def build_download_file(df, countries_list, regions_list, ages_list,
genders_list, metrics, start_date, end_date):
if 'All' in regions_list and ('UK' in countries_list):
# df0 = delete_multiple_sources(df, ages_list, genders_list)
df0 = df
else:
df0 = df
df0 = select_data(df, countries_list, regions_list, ages_list,
genders_list)
df0 = dfadaptDateRange(df0, start_date, end_date)
df0 = df0[df0['Metric'].isin(metrics)]
df0 = df0[[
'Date_format', 'Metric', 'Value', 'Country', 'Region', 'Age', 'Sex'
]]
print(df0.head())
# elif table_option:
# fig = go.Figure(data=
# [
# go.Table(header = dict(
# values = list(['Date', 'Metric', 'Value', 'Country', 'Region', 'Age', 'Gender']),
# fill_color = 'rgba(0,131,138,0.5)',
# ),
# cells = dict(
# values=[df0.Date_format, df0.Metric, df0.Value, df0.Country, df0.Region, df0.Age, df0.Sex],
# fill_color = 'white',
# )
# )
# ]
# )
# fig.update_layout(autosize=False,
# width= 1425,
# height=710,
# )
return None
def plot_world_map(df, age, gender, metric, selected_unit, end_date):
"""Plotting choropleth world map depending on criteria (parameters)
Parameters:
-----------
df : Pandas DataFrame
the original dataset
age : int
the age range to select
gender : str
the genders to select
metric : str
the metric that we want to plot
selected_unit : str
the selected unit with which data is divided
end_date : str
the last date to be considered
Returns:
--------
fig : plotly Figure
the corresponding plot
"""
Countries = list(set(df['Country']))
for country in [
'England', 'Scotland', 'Northern Ireland', 'Senegal', 'Eswatini'
]: # England, Scotland, N. Ireland are already in UK country
try:
Countries.remove(country)
except ValueError:
pass
df0 = select_data(df, Countries, ['All'], [age], [gender])
df0['Date_code'] = df0.apply(computeDatecode, axis=1)
df0 = df0.sort_values(by=['Date_code'])
df0['Date_format'] = df0.apply(computeDateFormat, axis=1)
df0 = df0[df0['Metric'] == metric]
df0 = df0[df0['gap_in_day'] <= end_date]
values_list = []
dates_list = []
for country in Countries:
df1 = select_data(df0, [country], ['All'], [age], [gender])
if selected_unit == 'Per million inhabitants':
column = 'Value_by_pop'
else:
column = 'Value'
if len(df1) == 0:
values_list.append('nan')
dates_list.append('nan')
else:
index = len(df1) - 1
value = list(df1[column])[index]
date = list(df1['Date_format'])[index]
while isnan(value) and index > 0:
index -= 1
value = list(df1[column])[index]
date = list(df1['Date_format'])[index]
values_list.append(value)
dates_list.append(date)
Code = [code_country[country] for country in Countries]
df2 = pd.DataFrame({
'Country': Countries,
'code': Code,
'Value': values_list,
'Date': dates_list
})
for col in df2.columns:
df2[col] = df2[col].astype(str)
df2['text'] = df2['Country'] + '<br>' + 'Value : ' + df2[
'Value'] + '<br>' + df2['Date'] + '<br>' + metric
fig = go.Figure(data=go.Choropleth(
locations=df2['code'],
z=df2['Value'],
hoverinfo='text',
text=df2['text'],
colorscale='Reds',
marker_line_color='rgb(0, 131, 138)',
colorbar_title=metric,
))
if selected_unit == 'Per million inhabitants':
unit_tag = ' (Per million inhabitants)'
else:
unit_tag = ''
fig.update_layout(
title_text='Number of ' + metric + ' by country ' + 'for ' + str(age) +
' - ' + str(age + 9) + ' year olds' + unit_tag,
geo=dict(
showframe=False,
showcoastlines=True,
),
)
fig.update_layout(autosize=False, width=1425, height=710)
return fig
def plot_usa_map(df, age, gender, metric, selected_unit, end_date):
"""Plotting choropleth map of the USA depending on criteria (parameters)
Parameters:
-----------
df : Pandas DataFrame
the original dataset
age : int
the age range to select
gender : str
the genders to select
metric : str
the metric that we want to plot
selected_unit : str
the selected unit with which data is divided
end_date : str
the last date to be considered
Returns:
--------
fig : plotly Figure
the corresponding plot
"""
States = regions_of_country(df, ['USA'])
df0 = select_data(df, ['USA'], States, [age], [gender])
df0['Date_code'] = df0.apply(computeDatecode, axis=1)
df0['Date_format'] = df0.apply(computeDateFormat, axis=1)
df0 = df0.sort_values(by=['Date_code'])
df0 = df0[df0['Metric'] == metric]
df0 = df0[df0['gap_in_day'] <= end_date]
