def plotCommunity(self,colorscale=False):
#make copy of node dataframe
df_temp = self.node_df.copy()
#change community label to string (needed for plot)
df_temp['community'] = df_temp['community'].map(lambda x: str(x))
#conditionals for plot type
if colorscale == False:
fig = px.sunburst(df_temp, path=['community', 'id'], values='adjacency_frequency',color='community',hover_name=None,
hover_data=None)
else:
fig = px.sunburst(df_temp, path=['community', 'id'], values='adjacency_frequency',
color='betweeness_centrality', hover_data=None,
color_continuous_scale='blugrn',
color_continuous_midpoint=np.average(df_temp['betweeness_centrality'], weights=df_temp['betweeness_centrality']))
#add margin to plot
fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))
#retain community plot as attribute
self.community_plot = fig
#offline sunburst plot
iplot(self.community_plot)
return
def getSunburst(path, channel, style):
df = pd.read_csv(path + channel + '.csv')
df['Time'] = pd.to_datetime(df['Time'])
import calendar
df['month'] = pd.DatetimeIndex(df['Time']).month
df_new = df.groupby(['month', 'Source', 'Target'
]).size().reset_index().rename(columns={0: 'count'})
df_new['month'] = df['month'].apply(lambda x: calendar.month_abbr[x])
#fig1 = px.sunburst(df_new, path=['Source','month', 'Target'], values=df_new['count'], color='month',color_continuous_scale='RdBu')
#fig2 = px.sunburst(df_new, path=['month','Source', 'Target'], values=df_new['count'], color='month',color_continuous_scale='RdBu')
if style == 1:
return px.sunburst(df_new,
path=['Source', 'month', 'Target'],
values=df_new['count'],
color='Source',
color_continuous_scale='RdBu')
elif style == 2:
return px.sunburst(df_new,
path=['month', 'Source', 'Target'],
values=df_new['count'],
color='month',
color_continuous_scale='RdBu')
else:
return px.sunburst(df_new,
path=['Target', 'month', 'Source'],
values=df_new['count'],
color='Target',
color_continuous_scale='RdBu')
def test_sunburst_treemap_with_path_and_hover():
df = px.data.tips()
fig = px.sunburst(df,
path=["sex", "day", "time", "smoker"],
color="smoker",
hover_data=["smoker"])
assert "smoker" in fig.data[0].hovertemplate
df = px.data.gapminder().query("year == 2007")
fig = px.sunburst(df,
path=["continent", "country"],
color="lifeExp",
hover_data=df.columns)
assert fig.layout.coloraxis.colorbar.title.text == "lifeExp"
df = px.data.tips()
fig = px.sunburst(df,
path=["sex", "day", "time", "smoker"],
hover_name="smoker")
assert "smoker" not in fig.data[
0].hovertemplate # represented as '%{hovertext}'
assert "%{hovertext}" in fig.data[
0].hovertemplate # represented as '%{hovertext}'
df = px.data.tips()
fig = px.sunburst(df,
path=["sex", "day", "time", "smoker"],
custom_data=["smoker"])
assert fig.data[0].customdata[0][0] in ["Yes", "No"]
assert "smoker" not in fig.data[0].hovertemplate
assert "%{hovertext}" not in fig.data[0].hovertemplate
def test_sunburst_treemap_column_parent():
vendors = ["A", "B", "C", "D", "E", "F", "G", "H"]
sectors = [
"Tech",
"Tech",
"Finance",
"Finance",
"Tech",
"Tech",
"Finance",
"Finance",
]
regions = [
"North", "North", "North", "North", "South", "South", "South", "South"
]
values = [1, 3, 2, 4, 2, 2, 1, 4]
df = pd.DataFrame(
dict(
id=vendors,
sectors=sectors,
parent=regions,
values=values,
))
path = ["parent", "sectors", "id"]
# One column of the path is a reserved name - this is ok and should not raise
px.sunburst(df, path=path, values="values")
def sunburst(self,
title,
colorscale=False,
color_col='betweeness_centrality',
color_continuous_scale='Oryel',
width=1100,
height=1100,
save=False) -> px.sunburst:
"""Plots of sunburst chart
Args:
title (str): title of plot
colorscale (bool): Size of the maker
color_col (str): Color-coded column names.
color_continuous_scale (str): cf.https://plotly.com/python/builtin-colorscales/
width (int): width of the graph
height (int): height of the graph
save (bool): Whether or not to save the HTML file.
