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 test_funnel():
fig = px.funnel(
x=[5, 4, 3, 3, 2, 1],
y=["A", "B", "C", "A", "B", "C"],
color=["0", "0", "0", "1", "1", "1"],
)
assert len(fig.data) == 2
def main():
data = {
'Product Development steps': [
'Requirement Elicitation', 'Requirement Analysis',
'Software Development', 'Debugging & Testing', 'Others'
],
'Time spent (in hours)': [50, 110, 250, 180, 70]
}
fig = px.funnel(data,
y='Product Development steps',
x='Time spent (in hours)')
fig.show()
def FunnelPlot(X, labels):
#DS
# Generate Funnel plot for data using PlotLY module
#DE
#IS
import plotly.express as px
#IE
data = dict(number=X, stage=labels)
fig = px.funnel(data, x='number', y='stage')
fig.show()
#FE
def upload_image(request):
# If image is uploaded & recieved by server
if request.method == 'POST':
form = imageInputForm(request.POST, request.FILES)
if form.is_valid():
fileObj = request.FILES['eye_image']
fs = FileSystemStorage()
filePathName = fs.save(fileObj.name, fileObj)
filePathName = fs.url(filePathName)
testimage = '.' + filePathName
img = image.load_img(testimage, target_size=(IMG_SIZE, IMG_SIZE))
img = preprocess_image(testimage)
img = np.expand_dims(img, axis=0)
with graph.as_default():
probs = PredictorConfig.MLmodel.predict_proba(img)
fiveProbs = []
labels = [
'No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative DR'
]
for i in range(5):
fiveProbs.append(probs[0, i])
label = fiveProbs.index(max(fiveProbs))
print(label)
data = pd.DataFrame(zip(fiveProbs, labels),
columns=['fiveProbs', 'labels'])
fig = px.funnel(data, x='fiveProbs', y='labels')
plt_div = plot(fig, output_type='div', include_plotlyjs=False)
context = {
'filePathName': filePathName,
'filename': fileObj.name,
'plt_div': plt_div,
'label': label,
}
return render(request, 'predictor/pred.html', context)
# If Image Upload Page Requested
else:
form = imageInputForm()
context = {'form': form}
return render(request, 'predictor/upload_image.html', context)
def set_data():
temp_list = get_data()
data = temp_list[0]
dict_keys = temp_list[1]
pages = dict_keys[0]
for i in range(4):
number1 = []
number2 = []
for j in range(4):
number1.append(
count_clicks(data, dict_keys[0][j], dict_keys[2][0],
dict_keys[1][i]))
number2.append(
count_clicks(data, dict_keys[0][j], dict_keys[2][1],
dict_keys[1][i]))
desktop = pd.DataFrame(dict(number=number1, page=pages))
desktop['device'] = 'Desktop'
mobile = pd.DataFrame(dict(number=number2, page=pages))
mobile['device'] = 'Mobile'
device = pd.concat([desktop, mobile], axis=0)
fig = px.funnel(device, x='number', y="page", color='device')
plotly.offline.plot(fig)
more_than_1000 = total_confirmed[total_confirmed.Sum > 1000].rename(
columns={
"Sum": "Confirmed cases"
}).sort_values(by=['Confirmed cases'], ascending=False)
last_date = str(df_Death_ch['Date'].iloc[-1].date().strftime("%b %d %Y"))
plt_bar(more_than_1000,
'Countries with more than 1000 COVID-19 cases as of ' + last_date,
'Country/Region', 'Confirmed cases', '#f54c4c')
# All the cases around the world
total_cases = total_Cases_ww + total_Cases_china
total_recovered = total_Recovered_china + total_Recovered_ww
total_death = total_Death_ww + total_Death_china
data = dict(number=[total_cases, total_recovered, total_death],
category=["Total Cases", "Total Recovered", "Deaths"])
pio.renderers.default = "browser"
fig = px.funnel(data, x='number', y='category', width=2000, height=1000)
fig.update_layout(
title={
'text': 'COVID-19 Statistics worldwide as of ' + last_date,
'xanchor': 'center',
'x': 0.5,
'yanchor': 'top'
},
font=dict(
#family="Courier New, monospace",
size=22,
color="#000000"))
fig.show()
fig.write_image(last_date + "/Worldwide totals.png")
