def country_info(country_name):
d = []
country = CountryInfo(country_name)
d.append(["name", country.name().capitalize()])
d.append(["capital", country.capital().capitalize()])
d.append(["region", country.region().capitalize()])
d.append(["currency", country.currencies()])
d.append(["area", country.area()])
d.append(["population", country.population()])
d.append(["languages", country.languages()])
d.append(["borders", country.borders()])
d.append(["calling code", country.calling_codes()])
d.append(["lat/long", country.capital_latlng()])
d.append(["code", country.iso(2)])
return d
currencies_pool = rand.sample(currencies_pool,
options[category_index] - 1)
currencies_pool.append(country.currencies()[0])
rand.shuffle(currencies_pool)
index = currencies_pool.index(country.currencies()[0])
else:
index = country_pool.index(countries_question[i])
answer = choices[index]
for x in range(options[category_index]):
country_choice = CountryInfo(country_pool[x])
if category == "area":
print('(' + choices[x] + ') ' + str(country_choice.area()) +
" sq kilometers")
elif category == "subregion":
print('(' + choices[x] + ') ' + subregion_pool[x])
elif category == "calling code":
print('(' + choices[x] + ') ' +
str(country_choice.calling_codes()[0]))
elif category == "population":
print('(' + choices[x] + ') ' + str(country_choice.population()))
elif category == "capital":
print('(' + choices[x] + ') ' + country_choice.capital())
def get_area_by_country(country):
country = CountryInfo(country)
return country.area()
data1 = country.alt_spellings()
print(data1)
data2 = country.capital()
print(data2)
data3 = country.currencies()
print(data3)
data4 = country.languages()
print(data4)
data5 = country.timezones()
print(data5)
data6 = country.area()
print(data6)
data7 = country.borders()
print(data7)
data8 = country.calling_codes()
print(data8)
data9 = country.wiki()
print(data9)
data10 = country.info()
for x, y in data10.items():
print(f'{x} --> {y}')
def getMobilityPlots(locations, data_type, shift_days, smoothing_range,
mobility_type, pop_type, location_num):
timeline = 'Days since X number of deaths/cases'
x_num = 10
if len(locations) <= location_num:
return None
else:
mob_loc_df = getGoogleMobilityReportsFromCountry(
locations[location_num])
#print (mob_reports_df.head(10))
#mob_loc_df = mob_reports_df[mob_reports_df['sub_region_1'].isnull()]
if data_type == 'Deaths':
df = getNewDeaths()
df_total = getTotalDeaths()
elif data_type == 'Cases':
df = getNewCases()
df_total = getTotalCases()
else:
return None
data = []
if not x_num: x_num = 0
location = locations[location_num]
location_key = location.lower().replace(' ', '_')
if pop_type == 'Per Million':
country = CountryInfo(location)
population = country.population()
area = country.area()
pop_density = float(population) / area
print(population)
print(area)
print(pop_density)
df[location_key] = 1000000.0 * df[location_key] / population
else:
population = 1.0
if timeline == 'Days since X number of deaths/cases':
df_total_loc = df_total.fillna(0)
df_total_loc['date'] = df_total_loc['date'] - datetime.timedelta(
days=shift_days)
df_total_loc = df_total_loc.drop(
df_total_loc[(df_total_loc[location_key] < x_num)].index)
df_total_loc = df_total_loc.reset_index()
df_total_loc = df_total_loc.drop([location_key], axis=1)
df[location_key] = df[location_key].fillna(0).rolling(
smoothing_range).mean()
df_loc = df[['date', location_key]].copy()
df_loc['date'] = df_loc['date'] - datetime.timedelta(
days=shift_days)
df_loc = pd.merge(df_total_loc, df_loc, how='left', on='date')
#mob_loc_df = mob_reports_df[mob_reports_df['country_region']==location]
print(mob_loc_df.info())
#print (mob_loc_df['sub_region_1'])