Returns:
px.sunburst: Figure of a sunburst graph
"""
# make copy of node dataframe
_df = self.node_df.copy()
# change community label to string (needed for plot)
_df.loc[:, 'community'] = _df['community'].map(lambda x: str(x))
# conditionals for plot type
if colorscale is False:
fig = px.sunburst(_df,
path=['community', 'id'],
values='adjacency_frequency',
color='community',
hover_name=None,
hover_data=None)
else:
fig = px.sunburst(_df,
path=['community', 'id'],
values='adjacency_frequency',
color=color_col,
hover_data=None,
color_continuous_scale=color_continuous_scale,
color_continuous_midpoint=np.average(
_df[color_col], weights=_df[color_col]))
fig.update_layout(
title=str(title),
width=width,
height=height,
)
if save:
self.save_plot(fig, title)
del _df
gc.collect()
return fig
def plot_sunbirst_charts(solved_data: pd.DataFrame):
sb1 = px.sunburst(solved_data, values="expense", path=["type",
"entity"])._data
sb2 = px.sunburst(
solved_data[solved_data["solution"] == 1],
values="expense",
path=["type", "entity"],
)._data
# use data and structure FROM sb1 and sb2 (trick)
fig = make_subplots(rows=1,
cols=2,
specs=[[{
"type": "domain"
}, {
"type": "domain"
}]])
fig.add_trace(
go.Sunburst(
branchvalues="total",
labels=sb1[0]["labels"],
parents=sb1[0]["parents"],
values=sb1[0]["values"],
),
1,
1,
)
fig.add_trace(
go.Sunburst(
branchvalues="total",
labels=sb2[0]["labels"],
parents=sb2[0]["parents"],
values=sb2[0]["values"],
),
1,
2,
)
fig.update_layout(
title_text="Expenses distribution in solution, sunbirst chart",
# Add annotations in the center of the donut pies.
annotations=[
dict(text="Original", x=0.17, y=1.1, font_size=20,
showarrow=False),
dict(text="Optimized",
x=0.84,
y=1.1,
font_size=20,
showarrow=False),
],
)
# fig = go.Figure(data = [trace1, trace2], layout = layout)
fig.show()
def sunburst_interactive(count_dict,lexique,lexique_class_labels):
df_viz = pd.DataFrame(count_dict.items(),columns=("word","freq"))
class_dict = dict(lexique["word class".split()].values)
if lexique_class_labels:
df_viz["class"] = df_viz.word.apply(lambda x:lexique_class_labels[class_dict[x]])
fig = px.sunburst(df_viz, path=["class",'word'], values='freq')
else:
fig = px.sunburst(df_viz, path=['word'], values='freq')
return fig
def refresh_overview_zone_chart(jsonified_cleaned_data,clickData,selected_axis):
# Init chart when none click
if json.dumps(clickData,indent=2) == "null":
return px.sunburst(), None, None
# Axe definition
geo_axis = "ZONE"
if selected_axis == 'Statut des PDVs':
analysis_axis = 'POS_STATUS'
else:
analysis_axis = 'IMPACT_VISITE'
# Clicked DACR
if clickData['points'][0]['currentPath'] == '/':
dacr_name = clickData['points'][0]['id']
elif clickData['points'][0]['currentPath'] in ['/Agadez/','/Diffa/','/Dosso/','/Maradi/','/Niamey/','/Tahoua/','/Tillaberi/','/Zinder/']:
dacr_name = clickData['points'][0]['parent']
else:
dacr_name = "Agadez"
# load pos data
df2 = pd.read_json(jsonified_cleaned_data)
df2 = df2[df2['DACR'] == dacr_name]
filtered_data = df2.to_json()
# AGG DATA
dacr_data = (
df2
.groupby(['MONTH','DACR',geo_axis,analysis_axis])
.agg({"POS" : "nunique"})
.reset_index()
