Develop Reference

Plotly express box plot hover data not working

Trying to add data to hover of boxplot express in plotly and following the instructions here in plotly 5.4.1. It is mentioned in the tutorial that additional information to be shown in the hover can be added by hover_data and hover_name argument. However, The additional hover data, in this case information from continent column, is not presented in the hover. I am not sure what is going wrong here? (Here is the code I test in Google colab)
import plotly.express as px
import pandas as pd
import numpy as np
np.random.seed(1234)
df = pd.DataFrame(np.random.randn(20, 1),columns=['Col1'])
df['country']=['canada','france']*10
df['continent']=['america','europe']*10
fig = px.box(df, x="country", y="Col1", hover_data=['continent'])
fig.show()
Here is what i get in google colab:
Error I get with suggested solution (this was solved with pip install plotly --upgrade):
The solution offered by #Rob works but to make it a generic function, here is what I wrote out of it:
def box_with_hover(df,x,y,hover_data):
fig = px.box(df, x=x, y=y, hover_data=[hover_data])
fig.add_traces(
px.bar(
df.groupby([x, hover_data], as_index=False).agg(
base=(y, "min"), y=(y, lambda s: s.max() - s.min())
),
x=x,
base="base",
y="y",
hover_data={hover_data:True, x:True, "base":False, "y":False},
)
.update_traces(opacity=0.1)
.data ).update_layout(bargap=0.8)
fig.show()

this is similar to Change Plotly Boxplot Hover Data
boxplot hover info is within javascript layer of plotly. Hence have overlayed a bar plot where hover can be controlled in way you require. When you hover over boxplot you get standard boxplot hover. bar different hover info
import plotly.express as px
import pandas as pd
import numpy as np
np.random.seed(1234)
df = pd.DataFrame(np.random.randn(20, 1), columns=["Col1"])
df["country"] = ["canada", "france"] * 10
df["continent"] = ["america", "europe"] * 10
fig = px.box(df, x="country", y="Col1", hover_data=["continent"])
fig.add_traces(
px.bar(
df.groupby(["country", "continent"], as_index=False).agg(
base=("Col1", "min"), y=("Col1", lambda s: s.max() - s.min())
),
x="country",
base="base",
y="y",
hover_data={"continent":True, "country":True, "base":False, "y":False},
)
.update_traces(opacity=0.1)
.data
).update_layout(bargap=0.8)
fig
generic function
import plotly.express as px
import pandas as pd
import numpy as np
np.random.seed(1234)
df = pd.DataFrame(np.random.randn(20, 1), columns=["Col1"])
df["country"] = ["canada", "france"] * 10
df["continent"] = ["america", "europe"] * 10
df["letter"] = list("AB") * 10
def box_with_hover(*args, **kwargs):
if isinstance(args[0], pd.DataFrame):
kwargs["data_frame"] = args[0]
fig = px.box(**kwargs)
fig.add_traces(
px.bar(
kwargs["data_frame"]
.groupby([kwargs["x"]], as_index=False)
.agg(
**{
**{
"base": (kwargs["y"], "min"),
"y": (kwargs["y"], lambda s: s.max() - s.min()),
},
**{c: (c, "first") for c in kwargs["hover_data"]},
}
),
x=kwargs["x"],
base="base",
y="y",
hover_data={
**{c: True for c in kwargs["hover_data"]},
**{kwargs["x"]: True, "base": False, "y": False},
},
)
.update_traces(opacity=0.1)
.data
).update_layout(bargap=0.8)
return fig
box_with_hover(
df.reset_index(), x="country", y="Col1", hover_data=["continent", "letter", "index"]
)

Plotly: How to create a barchart using group by?

