def usaMap(dataFrame, loc, var, color, title):
#make a map of the usa that shows relative amounts of var
#usaMap(dataSubset3, 'STATE_ABBR', 'AVG_REL_EST_TOTAL', 'Blues', 'Average Release Estimate Total Per State Over Time')
fig = go.Figure(data=go.Choropleth(
#set location equal to dataframe column (for example: dataFrame['STATE_ABBR'])
locations=dataFrame[loc],
#set data equal to datafram column corresponding to variable passed as arg
z=dataFrame[var],
#set location mode to United States
locationmode='USA-states',
#set colorscale to color scheme passed as arg
colorscale=color,
))
fig.update_layout(
#set the title to the title passed as arg
title_text=title,
#set the scope to usa so that fig only shows United States map
geo_scope='usa',
)
#make filename and write the figure to html – opens automatically
filename = 'usamap_' + title.replace(' ', '_') + '.html'
fig.write_html(filename, auto_open=True)
def make_plot(locations, z):
data = [
go.Choropleth(
locations=locations,
z=z,
text=locations,
locationmode="ISO-3",
autocolorscale=True,
marker=go.choropleth.Marker(
line=go.choropleth.marker.Line(color='rgb(0,0,0)', width=0.5)),
colorbar=go.choropleth.ColorBar(ticksuffix='%',
title='% of children',
len=0.5),
)
]
layout = go.Layout(
height=700,
width=700,
margin={
"t": 0,
"b": 0,
"l": 0,
"r": 0
},
geo=go.layout.Geo(
# resolution=50,
showframe=True,
showcoastlines=True,
showcountries=True,
projection=go.layout.geo.Projection(type='mercator')),
)
fig = go.Figure(data=data, layout=layout)
iplot(fig)
def choropleth_scores_plot(state_values,
date,
cmax,
cmin,
filename='choropleth.svg'):
fig = go.Figure(data=go.Choropleth(
locations=
states, #['AK','AZ','AR','CA','CO','CT','DE','DC','FL','GA','HI','ID','IL','IN','IA','KS','KY','LA','ME','MD','MA','MI','MN','MS','MO','MT','NE','NV','NH','NJ','NM','NY','NC','ND','OH','OK','OR','PA','RI','SC','SD','TN','TX','UT','VT','VA','WA','WV','WI','WY'],
locationmode='USA-states',
colorscale='Viridis',
z=state_values,
marker=dict(
line=dict(color='rgb(255,255,255)', width=2),
# cmax = cmax,
# cmin = cmin,
#colorbar= dict(title='Average Polarity<br> Scorescore/tweet'),
),
zmax=0,
zmin=cmin,
))
fig.update_layout(
title_text='Number of Tweets: ' + date,
font=dict(size=24),
geo_scope='usa',
)
iplot(fig)
fig.write_image(filename, format='png', width=1400, height=1000, scale=2)
def create_plotly(df):
df_st = df.copy()
for col in df_st.columns:
df_st[col] = df_st[col].astype(str)
scl = [
[0.0, 'rgb(242,240,247)'],
[0.2, 'rgb(218,218,235)'],
[0.4, 'rgb(188,189,220)'],
[0.6, 'rgb(158,154,200)'],
[0.8, 'rgb(117,107,177)'],
[1.0, 'rgb(84,39,143)']
]
df_st['text'] = df_st['State'] + '<br>' + \
'Profit' + df_st['Profit'] + ' Sales ' + df_st['Sales']
data = [go.Choropleth(
colorscale=scl,
autocolorscale=True,
locations=df_st['Code'],
z=df_st['Profit'].astype(float),
locationmode='USA-states',
text=df_st['text'],
marker=go.choropleth.Marker(
line=go.choropleth.marker.Line(
color='rgb(255,255,255)',
width=2
)),
colorbar=go.choropleth.ColorBar(title="Sale&Profit$$"))]
return data
def plotStates(colored, title, label):
"""
Docstring: Create plotly visualization of US states colored by the variable colored
Currently only using stateData, but could be generalized to accept any dataset
"""
#scl = [[0.0, '#ffffff'],[0.2, '#ff9999'],[0.4, '#ff4d4d'],[0.6, '#ff1a1a'],[0.8, '#cc0000'],[1.0, '#4d0000']] # reds
data = [
go.Choropleth(
#colorscale = scl,
colorscale='RdBu',