from plotly.subplots import make_subplots
import plotly.graph_objects as go
total_comarision = total_comarision.sort_values('confirmed', ascending=False)
total_comarision = total_comarision.sort_values('death', ascending=False)
stages = total_comarision.Pandemic
df_mtl = pd.DataFrame(dict(number=total_comarision.confirmed, stage=stages))
df_mtl['Category'] = 'Confirmed'
df_toronto = pd.DataFrame(dict(number=total_comarision.death, stage=stages))
df_toronto['Category'] = 'Death'
df = pd.concat([df_mtl, df_toronto], axis=0)
fig_confirm = px.funnel(df,
x='number',
y='stage',
color='Category',
title="COVID-19 with other known epedemics")
fig_confirm.show()
# In[ ]:
# Test performed by countryies per 1000 people
testing = pd.read_csv("test.csv")
testing = testing.rename(columns={'Total tests per thousand': 'TotalTest'})
testing['Date'] = pd.to_datetime(testing['Date'], errors='coerce')
testing.head()
# In[ ]:
def update_chart(selector_left, selector1, selector2, selector3,
rdbTypeChart):
# create a backup data
backup_df = df
if selector_left == "Explore Relationship Between Columns":
# return figure for the graph based on column x , y and type of chart.
if rdbTypeChart == "bar":
fig = px.bar(backup_df,
x=selector1,
y=selector2,
color='income_>50K')
# set transition when updating the graph to make it display smoothy.
fig.update_layout(transition_duration=500)
return fig, ''
if rdbTypeChart == "line":
fig = px.line(backup_df,
x=selector1,
y=selector2,
color='income_>50K')
fig.update_layout(transition_duration=500)
return fig, ''
if rdbTypeChart == "area":
fig = px.area(backup_df,
x=selector1,
y=selector2,
color='income_>50K')
fig.update_layout(transition_duration=500)
return fig, ''
if rdbTypeChart == "funnel":
fig = px.funnel(backup_df,
x=selector1,
y=selector2,
color='income_>50K')
fig.update_layout(transition_duration=500)
return fig, ''
else:
fig = px.scatter(backup_df,
x=selector1,
y=selector2,
color='income_>50K')
fig.update_layout(transition_duration=500)
return fig, ''
if selector_left == "Compare Different Between Original and Predict Data":
# return figure for the graph based on column x , y and type of chart.
if rdbTypeChart == "bar":
fig = px.bar(d_normal,
x=selector1,
y=selector2,
color='income_>50K')
fig2 = px.bar(d_3d,
x=selector1,
y=selector2,
color='income_>50K')
# set transition when updating the graph to make it display smoothy.
fig.update_layout(transition_duration=500)
fig2.update_layout(transition_duration=500)
return fig, fig2
if rdbTypeChart == "line":
fig = px.line(d_normal,
x=selector1,
y=selector2,
color='income_>50K')
fig2 = px.line(d_3d,
x=selector1,
y=selector2,
color='income_>50K')
# set transition when updating the graph to make it display smoothy.
fig.update_layout(transition_duration=500)
fig2.update_layout(transition_duration=500)
return fig, fig2
if rdbTypeChart == "area":
fig = px.area(d_normal,
x=selector1,
y=selector2,
color='income_>50K')
fig2 = px.area(d_3d,
x=selector1,
y=selector2,
color='income_>50K')
# set transition when updating the graph to make it display smoothy.
fig.update_layout(transition_duration=500)
fig2.update_layout(transition_duration=500)
return fig, fig2
if rdbTypeChart == "funnel":
fig = px.funnel(d_normal,
x=selector1,
y=selector2,
color='income_>50K')
fig2 = px.funnel(d_3d,
x=selector1,
y=selector2,
color='income_>50K')
# set transition when updating the graph to make it display smoothy.
fig.update_layout(transition_duration=500)
fig2.update_layout(transition_duration=500)
return fig, fig2
if rdbTypeChart == "3d":
fig = px.scatter_3d(d_normal,
x=selector1,
y=selector2,
z=selector3,
color='income_>50K')
fig2 = px.scatter_3d(d_3d,
x=selector1,
y=selector2,
z=selector3,
color='income_>50K')
