[Visualization of ratio] Plotly and dynamic visualization [python, pie, sunburst, sanky, treemap, fannele,]
python==3.8 plotly==4.10.0
pie (yen, donut)
Basic circle
You don't need to explain about pie charts anymore
import plotly.express as px
df = px.data.tips()
fig = px.pie(df, values='tip', names='day',
title='pie plot with PX')
fig.show()
with graph_objects
import plotly.graph_objects as go
df = px.data.tips()
fig = go.Figure(data=[go.Pie(labels=df['day'],
values=df['tip'])])
fig.update_traces(hoverinfo='label+percent',
textinfo='value',
textfont_size=20,
marker=dict(line=dict(width=2)))
fig.show()
Make donuts
import plotly.graph_objects as go
fig = go.Figure(data=[go.Pie(labels=df['day'],
values=df['tip'],
hole=.3)
])
fig.update_traces(hoverinfo='label+percent',
textinfo='value',
textfont_size=20,
marker=dict(line=dict(width=2)))
fig.show()
sunburst
Specify in the order specified as the parent in path The size of the value is specified by values
import plotly.express as px
df = px.data.tips()
fig = px.sunburst(df, path=['day', 'time', 'sex'], values='total_bill')
fig.show()
Change color
It also color-codes the size of the value
import plotly.express as px
df = px.data.tips()
fig = px.sunburst(df, path=['day', 'time', 'sex'], values='total_bill',color='total_bill')
fig.show()
alluvial (alluvial map)
Basic
import plotly.express as px
df = px.data.tips()
fig = px.parallel_categories(df)
fig.show()
parallel_categories
Count the categorical variables to make a parallel view Specify with color to visualize which category exists in other variables at what rate
import plotly.express as px
df = px.data.tips()
fig = px.parallel_categories(df, color="size",
color_continuous_scale=px.colors.sequential.Inferno)
fig.show()
parallel_coordinates
Check the distribution of continuous values one by one You can visually check where there are many and how much they vary, rather than a cohesive ratio.
import plotly.express as px
df = px.data.tips()
fig = px.parallel_coordinates(df, color="size",
dimensions=['total_bill', 'size', 'tip'],
color_continuous_midpoint=2)
fig.show()
sanky diagram (flow chart)
A graph that began when Mr. Sanky visualized the flow rate of energy
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(
go.Sankey(
node = dict(pad = 15,thickness = 20,line = dict(color = "black", width = 0.5),
label = ["n0","n1","n2","n3","n4"],
color = "blue"),
link = dict(
source = [0,1,2,3],
target =[1,2,3,4],
value = [5,10,15,20])
)
)
fig.update_layout(title_text="Sankey Diagram", font_size=10)
fig.show()
An arrow is drawn from the source entered as a real number to the target, and the thickness of the arrow is passed to the value. Specify only the number of uniques in source and target for label The specified order corresponds to the size of the source and target numbers.
If it is in the form of data
Make a state like Convert the passing relationship to a number
By doing such pretreatment, Which category holds how much flow Percentage distributed to other categories And so on
d1 = df.groupby(["sex","smoker"], as_index=False).sum()[["sex","smoker",'total_bill']]
d1.columns = ['sor','tar','total_bill']
d2 = df.groupby(["smoker","day"], as_index=False).sum()[["smoker","day",'total_bill']]
d2.columns = ['sor','tar','total_bill']
d3 = df.groupby(["day","time"], as_index=False).sum()[["day","time",'total_bill']]
d3.columns = ['sor','tar','total_bill']
concat_d = pd.concat([d1, d2, d3],axis=0, ignore_index=True)
label_list = pd.concat([concat_d['sor'],concat_d['tar']],axis=0).unique().astype('str')
for i in range(0,len(label_list)):
for j in range(0,len(concat_d['sor'])):
if concat_d['sor'].astype('str')[j]==label_list[i]:
concat_d['sor'][j]=i
if concat_d['tar'].astype('str')[j]==label_list[i]:
concat_d['tar'][j]=i
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(
go.Sankey(
node = dict(pad = 15,thickness = 20,line = dict(color = "black", width = 0.5),
label = label_list,
color = "blue"),
link = dict(
source = concat_d.sor,
target =concat_d.tar,
value = concat_d.total_bill)
)
)
fig.update_layout(title_text="Basic Sankey Diagram", font_size=10)
fig.show()
treemap
Express the ratio by area Use of expenses, confirmation of inventory ratio, etc.
import plotly.express as px
df = px.data.tips()
fig = px.treemap(df,
path=[px.Constant('back ground'),'sex','day','time'],
values='total_bill',
color='sex'
)
fig.show()
Branch in the order passed to path If you want to make a background, make an arbitrary one with constant
funnel basic
Can express the progress of the ratio Can express attenuation from website viewing to purchase
from plotly import graph_objects as go
fig = go.Figure()
fig.add_trace(go.Funnel(
name = 'Montreal',
orientation = "h",
y = ["2018-01-01", "2018-07-01", "2019-01-01", "2020-01-01"],
x = [100, 60, 40, 20],
textposition = "inside",
texttemplate = "%{y| %a. %_d %b %Y}"))
fig.update_layout(yaxis = {'type': 'date'})
fig.show()
Some at the same time
from plotly import graph_objects as go
fig = go.Figure()
fig.add_trace(go.Funnel(
name = 'Montreal',
orientation = "h",
y = ["2018-01-01", "2018-07-01", "2019-01-01", "2020-01-01"],
x = [100, 60, 40, 20],
textposition = "inside",
textinfo = "value+percent previous"))
fig.add_trace(go.Funnel(
name = 'Vancouver',
orientation = "h",
y = ["2018-01-01", "2018-07-01", "2019-01-01", "2020-01-01"],
x = [90, 70, 50, 10],
textposition = "inside",
textinfo = "value+percent previous"))
fig.add_trace(go.Funnel(
name = 'Toronto',
orientation = "h",
y = ["2018-01-01", "2018-07-01", "2019-01-01","2020-01-01","2021-01-01"],
x = [100, 60, 40,30, 20,10],
textposition = "inside",
textinfo = "value+percent previous"))
fig.update_layout(yaxis = {'type': 'date'})
fig.show()
If text info is percentage total, it shows the ratio with the whole as 100
Represent the funnel as a plane
import plotly.express as px
fig = px.funnel_area(names=["The 1st","The 2nd", "The 3rd", "The 4th", "The 5th"],
values=[5, 4, 3, 2, 1],
color=[5, 3, 3, 3, 1])
fig.show()
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