autocolorscale=False,
locations=stateData['Initials'],
z=stateData[colored].astype(float),
locationmode='USA-states',
text=stateData['State'],
marker=go.choropleth.Marker(line=go.choropleth.marker.Line(
color='rgb(255,255,255)', width=2)),
colorbar=go.choropleth.ColorBar(title=label))
]
layout = go.Layout(
title=go.layout.Title(text=title),
geo=go.layout.Geo(
scope='usa',
projection=go.layout.geo.Projection(type='albers usa'),
showlakes=True,
lakecolor='rgb(255, 255, 255)'),
)
fig = go.Figure(data=data, layout=layout)
plotly.offline.plot(fig, auto_open=True)
def worldgraph1(ctry, data, ccode, title="Worldwide Happiness", reverse=False):
fig = go.Figure(data=go.Choropleth(
locations=ccode,
z=data,
text=ctry,
autocolorscale=True,
reversescale=reverse,
marker_line_color="darkgray",
marker_line_width=0.5,
colorbar_title="Happiness",
))
fig.update_layout(
title_text=title,
geo=dict(showframe=False,
showcoastlines=False,
projection_type="equirectangular"),
annotations=[
dict(
x=0.55,
y=0.1,
xref="paper",
yref="paper",
text="Source: Worldwide Happiness Report, 2019",
showarrow=False,
)
],
)
fig.show()
def make_plot(x, z):
data = [
go.Choropleth(
locations=x,
z=z,
text=x,
locationmode="country names",
autocolorscale=True,
marker=go.choropleth.Marker(
line=go.choropleth.marker.Line(color='rgb(0,0,0)', width=0.5)),
colorbar=go.choropleth.ColorBar(len=0.5),
)
]
layout = go.Layout(height=700,
width=700,
margin={
"t": 0,
"b": 0,
"l": 0,
"r": 0
},
geo=go.layout.Geo(showframe=True,
showcoastlines=True,
showcountries=True,
projection=go.layout.geo.Projection(
type='mercator')))
fig = go.Figure(data=data, layout=layout)
iplot(fig)
def makeChoropleth(dates, types, countries, location):
chorodata = mannschaft.loc[
(mannschaft['Date'] >= dates[0]) & (mannschaft['Date'] <= dates[1]) &
(mannschaft['Opponent'].isin(types)) &
(mannschaft['Location'].isin(locations))].copy()
chorodata = chorodata.groupby(['code', 'Opponent'
]).sum()['German Goals'].reset_index()
chorodata = pd.merge(
mannschaft[['code',
'Opponent']].drop_duplicates().reset_index().drop('index',
axis=1),
chorodata,
on=['code', 'Opponent'],
how='outer')
chorodata = chorodata.loc[chorodata['Opponent'].isin(countries)]
data = [
go.Choropleth(
locations=chorodata['code'],
z=chorodata['German Goals'],
colorscale='Viridis',
zmin=0,
zmax=mannschaft.groupby('Opponent').sum()['German Goals'].max())
]
layout = go.Layout(title='I do not know',
geo=dict(center=dict(lon=-60, lat=-12),
projection=dict(scale=2)))
return go.Figure(data=data, layout=layout)
def update_figure(selected, cause, year):
def title(text):
if text == "fire_size":
return "Acres Burned"
elif text == "fire_count":
return "Wildfire Count"
else:
return "FIRE"
if year == 2016:
state_df = df.groupby(['state',
'stat_cause_descr']).sum().reset_index()
else:
df_f = df[df['fire_year'] == year]
state_df = df_f.groupby(['state', 'fire_year',
'stat_cause_descr']).sum().reset_index()
trace = go.Choropleth(
locations=state_df[state_df['stat_cause_descr'] == cause]['state'],
locationmode='USA-states',
z=state_df[state_df['stat_cause_descr'] == cause][selected],
text=state_df[state_df['stat_cause_descr'] == cause]['state'],
autocolorscale=False,
colorscale="Reds",
reversescale=False,
marker={'line': {
'color': 'rgb(180,180,180)',
'width': 0.5
}},
colorbar={
"thickness": 10,
"len": 0.4,
"x": -0.1,
"y": 0.7,
"xanchor": 'left',
'title': {
"text": title(selected),
"side": "top"
}
})
return {