.rename(columns={"POS" : "NOMBRE DE PDVs"})
# .pivot(index="DACR",columns='POS_STATUS',values='POS_CNT')
# .reset_index()
)
# Chart color dict
if selected_axis == 'Statut des PDVs':
chart_color_dict = {x:status_markers_colors[x] for x in dacr_data[analysis_axis]}
else:
chart_color_dict = {x:impact_markers_colors[x] for x in dacr_data[analysis_axis]}
fig = px.sunburst(
dacr_data,
path=[geo_axis, analysis_axis],
values='NOMBRE DE PDVs',
color=analysis_axis,
color_discrete_map=chart_color_dict
# hovertemplate='<extra>{fullData.DACR}</extra>'
)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
return fig,filtered_data, "Zones de {0}: ".format(dacr_name)
def refresh_overview_sector_chart(jsonified_cleaned_data,clickData,selected_axis):
# Init chart when none click
if json.dumps(clickData,indent=2) == "null":
return px.sunburst(), None
# Axis definition
geo_axis = "SECTEUR"
if selected_axis == 'Statut des PDVs':
analysis_axis = 'POS_STATUS'
else:
analysis_axis = 'IMPACT_VISITE'
# Clicked DACR
if clickData['points'][0]['currentPath'] == '/':
zone_name = clickData['points'][0]['id']
else:
zone_name = clickData['points'][0]['parent']
# load pos data
df2 = pd.read_json(jsonified_cleaned_data)
df2 = df2[df2['ZONE'] == zone_name]
dacr_name = list(df2['DACR'].unique())[0]
# AGG DATA
zone_data = (
df2
.groupby(['MONTH','DACR',geo_axis,analysis_axis])
.agg({"POS" : "nunique"})
.reset_index()
.rename(columns={"POS" : "NOMBRE DE PDVs"})
# .pivot(index="DACR",columns='POS_STATUS',values='POS_CNT')
# .reset_index()
)
# Chart color dict
if selected_axis == 'Statut des PDVs':
chart_color_dict = {x:status_markers_colors[x] for x in zone_data[analysis_axis]}
else:
chart_color_dict = {x:impact_markers_colors[x] for x in zone_data[analysis_axis]}
fig = px.sunburst(
zone_data,
path=[geo_axis, analysis_axis],
values='NOMBRE DE PDVs',
color=analysis_axis,
color_discrete_map=chart_color_dict
# hovertemplate='<extra>{fullData.DACR}</extra>'
)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
return fig, "Secteurs de {0} {1}: ".format(dacr_name,zone_name)
def test_sunburst_treemap_with_path_color():
vendors = ["A", "B", "C", "D", "E", "F", "G", "H"]
sectors = [
"Tech",
"Tech",
"Finance",
"Finance",
"Tech",
"Tech",
"Finance",
"Finance",
]
regions = ["North", "North", "North", "North", "South", "South", "South", "South"]
values = [1, 3, 2, 4, 2, 2, 1, 4]
calls = [8, 2, 1, 3, 2, 2, 4, 1]
total = ["total",] * 8
df = pd.DataFrame(
dict(
vendors=vendors,
sectors=sectors,
regions=regions,
values=values,
total=total,
calls=calls,
)
)
path = ["total", "regions", "sectors", "vendors"]
fig = px.sunburst(df, path=path, values="values", color="calls")
colors = fig.data[0].marker.colors
assert np.all(np.array(colors[:8]) == np.array(calls))
fig = px.sunburst(df, path=path, color="calls")
colors = fig.data[0].marker.colors
assert np.all(np.array(colors[:8]) == np.array(calls))
# Hover info
df["hover"] = [el.lower() for el in vendors]
fig = px.sunburst(df, path=path, color="calls", hover_data=["hover"])
custom = fig.data[0].customdata
assert np.all(custom[:8, 0] == df["hover"])
assert np.all(custom[8:, 0] == "(?)")