# set transition when updating the graph to make it display smoothy.
fig.update_layout(transition_duration=500)
fig2.update_layout(transition_duration=500)
return fig, fig2
else:
fig = px.scatter(d_normal,
x=selector1,
y=selector2,
color='income_>50K')
fig2 = px.scatter(d_3d,
x=selector1,
y=selector2,
color='income_>50K')
# set transition when updating the graph to make it display smoothy.
fig.update_layout(transition_duration=500)
fig2.update_layout(transition_duration=500)
return fig, fig2
else:
# return figure for the graph based on column y and type of chart.
fig = px.box(backup_df, y=selector1)
fig.update_layout(transition_duration=500)
return fig, ''
st.header("")
box_ = px.scatter(df, x='CGPA', y='LOR ', marginal_x='violin',
marginal_y='box', color='Research')
box_
count_p = sns.countplot(df['University Rating'])
count_p
st.header("Statistical analysis of the dataset")
st.write(df.describe().T)
fig = px.density_heatmap(df, x="University Rating", y='SOP')
fig
fig = px.funnel(df, x='SOP', y='LOR ', color='Research')
fig
bar = px.histogram(diabete, x='Pregnancies', color='Outcome', marginal='box')
bar
scat = px.scatter(diabete, x='SkinThickness', y='Age', color='Outcome',
marginal_x='box', marginal_y='violin')
scat
if st.checkbox('Show columns'):
st.write(diabete.columns.to_list())
if st.checkbox("Show number of classes to predict dataset"):
st.write(diabete.Outcome.value_counts())
row8_1, row8_2 = st.beta_columns((.9, 1.1))
with row8_1:
araclar = []
for i in filtered.index:
filt = eval(filtered.analitik_araclar[i])
araclar.append(filt)
araclar = list(chain.from_iterable(araclar))
araclar = pd.Series(araclar).value_counts().to_frame(name = 'İlan Sayısı').reset_index().rename(columns={'index': 'Analitik Araçlar'})
st.subheader('İş ilanlarında hangi analitik araçları bilmeniz isteniyor?')
fig = px.funnel(araclar.iloc[0:10, :], x = 'İlan Sayısı', y = 'Analitik Araçlar')
fig.update_layout(width=600,
margin=dict(t=5)
)
st.plotly_chart(fig)
with row8_2:
bolumler = []
for i in filtered.index:
bolum = eval(filtered.istenen_bölümler[i])
bolumler.append(bolum)
bolumler = list(chain.from_iterable(bolumler))
bolumler = pd.Series(bolumler).value_counts().to_frame(name = 'İlan Sayısı').reset_index().rename(columns={'index': 'Aranan Bölümler'})
def FunnelPlot(Data, labels):
data = dict(number=Data, stage=labels)
fig = px.funnel(data, x='number', y='stage')
fig.show()
def update2(input3, input4, input5, input21):
state1 = get_given_state_data(state_df, input3)
state2 = get_given_state_data(state_df, input4)
dfresult = state1
dfresult[input3] = state1[input5]
dfresult[input4] = state2[input5]
fig = None
if input21 == "plot":
fig = px.line(
dfresult,
x=get_dates(state_df),
y=[input3, input4],
title="Covid-19 : Cases comparison between %s and %s . " %
(input3, input4),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input21 == "bar":
fig = px.bar(dfresult,
x=get_dates(state_df),
y=[input3, input4],
title="Covid-19 : Cases comparison between %s and %s . " %
(input3, input4),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input21 == "histogram":
fig = px.histogram(
dfresult,
x=get_dates(state_df),
y=[input3, input4],
title="Covid-19 : Cases comparison between %s and %s . " %
(input3, input4),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input21 == "area":
fig = px.area(
dfresult,
x=get_dates(state_df),
y=[input3, input4],
title="Covid-19 : Cases comparison between %s and %s . " %
(input3, input4),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input21 == "funnel":
fig = px.funnel(
dfresult,
x=get_dates(state_df),
y=[input3, input4],
title="Covid-19 : Cases comparison between %s and %s . " %
(input3, input4),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input21 == "scatter":
fig = px.scatter(
dfresult,
x=get_dates(state_df),
y=[input3, input4],
title="Covid-19 : Cases comparison between %s and %s . " %
(input3, input4),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
fig.update_layout(font_family="Courier New",
font_color="blue",
title_font_family="Times New Roman",
title_font_color="red",
legend_title_font_color="green",
transition_duration=500)
return fig
funnel_by_groups = []
# for every experiment group
for i in new_data.exp_id.unique():
group = new_data[new_data.exp_id == i].groupby([
'event_name', 'exp_id'
])['user_id'].nunique().reset_index().sort_values(by='user_id',
ascending=False)
funnel_by_groups.append(group)
# concatenate the data to have one big dataframe
funnel_by_groups = pd.concat(funnel_by_groups)
# display funnel
fig = px.funnel(funnel_by_groups, x='user_id', y='event_name', color='exp_id')
fig.show()