"data": [trace],
"layout":
go.Layout(title={
'text': 'Wildfire Caused By ' + cause,
'yanchor': 'top',
'x': 0.5,
'y': 0.9
},
clickmode='event+select',
paper_bgcolor='white',
plot_bgcolor='white',
height=600,
width=800,
geo={
'scope': 'usa',
'bgcolor': 'rgba(0,0,0,0)'
},
margin=dict(l=0, r=0, b=0, t=0, pad=0))
}
def horoplethMap(df_data):
df = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv'
)
fig = go.Figure(data=go.Choropleth(
locations=df['CODE'],
z=df_data['KMeans'],
text=df_data['country'],
colorscale='Blues',
autocolorscale=False,
reversescale=True,
marker_line_color='darkgray',
marker_line_width=0.5,
colorbar_title='Clustering',
))
fig.update_layout(
title_text='KMeans Clustering Plot',
geo=dict(showframe=False,
showcoastlines=False,
projection_type='equirectangular'),
annotations=[
dict(
x=0.55,
y=0.1,
xref='paper',
yref='paper',
text=
'Source: <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\
CIA World Factbook</a>',
showarrow=False)
])
py.image.save_as(fig, filename='horoplethMap.png')
def makeChoropleth(dates, types, countries, locations):
chorodata = zika.loc[(zika['report_date'] >= dates[0]) & (zika['report_date'] <= dates[1]) &
(zika['report_type'].isin(types)) & (zika['location'].isin(locations))].copy()
chorodata = chorodata.groupby(['code', 'country']).sum()['value'].reset_index()
chorodata = pd.merge(zika[['code', 'country']].drop_duplicates().reset_index().drop('index', axis = 1),
chorodata, on=['code', 'country'], how='outer')
chorodata = chorodata.loc[chorodata['country'].isin(countries)]
data = [
go.Choropleth(
locations = chorodata['code'],
z = chorodata['value'],
colorscale = 'Viridis',
zmin = 0,
zmax = zika.groupby('country').sum()['value'].max()
)
]
layout = go.Layout(
title = 'Zika Cases by Country',
geo = dict(
center = dict(lon = -60, lat = -12),
projection = dict(scale = 2)
)
)
return go.Figure(data = data, layout = layout)
def update_figure(selected_si):
if selected_si == 0:
selected_si = '0.0'
elif selected_si == 1:
selected_si = '1.0'
column = 'SI_'+ str(selected_si)
trace = go.Choropleth(
locations=sus_df['code'],
z=sus_df[column].astype(float),
locationmode='USA-states',
colorscale='Greens',
autocolorscale=False,
# hovertext=sus_df['text'], # hover text
marker_line_color='white', # line markers between states
colorbar={"thickness": 10,"len": 0.55,"x": 0.9,"y": 0.55,'outlinecolor':'white',
'title': {#"text": 'SI',
"side": "top"}}
)
return {"data": [trace],
"layout": go.Layout(title={'text':'Sustainability Indexes of U.S. States',
'y':0.9,
},
height=350,
geo = dict(
scope='usa',
projection=go.layout.geo.Projection(type = 'albers usa'),
showlakes=False, # lakes
),
margin={'t':10,'b':0,'l':10,'r':10})}
def Plotter(mapName, scl, values):
data = [
go.Choropleth(
colorscale=scl,
autocolorscale=False,
locations=list(stateDict.values()),
z=values,
locationmode='USA-states',
text=[i[0] for i in locs],
marker=go.choropleth.Marker(line=go.choropleth.marker.Line(
color='rgb(255,255,255)', width=2)),
colorbar=go.choropleth.ColorBar(title=mapName))
]
layout = go.Layout(
title=go.layout.Title(text=mapName),
geo=go.layout.Geo(
scope='usa',
projection=go.layout.geo.Projection(type='albers usa'),
showlakes=True,
lakecolor='rgb(255, 255, 255)'),
)
fig = go.Figure(data=data, layout=layout)
plotly.offline.plot(fig, filename=(mapName + ".html"))
def location_map_function(country):
'''
Returns a world map where the given country is highlighted.
