def gen_report(file_path):
comps = ['腾讯', '阿里', '百度', '京东', '美团', '小米', '字节跳动', '滴滴']
df_dict = pd.read_excel(file_path, comps)
for k, v in df_dict.items():
v['投资主体'] = k
# domain_set = set().union(* [set(df['行业']) for df in df_dict.values()])
# df_all = pd.concat(df_dict.values())
df_all = pd.concat(df_dict)
df_all['海外'] = df_all['海外'].where(df_all['海外'].notnull(), '国内')
fig = px.treemap(df_all, path=['行业', '投资主体', '日期', '公司简称'])
fig.show()
def variable_directory():
"""
this plots the directory map of all variables in the code base
"""
# TODO: put it into the right data frame
var_id = []
var_alias = []
scheme_name = []
method_name = []
file_name = []
# file_var_count = []
for entry in Variable.objects.all():
var_id.append(entry.name)
var_alias.append(entry.metadata['alias'])
directory_list = entry.directory.split("/")
if (directory_list[0] == 'variables'):
scheme_name.append(directory_list[1])
if (directory_list[-1].endswith('.py')):
file_name.append(directory_list[-1])
if (len(directory_list) == 4):
method_name.append(directory_list[2])
else:
method_name.append(directory_list[-1].split(".py")[0])
df_var = pd.DataFrame(data={
'var_id': var_id,
'alias': var_alias,
'scheme': scheme_name,
'method': method_name,
'file': file_name,
})
df_var.reset_index()
# file_counts = df_var['file'].value_counts()
# df1 = df_var.groupby(by='method').agg('count')
fig = px.treemap(
df_var, path=['scheme', 'method', 'file'],
color='scheme',
color_discrete_map={
'(?)': colorScheme['highlight_color'],
'General_Appliances': colorScheme['trace2_color'], 'Home_Energy_Efficiency_Retrofits (HEER)': colorScheme['trace2_color'],
'High_Efficiency_Appliances_Business (HEAB)': colorScheme['trace2_color'],
'Other_ESS_methods': colorScheme['trace1_color'], 'Removal_of_Old_Appliances (RoOA)': colorScheme['trace2_color']},
title="Overview of Openfisca_nsw_safeguard Code Base",
height=700, width=1500)
fig.update_layout(uniformtext=dict(minsize=14, mode='hide'))
plot_div = fig.to_html(full_html=False)
return plot_div
def position_company_treemap(connections_df):
#connections_df = connections_df.dropna(subset=['Position', 'Company'])
connections_df['Company'] = connections_df['Company'].fillna("Other")
connections_df['Position'] = connections_df['Position'].fillna("Other")
connections_df["PositionCount"] = 1
connections_df["Name"] = connections_df["First Name"] + " " + connections_df["Last Name"]
fig = px.treemap(connections_df, path=['Position', "Company", "Name"],
values="PositionCount",
)
fig.update_layout(title='Job positions of your connected people for each company')
return fig
def covid_16():
fig = px.treemap(e_lat_grp.sort_values(
by='Cases', ascending=False).reset_index(drop=True),
path=["Country"],
values="Cases",
title='EBOLA',
color_discrete_sequence=px.colors.qualitative.Dark2)
fig.data[0].textinfo = 'label+text+value'
#fig.show()
plotly.offline.plot(fig, filename="choro", image='svg', auto_open=True)
return render_template("Vizualize.html")
def treemap(data):
return px.treemap(
data,
names=data["Short Callpath"],
parents=data["Parent"],
values=data["Time (Inclusive)"],
color=data["Time Imbalance"],
branchvalues="total",
)
import dash
def treemap_1(attribute):
plotdata = rawdata.sort_values('Tesla')
fig = px.treemap(plotdata,
path=[str(attribute), 'Manufacturer'],
color='Tesla',
hover_data=['Manufacturer', attribute],