I have a dataset as below:
import pandas as pd
data = dict(Pclass=[1,1,2,2,3,3],
Survived = [0,1,0,1,0,1],
CategorySize = [80,136,97,87,372,119] )
I need to create a barchart using plotly in python, which is grouped by Pclass. in each group, i have 2 columns for Survived=0 and Survived=1 and in Y axis i should have the CategorySize. Therefore, i must have 6 bars which are in 3 groups.
Here is what i have tried:
import plotly.offline as pyo
import plotly.graph_objects as go
data = [ go.Bar( x = PclassSurvived.Pclass, y = PclassSurvived.CategorySize ) ]
layout = go.Layout(title= 'Pclass-Survived', xaxis = dict(title = 'Pclass'), yaxis = dict(title = 'CategorySize'),barmode='group' )
fig = go.Figure(data = data, layout = layout)
pyo.plot( fig, filename='./Output/Pclass-Survived.html')
But, it is not what i need.

This could be easily done with Pandas's groupby and Plotly Express.
You should group your data by Pclass and Survived columns, and apply the sum aggregate function to the CategorySize column.
This way you'll get 6 groups, with their aggregate values, and you can easily plot for each group a pair of bar charts (side-byside) thanks to the barmode attribute (by using the 'group' value), you can read more about it in the documentation.
The code:
import pandas as pd
import plotly.express as px
data = pd.DataFrame(
dict(
Pclass=[1, 1, 2, 2, 3, 3],
Survived=[0, 1, 0, 1, 0, 1],
CategorySize=[80, 136, 97, 87, 372, 119],
)
)
Now you group the data:
grouped_df = data.groupby(by=["Pclass", "Survived"], as_index=False).agg(
{"CategorySize": "sum"}
)
And convert the Survived column values to strings (so plotly treat it as a discrete variable, rather than numeric variable):
grouped_df.Survived = grouped_df.Survived.map({0: "Died", 1: "Survived",})
Now, you should have:
Pclass
Survived
CategorySize
0
1
Died
80
1
1
Survived
136
2
2
Died
97
3
2
Survived
87
4
3
Died
372
5
3
Survived
119
Finally, you visualize your data:
fig = px.bar(
data_frame=grouped_df,
x="Pclass",
y="CategorySize",
color="Survived",
barmode="group",
)
fig.show()

I'm having trouble with your sample dataset. PclassSurvived.Pclass and PclassSurvived.CategorySize are not defined, and it's not 100% clear to me what you would like to accomplish here. But judging by your explanations and the structure of your dataset, it seems that this could get you somewhere:
Plot 1:
Code 1:
# imports
from plotly.subplots import make_subplots
import plotly.figure_factory as ff
import plotly.graph_objs as go
import pandas as pd
import numpy as np
data = dict(Pclass=[1,1,2,2,3,3],
Survived = [0,1,0,1,0,1],
CategorySize = [80,136,97,87,372,119] )
df=pd.DataFrame(data)
s0=df.query('Survived==0')
s1=df.query('Survived==1')
#layout = go.Layout(title= 'Pclass-Survived', xaxis = dict(title = 'Pclass'), yaxis = dict(title = 'CategorySize'),barmode='group' )
fig = go.Figure()
data=data['Pclass']
fig.add_trace(go.Bar(x=s0['Pclass'], y = s0['CategorySize'],
name='dead'
)
)
fig.add_trace(go.Bar(x=s1['Pclass'], y = s1['CategorySize'],
name='alive'
)
)
fig.update_layout(barmode='group')
fig.show()
Edit: You can produce the same plot using the plotly.offline module like this:
Code 2:
# Import the necessaries libraries
import plotly.offline as pyo
import plotly.graph_objs as go
import pandas as pd
# Set notebook mode to work in offline
pyo.init_notebook_mode()
# data
data = dict(Pclass=[1,1,2,2,3,3],
Survived = [0,1,0,1,0,1],
CategorySize = [80,136,97,87,372,119] )
df=pd.DataFrame(data)
#
s0=df.query('Survived==0')
s1=df.query('Survived==1')
fig = go.Figure()
data=data['Pclass']
fig.add_trace(go.Bar(x=s0['Pclass'], y = s0['CategorySize'],
name='dead'
)
)
fig.add_trace(go.Bar(x=s1['Pclass'], y = s1['CategorySize'],
name='alive'
)
)
pyo.iplot(fig, filename = 'your-library')
Alternative approach with stacked bars:
Plot 2:
Code 3:
# imports
from plotly.subplots import make_subplots
import plotly.figure_factory as ff
import plotly.graph_objs as go
import pandas as pd
import numpy as np
data = dict(Pclass=[1,1,2,2,3,3],
Survived = [0,1,0,1,0,1],
CategorySize = [80,136,97,87,372,119] )
df=pd.DataFrame(data)
s0=df.query('Survived==0')
s1=df.query('Survived==1')
#layout = go.Layout(title= 'Pclass-Survived', xaxis = dict(title = 'Pclass'), yaxis = dict(title = 'CategorySize'),barmode='group' )
fig = go.Figure()
data=data['Pclass']
fig.add_trace(go.Bar(x=s0['Pclass'], y = s0['CategorySize'],
name='dead'
)
)
fig.add_trace(go.Bar(x=s1['Pclass'], y = s1['CategorySize'],
name='alive'
)
)
df_tot = df.groupby('Pclass').sum()
annot1 = [dict(
x=xi,
y=yi,
text=str(yi),
xanchor='auto',
yanchor='bottom',
showarrow=False,
) for xi, yi in zip(df_tot.index, df_tot['CategorySize'])]
fig.update_layout(barmode='stack', annotations=annot1)
fig.show()