As only data provided are the ones of the country - only the
country is coloured in the output map.
'''
trace2 = go.Choropleth(locations=[country],
z=[1],
text=country,
autocolorscale=True,
locationmode='country names',
showscale=False)
return {
"data": [trace2],
"layout":
go.Layout(height=500,
margin=go.layout.Margin(l=0, r=0, b=0, t=0, pad=4),
geo={
'showframe': False,
'showcountries': True,
'showcoastlines': True,
'projection': {
'type': "miller"
}
})
}
def world_map(self, df, x_col, y_col, label_col, chart_title):
map_json = {
'data': [
go.Choropleth(locations=df[x_col],
z=df[y_col],
text=df[label_col],
colorscale='blues',
marker_line_color='darkgray',
marker_line_width=0.5,
colorbar_title="Total<br>RSVP's")
],
'layout':
go.Layout(
# title_text='Worldwide RSVP Map',
width=plot_w,
# height=plot_h,
margin={
'b': 0,
't': 0,
'r': 0,
'l': 0
},
clickmode='event+select',
geo=dict(showframe=False,
showcoastlines=False,
landcolor='rgb(230, 230, 230)',
showland=True))
}
return map_json
def display_output(rows, columns, titulo, varname):
df_ciudades_ant_cuenta = pd.DataFrame(rows,
columns=[c['name'] for c in columns])
fig = go.Figure(data=go.Choropleth(
locations=df_ciudades_ant_cuenta['ciudad'], # Spatial coordinates
z=df_ciudades_ant_cuenta['cuenta'].astype(
float), # Data to be color-coded
geojson=geoJSON,
featureidkey="properties.name",
colorscale='Blues',
colorbar_title=varname,
locationmode='geojson-id'))
fig.update_geos(fitbounds="locations",
visible=False,
showcountries=False,
countrycolor="Black",
showsubunits=False)
fig.update_layout(
width=1000,
height=1000,
geo=dict(
scope='south america',
projection=go.layout.geo.Projection(type='mercator'),
showlakes=True, # lakes
lakecolor='rgb(255, 255, 255)'),
title_text=titulo,
font=dict(family="Courier New, monospace", size=25, color="#7f7f7f"))
return fig
def map_maker(year):
dff = df[df['Year'] == year]
print(dff.head(5))
trace = [
go.Choropleth(
locations=dff['Code'],
locationmode='USA-states',
z=dff['Score_Classification'],
text=dff['Legal Name'],
colorscale="YlOrRd",
)
]
layout = go.Layout(autosize=True,
hovermode='closest',
geo_scope='usa',
showlegend=True,
height=700,
margin=dict(l=20, r=20, t=20, b=20),
mapbox={
'accesstoken': mapbox_access_token,
'bearing': 0,
'center': {
'lat': 38,
'lon': -94
},
'pitch': 30,
'zoom': 3,
'style': 'light'
})
return {'data': trace, 'layout': layout}
def update_figure(selected):
"""Generate choropleth based on selected option."""
