I would like to create a subplot with 2 plot generated with the function plotly.express.line, is it possible? Given the 2 plot:
fig1 =px.line(df, x=df.index, y='average')
fig1.show()
fig2 = px.line(df, x=df.index, y='Volume')
fig2.show()
I would like to generate an unique plot formed by 2 subplot (in the example fig1 and fig2)
Yes, you can build subplots using plotly express. Either
1. directly through the arguments facet_row and facet_colums (in which case we often talk about facet plots, but they're the same thing), or
2. indirectly through "stealing" elements from figures built with plotly express and using them in a standard make_subplots() setup with fig.add_traces()
Method 1: Facet and Trellis Plots in Python
Although plotly.express supports data of both wide and long format, I often prefer building facet plots from the latter. If you have a dataset such as this:
Date variable value
0 2019-11-04 average 4
1 2019-11-04 average 2
.
.
8 2019-12-30 volume 5
9 2019-12-30 volume 2
then you can build your subplots through:
fig = px.line(df, x='Date', y = 'value', facet_row = 'variable')
Plot 1:
By default, px.line() will apply the same color to both lines, but you can easily handle that through:
fig.update_traces(line_color)
This complete snippet shows you how:
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
df = pd.DataFrame({'Date': ['2019-11-04', '2019-11-04', '2019-11-18', '2019-11-18', '2019-12-16', '2019-12-16', '2019-12-30', '2019-12-30'],
'variable':['average', 'volume', 'average', 'volume', 'average','volume','average','volume'],
'value': [4,2,6,5,6,7,5,2]})
fig = px.line(df, x='Date', y = 'value', facet_row = 'variable')
fig.update_traces(line_color = 'red', row = 2)
fig.show()
Method 2: make_subplots
Since plotly express can do some pretty amazing stuff with fairly complicated datasets, I see no reason why you should not stumple upon cases where you would like to use elements of a plotly express figure as a source for a subplot. And that is very possible.
Below is an example where I've built to plotly express figures using px.line on the px.data.stocks() dataset. Then I go on to extract some elements of interest using add_trace and go.Scatter in a For Loop to build a subplot setup. You could certainly argue that you could just as easily do this directly on the data source. But then again, as initially stated, plotly express can be an excellent data handler in itself.
Plot 2: Subplots using plotly express figures as source:
Complete code:
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
from plotly.subplots import make_subplots
df = px.data.stocks().set_index('date')
fig1 = px.line(df[['GOOG', 'AAPL']])
fig2 = px.line(df[['AMZN', 'MSFT']])
fig = make_subplots(rows=2, cols=1)
for d in fig1.data:
fig.add_trace((go.Scatter(x=d['x'], y=d['y'], name = d['name'])), row=1, col=1)
for d in fig2.data:
fig.add_trace((go.Scatter(x=d['x'], y=d['y'], name = d['name'])), row=2, col=1)
fig.show()
There is no need to use graph_objects module if you have just already generated px figures for making subplots. Here is the full code.
import plotly.express as px
import pandas as pd
from plotly.subplots import make_subplots
df = px.data.stocks().set_index('date')
fig1 = px.line(df[['GOOG', 'AAPL']])
fig2 = px.line(df[['AMZN', 'MSFT']])
fig = make_subplots(rows=2, cols=1)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.show()
If there are more than two variables in each plot, one can use for loop also to add the traces using fig.add_trace method.
From the documentation, Plotly express does not support arbitrary subplot capabilities. You can instead use graph objects and traces (note that go.Scatter is equivalent):
import pandas as pd
from plotly.subplots import make_subplots
import plotly.graph_objects as go
## create some random data
df = pd.DataFrame(
data={'average':[1,2,3], 'Volume':[7,3,6]},
index=['a','b','c']
)
fig = make_subplots(rows=1, cols=2)
fig.add_trace(
go.Scatter(x=df.index, y=df.average, name='average'),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=df.index, y=df.Volume, name='Volume'),
row=1, col=2
)
fig.show()
Related
I would like to add a second Y axis to my bar plot bellow, that is the number of citizens in integer:
this graph was made using plotly:
import plotly.express as px
fig = px.bar(df, x="country",y="pourcent_visit",color="city",barmode='group')
# fig.add_hline(y=10)
fig.show()
To my knowledge, there's no direct way to do this. But you can easily build a Plotly Express figure, grab the traces (and data structures) from there and combine them in a figure that allows multiple axes using fig = make_subplots(specs=[[{"secondary_y": True}]]). With no provided data sample, I'll use the built-in dataset px.data.tips() that I'm guessing to a large part resembles the structure of your real world dataset judging by the way you've applied the arguments in px.bar(). Details in the comments, but please don't hesitate to let me know if something is unclear.