# ## Study the results of the experiment
# ### How many users are there in each group?
usersExp = new_data.groupby('exp_id')['user_id'].nunique().reset_index()
usersExp.columns = ['exp_id', 'unique_users']
display(usersExp)
# ### In each of the control groups, find the number of users who performed each event.
# create a pivot table with the number of unique users in each control gorup that goes through each action
expGroups = new_data.pivot_table(index='event_name',
values='user_id',
columns='exp_id',
aggfunc=lambda x: x.nunique()).reset_index()
expGroups.columns = ['event_name', '246', '247', '248']
st.write(iris.target_names)
st.subheader('Prediction')
st.write(iris.target_names[prediction])
#st.write(prediction)
st.subheader('Prediction Probability')
st.write(prediction_proba)
import plotly.express as px
data = dict(number=[39, 24, 21, 14, 2],
stage=[
"Visit", "Downloads", "Potential customers", "Requested price",
"Invoice sent"
])
fig = px.funnel(data, x='number', y='stage', width=600, height=400)
#st.fig.show()
#st.plotly
st.plotly_chart(fig)
import plotly.express as px
#import pandas as pd
stages = [
"Visit", "Downloads", "Potential customers", "Requested price",
"Invoice sent"
]
df_mtl = pd.DataFrame(
dict(number=[39, 24, 21, 14, 2],
stage=[
'Visit', "Downloads", "Potential customers", "Requested price",
"Invoice sent"
''' 18-01-2020
Shukrithi Rathna
Assignment 2
Q8. Generating Funnel Plot
'''
import plotly.express as px
data = dict(number=[50, 110, 250, 180, 70],
stage=[
"Requirement Elicitation", "Requirement Anlaysis",
"Software Development", "Debugging and Testing", "Others"
])
fig = px.funnel(data, x='number', y='stage')
fig.show()
connector = {"mode":"between", "line":{"width":4, "color":"rgb(0, 0, 0)", "dash":"solid"}}
))
fig.update_layout(title = "Profit and loss statement 2018")
fig.show()
# In[ ]:
import plotly.express as px
data = dict(
number=[39, 27.4, 20.6, 11, 2],
stage=["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"])
fig = px.funnel(data, x='number', y='stage')
fig.show()
# In[ ]:
import plotly.express as px
import pandas as pd
stages = ["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"]
df_mtl = pd.DataFrame(dict(number=[39, 27.4, 20.6, 11, 3], stage=stages))
df_mtl['office'] = 'Montreal'
df_toronto = pd.DataFrame(dict(number=[52, 36, 18, 14, 5], stage=stages))
df_toronto['office'] = 'Toronto'
df = pd.concat([df_mtl, df_toronto], axis=0)
fig = px.funnel(df, x='number', y='stage', color='office')
# 8th Question:
# $ pip install plotly==4.4.1
import plotly.express as px
import matplotlib.pyplot as plt
steps = [
'Requirement Elicitation', 'Requirement Analysis', 'Software Development',
'Debugging & Testing', 'Others'
]
TimeSpend = [50, 110, 250, 180, 70]
data = {'Product Development steps': steps, 'Time spent': TimeSpend}
fig = px.funnel(data, x='Time spent', y='Product Development steps')
fig.show()
plt.show()
country_count = pd.DataFrame(country_count)
top_country = country_count[0:10]
top_country
# In[27]:
# Funnel chart using plotly
import plotly.express as px
data = dict(number=[1063, 619, 135, 60, 47, 44, 41, 40, 40, 38],
country=[
"United States", "India", "United Kingdom", "Canada", "UK,US",
"Spain", "Turkey", "Philippines", "France", "South Korea"
])