x_values = [x[0] for x in price_levels[selected]]
y_values = [x[1] for x in price_levels[selected]]
trace = go.Choropleth(
type="choropleth",
locations=x_values,
locationmode="country names",
colorscale=complete[selected],
colorbar=go.choropleth.ColorBar(ticksuffix="", title="Percent", len=0.5),
z=y_values,
)
return {
"data": [trace],
"layout": go.Layout(
height=700,
width=700,
font={"size": 16},
margin={"t": 0, "b": 0, "l": 0, "r": 0},
geo={
"lataxis": {"range": [36.0, 71.0]},
"lonaxis": {"range": [-10.0, 35.0]},
"projection": {"type": "transverse mercator"},
"resolution": 50,
"showcoastlines": True,
"showframe": True,
"showcountries": True,
},
),
}
def choro_map(d_frame, metric):
state_data = d_frame[['State', 'State-abbreviation',
metric]].groupby(['State',
'State-abbreviation']).sum()
state_data = state_data.reset_index()
locations = state_data['State-abbreviation'].values
z_data = state_data[metric].values
text = state_data.State.values
ch_map = [
go.Choropleth(locations=locations,
locationmode='USA-states',
z=z_data,
autocolorscale=False,
colorscale='YlOrRd',
colorbar={'tickprefix': '$'},
text=text)
]
layout = go.Layout(height=240,
margin={
'l': 5,
'r': 5,
'b': 5,
't': 5
},
geo={
'scope': 'usa',
'projection': {
'type': 'albers usa'
},
'showframe': False
})
return ch_map, layout
def hu_run_select() -> 'html':
the_region = request.form["the_region_selected"] ## 取得用户交互输入
print(the_region) ## 检查用户输入, 在后台
dfs = df.query("code=='{}'".format(
the_region)) ## 使用df.query()方法. 按用户交互输入the_region过滤
data_str = dfs.to_html() # 数据产出dfs, 完成互动过滤
fig = go.Figure(data=go.Choropleth(
locations=dfs['code'], # Spatial coordinates
z=dfs['total'].astype(float), # Data to be color-coded
locationmode=
'USA-states', # set of locations match entries in `locations`
colorscale='Reds',
colorbar_title="Millions USD",
))
fig.update_layout(
title_text='美国大型枪击事件',
geo_scope='usa', # limite map scope to USA
)
fig.show()
py.offline.plot(fig, filename="map_details.html", auto_open=False)
with open("map_details.html", encoding="utf8",
mode="r") as f: # 把"map_details.html"當文字檔讀入成字符串
plot_all = "".join(f.readlines())
regions_available = regions_available_loaded # 下拉选单有内容
return render_template(
'page_03_details.html',
the_plot_all=plot_all,
the_res=data_str,
the_select_region=regions_available,
)
def update_figure(filter_choice, level_choice):
average = all_options[filter_choice][0]
text = all_options[filter_choice][1]
title_text = all_options[filter_choice][2]
dff = data_viz[data_viz['level'] == level_options[level_choice]]
bar_title = all_options[filter_choice][4]
return {
'data': [
go.Choropleth(
colorscale=scl,
autocolorscale=False,
locations=dff['code'].unique(),
z=[
'%.1f' % round(x, 1)
for x in dff[average].astype(float).unique()
],
locationmode='USA-states',
text=dff[text].unique(),
marker=dict(line=dict(color='rgb(255,255,255)', width=2)),
colorbar=dict(title=bar_title))
],
'layout':
go.Layout(
title=title_text,
geo=dict(scope='usa',
projection=dict(type='albers usa'),
showlakes=True,
lakecolor='rgb(255, 255, 255)'),
)
}
def update_ds_map(disaster, year_value):
df_count_year = disaster_nums[disaster_nums['incidentType'] == disaster].groupby(['state', 'yearofloss'])['incidentType'].count().reset_index()
df_ds_year = df_count_year[df_count_year['yearofloss'].between(year_value[0], year_value[1])]
dff_year_claims = df_ds_year.groupby('state')['incidentType'].sum()
return {
'data': [
go.Choropleth(
locations = dff_year_claims.index,
z = dff_year_claims,
locationmode = 'USA-states',
colorscale = 'Blues'
)
],
'layout': go.Layout(
title_text = str(disaster) + " Count by State (" + str(year_value[0]) + " - " + str(year_value[1]) + ")",
geo_scope = 'usa',
height=730,
margin={
'l': 25,
'r': 0,
'b': 25,
't': 100,
}
)
}
def update_map(year_value, radio_item):
year_value1 = year_value[0]
year_value2 = year_value[1]
category_meaning = {1: "successful", 2: "failed", 3: "canceled"}
