidall['date'],covidall['totcases'])\n",
    "   #     plt.plot(covidall[covidall['state']==state1]['date'],covidall[covidall['state']==state1]['cases'])\n",
    "\n",
    "    if US is False:\n",
    "        plt.plot(covidall[covidall['state']==State1]['date'],covidall[covidall['state']==State1]['cases'], label=State1)\n",
    "        plt.plot(covidall[covidall['state']==State2]['date'],covidall[covidall['state']==State2]['cases'], label=State2)\n",
    "        plt.xticks(rotation='vertical')\n",
    "        plt.title(\"COVID-19 Cases Over Time in \" +State1+ \" and \"+ State2)\n",
    "        plt.legend(loc=\"upper left\")\n",
    "        plt.figure()\n",
    "\n",
    "    else:\n",
    "        plt.plot(covidall['date'],covidall['totcases'], label=\"US\")\n",
    "        plt.plot(covidall[covidall['state']==State1]['date'],covidall[covidall['state']==State1]['cases'], label=State1)\n",
    "        plt.plot(covidall[covidall['state']==State2]['date'],covidall[covidall['state']==State2]['cases'], label=State2)\n",
    "        plt.xticks(rotation='vertical')\n",
    "        plt.title(\"COVID-19 Cases Over Time in \" +State1+ \", \"+ State2+\" and the US\")\n",
    "        plt.legend(loc=\"upper left\")\n",
    "        plt.figure()\n",
    "\n",
    "w2=interactive(state_compare2, State1=sorted(covidall['state'].unique()), State2=sorted(covidall['state'].unique()), US=True)\n",
    "display(w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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