#mob_loc_df = mob_loc_df[mob_loc_df['sub_region_1'].isnull()]
mob_loc_df['date'] = pd.to_datetime(mob_loc_df.date)
mob_loc_df.sort_values(by=['date'])
mob_loc_df[mobility_type] = mob_loc_df[mobility_type].fillna(
0).rolling(smoothing_range).mean()
country = pycountry.countries.get(name=location).alpha_3
stringency_df = getStringencyDataFrame(country)
stringency_df['date'] = pd.to_datetime(stringency_df.date)
stringency_df.sort_values(by=['date'])
df_loc = pd.merge(df_loc, mob_loc_df, how='left', on='date')
df_loc = pd.merge(df_loc, stringency_df, how='left', on='date')
if mobility_type != 'residential_percent_change_from_baseline':
df_loc[mobility_type] = -df_loc[mobility_type]
mobility_name = '% Time less at ' + mobility_dict[mobility_type]
else:
mobility_name = '% Time more at ' + mobility_dict[mobility_type]
#print (df_loc[mobility_type])
data.append(
go.Bar(
x=df_loc.index,
y=df_loc[location_key],
marker=dict(color=colour_palette[location_num], ),
opacity=0.7,
text=location,
#name=location + ' ' + data_type,
yaxis='y2',
showlegend=False))
data.append(
go.Scatter(x=df_loc.index,
y=df_loc['stringency'],
mode='lines',
marker=dict(color='rgb(0,0,0)', ),
line=dict(color='rgb(0,0,0)', dash='dot'),
opacity=1.0,
text=location,
name='Stringency Index',
yaxis='y1',
showlegend=True))
data.append(
go.Scatter(x=df_loc.index,
y=df_loc[mobility_type],
mode='lines',
marker=dict(color='rgb(0,0,0)', ),
line=dict(color='rgb(0,0,0)', dash='solid'),
opacity=1.0,
text=location,
name=mobility_name,
yaxis='y1',
showlegend=True))
else:
return None
if mobility_type == 'residential_percent_change_from_baseline':
y_range = [0, 100]
else:
y_range = [0, 100]
if pop_type == "Per Million":
title_sub = ' (per million)'
else:
title_sub = ''
figure = {
'data':
data,
'layout':
go.Layout(
title={
'text': location,
'y': 1.0,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
},
showlegend=True, #show_legend,
legend=dict(
orientation="h",
x=0,
y=1.1,
font=dict(family="Arial", size=14, color="#000000"),
),
font=dict(family='Arial', size=12, color='#000000'),
hovermode='closest',
margin=dict(t=50),
xaxis=dict(tickfont=dict(family='Arial',
size=14,
color='#000000'), ),
yaxis2=dict(
#range=[0,1200],
rangemode="tozero",
tickfont=dict(family='Arial', size=14, color='#000000'),
side='left',
title=data_type + title_sub,
showgrid=False),
yaxis=dict(
#autorange='reversed',
range=y_range,
rangemode='tozero',
anchor='x',
overlaying='y',
tickfont=dict(family='Arial', size=14, color='#000000'),
side='right',
title='Mobility/Stringency',
showgrid=False),
height=400,
autosize=True,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
return dcc.Graph(figure=figure,
config={'displayModeBar': False},
id='reg-str-graph')
# coding=utf-8
from countryinfo import CountryInfo
country = CountryInfo('Austria')
print (country.population())
print (country.area())
def updateTotalDeathsTimeline(locations, timeline, data_type, x_num,
smoothing_range, x_days, pop_type):
if locations:
if data_type == 'Deaths':
df = getTotalDeaths()
elif data_type == 'Cases':
df = getTotalCases()
else:
return None
df['sum_data'] = df.apply(lambda x: sumLocations(x, locations), axis=1)
df = df.drop(df[(df['sum_data'] == 0)].index)
data = []
count = 0
for location in locations:
location_key = location.lower().replace(' ', '_')
if pop_type == '% of Population':
country = CountryInfo(location)
population = country.population()
area = country.area()
pop_density = float(population) / area
print(population)
print(area)
print(pop_density)
df[location_key] = 100.0 * df[location_key] / population
else:
population = 1.0
if not x_num: x_num = 0
if timeline == 'Days since X number of deaths/cases':
df_location = df.fillna(0)
