def plot_stacked_funnel(events, steps, col=None, from_date=None, to_date=None, step_interval=0):
"""
Function used for producing a funnel plot
:param events: (DataFrame)
events dataframe
:param steps: (list)
list containing funnel steps as strings
:param col: (str)
column to be used for grouping the funnel dataframes
:return: (plt.figure) funnel plot
"""
# create list to append each trace to
# this will be passed to "go.Figure" at the end
data = []
# if col is provided, create a funnel_df for each entry in the "col"
if col:
# generate dict of funnel dataframes
dict_ = group_funnel_dfs(events, steps, col)
title = 'Funnel plot per {}'.format(col)
else:
funnel_df = create_funnel_df(events, steps, from_date=from_date, to_date=to_date, step_interval=step_interval)
dict_ = {'Total': funnel_df}
title = 'Funnel plot'
for t in dict_.keys():
trace = go.Funnel(
name=t,
y=dict_[t].step.values,
x=dict_[t].val.values,
textinfo="value+percent previous"
)
data.append(trace)
layout = go.Layout(margin={"l": 180, "r": 0, "t": 30, "b": 0, "pad": 0},
funnelmode="stack",
showlegend=True,
hovermode='closest',
plot_bgcolor='rgba(228, 222, 239, 0.65)',
title='Funnel Plot per {}'.format(col),
legend=dict(orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1,
font=dict(
size=12,
color="black"),
bgcolor="LightSteelBlue",
bordercolor="Black",
borderwidth=2
)
)
return go.Figure(data, layout)
def update_funnel(ad_set1, ad_set2):
'''
Function to update funnel visual based on ad set selections
Input: (first ad set selection, optional: second ad set selection)
Output: Funnel visualization
'''
#Identify features to include in funnel visual
funnel_features = ["Impressions", "Link Clicks", "Website Leads",
"Website Registrations Completed", 'Ad Set Name']
#If only one ad set is selected, update chart title accordingly
if not ad_set2:
selected_ads = [ad_set1]
title = 'Ad Set ' + str(ad_set1) + ' Conversion Cycle'
#If two ad sets are selected, update chart title accordingly
else:
selected_ads = [ad_set1, ad_set2]
title = 'Comparison of Ad Sets ' + str(ad_set1) + ' & ' \
+ str(ad_set2) + ' Conversion Cycles'
#Filter data to only include ad sets selected with drop-downs
data = ads[ads['Ad Set Name'].isin(selected_ads)]
#Group data by ad set name and sum
data = data[funnel_features].groupby('Ad Set Name').sum()
#Define figure layout
layout = go.Layout(title=dict(text=title, x=0.6),
legend_title_text='Ad Set')
fig = go.Figure(layout=layout)
#Add funnel plot to figure for first add set
fig.add_trace(go.Funnel(
name = ad_set1,
y = data.loc[ad_set1,:].index,
x = data.loc[ad_set1,:].values,
textinfo = "value+percent initial"))
#If second ad set is selected, add another trace to figure for that set
if len(selected_ads) > 1:
fig.add_trace(go.Funnel(
name = ad_set2,
y = data.loc[ad_set2,:].index,
x = data.loc[ad_set2,:].values,
textinfo = "value+percent initial"))
return(dcc.Graph(id='funnel-1', figure=fig))
def plot_new_funnel(phases, values):
fig = go.Figure(go.Funnel(
y = phases,
x = values,
textposition = "inside",
textinfo = "value+percent initial",
opacity = 0.85, marker = {"color": colors[:len(phases)],
},
connector = {"line": {"color": "royalblue", "dash": "dot", "width": 3}})
)
fig.show()
def funnel_plot(df, steps, col=None):
"""
Функция для построения воронки с помощью plotly
Parameters
---------
df : pandas.DataFrame
Объект pandas df.
steps : list
Список исследуемых событий. Список необходимо формировать в порядке воронки, от стартового события, до завершающего.
col : str
Фича, по которой будет разделение воронки. Например - OS, воронка будет разделена на iOS и Android.
