Develop Reference

Adding a market to a line chart Plotly python

I'm trying to add a point to the last observation on a time series chart with plotly. It is not very different from the example here https://stackoverflow.com/a/72539011/3021252 for instance. Except it is the last observation. Unfortunately following such pattern modifies the axis range.
Here is an example of an original chart
import plotly.express as px
df = px.data.gapminder().query("country=='Canada'")
fig = px.line(df, x="year", y="lifeExp", title='Life expectancy in Canada')
fig.show()
But after adding a marker
import plotly.graph_objects as go
fig.add_trace(
go.Scatter(
x=[df["year"].values[-1]],
y=[df["lifeExp"].values[-1]],
mode='markers'
)
)
It looks like that
Has anyone have an idea how not to introduce this gap on the right?

How to update range_color in Plotly Express?

I am using plotly express and I want to display some data:
import plotly.express as px
dfx = px.data.tips()
fig = px.scatter(dfx,
x='total_bill', y='tip',
color='size',
template='plotly_dark',
range_color=[2,4])
My goal is to update the range color after it has been defined.
I tried something like this:
fig.update_layout(range_color=[3,6])
ValueError: Invalid property specified for object of type plotly.graph_objs.Layout: 'range'
but without success.
Are you aware of what I need to write in order to update the range color values?

To change the range of the color bar, you would change the maximum and minimum values of the color axis. This is different from the description of the graph settings, which can be found in fig.layout.
import plotly.express as px
dfx = px.data.tips()
fig = px.scatter(dfx,
x='total_bill', y='tip',
color='size',
template='plotly_dark',
range_color=[2,4])
fig.update_layout(coloraxis=dict(cmax=6, cmin=3))
fig.show()

I think you need to use range_color in the px.scatter function
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df,
x="sepal_width",
y="sepal_length",
color="sepal_length",
color_continuous_scale=["red",
"green", "blue"])
fig.show()
Here is a link to the documentation plotly
You can also look here at fig.update_coloraxes
update coloraxes
Also just found this in the documentation for update_layout
fig.update_layout(colorscale=dict(...))
update colorscale

How to specify the x coordinate on a grouped bar chart on plotly?

I made a bar chart with python plotly, and I want to put a marker on a particular bar, example non-smoking females.
Does anyone know how to specify this?
I took an example from the plotly documentation, if I try to put the marker it just takes the center of the main category.
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="sex", y="total_bill",
color='smoker', barmode='group',
height=400)
#trying to set the marker
fig.add_trace(
go.Scatter(x=["Female"],
y=[1100]
))
fig.show()

inspired by this: https://community.plotly.com/t/grouped-bar-charts-with-corresponding-line-chart/19562/4
use xaxis2, work out position, have hardcoded it, but 0.15 has relationship to number of traces in bargoup and x value
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
df = px.data.tips()
fig = px.histogram(
df, x="sex", y="total_bill", color="smoker", barmode="group", height=400
)
# trying to set the marker
fig.add_trace(
go.Scatter(
x=[0.15],
y=[1100],
customdata=[["No", "Female"]],
xaxis="x2",
hovertemplate="smoker=%{customdata[0]}<br>sex=%{customdata[1]}<br>sum of total_bill=%{y}<extra></extra>",
)
)
fig.update_layout(xaxis2={"overlaying": "x", "range": [0, 1], "showticklabels": False})
fig

Plotly: How to use scatter chart to display a multiIndex dataframe?

I'm using a multi-index data frame to plot a line chart. I can see the correct result when I plot the graph using Matplotlib but the data frame shows the wrong output when plotted using Plotly scatter charts- why?
import pandas as pd
data = pd.DataFrame([
('Q1','Blue',100),
('Q1','Green',300),
('Q2','Blue',200),
('Q2','Green',350),
('Q3','Blue',300),
('Q3','Green',400),
('Q4','Blue',400),
('Q4','Green',450),
],
columns=['quarter', 'company', 'value']
)
data = data.set_index(['quarter', 'company']).value
data.unstack().plot(kind='bar', stacked=True)
The above code plots the right chart. The below code also generate the right result.
fig = go.Figure()
fig.add_trace(go.Scatter(x=sample.index, y=sample['Blue'],
#mode='lines+markers', name='',
#line=dict(color=colors_list[1],width=2,) ,
)
)
but I don't know how to plot both Blue and Green in the scatter plot at the same time?
example:
fig = go.Figure()
fig.add_trace(go.Scatter(x=sample.index, y=sample,
#mode='lines+markers', name='',
#line=dict(color=colors_list[1],width=2,) ,
)
)
Can anyone help how to plot both Blue and Green together?