assert np.all(custom[:8, 1] == df["calls"])
# Discrete color
fig = px.sunburst(df, path=path, color="vendors")
assert len(np.unique(fig.data[0].marker.colors)) == 9
# Numerical column in path
df["regions"] = df["regions"].map({"North": 1, "South": 2})
path = ["total", "regions", "sectors", "vendors"]
fig = px.sunburst(df, path=path, values="values", color="calls")
colors = fig.data[0].marker.colors
assert np.all(np.array(colors[:8]) == np.array(calls))
def draw(self):
"""
Generate SunBurstChart visualization
"""
label_column, numerical_var = self._check_requirements()
if numerical_var is not None:
#plot
fig = px.sunburst(self.dataframe, values=numerical_var, path=label_column)
fig.show()
else:
fig = px.sunburst(self.dataframe, path=label_column)
fig.show()
def makeSunburstCharts(DN_DF):
# DN_DF = DN_DF[DN_DF['DT Hemisphere'] != "check DT"]
DN_DF_FindSoma_Filter = DN_DF[DN_DF['Soma Hemisphere'] != "FindSoma"]
DN_DF_RightDT = DN_DF_FindSoma_Filter[DN_DF_FindSoma_Filter['DT Hemisphere'] == "Right"]
fig = px.sunburst(DN_DF_FindSoma_Filter, path=['DT Hemisphere', 'Soma Hemisphere', 'DN Type'],
title="Both DT - Soma Hem - DN Type")
py.plot(fig, filename="Both DT - Soma Hem - DN Type")
DN_DF_RightDT.Classification = DN_DF_RightDT['Classification'].fillna('No Classification')
fig = px.sunburst(DN_DF_RightDT, path=['Soma Hemisphere', 'DN Type', 'Classification'],
title="Right DT - Soma Hem - DN Type - Classification")
py.plot(fig, filename="Right DT - Soma Hem - DN Type - Classification")
return
def exibe_instrucao(cpf):
st.subheader("Considerando o grau de instrução")
parlamentar = df_parlamentares[df_parlamentares["CPF"] == cpf]
grau = parlamentar["DS_GRAU_INSTRUCAO"].iloc[0]
uf = parlamentar["SG_UE"].iloc[0]
x_brasil = pd.crosstab(index=[df_parlamentares['DS_GRAU_INSTRUCAO']],
columns=[df_parlamentares['DS_GRAU_INSTRUCAO']],
normalize=True).T
perc_brasil = x_brasil[grau][grau].round(3) * 100
x_estado = pd.crosstab(index=[df_parlamentares['DS_GRAU_INSTRUCAO']],
columns=[
df_parlamentares[df_parlamentares["SG_UE"] ==
uf]['DS_GRAU_INSTRUCAO']
],
normalize=True).T
perc_estado = x_estado[grau][grau].round(3) * 100
st.write(
"Faz parte dos {}% dos parlamentares com {} no Brasil, {}% no estado {}."
.format(perc_brasil.round(1), grau.lower(), perc_estado.round(1), uf))
df_instruc = df_parlamentares[[
"CPF", "NM_CANDIDATO", "SG_UE", "SG_PARTIDO", "DS_GRAU_INSTRUCAO"
]].copy()
df_instruc = df_instruc[df_instruc["SG_UE"] == uf].copy()
data = df_instruc
fig = px.sunburst(data,
path=['SG_UE', "DS_GRAU_INSTRUCAO", "NM_CANDIDATO"])
st.plotly_chart(fig, use_container_width=True)
#
#
return
def exibe_estado_civil(cpf):
st.subheader("Considerando o Estado Civil")
parlamentar = df_parlamentares[df_parlamentares["CPF"] == cpf]
civil = parlamentar["DS_ESTADO_CIVIL"].iloc[0]
uf = parlamentar["SG_UE"].iloc[0]
x_brasil = pd.crosstab(index=[df_parlamentares['DS_ESTADO_CIVIL']],
columns=[df_parlamentares['DS_ESTADO_CIVIL']],
normalize=True).T
perc_brasil = x_brasil[civil][civil].round(3) * 100
x_estado = pd.crosstab(index=[df_parlamentares['DS_ESTADO_CIVIL']],
columns=[
df_parlamentares[df_parlamentares["SG_UE"] ==
uf]['DS_ESTADO_CIVIL']
],
normalize=True).T
perc_estado = x_estado[civil][civil].round(3) * 100
st.write(
"Faz parte dos {}% dos parlamentares com estado civil {} no Brasil, {}% no estado {}."
.format(perc_brasil.round(1), civil.lower(), perc_estado.round(1), uf))
df_civil = df_parlamentares[[
"CPF", "NM_CANDIDATO", "SG_UE", "SG_PARTIDO", "DS_ESTADO_CIVIL"
]].copy()
df_civil = df_civil[df_civil["SG_UE"] == uf].copy()
data = df_civil
fig = px.sunburst(data, path=['SG_UE', "DS_ESTADO_CIVIL", "NM_CANDIDATO"])
st.plotly_chart(fig, use_container_width=True)
#
#
return
def exibe_cor(cpf):
st.subheader("Considerando a cor declarada")
parlamentar = df_parlamentares[df_parlamentares["CPF"] == cpf]
cor = parlamentar["DS_COR_RACA"].iloc[0]
uf = parlamentar["SG_UE"].iloc[0]
x_brasil = pd.crosstab(index=[df_parlamentares['DS_COR_RACA']],
columns=[df_parlamentares['DS_COR_RACA']],
normalize=True).T
perc_brasil = x_brasil[cor][cor].round(3) * 100
x_estado = pd.crosstab(
index=[df_parlamentares['DS_COR_RACA']],
columns=[
df_parlamentares[df_parlamentares["SG_UE"] == uf]['DS_COR_RACA']
],
normalize=True).T
perc_estado = x_estado[cor][cor].round(3) * 100
st.write(
"Faz parte dos {}% dos parlamentares de cor {} no Brasil e {}% do estado {}."
