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import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import json
#https://github.com/nytimes/covid-19-data/tree/master/rolling-averages
st.set_page_config(layout="wide")
def strip_fips(input_string):
try:
return( input_string.split('-')[1] )
except:
return(00000)
@st.cache_data
def load_geos(fp):
print('Running load_geos function...')
with open(fp) as response:
counties = json.load(response)
return(counties)
@st.cache_data
def load_counties(fp):
print('Running load_counties function...')
df = pd.read_csv(fp)
# extracting the fips code
df['fips'] = df['geoid'].apply(strip_fips)
# converting to date-time cause date_input needs to be a datetime object
df['date'] = pd.to_datetime(df['date'])
return(df)
county_fp = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/rolling-averages/us-counties-recent.csv'
df_counties = load_counties(county_fp)
geo_fp = 'data/geojson-counties-fips.json'
counties = load_geos(geo_fp)
# getting the valid dates
max_date = df_counties.date.max()
min_date = df_counties.date.min()
# setting the default value to the max date
# and limiting selection to only dates in dataframe
selected_date = st.date_input(
"Select date to see daily covid rate",
min_value=min_date,
max_value=max_date,
value=max_date)
st.write("Displaying data for %s" % selected_date)
# selecting just the selected date
df_to_plot = df_counties[ df_counties['date'] == str(selected_date)].copy()
# Creating the choropleth map
fig = px.choropleth(df_to_plot,
geojson=counties,
locations='fips',
color='cases_avg_per_100k',
color_continuous_scale="OrRd",
scope="usa",
range_color=(0, 50),
hover_data=["county", "state", "cases_avg_per_100k"]
)
# changing the charts background color
fig.update_layout(geo=dict(bgcolor= 'rgba(0,0,0,0)'))
fig.update_traces(marker_line_width=0)
# pushing the chart to the page
st.plotly_chart(fig, use_container_width=True)
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