How to properly handle datetime and categorical axes in bokeh/holoviews heatmap plot?

I'm trying to plot a simple heatmap using bokeh/holoviews. My data (pandas dataframe) has categoricals (on y) and datetime (on x). The problem is that the number of categorical elements is >3000 and the resulting plot appears with messed overlapped tickers on the y axis that makes it totally useless. Currently, is there a reliable way in bokeh to select only a subset of the tickers based on the zoom level?
I've already tried plotly and the result looks perfect but however I need to use bokeh/holoviews and datashader. I want also avoid to replace categoricals with numericals tickers.
I've also tried this solution but actually it doesn't work (bokeh 1.2.0).
This is a toy example representing my use case (Actually here #y is 1000 but it gives the idea)
from datetime import datetime
import pandas as pd
import numpy as np
from bokeh.plotting import figure, show
from bokeh.transform import linear_cmap
from bokeh.io import output_notebook
output_notebook()
# build sample data
index = pd.date_range(start='1/1/2019', periods=1000, freq='T')
data = np.random.rand(1000,100)
columns = ['col'+ str(n) for n in range(100)]
# initial data format
df = pd.DataFrame(data=data, index=index, columns=columns)
# bokeh
df = df.stack().reset_index()
df.rename(columns={'level_0':'x','level_1':'y', 0:'z'},inplace=True)
df.sort_values(by=['y'],inplace=True)
x = [
date.to_datetime64().astype('M8[ms]').astype('O')
for date in df.x.to_list()
]
data = {
'value': df.z.to_list(),
'x': x,
'y': df.y.to_list(),
'date' : df.x.to_list()
}
p = figure(x_axis_type='datetime', y_range=columns, width=900, tooltips=[("x", "#date"), ("y", "#y"), ("value", "#value")])
p.rect(x='x', y='y', width=60*1000, height=1, line_color=None,
fill_color=linear_cmap('value', 'Viridis256', low=df.z.min(), high=df.z.max()), source=data)
show(p)