co = table[table['launched year'].between(year_value1,
year_value2,
inclusive=True)]
co = co.drop(["launched year"], axis=1)
co = co[co["state"] == category_meaning[radio_item]]
co["ID"] = pd.to_numeric(co["ID"])
co = co.drop("state", axis=1)
new_co = co.groupby(["country"])['ID'].agg(sum)
# percentage=table.groupby(["country"]).agg({"ID":'sum'}) new_table.div(country, level="country")*100
new_co = pd.DataFrame(new_co)
new_co = new_co.reset_index()
sum_ = new_co["ID"].sum()
new_co["percent"] = (new_co["ID"].div(sum_)).apply(lambda x: x * 100)
fig_map = go.Figure(data=go.Choropleth(
locations=new_co["country"],
z=new_co["percent"],
# text = table[(table["state"]=="failed") & (table['launched year']==2017)]['country'],
colorscale='Blues',
autocolorscale=False,
reversescale=False,
marker_line_color='darkgray',
marker_line_width=0.5,
))
return fig_map
def getFig(value):
fig = go.Figure(data=go.Choropleth(
locations=df.loc[df["Year"] == value]["Country Code"],
z=df.loc[df["Year"] == value]["Annual_CO2_Emissions"],
text=df.loc[df["Year"] == value]["Country"],
colorscale="RdYlBu",
autocolorscale=False,
reversescale=True,
marker_line_color="darkgray",
marker_line_width=0.5,
colorbar_tickprefix="-",
colorbar_title="CO2 Emissions<br>(tonnes)",
))
fig.update_layout(
title_text="Annual CO2 Emissions",
geo=dict(showframe=False,
showcoastlines=False,
projection_type="equirectangular"),
annotations=[
dict(
x=0.55,
y=0.1,
xref="paper",
yref="paper",
showarrow=False,
)
],
)
return fig
def update_chart(selection):
if selection == 'dominant':
df = df_dom
df = df.merge(df_dep,
how="left",
left_on=["country", "dominant_source"],
right_on=["country", "label"])
df["percent_dep"] = round(100 * df["dependence"], 0).astype("int")
df.drop(df[df["dependence"] == 0].index.values, inplace=True)
df["text"] = df["country"] + "<br>" + df[
"dominant_source"] + " (" + df["percent_dep"].astype(
"str") + "%" + " dependent" + ")"
fig = go.Figure(data=go.Choropleth(
locations=df['iso'],
z=df['score'],
text=df['text'],
hoverinfo='text',
showscale=False,
colorscale=[[0, colors["color"][3]], [0.5, colors["color"][2]],
[1.0, colors["color"][1]]],
marker_line_width=0.5,
marker_line_color='white',
))
fig.update_layout({
"geo": {
"projection_type": "natural earth",
"lataxis_range": [-60, 85]
}
})
fig.update_layout(l_map)
else:
fig = make_fig_1(selection)
return fig
def first():
# data = [
# go.Bar(x=df['Country/Region'],y=df['Confirmed'])
#
# ]
df_new['text'] = '<br>' + 'Confirmed :' + df_new['Confirmed'].astype(
str) + '<br>' + 'Recovered :' + df_new['Recovered'].astype(
str) + '<br>' + 'Deaths :' + df_new['Deaths'].astype(str)
fig = go.Choropleth(
locations=df_new.index, # Spatial coordinates
z=df_new['Confirmed'], # Data to be color-coded
locationmode=
'country names', # set of locations match entries in `locations`
colorscale='Blues',
showlegend=False,
text=df_new['text'],
hovertext=df_new.index,
hovertemplate="Country:%{hovertext},%{text}",
colorbar_title='Confirmed<br>Cases',
)
d4 = [fig]
graphJSON_1 = json.dumps(d4, cls=plotly.utils.PlotlyJSONEncoder)
schedule.every(600).minutes.do(get_data)
return render_template('index.html', graphJSON=graphJSON_1)
def update_map(var_selected, date_selected):
if var_selected is None:
raise PreventUpdate
df = covid_data[[var_selected, 'day_of_year', 'date',
'country_name']].query(f"day_of_year=={date_selected}")
map = go.Figure(
data=go.Choropleth(locations=df['country_name'],
locationmode='country names',
z=np.log(df[var_selected]),
colorscale='Reds',
marker_line_color='black',
marker_line_width=0.5,
zmin=0,
zmax=max(np.log(covid_data[var_selected])),
text=df[var_selected],
hoverinfo='location+text'))
map.update_layout(margin=dict(l=5, r=50, b=0, t=0, pad=2),
geo=dict(
showframe=False,
showcoastlines=True,
projection_type='equirectangular',
))
return map
def update_choropleth(selector):
"""
Updates the choropleth map in the "Browse" section based on user's metric choice.