Plot:
Complete code:
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# sample data
df = px.data.tips()
# figure setup with multiple axes
fig = make_subplots(specs=[[{"secondary_y": True}]])
# build plotly express plot
fig2 = px.bar(df, x="day", y="total_bill", color="smoker", barmode="group")
# add traces from plotly express figure to first figure
for t in fig2.select_traces():
fig.add_trace(t, secondary_y = False)
# handle data for secondary axis
df2 = df.groupby('day').agg('sum')#.reset_index()
df2 = df2.reindex(index = df['day'].unique()).reset_index()
#
fig.add_trace(go.Scatter(x = df2['day'], y = df2['size'], mode = 'lines'), secondary_y = True)
# fix layout
fig.update_layout(legend_title_text = 'smoker')
fig.show()
I have started from the following example:
from plotly.subplots import make_subplots
from plotly import graph_objects as go
fig = make_subplots(rows=3, cols=1, subplot_titles=["foo", "bar", "goo"])
for i in range(3):
fig.add_trace(go.Box(x=list(range(100)), boxmean="sd", showlegend=False), row=i + 1, col=1)
fig.update_layout(height=600, width=1200, title_text="Yo Yo")
fig
It yields three box plots in three rows of a subplots Plotly container:
My objective is:
Get rid of the trace X strings on the left.
Use the same color for all three subplots.
By using:
fig.add_trace(go.Box(x=list(range(100)), boxmean="sd", showlegend=False, fillcolor="blue"), row=i + 1, col=1)
I'm getting closer to the second objective, but it is not yet there:
I'm guessing I can ask for a color cycle consisting of a single color; but I didn't manage to do that.
We have already tried the fill and obtained results, so I think the remaining task is to align the line colors. The y-axis labels can be set to empty by name. There are other ways to do this, but I think this is the easiest.
from plotly.subplots import make_subplots
from plotly import graph_objects as go
fig = make_subplots(rows=3, cols=1, subplot_titles=["foo", "bar", "goo"])
for i in range(3):
fig.add_trace(go.Box(x=list(range(100)),
boxmean="sd",
fillcolor='blue',
line={'color':'red'},
name='',
showlegend=False), row=i + 1, col=1)
fig.update_layout(height=600, width=1200, title_text="Yo Yo")
fig.show()
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
I'm keen to know if there is an equivalent to:
import pandas as pd
import numpy as np
data = pd.DataFrame({'Day':range(10),
'Temperature': np.random.rand(10),
'Wind': np.random.rand(10),
'Humidity': np.random.rand(10),
'Pressure': np.random.rand(10)})
data.set_index('Day').plot(subplots=True, layout=(2,2), figsize=(10,5))
plt.tight_layout()
That generates Plotly graphs as opposed to matplotlib charts.
For a plotly express solution:
You could use pd.melt() to get all your variables in the same column:
import pandas as pd
import plotly.express as px
df = pd.DataFrame({
'Day':range(10),
'Temperature': np.random.rand(10),
'Wind': np.random.rand(10),
'Humidity': np.random.rand(10),
'Pressure': np.random.rand(10),})
df_melt = df.melt(
id_vars='Day',
value_vars=['Temperature', 'Wind', 'Humidity', 'Pressure'])
Your dataframe now looks like this with the variable names in a column named 'variable' and the values in a column named 'value':
Day variable value
0 0 Temperature 0.609
1 1 Temperature 0.410
2 2 Temperature 0.194
3 3 Temperature 0.663
4 4 Temperature 0.351
Now you can use px.scatter() with argument facet_col to get the multiple plots:
fig = px.scatter(
df_melt,
x='Day',
y='value',
facet_col='variable',
facet_col_wrap=2,
color='variable',
width=800,
)
This results in the following plot:
Now in your example all variables have the same range of values. But if this is not the case then you might want to make sure that every plot gets its own range on the y-axis. This can be done as follows:
fig.update_yaxes(showticklabels=True, matches=None)
More info on facet plots can be found here:
https://plotly.com/python/facet-plots/
As per the documentation, Plotly Express does not support arbitrary subplot capabilities, instead it supports faceting by a given data dimension, and it also supports marginal charts to display distribution information.
