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README.md

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+# USA COVID-19 Dashboard
+Link to the app: https://share.streamlit.io/idsf21/assignment-2-anveshrithaa/main

requirements.txt

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+pandas
+streamlit
+plotly==5.3.1
+plotly_express==0.4.0

streamlit_app.py

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+import pandas as pd
+import io 
+import requests 
+import streamlit as st 
+import plotly.express as px
+
+
+header = st.container()
+user_input = st.container()
+output_graphs = st.container()
+
+with header:
+    st.title("USA Covid-19 Dashboard")
+    st.markdown("""
+    This is an interavtive application to track the number of daily covid-19 cases, death counts and the moving average for every state and county in the United States.
+    Select the state and county from the dropdown menu to view its covid-19 statistics. 
+    """)
+
+# Fetch Dataset from the New York Times Github Repository
+url = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv'
+s = requests.get(url).content
+df = pd.read_csv(io.StringIO(s.decode('utf-8')), parse_dates=True, index_col='date')
+
+with user_input:
+    st.sidebar.header('User Selection') 
+
+    # Generating the list for states
+    states_list = []
+    counties_list = []
+
+    states_list = df.state.unique()
+    states_list.sort()
+
+    select = st.sidebar.selectbox('Choose Bar Chart',['Total Cases','Total Deaths'],key='2')
+
+    state = st.sidebar.selectbox('State:',states_list) # We define the state variable
+
+    df_states = df[(df.state == state)].copy()
+    counties_list = df_states.county.unique()
+    conties_list = counties_list.sort()
+    us_state_abbrev = {'Alabama': 'AL', 'Alaska': 'AK', 'Arizona': 'AZ', 'Arkansas': 'AR', 'California': 'CA', 'Colorado': 'CO',
+'Connecticut': 'CT', 'Delaware': 'DE', 'Florida': 'FL', 'Georgia': 'GA', 'Hawaii': 'HI', 'Idaho': 'ID',
+'Illinois': 'IL', 'Indiana': 'IN', 'Iowa': 'IA', 'Kansas': 'KS', 'Kentucky': 'KY', 'Louisiana': 'LA',
+'Maine': 'ME', 'Maryland': 'MD', 'Massachusetts': 'MA', 'Michigan': 'MI', 'Minnesota': 'MN', 'Mississippi': 'MS',
+'Missouri': 'MO', 'Montana': 'MT', 'Nebraska': 'NE', 'Nevada': 'NV', 'New Hampshire': 'NH', 'New Jersey': 'NJ',
+'New Mexico': 'NM', 'New York': 'NY', 'North Carolina': 'NC', 'North Dakota': 'ND', 'Ohio': 'OH', 'Oklahoma': 'OK',
+'Oregon': 'OR', 'Pennsylvania': 'PA', 'Rhode Island': 'RI', 'South Carolina': 'SC', 'South Dakota': 'SD',
+'Tennessee': 'TN', 'Texas': 'TX', 'Utah': 'UT', 'Vermont': 'VT', 'Virginia': 'VA', 'Washington': 'WA',
+'West Virginia': 'WV', 'Wisconsin': 'WI', 'Wyoming': 'WY'}
+
+    df_total0 = df.groupby(['state', 'county']).max()
+    #df_total = df_total.to_frame()
+    df_total0.reset_index(level=0, inplace=True)
+    df_total = df_total0.groupby('state').sum()
+    df_total.reset_index(level=0, inplace=True)
+    df_total['state'] = df_total['state'].map(us_state_abbrev).fillna(df_total['state'])
+
+
+    county = st.sidebar.selectbox('County:',counties_list) 
+
+    num_states = st.sidebar.slider('Number of states to be displayed in bar graph. ', min_value = 1, max_value= 50, value= 10, step=1)
+    table_days = st.sidebar.slider('Number of days to be displayed in Summary Statistics. ', min_value = 3, max_value= 20, value= 7, step=1)
+
+    moving_average_day = st.sidebar.slider('Number of days for moving average', min_value = 5, max_value = 20, value = 7, step=1)
+
+    # Creating the dataframe for the county
+    df_county = df[(df.county == county)& (df.state == state)].copy()
+
+    #Create a new column with new cases
+    df_county['New Cases'] = df_county.loc[:, 'cases'].diff()
+
+    #Create a new column for 7-day moving average
+    df_county['Moving Average'] = df_county.loc[:,'New Cases'].rolling(window=moving_average_day).mean()
+    df_county['deaths'] = df_county['deaths'].astype(int)
+    df_county1 = df_county.rename(columns={'cases': 'Total Cases', 'deaths': 'Total Deaths'})
+
+with output_graphs:
+    st.subheader(f'Total number of cases vs Total number of deaths for {num_states} states')
+    st.markdown("""Select number of states to be displayed from the user selection menu""")
+    df_total2 = df_total.sort_values(by=['cases'], ascending=False)
+    df_total2 = df_total2.head(num_states)
+    plot = px.scatter(data_frame = df_total2, x = 'cases', y = 'deaths', color = 'state', size = 'cases')
+    st.plotly_chart(plot)
+
+    st.markdown("""""")
+    if select == "Total Cases": 
+        st.subheader(f'Top {num_states} states with highest number of covid-19 cases')
+        st.markdown("""Choose bar chart type from user selection menu""")
+        df_total = df_total.sort_values(by=['cases'], ascending=False)
+        fig = px.bar(df_total.head(num_states), y='cases',x='state',color='state',height=400)
+        fig.update_layout(xaxis_title='State',yaxis_title='Total Cases',template="plotly_dark")
+        st.plotly_chart(fig)
+
+
+
+    elif select == "Total Deaths":
+        st.subheader(f'Top {num_states} states with highest number of deaths')
+        st.markdown("""Choose bar chart type from user selection menu""")
+        df_total = df_total.sort_values(by=['deaths'], ascending=False)   
+        fig = px.bar(df_total.head(num_states), y='deaths',x='state',color='state',height=400)
+        fig.update_layout(title=f'Top {num_states} states highest number of COVID-19 deaths',xaxis_title='Country',yaxis_title='Total deaths',template="plotly_dark")
+        st.plotly_chart(fig)
+
+    a = df_county1.iloc[-table_days:, -4:]
+    st.header(f'Summary Table for the last {table_days} days for {county} county')
+    st.markdown(""" Number of cases, deaths, new cases and moving average for the selected county.""")
+
+    my_table = st.table(a)
+    #t1 = st.table(df_total)
+
+    st.header(f'Total Cases for {county}, {state}.')
+
+    total_cases_chart = df_county['cases']
+
+    st.line_chart(total_cases_chart)
+
+    st.header(f'{moving_average_day} day moving average for {county}, {state}.')
+
+    moving_average_chart = df_county['Moving Average']
+
+    st.line_chart(moving_average_chart)
+
+
+