Returns
-------
fig : plotly.graph_objs._figure.Figure
В качестве вывода будет объект Figure библиотеки plotly
"""
data = []
if col:
dict_ = funnel.stacking_funnel(df, steps, col)
title = 'Воронка заявки в разрезе {}'.format(col)
else:
funnel_df = funnel.create_funnel_df(df, steps)
dict_ = {'Total': funnel_df}
title = 'Воронка заявки'
for t in dict_.keys():
trace = go.Funnel(
name=t,
y=dict_[t].step.values,
x=dict_[t].val.values,
textinfo='value+percent previous'
)
data.append(trace)
layout = go.Layout(margin={"l": 180, "r": 0, "t": 30, "b": 0, "pad": 0},
funnelmode="stack",
showlegend=True,
hovermode='closest',
title=title,
legend=dict(orientation="v",
bgcolor='#E2E2E2',
xanchor='left',
font=dict(
size=12)
)
)
fig = go.Figure(data, layout)
return fig
def save_funnel(self):
timeseries = data_obj.timeseries_data()
fig = go.Figure(go.Funnel(
x = [timeseries['totalconfirmed'].iloc[-1],timeseries['totalrecovered'].iloc[-1],
timeseries['totaldeceased'].iloc[-1]],
y = ["Total Cases", "Total Recovered", "Deaths"],
textposition = "inside",
textinfo = "value",
opacity = 0.8,
marker = {"color": [self.confirmed_color,self.recovered_color,self.death_color],
"line": {"width": 2.5, "color": 'Black'}},
connector = {"line": {"color": "navy", "dash": "dot", "width": 2.5}}))
fig.update_layout(
template="simple_white",
title={'text': "COVID19: Pandemic in India",'x':0.5,'y':0.9,
'xanchor': 'center','yanchor': 'top'})
fig.update_layout(height=700, template='simple_white',width=600)
return fig
def update_graph_funnel(n_intervals):
conn = MySQLdb.connect(host="localhost", user="******", db="qpipe")
cursor = conn.cursor()
cursor.execute('''SELECT COUNT(p.sales_cycle_id) as Total,
sc.name FROM projects as p
RIGHT JOIN sale_cycles as sc
ON p.sales_cycle_id = sc.id
GROUP BY sc.id ORDER BY Total DESC''')
rows = cursor.fetchall()
cursor.close()
df3 = pd.DataFrame([[ij for ij in i] for i in rows])
df3.rename(columns={0: 'Total', 1: 'Name'}, inplace=True)
funnel = []
funnel.append(go.Funnel(
y=df3['Name'],
x=df3['Total'],
))
return {
'data': funnel,
}
'displayModeBar': False
},
)
], style={'width': '43%', 'display': 'inline-block','margin-top':'35px','margin-left':'68px'}, className="class_g3"),
# *************************************************************************
html.Div([
html.H5(''),
dcc.Graph(
id='g4',
figure=go.Figure(go.Funnel(
y = df_Top5_Group['Group'].tolist(),
x = df_Top5_Group['Count'].tolist(),
),
layout=go.Layout(title='Terrorism By Most Influential Groups')
),
config={
'displayModeBar': False
},
)
], style={'width': '43%', 'display': 'inline-block'}, className="class_g4"),
# ###########################################################################
if i == 0:
funnel["单一转化率"][i] = 1.0
else:
funnel["单一转化率"][i] = funnel["人数"][i] / funnel["人数"][i - 1]
print("funnel", funnel)
# 绘制漏斗分析图
import plotly.express as px
import plotly.graph_objs as go
trace = go.Funnel(
y=["点击", "收藏及加入购物车", "购买"],
x=[funnel["人数"][0], funnel["人数"][1], funnel["人数"][2]],
textinfo="value+percent initial",
marker=dict(color=["deepskyblue", "lightsalmon", "tan"]),
connector={"line": {
"color": "royalblue",
"dash": "solid",
"width": 3
}})
print("trace", trace) #a dictionary for Funnel with all parameters above
data1 = [trace]
print("data1", data1) #put funnel in a list
fig = go.Figure(
data1) #create a webpge http://127.0.0.1:57577/ and show the figure
fig.show() #
# RFM 模型是数据分析中衡量客户价值和客户创利能力的一个重要模型。为什么叫做RFM模型呢？
# 因为我们会搜集用户三个方面的数据来综合评定用户的价值等级，而这三项数据分别就是这里的RFM：
# R（Recency）——最近一次购买的时间有多远
# F（Frequency）——最近一段时间内的消费频率
md=5,
style={'padding':'0.5% 0.5% 0.5% 0.5%'}
)
]),
#second row
dbc.Row([
dbc.Col(
[
dcc.Graph(
id='FUN',
figure = {'data':[
go.Funnel(
y = bytype['type'].unique(),
x = bytype['GROSS'])
],
'layout':go.Layout(paper_bgcolor = '#ffffff', plot_bgcolor='#ffffff')}
), html.H5("Part by Role",style={'text-align':'center','padding':'0.5% 0.5% 0.5% 0.5%'})
],
md=6,
),
dbc.Col([
dcc.Graph(
id='FUN1',
figure = {'data':[
y=1.02,
yanchor="bottom",
x=1,
xanchor="right",
orientation="h",
font=dict(size=10)))
fta = dict(data=trace2, layout=layout2)
############################################
#Funnel figure
dfp = dfp.reset_index()
trace3 = [
go.Funnel(
y=dfp["grade"],
x=dfp[i],
name=i,
orientation="h",
textposition="inside",
textinfo="value+percent total",
) for i in genders
]
layout3 = go.Layout(title="Percentage of Degree Classifications by gender",
legend=dict(title=None,
y=1.02,
yanchor="bottom",
x=1,
xanchor="right",
orientation="h",
font=dict(size=10)))
ftgf = dict(data=trace3, layout=layout3)