color_discrete_sequence=[color1, color6],
height=800,
title=('Category:{}').format(attribute))
return\
fig
def treemap_2(attribute='value'):
plotdata = rawdata.sort_values('Range')
fig = px.treemap(plotdata,
path=[attribute, 'Manufacturer'],
color='Range',
hover_data=['Manufacturer', attribute],
color_continuous_scale=[color1, color3],
title=('Category:{}').format(attribute),
height=800) #.update(layout_showlegend=False)
return\
fig#.update(showlegend=False)
def covid_21():
fig = px.treemap(s_lat_grp.sort_values(
by='Deaths', ascending=False).reset_index(drop=True),
path=["Country"],
values="Deaths",
title='SARS',
color_discrete_sequence=px.colors.qualitative.Dark2)
fig.data[0].textinfo = 'label+text+value'
#fig.show()
fig.write_html("templates/choro21.html")
return render_template("choro21.html")
def company_position_treemap(connections_df):
#connections_df = connections_df.dropna(subset=['Position', 'Company'])
connections_df['Company'] = connections_df['Company'].fillna("Other")
connections_df['Position'] = connections_df['Position'].fillna("Other")
connections_df["PositionCount"] = 1
connections_df["Name"] = connections_df["First Name"] + " " + connections_df["Last Name"]
fig = px.treemap(connections_df, path=['Company', 'Position', 'Name'],
values="PositionCount",
)
fig.update_layout(title='Companies where your connected people work for each job position')
return fig
def gen_report_all(file_path):
df = pd.read_excel(file_path)
# 转时间序列
df['日期'] = pd.to_datetime(df['日期'])
df = df.set_index('日期').to_period('A')
df['日期'] = df.index
# print(df)
fig = px.treemap(df, path=['行业', '投资主体', '日期', '公司简称'])
fig.write_html(str(file_path.parent.joinpath('all.html')))
fig.write_image(str(file_path.parent.joinpath('all.png')), scale=20)
fig.show()
def single_graph_tree(feature):
title = 'Trade Performance of Malaysia based on {} from 2013 to 2019'.format(
feature)
if feature == 'COUNTRY':
df_group = df.groupby('COUNTRY')[show].sum().reset_index()
df_merge = df_group.merge(df_country, on='COUNTRY', how='inner')
df_merge = df_merge[df_merge[show] > 0]
df_merge["WORLD"] = "WORLD"
fig = px.treemap(df_merge,
path=['WORLD', 'Region', feature],
values=show,
color=show,
hover_data=[show],
color_continuous_scale='RdBu',
title=title)
fig.update_layout(height=600, width=900)
st.plotly_chart(fig)
elif feature == 'SITC 2 DIGIT':
df_merge = df.groupby([feature,
'SITC 1 DIGIT'])[show].sum().reset_index()
df_merge = df_merge[df_merge[show] > 0]
df_merge["COMMODITY"] = "COMMODITY"
fig = px.treemap(df_merge,
path=['COMMODITY', 'SITC 1 DIGIT', feature],
values=show,
color=show,
hover_data=[show],
color_continuous_scale='RdBu',
title=title)
fig.update_layout(height=600, width=900)
st.plotly_chart(fig)
else:
fig = px.treemap(df,
path=[feature],
values=show,
color=show,
hover_data=[show],
color_continuous_scale='RdBu',
title=title)
fig.update_layout(height=600, width=900)
st.plotly_chart(fig)
def makeChart(colorWithCats, name):
df = pd.DataFrame(colorWithCats) # columns=cols)
print(df)
fig = px.treemap(df, path=df.columns[-1:0:-1],
values=0,
color=0,
color_continuous_scale=getColorScale(name),
color_continuous_midpoint=df[0].mean(),
title='Nouns and their WordNet categories commonly associated with the adjective ' + name
)
with open(name+'.html', 'w') as f:
f.write(fig.to_html())
def print_treemap(dataframe):
figure = px.treemap(