:param clickData: Selected metric (ie. MPI or GII).
:return: A choropleth map.
"""
if selector == "MPI":
return {
'data': [
go.Choropleth(locations=totals_df['ISO'],
z=totals_df['MPI'].astype(float),
colorscale='Reds',
colorbar={
"thickness": 10,
"len": 0.65,
"x": 0.95,
"y": 0.5
},
text=totals_df['country'])
],
'layout': {
'height': 800,
'width': 1300,
'title': 'MPI by Country'
}
}
else:
return {
'data': [
go.Choropleth(locations=totals_df['ISO'],
z=totals_df['GII'].astype(float),
colorscale='Blues',
colorbar={
"thickness": 10,
"len": 0.65,
"x": 0.85,
"y": 0.5
},
text=totals_df['country'])
],
'layout': {
'height': 800,
'width': 1300,
'title': 'GII by Country'
}
}
def us_plot():
response = requests.get(us_url)
df = pd.read_html(response.text, flavor=['bs4','html5lib'])
state_code = ['NY','NJ','MA','CA','PA','IL','MI','FL','LA','CT','TX','GA','MD','OH','IN','WA','VA','CO','TN','NC','MO','RI','AZ','AL','MS','WI','SC','NV','IA','UT','KY','DC','DE','OK','MN','KS','AR','NM','OR','SD','NE','ID','NH','WV','ME','VT','ND','HI','WY','MT','AK']
new_df = df[0].iloc[1:52]
new_df['code'] = state_code
fig = go.Figure(data=go.Choropleth(
locations=new_df['code'], # Spatial coordinates
z = new_df['TotalCases'].astype(int), # Data to be color-coded
locationmode = 'USA-states', # set of locations match entries in `locations`
colorscale = [[0, 'rgb(240, 239, 239)'],[0.1, 'rgb(222, 146, 139 )'],[0.2, 'rgb(222, 146, 139 )'],[0.3, 'rgb(207, 125, 117)'],[0.4, 'rgb(207, 125, 117)'],[0.5, 'rgb(207, 125, 117)'],[0.6, 'rgb(202, 114, 105 )'],[0.7, 'rgb(190, 102, 93 )'],[0.8, 'rgb(191, 87, 77 )'],[0.9, 'rgb(191, 73, 61 )'],[1, 'rgb(183, 53, 40)']],
marker_line_color='white',
colorbar_title = "Total Cases",
))
fig.update_layout(
geo_scope='usa' # limite map scope to USA
)
fig.update_layout(margin={"r":0,"t":50,"l":0,"b":0})
div = fig.to_html(full_html=False)
results = get_us_data()
#donut chart
totalcase = []
state_list = [0,1,2,3,4,5,6,7,8,9]
for state in state_list:
totalcase.append(new_df.iloc[state]['TotalCases'])
other_states = df[0].iloc[11:52]
sum = other_states.sum(axis=0,skipna = True)
labels = ['New York','New Jersey','Massachusetts','Illinois','California','Pennsylvania','Michigan','Florida','Louisiana','Connecticut','Other States']
values = [totalcase[0],totalcase[1] , totalcase[2], totalcase[3],totalcase[4],totalcase[5],totalcase[6],totalcase[7],totalcase[8],totalcase[9],sum['TotalCases']]
# Use `hole` to create a donut-like pie chart
fig_pie = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.5, marker={'colors': [
'#a43820',
'#364761',
'#627da8',
'#a87262',
'#61917f',
'#8fbaa5',
'#a1834d',
'#c9bbb3',
'#537a78',
'#364d4b',
'#dedcdc'
]
},)])
fig_pie.update_layout(margin={"r":0,"t":150,"l":0,"b":20})
div_pie = fig_pie.to_html(full_html=False)
div = fig.to_html(full_html=False)
return render_template("us.html", results=results, plot_div=div,plot_div_pie=div_pie)
def choropleth_plot():
df = reviews["country"].replace("US", "United States").value_counts()
plot([go.Choropleth(
locationmode="country names",
locations=df.index.values,
text=df.index,
z=df.values
)])