values_list = []
dates_list = []
for state in States:
df1 = select_data(df0, ['USA'], [state], [age], [gender])
df1 = divide_US_Dataframe(df1, selected_unit)
if len(df1) == 0:
values_list.append('nan')
dates_list.append('nan')
else:
index = len(df1) - 1
value = list(df1['Value'])[index]
date = list(df1['Date_format'])[index]
while isnan(value) and index > 0:
index -= 1
value = list(df1['Value'])[index]
date = list(df1['Date_format'])[index]
values_list.append(value)
dates_list.append(date)
Code = [code_state[state] for state in States]
df2 = pd.DataFrame({
'state': States,
'code': Code,
'Value': values_list,
'Date': dates_list
})
for col in df2.columns:
df2[col] = df2[col].astype(str)
df2['text'] = df2['state'] + '<br>' + 'Value : ' + df2[
'Value'] + '<br>' + df2['Date'] + '<br>' + metric
fig = go.Figure(data=go.Choropleth(
locations=df2['code'],
z=df2['Value'].astype(float),
locationmode='USA-states',
colorscale='Reds',
autocolorscale=False,
text=df2['text'],
marker_line_color='rgb(0, 131, 138)',
colorbar_title=metric,
hoverinfo='text',
))
if selected_unit == 'Per million inhabitants':
unit_tag = ' (Per million inhabitants)'
else:
unit_tag = ''
fig.update_layout(
title_text='Number of ' + metric + ' by state ' + 'for ' + str(age) +
' - ' + str(age + 9) + ' year olds' + unit_tag,
geo=dict(scope='usa',
projection=go.layout.geo.Projection(type='albers usa'),
showlakes=False,
lakecolor='rgb(255, 255, 255)'),
),
fig.update_layout(autosize=False, width=1425, height=710)
return fig
def plot_histogram(df, country, region, gender, metric, logvalue, trend,
table_option, end_date):
"""Plotting histogram by age ranges depending on criteria (parameters)
Parameters:
-----------
df : Pandas DataFrame
the original dataset
country : str
the selected_coutry
region : str
the selected region
gender : str
the selected gender
metric : list
the selected metric
logvalue : bool
activates logarithmic scale for y axis
trend : int
the degree of the polynom we want to modelize
table_option : bool
activates the table displaying option
end_date : int
gap_in_day value of the last date to consider
Returns:
--------
fig : plotly Figure
the corresponding plot
"""
df0 = df.copy()
df0 = select_data(df, [country], [region], [i * 10 for i in range(11)],
[gender])
df0 = df0[df0['Metric'] == metric]
df0 = adaptDataframeHistogram(df0, end_date)
if df0.empty:
fig = go.Figure(go.Indicator(title={"text": "No data available"}))
fig.update_layout(paper_bgcolor="rgba(188,188,188, 0.33)")
return fig
elif table_option:
fig = go.Figure(data=[
go.Table(header=dict(
values=list([
'Date', 'Metric', 'Value', 'Country', 'Region', 'Age',
'Gender'
]),
fill_color='rgba(0,131,138,0.5)',
),
cells=dict(
values=[
df0.Date_format, df0.Metric, df0.Value,
df0.Country, df0.Region, df0.Age, df0.Sex
],
fill_color='white',
))
])
fig.update_layout(
autosize=False,
width=1425,
height=710,
)
return fig
else:
if region == 'All':
region_tag = ''
else:
region_tag = region + ', '
df0['Age range'] = df0.apply(ageRange, axis=1)
df0.sort_values(by=['Date_code'], ascending=False)
fig = px.histogram(
df0,
x='Age',
y='Value',
category_orders={
'Age': [10 * i for i in range(11)],
},
color='Date_format',
template='simple_white',
log_y=logvalue,
title='COVID-19 : ' + metric + ' in ' + region_tag + country,
nbins=11,
color_discrete_sequence=[
'rgba(0,131,138,0.87)', 'rgba(0,166,170,0.87)'
],
labels={
'Date_format': 'Latest data : ',
},
)
fig.update_layout(
autosize=False,
width=1425,
height=710,
)
fig.update_layout(hovermode="x unified")
fig.update_traces(hovertemplate=None)
fig.update_xaxes(
tickvals=[10 * i + 5 for i in range(11)],
ticktext=[
str(10 * i) + ' - ' + str(10 * i + 9) + ' y.o'
for i in range(10)
] + ['100 - 105 y.o'],
tickwidth=0,
ticklen=0,
showline=True,
linewidth=2,
linecolor='black',
title_font=dict(size=18),
)
y_axis = ', '.join([label_dic[i] for i in [metric]])
fig.update_yaxes(
showline=True,
linewidth=2,
linecolor='black',
title_text=y_axis,
title_font=dict(size=18),
)
if trend > 0:
X, Y = regression_histogram(df0, trend)
fig.add_trace(
go.Scatter(x=X,
y=Y,
mode='lines',
line={
'color': 'DarkSlateGrey',
'width': 2
},
name='',
showlegend=False))
return fig
def plot_metrics(df, countries_list, regions_list, ages_list, genders_list,