This demonstrates the usage of the lower-level plotly.subplots module and the make_subplots function it exposes to construct figures with arbitrary subplots.
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# using your sample data
fig = make_subplots(rows=2, cols=2, start_cell="bottom-left")
fig.add_trace(go.Scatter(x=data.index, y=data.Temperature, name='Temp'),
row=1, col=1, )
fig.add_trace(go.Scatter(x=data.index, y=data.Wind, name='Wind'),
row=1, col=2)
fig.add_trace(go.Scatter(x=data.index, y=data.Humidity, name='Humidity'),
row=2, col=1)
fig.add_trace(go.Scatter(x=data.index, y=data.Pressure, name='Pressure'),
row=2, col=2)
fig.show()
I wanted simply to plot quickly multiple distribution subplots one after another like in sns, pyplot. For loop works. Works of course also with scatter. Nice touch: even the xlables are printed.
for col in boston_df.columns.tolist():
boston_dis = px.histogram(boston_df,
x=col, color_discrete_sequence=['lavenderblush'],
title='Distribution',
histnorm='probability density', template='plotly_dark',
width=400, height=300)
boston_dis.show()
I am wondering what is best practice to create subplots using Python Plotly. Is it to use plotly.express or the standard plotly.graph_objects?
I'm trying to create a figure with two subplots, which are stacked bar charts. The following code doesn't work. I didn't find anything useful in the official documentation. The classic Titanic dataset was imported as train_df here.
import plotly.express as px
train_df['Survived'] = train_df['Survived'].astype('category')
fig1 = px.bar(train_df, x="Pclass", y="Age", color='Survived')
fig2 = px.bar(train_df, x="Sex", y="Age", color='Survived')
trace1 = fig1['data'][0]
trace2 = fig2['data'][0]
fig = make_subplots(rows=1, cols=2, shared_xaxes=False)
fig.add_trace(trace1, row=1, col=1)
fig.add_trace(trace2, row=1, col=2)
fig.show()
I got the following figure:
What I expect is as follows:
I'm hoping that the existing answer suits your needs, but I'd just like to note that the statement
it's not possible to subplot stakedbar (because stacked bar are in facted figures and not traces
is not entirely correct. It's possible to build a plotly subplot figure using stacked bar charts as long as you put it together correctly using add_trace() and go.Bar(). And this also answers your question regarding:
I am wondering what is best practice to create subplots using Python Plotly. Is it to use plotly.express or the standard plotly.graph_objects?
Use plotly.express ff you find a px approach that suits your needs. And like in your case where you do not find it; build your own subplots using plotly.graphobjects.
Below is an example that will show you one such possible approach using the titanic dataset. Note that the column names are noe the same as yours since there are no capital letters. The essence of this approav is that you use go.Bar() for each trace, and specify where to put those traces using the row and col arguments in go.Bar(). If you assign multiple traces to the same row and col, you will get stacked bar chart subplots if you specify barmode='stack' in fig.update_layout(). Usingpx.colors.qualitative.Plotly[i]` will let you assign colors from the standard plotly color cycle sequentially.
Plot:
Code:
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
url = "https://raw.github.com/mattdelhey/kaggle-titanic/master/Data/train.csv"
titanic = pd.read_csv(url)
#titanic.info()
train_df=titanic
train_df
# data for fig 1
df1=titanic.groupby(['sex', 'pclass'])['survived'].aggregate('mean').unstack()
# plotly setup for fig
fig = make_subplots(2,1)
fig.add_trace(go.Bar(x=df1.columns.astype('category'), y=df1.loc['female'],
name='female',
marker_color = px.colors.qualitative.Plotly[0]),
row=1, col=1)
fig.add_trace(go.Bar(x=df1.columns.astype('category'), y=df1.loc['male'],
name='male',
marker_color = px.colors.qualitative.Plotly[1]),
row=1, col=1)
# data for plot 2
age = pd.cut(titanic['age'], [0, 18, 80])
df2 = titanic.pivot_table('survived', [age], 'pclass')
groups=['(0, 18]', '(18, 80]']
fig.add_trace(go.Bar(x=df2.columns, y=df2.iloc[0],
name=groups[0],
marker_color = px.colors.qualitative.Plotly[3]),
row=2, col=1)
fig.add_trace(go.Bar(x=df2.columns, y=df2.iloc[1],
name=groups[1],
marker_color = px.colors.qualitative.Plotly[4]),
row=2, col=1)
fig.update_layout(title=dict(text='Titanic survivors by sex and age group'), barmode='stack', xaxis = dict(tickvals= df1.columns))
fig.show()
fig.show()
From what I know, it's not possible to subplot stakedbar (because stacked bar are in facted figures and not traces)...