dataframe.dropna(),
path=['Province', 'naics_sect', 'Funding_Program_Name'],
values='$_Amount',
color='$_Amount',
color_continuous_scale='Oranges',
#hover_data={'$_Amount': True} # This statement doesnt work -DFL
# perhaps can turn off hover in general, and display $_Amount as text instead
# width=400,
)
return figure
def generateCountryInfectionTreemap():
sample = covid_data.rename(columns={
"location": "Kraj",
"num_sequences_total": "Liczba przypadków"
})
fig = px.treemap(
sample,
path=[px.Constant('Liczba przypadków'), 'Kraj'],
values='Liczba przypadków',
hover_data=['Kraj'],
)
fig.show()
def BTGpactual_printTreemap(df, bins):
# Simple treemap
fig1 = px.treemap(df, path=["Ação"], values="Taxa %", color="Ação")
fig1.show()
# Elaborate treemap
df99 = BTGpactual_get99percentileDF(df)
# Create categories
min_value = df99["Taxa %"].min()
max_value = df99["Taxa %"].max()
bin_interval = (max_value - min_value) / bins
ranges = [bin_interval * i for i in range(bins + 2)]
group_names = ["Group" + str(i + 1) for i in range(bins + 1)]
# Prepare treemap
df99["Group"] = pd.cut(df99["Taxa %"], bins=ranges, labels=group_names)
df99["BTC"] = "BTC" # In order to have a single root node
fig = px.treemap(df99, path=["BTC", "Group", "Ação"], values="Taxa %", color="Ação")
fig.show()
def simpletreemap():
dataset = pd.read_csv('Iris.csv')
fig = px.treemap(dataset,
path=['SepalLengthCm', 'SepalWidthCm', 'Species'],
values='PetalWidthCm')
graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
header = "Plotting the results"
description = """Iris dataset each data point is represented as a marker point."""
return render_template('index.html',
graphJSON=graphJSON,
header=header,
description=description)
def plot_treemap():
data = system_info[system_info['PLN_AREA_N'] != 'NOT SPECIFIED']
fig = px.treemap(
data,
path=['Country', 'Region', 'PLN_AREA_N', 'System type'],
values='System size',
color='Region',
color_continuous_scale='Reds',
hover_data=["PLN_AREA_N"],
title='Cumulative installed capacity (kWp) by planning area')
fig.show()
pio.write_html(fig, file='treemap.html', auto_open=True)
def portfolio_treemap():
tbic_port = get_portfolio_data()
tbic_port[
"portfolio"] = "The TBIC Portfolio - Go Bears!! (Click to drill down...)"
# tbic_port.to_pickle(cwd.joinpath("assets/sector_tree.pkl"))
fig = px.treemap(tbic_port,
path=['portfolio', 'Sector', 'Industry', 'Company Name'],
values='GBP Value',
color='% Gain',
color_continuous_scale='thermal')
return dcc.Graph(figure=fig)
def undervalued_treemap():
df = si.get_undervalued_large_caps()
df['portfolio'] = "Undervalued Large Caps. Trailing P/E < 20, Price/Earnings to Growth < 1"
df['MarketCap'] = df['Market Cap'].map(lambda x: x.split("B")[0]).astype(
float)
df['1d chg'] = df['% Change'].map(lambda x: x.split("%")[0]).astype(float)
fig = px.treemap(df,
path=['portfolio', 'Name'],
values='MarketCap',
color='1d chg',
color_continuous_scale='thermal')
return dcc.Graph(figure=fig)
def projects_treemap_chart(raw_data: PomodorosProcessed):
data = raw_data.df.groupby('Activity').agg({
'Pomodoros': 'sum',
'Parent': 'max'
})
fig = px.treemap(data.reset_index(),
values='Pomodoros',
names='Activity',
parents='Parent',
color_discrete_sequence=px.colors.qualitative.Pastel +
px.colors.qualitative.D3)
st.plotly_chart(fig, use_container_width=True)
def currency_treemap(value):
tbic_port = get_portfolio_data(value)