metrics, logvalue, start_date, end_date, rug_value, reverse,
trend, forecast, unit, table_option):
"""Plotting several metrics (e.g cases, deaths or tests) depending on criteria (parameters)
Parameters:
-----------
df : Pandas DataFrame
the original dataset
countries_list : str list
the list of the countries to select
regions_list : str list
the list of the region to select
ages_list : int list
the list of the age ranges to select
genders_list : str list
the list of the genders to select
metrics : str list
the metrics that we want to plot
logvalue : bool
activates logarithmic scale for y axis
start_date : str
the first date to be considered
end_date : str
the last date to be considered
rug_value : bool
activates the rug plot option
reverse : bool
activates the reverse axis option
trend : int
the degree of the polynom we want to modelize
forecast : int
the number of forcasted days to display
unit : str
unit used to divide data
table_option : bool
activates the table displaying option
Returns:
--------
fig : plotly Figure
the corresponding plot
"""
if 'All' in regions_list and ('UK' in countries_list):
# df0 = delete_multiple_sources(df, ages_list, genders_list)
df0 = df
else:
df0 = df
df0 = select_data(df, countries_list, regions_list, ages_list,
genders_list)
df0 = dfadaptDateRange(df0, start_date, end_date)
df0 = df0[df0['Metric'].isin(metrics)]
if df0.empty:
fig = go.Figure(go.Indicator(title={"text": "No data available"}))
fig.update_layout(paper_bgcolor="rgba(188,188,188, 0.33)")
return fig
elif table_option:
fig = go.Figure(data=[
go.Table(header=dict(
values=list([
'Date', 'Metric', 'Value', 'Country', 'Region', 'Age',
'Gender'
]),
fill_color='rgba(0,131,138,0.5)',
),
cells=dict(
values=[
df0.Date_format, df0.Metric, df0.Value,
df0.Country, df0.Region, df0.Age, df0.Sex
],
fill_color='white',
))
])
fig.update_layout(
autosize=False,
width=1425,
height=710,
)
return fig
else:
pass
if unit == 'Per million inhabitants' and regions_list == ['All']:
unit_tag = ' (Per million inhabitants)'
Y = df0['Value_by_pop']
else:
unit_tag = ''
Y = df0['Value']
if reverse:
X, Y = Y, df0['gap_in_day']
else:
X, Y = df0['gap_in_day'], Y
if regions_list == ['All']:
region_tag = ''
else:
region_tag = ' (Regions)'
fig = px.scatter(
df0,
x=X,
y=Y,
log_y=logvalue,
opacity=1 / (len(metrics)**0.2),
color='Country - Region - Age - Gender',
symbol='Metric',
symbol_sequence=[
'circle', 'cross', 'x', 'star-triangle-up', 'star', 'diamond',
'hexagon'
],
hover_name='Country - Region - Age - Gender',
labels={
'Date_format': 'Date',
'gap_in_day': 'Date',
'Value_by_pop': 'Value by M inhabitants'
},
hover_data={
'gap_in_day': True,
'Date': False,
'Date_format': True,
'Metric': True,
'Value': True,
'Country - Region - Age - Gender': False,
},
title='COVID-19 : ' + ', '.join(metrics) + ' in ' +
', '.join(countries_list) + region_tag,
marginal_y=rug_value,
template='plotly_white',
)
fig.update_traces(marker=dict(size=8, line=dict(width=.25, color='grey')),
selector=dict(mode='markers'))
fig.update_layout(
autosize=False,
width=1425,
height=710,
)
if reverse:
fig.update_yaxes(
tickvals=[i - 1 for i in firsts_of_the_month],
ticktext=[i + ' 2020' for i in months_list],
tickwidth=2,
tickcolor='grey',
ticklen=10,
col=1,
showline=True,
linewidth=2,
linecolor='black',
title_font=dict(size=18),
)
x_axis = ', '.join([label_dic[i] for i in metrics]) + unit_tag
fig.update_xaxes(
showline=True,
linewidth=2,
linecolor='black',
title_text=x_axis,
title_font=dict(size=18),
)
else:
fig.update_xaxes(
tickvals=[i - 1 for i in firsts_of_the_month],
ticktext=[i + ' 2020' for i in months_list],
tickwidth=2,
tickcolor='grey',
ticklen=10,
col=1,
showline=True,
linewidth=2,
linecolor='black',
title_font=dict(size=18),
)
y_axis = ', '.join([label_dic[i] for i in metrics]) + unit_tag
fig.update_yaxes(
showline=True,
linewidth=2,
linecolor='black',
title_text=y_axis,
title_font=dict(size=18),
)
if trend > 0:
reg = regression(df0, trend, forecast,
unit == 'Per million inhabitants')
for X, Y in reg:
if reverse:
Y, X = X, Y
else:
pass
fig.add_trace(
go.Scatter(x=X,
y=Y,
mode='lines',
line={
'color': 'DarkSlateGrey',
'width': 2
},
name='',
showlegend=False))
else:
pass
return fig