On behalf of fig.show(), you can put to check if the html file is okay for you (The plots are unfortunately one under the other...) :
with open('p_graph.html', 'a') as f:
f.write(fig1.to_html(full_html=False, include_plotlyjs='cdn',default_height=500))
f.write(fig2.to_html(full_html=False, include_plotlyjs='cdn',default_height=500))
try the code below to check if the html file generate can be okay for you:
import pandas as pd
import plotly.graph_objects as go
#Remove the .astype('category') to easily
#train_df['Survived'] = train_df['Survived'].astype('category')
Pclass_pivot=pd.pivot_table(train_df,values='Age',index='Pclass',
columns='Survived',aggfunc=lambda x: len(x))
Sex_pivot=pd.pivot_table(train_df,values='Age',index='Sex',
columns='Survived',aggfunc=lambda x: len(x))
fig1 = go.Figure(data=[
go.Bar(name='Survived', x=Pclass_pivot.index.values, y=Pclass_pivot[1]),
go.Bar(name='NotSurvived', x=Pclass_pivot.index.values, y=Pclass_pivot[0])])
# Change the bar mode
fig1.update_layout(barmode='stack')
fig2 = go.Figure(data=[
go.Bar(name='Survived', x=Sex_pivot.index.values, y=Sex_pivot[1]),
go.Bar(name='NotSurvived', x=Sex_pivot.index.values, y=Sex_pivot[0])])
# Change the bar mode
fig2.update_layout(barmode='stack')
with open('p_graph.html', 'a') as f:
f.write(fig1.to_html(full_html=False, include_plotlyjs='cdn',default_height=500))
f.write(fig2.to_html(full_html=False, include_plotlyjs='cdn',default_height=500))
I managed to generate the subplots using the add_bar function.
Code:
from plotly.subplots import make_subplots
# plotly can only support one legend per graph at the moment.
fig = make_subplots(
rows=1, cols=2,
subplot_titles=("Pclass vs. Survived", "Sex vs. Survived")
)
fig.add_bar(
x=train_df[train_df.Survived == 0].Pclass.value_counts().index,
y=train_df[train_df.Survived == 0].Pclass.value_counts().values,
text=train_df[train_df.Survived == 0].Pclass.value_counts().values,
textposition='auto',
name='Survived = 0',
row=1, col=1
)
fig.add_bar(
x=train_df[train_df.Survived == 1].Pclass.value_counts().index,
y=train_df[train_df.Survived == 1].Pclass.value_counts().values,
text=train_df[train_df.Survived == 1].Pclass.value_counts().values,
textposition='auto',
name='Survived = 1',
row=1, col=1
)
fig.add_bar(
x=train_df[train_df.Survived == 0].Sex.value_counts().index,
y=train_df[train_df.Survived == 0].Sex.value_counts().values,
text=train_df[train_df.Survived == 0].Sex.value_counts().values,
textposition='auto',
marker_color='#636EFA',
showlegend=False,
row=1, col=2
)
fig.add_bar(
x=train_df[train_df.Survived == 1].Sex.value_counts().index,
y=train_df[train_df.Survived == 1].Sex.value_counts().values,
text=train_df[train_df.Survived == 1].Sex.value_counts().values,
textposition='auto',
marker_color='#EF553B',
showlegend=False,
row=1, col=2
)
fig.update_layout(
barmode='stack',
height=400, width=1200,
)
fig.update_xaxes(ticks="inside")
fig.update_yaxes(ticks="inside", col=1)
fig.show()
Resulting plot:
Hope this is helpful to the newbies of plotly like me.