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utils.py
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from plotly.subplots import make_subplots
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import plotly.express as px
from country_utils import country_utils
import us_states
import math
import requests
def clean_df(df):
df.rename(columns={'ObservationDate': 'Date', 'Province/State': 'Province_State',
'Country/Region': 'Country_Region', 'Confirmed': 'ConfirmedCases',
'Deaths': 'Fatalities'}, inplace=True)
df.loc[df['Country_Region'] == 'Mainland China', 'Country_Region'] = 'China'
# df['Date'] = pd.to_datetime(df['Date'],format='%m/%d/%Y')
# df['Day'] = df.Date.dt.dayofyear
df['cases_lag_1'] = df.groupby(['Country_Region', 'Province_State'])[
'ConfirmedCases'].shift(1)
df['deaths_lag_1'] = df.groupby(['Country_Region', 'Province_State'])[
'Fatalities'].shift(1)
df['Daily Cases'] = df['ConfirmedCases'] - df['cases_lag_1']
df['Daily Deaths'] = df['Fatalities'] - df['deaths_lag_1']
return df
def share_world_cases(df):
df.ConfirmedCases = np.abs(df.ConfirmedCases)
df_tm = df.copy()
date = df_tm.Date.max() # get current date
df_tm = df_tm[df_tm['Date'] == date]
obj = country_utils()
df_tm.Province_State.fillna('', inplace=True)
df_tm['continent'] = df_tm.apply(
lambda x: obj.fetch_continent(x['Country_Region']), axis=1)
df_tm["world"] = "World" # in order to have a single root node
fig = px.treemap(df_tm, path=['world', 'continent', 'Country_Region'], values='ConfirmedCases',
color='ConfirmedCases', hover_data=['Country_Region'],
color_continuous_scale='dense', title='Current share of Worldwide COVID19 Cases')
fig.update_layout(width=700, template='seaborn')
return fig
def compile_options(df):
options = []
countries = df['Country_Region'].unique()
for country in countries:
options.append({'label': country, 'value': country})
return options
def world_rolling_avg(df):
df_daily = df.copy()
df_daily = df_daily.groupby('Date', as_index=False)[
'ConfirmedCases', 'Fatalities', 'Daily Cases', 'Daily Deaths'].sum()
df_daily = daily_metrics_world(df_daily)
fig = world_graph(df_daily, 'Date', 'Daily Cases', 'Daily Deaths',
'<b>Worldwide: Daily Cases & Deaths</b><br> With 7-Day Rolling averages')
return fig
def daily_metrics_world(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fatalities']
for i in range(1, len(df)):
daily_cases = (df.loc[i, 'ConfirmedCases'] -
df.loc[i-1, 'ConfirmedCases'])
daily_deaths = (df.loc[i, 'Fatalities']-df.loc[i-1, 'Fatalities'])
df.loc[i, 'Daily Cases'] = daily_cases
df.loc[i, 'Daily Deaths'] = daily_deaths
df.loc[0, 'Daily Cases'] = 0
df.loc[0, 'Daily Deaths'] = 0
return df
def world_graph(df, x, y1, y2, title, days=7):
colors = dict(case='#4285F4', death='#EA4335')
df['cases_roll_avg'] = df[y1].rolling(days).mean()
df['deaths_roll_avg'] = df[y2].rolling(days).mean()
fig = make_subplots(specs=[[{'secondary_y': True}]])
fig.add_trace(go.Scatter(name='Daily Cases',
x=df[x], y=df[y1], mode='lines',
line=dict(width=0.5, color=colors['case'])),
secondary_y=False)
fig.add_trace(go.Scatter(name='Daily Deaths',
x=df[x], y=df[y2], mode='lines',
line=dict(width=0.5, color=colors['death'])),
secondary_y=True)
fig.add_trace(go.Scatter(name='Cases: '+str(days)+'-Day Rolling average',
x=df[x], y=df['cases_roll_avg'], mode='lines',
line=dict(width=3, color=colors['case'])),
secondary_y=False)
fig.add_trace(go.Scatter(name='Deaths: '+str(days)+'-Day rolling average',
x=df[x], y=df['deaths_roll_avg'], mode='lines',
line=dict(width=3, color=colors['death'])),
secondary_y=True)
fig.update_yaxes(title_text='Cases', title_font=dict(color=colors['case']), secondary_y=False, nticks=5,
tickfont=dict(color=colors['case']), linewidth=2, linecolor='black', gridcolor='darkgrey',
zeroline=False)
fig.update_yaxes(title_text='Deaths', title_font=dict(color=colors['death']), secondary_y=True, nticks=5,
tickfont=dict(color=colors['death']), linewidth=2, linecolor='black', gridcolor='darkgray',
zeroline=False)
fig.update_layout(title=title, margin=dict(l=0, r=0, t=100, b=30), autosize=True, hovermode='x',
legend=dict(x=0.01, y=0.99, bordercolor='black', borderwidth=1, bgcolor='#EED8E4',
font=dict(family='arial', size=10)),
xaxis=dict(mirror=True, linewidth=2,
linecolor='black', gridcolor='darkgray'),
plot_bgcolor='rgb(255,255,255)')
return fig
def world_map(df):
df_map = df.copy()
obj = country_utils()
df_map['Date'] = df_map['Date'].astype(str)
df_map = df_map.groupby(['Date', 'Country_Region'], as_index=False)[
'ConfirmedCases', 'Fatalities'].sum()
obj = country_utils()
df_map['iso_alpha'] = df_map.apply(