tbic_port["portfolio"] = "The TBIC Portfolio - By Currency"
fig = px.treemap(
tbic_port,
path=['portfolio', 'CCY', 'Sector', 'Industry', 'Company Name'],
values='GBP Value',
color='% Gain',
color_continuous_scale='thermal',
height=700)
return fig
def tab4_content():
fig1 = px.treemap(
df_cost_rating,
path=[px.Constant('All'), 'Rating text', 'Price range text'],
values='Price range',
title='Price range percentage according to Rating')
fig2 = px.scatter(df_cost_rating[[
'Rating text', 'Price range', 'Aggregate rating'
]].groupby('Rating text').mean().reset_index().sort_values('Price range'),
x='Price range',
y='Aggregate rating',
color='Rating text',
title='Rating and Cost Correlation')
fig2.update_traces(marker=dict(size=12, line=dict(width=2)),
selector=dict(mode='markers'))
fig3 = px.scatter(df_cost_rating,
x='Aggregate rating',
y='Average Cost for two in dollars',
color='Aggregate rating',
title='Average Cost for two in dollars vs Rating')
fig3.update_traces(marker=dict(size=12, ), selector=dict(mode='markers'))
return html.Div([
dbc.Card(
dbc.CardBody([
html.H4("Restaurants", className="card-title"),
html.H6("There is a good partion of Rating unrated",
className="card-text"),
html.H6("There is correlation between Rating and cost",
className="card-text"),
html.
H6("Restaurants with high price range tend to have good rating",
className="card-text"),
]),
className="mt-3",
),
dbc.Card(
dbc.CardBody(
[dcc.Graph(id='bar-graph-restaurant-rating', figure=fig1)]),
className="mt-3",
),
dbc.Card(
dbc.CardBody(
[dcc.Graph(id='scatter-graph-cost-rating', figure=fig2)]),
className="mt-3",
),
dbc.Card(
dbc.CardBody([dcc.Graph(id='scatter-graph-cost-two',
figure=fig3)]),
className="mt-3",
),
])
def set_table_data(n_clicks, locations):
if not n_clicks:
raise PreventUpdate
log_loc = trend_locs['name'][locations]
logging.info(msg=list(log_loc))
try:
woeid = trend_locs['woeid'][locations]
df = adv.twitter.get_place_trends(woeid)
n_countries = df['country'].nunique()
countries = df['country'].unique()
fig = make_subplots(
rows=n_countries,
cols=1,
subplot_titles=['Worldwide' if not c else c for c in countries],
specs=[[{
'type': 'treemap'
}] for i in range(n_countries)],
vertical_spacing=0.05)
for i, c in enumerate(countries):
sub_fig_df = df[df['country'] == c]
sub_fig = px.treemap(sub_fig_df,
path=['country', 'location', 'name'],
values='tweet_volume')
sub_fig.layout.margin = {'b': 5, 't': 5}
sub_fig.data[0][
'hovertemplate'] = '<b>%{label}</b><br>Tweet volume: %{value}'
last_line = '' if c == '' else '<br>%{percentRoot} of %{root}'
sub_fig.data[0][
'texttemplate'] = '<b>%{label}</b><br><br>Tweet volume: %{value}<br>%{percentParent} of %{parent}' + last_line
fig.add_trace(sub_fig.to_dict()['data'][0], row=i + 1, col=1)
fig.layout.height = 400 * n_countries
fig.layout.template = 'none'
fig.layout.margin = {'t': 40, 'b': 40}
fig.layout.paper_bgcolor = '#000000'
fig.layout.plot_bgcolor = '#000000'
final_df = df.drop(['promoted_content', 'woeid', 'parentid'], axis=1)
final_df = final_df.rename(columns={'name': 'Topic'})
final_df.columns = [
x.title() for x in final_df.columns.str.replace('_', ' ')
]
url_search = '?q=' + '+'.join(log_loc)
return final_df.to_dict('rows'), url_search, fig.to_dict()
except Exception as e:
return pd.DataFrame(
{
'Name':
['Too many requests please '
'try again in 15 minutes.']