Finally, I partially followed the suggestion from James and managed to get it to work using a python callback for the ticker. This solution was hard to find for me. I really searched all the Bokeh docs, examples and source code for days.
The main problem for me is that in the doc is not mentioned how I can use "ColumnDataSource" objects in the custom callback.
https://docs.bokeh.org/en/1.2.0/docs/reference/models/formatters.html#bokeh.models.formatters.FuncTickFormatter.from_py_func
Finally, this helped a lot:
https://docs.bokeh.org/en/1.2.0/docs/user_guide/interaction/callbacks.html#customjs-with-a-python-function.
So, I modified the original code as follow in the hope it can be useful to someone:
from datetime import datetime
import pandas as pd
import numpy as np
from bokeh.plotting import figure, show
from bokeh.transform import linear_cmap
from bokeh.io import output_notebook
from bokeh.models import FuncTickFormatter
from bokeh.models import ColumnDataSource
output_notebook()
# build sample data
index = pd.date_range(start='1/1/2019', periods=1000, freq='T')
data = np.random.rand(1000,100)
columns_labels = ['col'+ str(n) for n in range(100)]
columns = [n for n in range(100)]
# initial data format
df = pd.DataFrame(data=data, index=index, columns=columns)
# bokeh
df = df.stack().reset_index()
df.rename(columns={'level_0':'x','level_1':'y', 0:'z'},inplace=True)
df.sort_values(by=['y'],inplace=True)
x = [
date.to_datetime64().astype('M8[ms]').astype('O')
for date in df.x.to_list()
]
data = {
'value': df.z.to_list(),
'x': x,
'y': df.y.to_list(),
'y_labels_tooltip' : [columns_labels[k] for k in df.y.to_list()],
'y_ticks' : columns_labels*1000,
'date' : df.x.to_list()
}
cd = ColumnDataSource(data=data)
def ticker(source=cd):
labels = source.data['y_ticks']
return "{}".format(labels[tick])
#p = figure(x_axis_type='datetime', y_range=columns, width=900, tooltips=[("x", "#date{%F %T}"), ("y", "#y_labels"), ("value", "#value")])
p = figure(x_axis_type='datetime', width=900, tooltips=[("x", "#date{%F %T}"), ("y", "#y_labels_tooltip"), ("value", "#value")])
p.rect(x='x', y='y', width=60*1000, height=1, line_color=None,
fill_color=linear_cmap('value', 'Viridis256', low=df.z.min(), high=df.z.max()), source=cd)
p.hover.formatters = {'date': 'datetime'}
p.yaxis.formatter = FuncTickFormatter.from_py_func(ticker)
p.yaxis[0].ticker.desired_num_ticks = 20
show(p)
The result is this:

Style Seaborn and Plotly heatmaps

I created a plot with Seaborn:
# imports
import random
%matplotlib inline
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# create some random data
N = 20
rand_matrix = np.asarray([random.randrange(1,11)/10 for _ in range(1, N*N+1) ]).reshape(N,N)
data = np.flip(np.triu(rand_matrix), 1)
df = pd.DataFrame(data, index=pd.date_range(start='2015-01-01', freq='MS',\
periods=N), columns = range(1,N+1))
df[1]=1
# seaborn plot
plt.figure(figsize=(20,20))
sns.heatmap(data = df,
annot = True,
vmin = 0.0,
vmax = 1.0,
cmap = 'PuBuGn')
Here is how I create the Plotly plot:
import plotly.offline as py
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
data = [
go.Heatmap(
z=df.values,
x=df.columns.tolist(),
y=df.index.tolist()
)
]
layout = go.Layout(
xaxis = dict(ticks='', nticks=N),
yaxis = dict(ticks='', nticks=N))
fig = go.Figure(data=data, layout=layout)
iplot(fig)
I have following questions:
Seaborn: How do I change the y axis labels to YYYY-MM?
Plotly: How do I get the older data to the top (like with Seaborn)
Plotly: How do I get the data labels displayed in the heatmap (like Seaborn does).
Plotly: I noticed that rendering is much slower than Seaborn. Can it be optimized, if yes how?

2. How do I get the older data to the top?
fig.update_yaxes(autorange="reversed")
3. How do i get the data labels displayed in the heatmap?
ff.create_annotated_heatmap()
4. Can plotly be optimized?
No quick-fixes here.
Plot:
Plotly:
from plotly.offline import iplot
import plotly.graph_objs as go
import plotly.figure_factory as ff
#py.init_notebook_mode(connected=True)
data = [
go.Heatmap(
z=df.values,
x=df.columns.tolist(),
y=df.index.tolist()
)
]
font_colors = ['white', 'black']
fig = ff.create_annotated_heatmap(z=df.values, colorscale='Jet', font_colors=font_colors)
fig.update_layout( yaxis = dict(ticks='', nticks=N))
# Make text size smaller
for i in range(len(fig.layout.annotations)):
fig.layout.annotations[i].font.size = 8
# reverse scales
fig.update_yaxes(autorange="reversed")
fig.show()

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')