fig = px.funnel(data, x='number', y='country')
fig.show()
# # TV shows with Largest Number of Seasons
# In[39]:
features = ['title', 'duration']
durations = netflix_Tv_shows[features]
durations['no_of_season'] = durations['duration'].str.replace('Season', '')
durations['no_of_season'] = durations['no_of_season'].str.replace('s', '')
durations['no_of_season'] = durations['no_of_season'].astype(str).astype(int)
top = ['title', 'no_of_season']
top_ = durations[top]
top_ = top_.sort_values(by='no_of_season', ascending=False)
top20 = top_[0:20]
import plotly.express as px
fig = px.funnel(y=[
"завтрак", "перекус1", "обед", "перекус2", "ужин", "завтрак", "перекус1",
"обед", "перекус2", "ужин", "завтрак", "перекус1", "обед", "перекус2",
"ужин", "завтрак", "перекус1", "обед", "перекус2", "ужин"
],
x=[
15, 10, 25.4, 10, 26, 9.8, 15, 36.2, 9.6, 7.6, 1.9, 4, 7.1,
1.6, 1.97, 3, 0.5, 11.7, 3, 2
],
color=[
"белок", "белок", "белок", "белок", "белок", "углеводы",
"углеводы", "углеводы", "углеводы", "углеводы", "жир",
"жир", "жир", "жир", "жир", "пищевые волокна",
"пищевые волокна", "пищевые волокна", "пищевые волокна",
"пищевые волокна"
])
fig.show()
def update_figure(sta1, input2):
fig = None
state = get_given_state_data(state_df, sta1)
if input2 == 'plot':
fig = px.line(state,
x=get_dates(state_df),
y=['Confirmed', 'Active', 'Recovered', 'Deceased'],
title="Covid-19 data of state.",
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input2 == 'bar':
fig = px.bar(state,
x=get_dates(state_df),
y=['Confirmed', 'Active', 'Recovered', 'Deceased'],
title="Covid-19 data of state.",
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input2 == 'area':
fig = px.area(state,
x=get_dates(state_df),
y=['Confirmed', 'Active', 'Recovered', 'Deceased'],
title="Covid-19 data of state.",
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input2 == 'histogram':
fig = px.histogram(state,
x=get_dates(state_df),
y=['Confirmed', 'Active', 'Recovered', 'Deceased'],
title="Covid-19 data of state.",
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input2 == 'funnel':
fig = px.funnel(state,
x=get_dates(state_df),
y=['Confirmed', 'Active', 'Recovered', 'Deceased'],
title="Covid-19 data of state.",
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input2 == 'scatter':
fig = px.scatter(state,
x=get_dates(state_df),
y=['Confirmed', 'Active', 'Recovered', 'Deceased'],
title="Covid-19 data of state.",
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
fig.update_layout(font_family="Courier New",
font_color="blue",
title_font_family="Times New Roman",
title_font_color="red",
legend_title_font_color="green",
transition_duration=500)
return fig
import dash
from dash import dcc
from dash import html
import plotly.express as px
app = dash.Dash(__name__)
# Connect to Materialize as a regular database
conn = psycopg2.connect("dbname=materialize user=materialize port=6875 host=localhost")
# Read the materialized view with Pandas
sql = "select * from consolidated_funnel order by cnt desc;"
df = pd.read_sql_query(sql, conn)
# Plot a funnel chart
fig = px.funnel(df, x="step", y="cnt")
# Main UI scaffolding for the dashboard
app.layout = html.Div(children=[
html.H1(children='Conversion Funnel'),
html.Div(children='''
Dash: A web application framework for your data.