.format(perc_brasil.round(1), cor.lower(), perc_estado.round(1), uf))
df_cor = df_parlamentares[[
"CPF", "NM_CANDIDATO", "SG_UE", "SG_PARTIDO", "DS_COR_RACA"
]].copy()
df_cor = df_cor[df_cor["SG_UE"] == uf].copy()
data = df_cor
fig = px.sunburst(data, path=['SG_UE', "DS_COR_RACA", "NM_CANDIDATO"])
st.plotly_chart(fig, use_container_width=True)
#
#
return
def exibe_genero(cpf):
st.subheader("Considerando o gênero")
parlamentar = df_parlamentares[df_parlamentares["CPF"] == cpf]
genero = parlamentar["DS_GENERO"].iloc[0]
uf = parlamentar["SG_UE"].iloc[0]
gen_brasil = pd.crosstab(index=[df_parlamentares['DS_GENERO']],
columns=[df_parlamentares['DS_GENERO']],
normalize=True).T
perc_brasil = (gen_brasil[genero][genero]) * 100
gen_estado = pd.crosstab(
index=[df_parlamentares['DS_GENERO']],
columns=[
df_parlamentares[df_parlamentares["SG_UE"] == uf]['DS_GENERO']
],
normalize=True).T
perc_estado = (gen_estado[genero][genero]) * 100
st.write(
"Faz parte dos {}% dos parlamentares do sexo {} no Brasil e {}% do estado {}."
.format(perc_brasil.round(1), genero.lower(), perc_estado.round(1),
uf))
df_genero = df_parlamentares[[
"CPF", "NM_CANDIDATO", "SG_UE", "SG_PARTIDO", "DS_GENERO"
]].copy()
df_genero = df_genero[df_genero["SG_UE"] == uf].copy()
data = df_genero
fig = px.sunburst(data, path=['SG_UE', "DS_GENERO", "NM_CANDIDATO"])
st.plotly_chart(fig, use_container_width=True)
#
#
return
def update_current_ratio(entity):
ticker = Ticker(entity)
df = ticker.balance_sheet()
print("df: ", df)
df = df.reset_index(drop=True)
cols = df.columns
totalCurrentAssets = df["totalCurrentAssets"][0]
totalCurrentLiabilities = df["totalCurrentLiabilities"][0]
ratio = float(totalCurrentAssets) / float(totalCurrentLiabilities)
if ratio > 2:
financial_status = "Conservatively financed"
elif ratio > 1:
financial_status = "Be careful"
else:
financial_status = "indebted"
data = dict(
asset_and_liability=[financial_status, "totalCurrentAssets", "totalCurrentLiabilities"],
parent=["", financial_status, financial_status],
value=[ratio, totalCurrentAssets, totalCurrentLiabilities]
)
fig = px.sunburst(
data,
names='asset_and_liability',
parents='parent',
values='value',
)
return fig
def test_pie_like_px():
# Pie
labels = ["Oxygen", "Hydrogen", "Carbon_Dioxide", "Nitrogen"]
values = [4500, 2500, 1053, 500]
fig = px.pie(names=labels, values=values)
trace = go.Pie(labels=labels, values=values)
_compare_figures(trace, fig)
labels = [
"Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"
]
parents = ["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"]
values = [10, 14, 12, 10, 2, 6, 6, 4, 4]
# Sunburst
fig = px.sunburst(names=labels, parents=parents, values=values)
trace = go.Sunburst(labels=labels, parents=parents, values=values)
_compare_figures(trace, fig)
# Treemap
fig = px.treemap(names=labels, parents=parents, values=values)
trace = go.Treemap(labels=labels, parents=parents, values=values)
_compare_figures(trace, fig)
# Funnel
x = ["A", "B", "C"]
y = [3, 2, 1]
fig = px.funnel(y=y, x=x)
trace = go.Funnel(y=y, x=x)
_compare_figures(trace, fig)
# Funnelarea
fig = px.funnel_area(values=y, names=x)
trace = go.Funnelarea(values=y, labels=x)
_compare_figures(trace, fig)
def draw_fig_ad(df_yesterday):
# sunburst for ad click
df_yesterday_ad = df_yesterday.iloc[:, [1, -2, -1]].groupby(
'訪問網站').sum().reset_index().melt(id_vars=["訪問網站"],
var_name="行為",