df_location = df_location.drop(df_location[(
df_location[location_key] < x_num / population)].index)
df_location = df_location.reset_index()
data.append(
go.Scatter(x=df_location.index,
y=df_location[location_key],
mode='lines',
marker=dict(color=colour_palette[count], ),
opacity=1.0,
text=location,
name=location))
prediction_df = df_location[['date', location_key]]
prediction_df[
'rolling_mean'] = 1.0 + df_location[location_key].fillna(
0).pct_change().rolling(smoothing_range).mean()
prediction_df = prediction_df.iloc[[-1]]
r_index = prediction_df.index.values[0]
r_mean = prediction_df['rolling_mean'].values[0]
r_num = prediction_df[location_key].values[0]
for x in range(1, x_days):
extra_df = pd.DataFrame(
[[float(r_num) * r_mean**x, r_mean]],
columns=[location_key, "rolling_mean"])
prediction_df = pd.concat([prediction_df, extra_df],
ignore_index=True,
axis=0)
prediction_df.index = pd.RangeIndex(start=r_index,
stop=r_index + x_days,
step=1)
data.append(
go.Scatter(x=prediction_df.index,
y=prediction_df[location_key],
mode='lines',
marker=dict(color=colour_palette[count], ),
line=dict(color=colour_palette[count],
dash='dot'),
opacity=1.0,
text=location,
name=location,
showlegend=False))
else:
data.append(
go.Scatter(x=df['date'],
y=df[location_key].fillna(0),
mode='lines',
marker=dict(color=colour_palette[count], ),
opacity=1.0,
text=location,
name=location))
prediction_df = df[['date', location_key]]
prediction_df['rolling_mean'] = 1.0 + df[location_key].fillna(
0).pct_change().rolling(smoothing_range).mean()
prediction_df = prediction_df.iloc[[-1]]
r_date = prediction_df['date'].values[0]
r_mean = prediction_df['rolling_mean'].values[0]
r_num = prediction_df[location_key].values[0]
for x in range(1, x_days):
r_dt = convert_to_datetime(
r_date
) #datetime.datetime.strptime(r_date, '%Y-%m-%d')
extra_df = pd.DataFrame(
[[
r_dt + datetime.timedelta(days=x),
float(r_num) * r_mean**x, r_mean
]],
columns=["date", location_key, "rolling_mean"])
prediction_df = pd.concat([prediction_df, extra_df],
ignore_index=True,
axis=0)
data.append(
go.Scatter(x=prediction_df['date'],
y=prediction_df[location_key],
mode='lines',
marker=dict(color=colour_palette[count], ),
line=dict(color=colour_palette[count],
dash='dot'),
opacity=1.0,
text=location,
name=location,
showlegend=False))
count += 1
if pop_type == "% of Population":
title_sub = ' (% of population)'
else:
title_sub = ''
figure = {
'data':
data,
'layout':
go.Layout(
title='Cumulative ' + data_type + title_sub,
legend=dict(orientation="h", x=0, y=1.1),
font=dict(family='Arial', size=15, color='#000000'),
hovermode='closest',
margin=dict(t=50),
xaxis=dict(tickfont=dict(family='Arial',
size=14,
color='#000000'), ),
yaxis=dict(
#range=y_axis_range,
tickfont=dict(family='Arial', size=14, color='#000000'), ),
height=400,
autosize=True,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
return dcc.Graph(figure=figure,
config={'displayModeBar': False},
id='total-deaths-t-graph')
country_currency = country.currencies()
print('\nCurrency:')
for currency in country_currency:
print(currency, end=', ')
country_lang = country.languages()
print('\n\nLanguages:')
for languages in country_lang:
print(languages, end=', ')
country_timezone = country.timezones()
print('\n\nTime-Zone:')
for timezones in country_timezone:
print(timezones, end=', ')
country_area = country.area()
print(f'\n\nCountry Area:\n{country_area}')
country_borders = country.borders()
print('\nBorders:')
for border in country_borders:
print(border, end=', ')
calling_codes = country.calling_codes()
print('\n\nCall Code:')
for call_code in calling_codes:
print(call_code)
country_region = country.region()
print(f'\nRegion:\n{country_region}')