'''),
dcc.Graph(
id='funnel-chart',
figure=fig
)
])
def update3(input6, input7, input8, input31):
city1 = get_given_city_data(city_df, input6)
city2 = get_given_city_data(city_df, input7)
dfresult = city1
dfresult[input6] = city1[input8]
dfresult[input7] = city2[input8]
fig = None
if input31 == 'plot':
fig = px.line(
dfresult,
x=get_dates(city_df),
y=[input6, input7],
title="Covid-19 : Cases comparison between %s and %s . " %
(input6, input7),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input31 == 'bar':
fig = px.bar(dfresult,
x=get_dates(city_df),
y=[input6, input7],
title="Covid-19 : Cases comparison between %s and %s . " %
(input6, input7),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input31 == 'histogram':
fig = px.histogram(
dfresult,
x=get_dates(city_df),
y=[input6, input7],
title="Covid-19 : Cases comparison between %s and %s . " %
(input6, input7),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input31 == 'area':
fig = px.area(
dfresult,
x=get_dates(city_df),
y=[input6, input7],
title="Covid-19 : Cases comparison between %s and %s . " %
(input6, input7),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input31 == 'funnel':
fig = px.funnel(
dfresult,
x=get_dates(city_df),
y=[input6, input7],
title="Covid-19 : Cases comparison between %s and %s . " %
(input6, input7),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
if input31 == 'scatter':
fig = px.scatter(
dfresult,
x=get_dates(city_df),
y=[input6, input7],
title="Covid-19 : Cases comparison between %s and %s . " %
(input6, input7),
labels={
"x": "Time",
"value": "Number of Cases"
},
template="presentation")
fig.update_layout(font_family="Courier New",
font_color="blue",
title_font_family="Times New Roman",
title_font_color="red",
legend_title_font_color="green",
transition_duration=500)
return fig
import plotly.express as px
import pandas as pd
stages = ["访问数", "下载数", "注册数", "搜索数", "付款数"]
data = dict(number=[59, 32, 18, 9, 2], stage=stages)
px.funnel(data, x="number", y="stage").show()
df_male = pd.DataFrame(
dict(number=[59, 32, 18, 9, 2], stage=stages, gender=["男"] * 5))
df_female = pd.DataFrame(
dict(number=[29, 17, 8, 3, 1], stage=stages, gender=["女"] * 5))
px.funnel(pd.concat([df_male, df_female]),
x="number",
y="stage",
color="gender").show()
import plotly.express as px
fig = px.sunburst(
names=[
"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],
)
fig.write_html(os.path.join(dir_name, "sunburst.html"), auto_play=False)
import plotly.express as px
fig = px.funnel(y=["first", "second", "first", "second"],
x=[3, 1, 4, 2],
color=["A", "A", "B", "B"])
fig.write_html(os.path.join(dir_name, "funnel.html"), auto_play=False)
import plotly.express as px
fig = px.scatter(x=[1, 2, 1, 2],
y=[4, 3, 2, 4],
color=[True, True, False, False])
fig.write_html(os.path.join(dir_name, "scatter_bool_color.html"),
auto_play=False)
import plotly.express as px
fig = px.pie(values=[1, 2, 3, 4], color=[True, False, True, False])
fig.write_html(os.path.join(dir_name, "pie_bool_color.html"), auto_play=False)
Например, он используется для наблюдения за доходом или убытком в процессе продаж
для каждого этапа и отображает значения, которые постепенно уменьшаются.
Каждый этап иллюстрируется как процент от общего числа всех значений.
"""
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
# 1) Basic Funnel Plot with plotly.express
data = dict(number=[39, 27.4, 20.6, 11, 2],
stage=[
"Посещения сайта", "Загрузки", "Потенциальные клиенты",
"Запрос о цене", "Выставлен счёт"
])
fig = px.funnel(data, x='number', y='stage', title='Основная воронка продаж')
fig.show()
# 2) Stacked Funnel Plot with plotly.express
stages = [
"Посещения сайта", "Загрузки", "Потенциальные клиенты", "Запрос о цене",
"Выставлен счёт"
]
df_mtl = pd.DataFrame(dict(number=[39, 27.4, 20.6, 11, 3], stage=stages))
df_mtl['office'] = 'Санкт-Перербург'
df_toronto = pd.DataFrame(dict(number=[52, 36, 18, 14, 5], stage=stages))
df_toronto['office'] = 'Москва'
df = pd.concat([df_mtl, df_toronto], axis=0)
fig = px.funnel(df,
x='number',
y='stage',