value_name="人次")
fig_ad = px.sunburst(
df_yesterday_ad,
path=['訪問網站', '行為'], # 分類變數
values='人次', # 面積依據資料
color='訪問網站', # 分色變數
# hover_data=['訪問網站'], # 游標顯示資料
)
fig_ad.update_traces(marker=dict(
# colors=themeColor,
line=dict(color='#ffffff', width=2)))
fig_ad.update_layout(
margin=dict(l=10, r=10, t=10, b=10), # 圖形在畫布的邊界設定
paper_bgcolor="rgba(0,0,0,0)", # 畫布背景顏色
plot_bgcolor="rgba(0,0,0,0)", # 圖形背景顏色
autosize=True, # 自動調整大小
font_color='white', # 文字顏色
font_size=20, # 文字大小
font_family='Microsoft JhengHei', # 字體
)
return fig_ad
def test_sunburst_treemap_with_path_and_hover():
df = px.data.tips()
fig = px.sunburst(df,
path=["sex", "day", "time", "smoker"],
color="smoker",
hover_data=["smoker"])
assert "smoker" in fig.data[0].hovertemplate
def plot_sunburst(
df: pd.DataFrame,
col: str,
path: List[str]=['state_id', 'store_id', 'cat_id', 'dept_id'],
color_discrete_sequence: List[str]=px.colors.qualitative.Prism
) -> go.Figure:
"""
Expected columns:
- 'state_id'
- 'store_id'
- 'cat_id'
- 'dept_id'
- col
"""
fig = px.sunburst(
df,
path=path,
values=col,
color_discrete_sequence=color_discrete_sequence
)
fig.update_layout(
title='USD sales repartition',
height=1000
)
return fig
def plot(drive):
# The drive itself
sections = [drive.name]
parents = ['']
values = [drive.disk_size]
# Game libraries on the drive
#library_names = [f'{library[0]} ({len(drive.game_sizes[i])} games)' for i, library in enumerate(drive.game_libraries)]
library_names = [library[0] for library in drive.game_libraries]
sections.extend(library_names)
parents.extend((len(library_names)) * [drive.name])
values.extend(drive.game_library_sizes)
# OS/other apps and free space
sections.extend(['Other', 'Free Space'])
parents.extend(2 * [drive.name])
values.append(round(drive.disk_usage - sum(drive.game_library_sizes), 1))
values.append(round(drive.disk_size - drive.disk_usage, 1))
# Games
for i, library_name in enumerate(library_names):
for game in drive.game_sizes[i]:
sections.append(game[0])
parents.append(library_name)
values.append(game[1])
data = dict(sections=sections,
parents=parents,
values=values)
fig = px.sunburst(data, names='sections', parents='parents', values='values', branchvalues='total')
fig.show()
def update_graph(value, df):
df = df.query("year == {}".format(value)) # no JSON de-serialization here
return px.sunburst(df,
path=['continent', 'country'],
values='pop',
color='lifeExp',
hover_data=['iso_alpha'])
def sunburstgraph(self, df):
fig = px.sunburst(
df,
path=['Continent', 'sex', 'age'],
values='suicides_no',
title="Suicides with respect to Continent, Sex and Age")
return fig
def make_sunburst_plot(clickData=None, toAdd=None, col=None, val=None):
if clickData != None:
col = clickData["points"][0]['x'].split(": ")[0]
val = clickData["points"][0]['x'].split(": ")[1]
elif col == None:
col = 'Thickness Material A'
val = '47'
desc = list(descriptors[:-2])
if col in desc:
desc.remove(col)
if toAdd != None:
for item in toAdd:
desc.append(item)
test = production_df.loc[production_df[col] == val]
fig = px.sunburst(test, path=desc[:], color='Adjusted EBITDA', title='{}: {}'.format(
col, val),
color_continuous_scale=px.colors.sequential.Viridis)
fig.update_layout({
"plot_bgcolor": "#F9F9F9",
"title": '(Select in Violin) {}: {}'.format(col,val),
"paper_bgcolor": "#F9F9F9",
"height": 400,
"margin": dict(
l=0,
r=0,
b=0,
t=30,
pad=4
),
})
return fig