lambda x: obj.fetch_iso3(x['Country_Region']), axis=1)
df_map['Confirmed_Cases'] = np.log(df_map.ConfirmedCases+1)
df_map['World_Fatalities'] = np.log(df_map.Fatalities+1)
fig_cases = px.choropleth(df_map,
locations="iso_alpha",
color="Confirmed_Cases",
hover_name="Country_Region",
hover_data=["ConfirmedCases"],
animation_frame="Date",
color_continuous_scale=px.colors.sequential.dense,
title='Total Confirmed Cases growth(Logarithmic Scale)')
fig_deaths = px.choropleth(df_map,
locations='iso_alpha',
color='World_Fatalities',
hover_name='Country_Region',
hover_data=["Fatalities"],
animation_frame='Date',
color_continuous_scale=px.colors.sequential.dense,
title='Total Deaths growth (Logarithmic Scale)')
return fig_cases, fig_deaths
def usa_map(df):
df_us = df.copy()
df_us = df_us[df_us['Country_Region'] == 'US']
df_us['Date'] = df_us['Date'].astype(str)
df_us['state_code'] = df_us.apply(
lambda x: us_states.us_state_abbrev.get(x.Province_State, float('nan')), axis=1)
df_us['log(ConfirmedCases)'] = np.log(df_us.ConfirmedCases + 1)
df_us['log(Fatalities)'] = np.log(df_us.Fatalities + 1)
fig_cases = px.choropleth(df_us,
locationmode="USA-states",
scope="usa",
locations="state_code",
color="log(ConfirmedCases)",
hover_name="Province_State",
hover_data=["ConfirmedCases"],
animation_frame="Date",
color_continuous_scale=px.colors.sequential.Darkmint,
title='Total Cases growth for USA(Logarithmic Scale)')
fig_deaths = px.choropleth(df_us,
locationmode="USA-states",
scope="usa",
locations="state_code",
color="log(Fatalities)",
hover_name="Province_State",
hover_data=["ConfirmedCases"],
animation_frame="Date",
color_continuous_scale=px.colors.sequential.Darkmint,
title='Total Fatalities growth for USA(Logarithmic Scale)')
fig_state_cases = px.line(df_us, x='Date', y='ConfirmedCases',
color='Province_State', title='COVID19 Confirmed Cases US')
fig_state_cases.update_layout(hovermode='closest', template='seaborn', width=700, xaxis=dict(mirror=True, linewidth=2, linecolor='black', showgrid=False),
yaxis=dict(mirror=True, linewidth=2, linecolor='black'))
return fig_cases, fig_deaths, fig_state_cases
def find_minimum(np_log):
if np_log < 1:
return 0
else:
return math.log(np_log)
def canada_map(df):
df_ca = df.copy()
df_ca = df_ca[df_ca['Country_Region'] == 'Canada']
df_ca['Date'] = df_ca['Date'].astype(str)
log_Cases = np.log(df_ca.ConfirmedCases + 1)
df_ca['log(ConfirmedCases)'] = df_ca.apply(
lambda x: find_minimum(x.ConfirmedCases), axis=1)
df_ca['log(Fatalities)'] = np.log(df_ca.Fatalities + 1)
r = requests.get(
url='https://raw.githubusercontent.com/codeforamerica/click_that_hood/master/public/data/canada.geojson')
canada_geojson = r.json()
cad_cases = px.choropleth(df_ca,
geojson=canada_geojson,
color="log(ConfirmedCases)",
locations='Province_State',
# hover_data=['log(ConfirmedCases)'],
animation_frame='Date',
featureidkey='properties.name',
color_continuous_scale=px.colors.sequential.dense,
range_color=(0, 10),
title='Confirmed cases of Canada (Log Scale)'
)
cad_cases.update_geos(fitbounds="locations", visible=True)
cad_cases.update_geos(projection_type="orthographic")
cad_cases.update_layout(
height=600, margin={"r": 0, "t": 30, "l": 0, "b": 30})
cad_deaths = px.choropleth(df_ca,
geojson=canada_geojson,
color="log(Fatalities)",
locations='Province_State',
# hover_data=['log(ConfirmedCases)'],
animation_frame='Date',
featureidkey='properties.name',
color_continuous_scale=px.colors.sequential.dense,
range_color=(0, 10),
title='Canada'
)
cad_deaths.update_geos(fitbounds="locations", visible=True)
cad_deaths.update_geos(projection_type="orthographic")
cad_deaths.update_layout(
height=600, margin={"r": 0, "t": 30, "l": 0, "b": 30})
return cad_cases, cad_deaths
def add_daily_measures_country(df, country):
df = df[df.Country_Region == country]
df = df.groupby('Date', as_index=False)[
'ConfirmedCases', 'Fatalities'].sum()
df['Daily Cases'] = df['ConfirmedCases'] - df['ConfirmedCases'].shift(1)
df['Daily Deaths'] = df['Fatalities'] - df['Fatalities'].shift(1)
return df
def usa_daily_counts(df):
df_usa = df.copy()
df_usa = add_daily_measures_country(df_usa, 'US')
fig = go.Figure(data=[
go.Bar(name='Cases', x=df_usa['Date'], y=df_usa['Daily Cases']),
go.Bar(name='Deaths', x=df_usa['Date'], y=df_usa['Daily Deaths'])
])
# Change the bar mode
fig.update_layout(barmode='overlay',
title='Daily Case and Death count(USA)')
fig.update_layout(hovermode='closest', template='seaborn', width=700, xaxis=dict(mirror=True, linewidth=2, linecolor='black', showgrid=False),
yaxis=dict(mirror=True, linewidth=2, linecolor='black'))
return fig