Your question is a bit unclear, but I'm assuming that your primary objective here is to display values accross an array of quarters where values are split in two groups ['blue', 'red']. (I can't quite understand why you're asking for a plotly scatter figure but showing a matplotlib bar chart...). Anyway, If I'm right, then your use case is very well suited for plotly.express, specifically px.scatter with which the following snippet will produce the figure below.
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
data = pd.DataFrame([
('Q1','Blue',100),
('Q1','Green',300),
('Q2','Blue',200),
('Q2','Green',350),
('Q3','Blue',300),
('Q3','Green',400),
('Q4','Blue',400),
('Q4','Green',450),
],
columns=['quarter', 'company', 'value']
)
fig = px.scatter(data, x = 'quarter', y = 'value',
color = 'company',
color_discrete_sequence=['Blue', 'Green'])
fig.show()

As vestland pointed in his good answer you can use Plotly Express to do it simply.
However, if you do not want to use Plotly Express, you have to add plots (traces) to the figure and configure the figure keys as below.
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=sample.index, y=sample['Green'], mode='markers', name = 'Green', marker_color = 'Green'))
fig.add_trace(go.Scatter(x=sample.index, y=sample['Blue'], mode='markers', name = 'Blue', marker_color = 'Blue'))
fig.update_layout(title="The Title", xaxis_title="Quarter", yaxis_title="Value", legend_title="Company")
gives

Plotly: Plot multiple figures as subplots

These resources show how to take data from a single Pandas DataFrame and plot different columns subplots on a Plotly graph. I'm interested in creating figures from separate DataFrames and plotting them to the same graph as subplots. Is this possible with Plotly?
https://plot.ly/python/subplots/
https://plot.ly/pandas/subplots/
I'm creating each figure from a dataframe like this:
import pandas as pd
import cufflinks as cf
from plotly.offline import download_plotlyjs, plot,iplot
cf.go_offline()
fig1 = df.iplot(kind='bar',barmode='stack',x='Type',
y=mylist,asFigure=True)
Edit:
Here is an example based on Naren's feedback:
Create the dataframes:
a={'catagory':['loc1','loc2','loc3'],'dogs':[1,5,6],'cats':[3,1,4],'birds':[4,12,2]}
df1 = pd.DataFrame(a)
b={'catagory':['loc1','loc2','loc3'],'dogs':[12,3,5],'cats':[4,6,1],'birds':[7,0,8]}
df2 = pd.DataFrame(b)
The plot will just show the information for the dogs, not the birds or cats:
fig = tls.make_subplots(rows=2, cols=1)
fig1 = df1.iplot(kind='bar',barmode='stack',x='catagory',
y=['dogs','cats','birds'],asFigure=True)
fig.append_trace(fig1['data'][0], 1, 1)
fig2 = df2.iplot(kind='bar',barmode='stack',x='catagory',
y=['dogs','cats','birds'],asFigure=True)
fig.append_trace(fig2['data'][0], 2, 1)
iplot(fig)

Here's a short function in a working example to save a list of figures all to a single HTML file.
def figures_to_html(figs, filename="dashboard.html"):
with open(filename, 'w') as dashboard:
dashboard.write("<html><head></head><body>" + "\n")
for fig in figs:
inner_html = fig.to_html().split('<body>')[1].split('</body>')[0]
dashboard.write(inner_html)
dashboard.write("</body></html>" + "\n")
# Example figures
import plotly.express as px
gapminder = px.data.gapminder().query("country=='Canada'")
fig1 = px.line(gapminder, x="year", y="lifeExp", title='Life expectancy in Canada')
gapminder = px.data.gapminder().query("continent=='Oceania'")
fig2 = px.line(gapminder, x="year", y="lifeExp", color='country')
gapminder = px.data.gapminder().query("continent != 'Asia'")
fig3 = px.line(gapminder, x="year", y="lifeExp", color="continent",
line_group="country", hover_name="country")
figures_to_html([fig1, fig2, fig3])

You can get a dashboard that contains several charts with legends next to each one:
import plotly
import plotly.offline as py
import plotly.graph_objs as go
fichier_html_graphs=open("DASHBOARD.html",'w')
fichier_html_graphs.write("<html><head></head><body>"+"\n")
i=0
while 1:
if i<=40:
i=i+1
#______________________________--Plotly--______________________________________
color1 = '#00bfff'
color2 = '#ff4000'
trace1 = go.Bar(
x = ['2017-09-25','2017-09-26','2017-09-27','2017-09-28','2017-09-29','2017-09-30','2017-10-01'],
y = [25,100,20,7,38,170,200],
name='Debit',
marker=dict(
color=color1
)
)
trace2 = go.Scatter(
x=['2017-09-25','2017-09-26','2017-09-27','2017-09-28','2017-09-29','2017-09-30','2017-10-01'],
y = [3,50,20,7,38,60,100],
name='Taux',
yaxis='y2'
)
data = [trace1, trace2]
layout = go.Layout(
title= ('Chart Number: '+str(i)),
titlefont=dict(
family='Courier New, monospace',
size=15,
color='#7f7f7f'
),
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
yaxis=dict(
title='Bandwidth Mbit/s',
titlefont=dict(
color=color1
),
tickfont=dict(
color=color1
)
),
yaxis2=dict(
title='Ratio %',
overlaying='y',
side='right',
titlefont=dict(
color=color2
),
tickfont=dict(
color=color2
)
)
)
fig = go.Figure(data=data, layout=layout)
plotly.offline.plot(fig, filename='Chart_'+str(i)+'.html',auto_open=False)
fichier_html_graphs.write(" <object data=\""+'Chart_'+str(i)+'.html'+"\" width=\"650\" height=\"500\"></object>"+"\n")
else:
break
fichier_html_graphs.write("</body></html>")
print("CHECK YOUR DASHBOARD.html In the current directory")
Result:

You can also try the following using cufflinks:
cf.subplots([df1.figure(kind='bar',categories='category'),
df2.figure(kind='bar',categories='category')],shape=(2,1)).iplot()
And this should give you:

New Answer:
We need to loop through each of the animals and append a new trace to generate what you need. This will give the desired output I am hoping.
import pandas as pd
import numpy as np
import cufflinks as cf
import plotly.tools as tls
from plotly.offline import download_plotlyjs, plot,iplot
cf.go_offline()
import random
def generate_random_color():
r = lambda: random.randint(0,255)
return '#%02X%02X%02X' % (r(),r(),r())
a={'catagory':['loc1','loc2','loc3'],'dogs':[1,5,6],'cats':[3,1,4],'birds':[4,12,2]}
df1 = pd.DataFrame(a)
b={'catagory':['loc1','loc2','loc3'],'dogs':[12,3,5],'cats':[4,6,1],'birds':[7,0,8]}
df2 = pd.DataFrame(b)
#shared Xaxis parameter can make this graph look even better
fig = tls.make_subplots(rows=2, cols=1)
for animal in ['dogs','cats','birds']:
animal_color = generate_random_color()
fig1 = df1.iplot(kind='bar',barmode='stack',x='catagory',
y=animal,asFigure=True,showlegend=False, color = animal_color)
fig.append_trace(fig1['data'][0], 1, 1)
fig2 = df2.iplot(kind='bar',barmode='stack',x='catagory',
y=animal,asFigure=True, showlegend=False, color = animal_color)
#if we do not use the below line there will be two legend
fig2['data'][0]['showlegend'] = False
fig.append_trace(fig2['data'][0], 2, 1)
#additional bonus
#use the below command to use the bar chart three mode
# [stack, overlay, group]
#as shown below
#fig['layout']['barmode'] = 'overlay'
iplot(fig)
Output:
Old Answer:
This will be the solution
Explanation:
Plotly tools has a subplot function to create subplots you should read the documentation for more details here. So I first use cufflinks to create a figure of the bar chart. One thing to note is cufflinks create and object with both data and layout. Plotly will only take one layout parameter as input, hence I take only the data parameter from the cufflinks figure and append_trace it to the make_suplots object. so fig.append_trace() the second parameter is row number and third parameter is column number
import pandas as pd
import cufflinks as cf
import numpy as np
import plotly.tools as tls
from plotly.offline import download_plotlyjs, plot,iplot
cf.go_offline()
fig = tls.make_subplots(rows=2, cols=1)
df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
fig1 = df.iplot(kind='bar',barmode='stack',x='A',
y='B',asFigure=True)
fig.append_trace(fig1['data'][0], 1, 1)
df2 = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('EFGH'))
fig2 = df2.iplot(kind='bar',barmode='stack',x='E',
y='F',asFigure=True)
fig.append_trace(fig2['data'][0], 2, 1)
iplot(fig)
If you want to add a common layout to the subplot I suggest that you do
fig.append_trace(fig2['data'][0], 2, 1)
fig['layout']['showlegend'] = False
iplot(fig)
or even
fig.append_trace(fig2['data'][0], 2, 1)
fig['layout'].update(fig1['layout'])
iplot(fig)
So in the first example before plotting, I access the individual parameters of the layout object and change them, you need to go through layout object properties for refernce.
In the second example before plotting, I update the layout of the figure with the cufflinks generated layout this will produce the same output as we see in cufflinks.

You've already received a few suggestions that work perfectly well. They do however require a lot of coding. Facet / trellis plots using px.bar() will let you produce the plot below using (almost) only this:
px.bar(df, x="category", y="dogs", facet_row="Source")
The only extra steps you'll have to take is to introduce a variable on which to split your data, and then gather or concatenate your dataframes like this:
df1['Source'] = 1
df2['Source'] = 2
df = pd.concat([df1, df2])
And if you'd like to include the other variables as well, just do:
fig = px.bar(df, x="category", y=["dogs", "cats", "birds"], facet_row="Source")
fig.update_layout(barmode = 'group')
Complete code:
# imports
import plotly.express as px
import pandas as pd
# data building
a={'category':['loc1','loc2','loc3'],'dogs':[1,5,6],'cats':[3,1,4],'birds':[4,12,2]}
df1 = pd.DataFrame(a)
b={'category':['loc1','loc2','loc3'],'dogs':[12,3,5],'cats':[4,6,1],'birds':[7,0,8]}
df2 = pd.DataFrame(b)
# data processing
df1['Source'] = 1
df2['Source'] = 2
df = pd.concat([df1, df2])
# plotly figure
fig = px.bar(df, x="category", y="dogs", facet_row="Source")
fig.show()
#fig = px.bar(df, x="category", y=["dogs", "cats", "birds"], facet_row="Source")
#fig.update_layout(barmode = 'group')