},
columns=df.columns).to_dict('rows')
def covid_13():
global c_lat_grp
fig = px.treemap(c_lat_grp.sort_values(
by='Deaths', ascending=False).reset_index(drop=True),
path=["Country"],
values="Deaths",
title='COVID-19 Deaths',
color_discrete_sequence=px.colors.qualitative.Dark2)
fig.data[0].textinfo = 'label+text+value'
#fig.show()
#fig.savefig(img)
#img.seek(0)
plotly.offline.plot(fig, filename="choro", image='svg', auto_open=True)
return render_template("Vizualize.html")
def graph():
fig = px.treemap(
df,
path=['state', 'confirmed', 'active', 'recovered'],
values='active',
color='active',
hover_data=['confirmed', 'recovered'],
color_continuous_scale='RdBu',
color_continuous_midpoint=np.average(df['confirmed'],
weights=df['active']),
title='Statewise Cases',
)
fig.show()
def main():
'''
生成树状图
'''
df = px.data.gapminder().query("year == 2007")
fig = px.treemap(df,
path=['continent', 'country'],
values='pop',
color='lifeExp',
hover_data=['iso_alpha'],
color_continuous_scale='RdBu',
color_continuous_midpoint=np.average(df['lifeExp'],
weights=df['pop']))
fig.show()
def update_expl_vis_treemap(features, counts, df_input):
# print('Draw treemap')
clscl = ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)', 'rgb(51,160,44)', 'rgb(251,154,153)',
'rgb(227,26,28)', 'rgb(253,191,111)', 'rgb(255,127,0)', 'rgb(202,178,214)', 'rgb(106,61,154)',
'rgb(255,255,153)', 'rgb(177,89,40)']
order = ['Country'] + [feature for feature in features]
group_by = ['Country', 'City', 'Group', 'Attack Type', 'Target Type', 'Weapon Type', 'Suicide', 'Success']
agg_on = {'eventid': ['size'], 'Killed': ['sum'], 'Wounded': ['sum']}
df_tmp = df_input.groupby(group_by).agg(agg_on).reset_index()
df_tmp.columns = ['Country', 'City', 'Group', 'Attack Type', 'Target Type', 'Weapon Type', 'Suicide', 'Success',
'Attack', 'Killed', 'Wounded']
fig = px.treemap(df_tmp, path=order, values=counts, color_discrete_sequence=clscl)
fig.update_layout(margin={'l': 40, 'b': 40, 't': 40, 'r': 40}, height=800, hovermode='closest')
return fig
def generate_treemap(df):
return dcc.Graph(figure=px.treemap(df,
path=[df.columns[0]],
values=df.columns[1]).update_layout(
height=320,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
paper_bgcolor="#F8F9F9",
autosize=True),
id='treemap_plot')
def generate_pl_plot(data):
data = generate_pl_inputs(data)
plotly_fig = px.treemap(data,
path=['Investment', 'AllocationType', 'Tickers'],
values='Allocation',
color='Allocation',
color_continuous_scale='Greys')
plotly_fig.update_traces(textfont_size=14,
hovertemplate='<b>%{label}</b>' +
'<br>%{value:.2f}% <br>')
plotly_fig.update_layout(coloraxis_showscale=False)
plotly_fig.data[0].textinfo = 'label+text+value'
graphJSON = json.dumps(plotly_fig, cls=plotly.utils.PlotlyJSONEncoder)
return graphJSON
def update_treemap(slider_value, columns):
df = combined_data
df = df[(df['year'] >= slider_value[0]) & (df['year'] <= slider_value[1])]
df = df.sample(frac=0.1, replace=False, random_state=1)
df = df.dropna(axis=0)
for col in columns:
if df[col].dtype == 'float64':
df[col] = df[col].astype(int)
df['all'] = ''
path = ['all'] + columns
fig = px.treemap(df, path=path, values='Number_of_Casualties')
return fig
