I would like to plot several plots in a subplot, specifically ecdf plots which are found under plotly express. Unfortunately I cannot get it to work because it appears subplot expects a graph objects plotly plot. The error says it receives invalid data, specifically:
"Invalid element(s) received for the 'data' property"
Obviously that means that of the following, ecdf is not included:
['bar', 'barpolar', 'box', 'candlestick',
'carpet', 'choropleth', 'choroplethmapbox',
'cone', 'contour', 'contourcarpet',
'densitymapbox', 'funnel', 'funnelarea',
'heatmap', 'heatmapgl', 'histogram',
'histogram2d', 'histogram2dcontour', 'icicle',
'image', 'indicator', 'isosurface', 'mesh3d',
'ohlc', 'parcats', 'parcoords', 'pie',
'pointcloud', 'sankey', 'scatter',
'scatter3d', 'scattercarpet', 'scattergeo',
'scattergl', 'scattermapbox', 'scatterpolar',
'scatterpolargl', 'scatterternary', 'splom',
'streamtube', 'sunburst', 'surface', 'table',
'treemap', 'violin', 'volume', 'waterfall']
Great, now, is there a work around that will allow me to plot a few of these guys next to each other?
Here's the code for a simple ecdf plot as from the documentation.
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill", color="sex", markers=True, lines=False, marginal="histogram")
fig.show()
If I wanted to plot two of this same plot together for example, I would expect the following code (basically copied from the documentation) to work, probably, (if it accepted ecdf) but I cannot get it to work for the aforementioned reasons.
from plotly.subplots import make_subplots
import plotly.graph_objects as go
df = px.data.tips()
fig = make_subplots(rows=1, cols=2)
fig.add_trace(
px.ecdf(df, x="total_bill", color="sex", markers=True, lines=False, marginal="histogram"),
row=1, col=1
)
fig.add_trace(
px.ecdf(df, x="total_bill", color="sex", markers=True, lines=False, marginal="histogram"),
row=1, col=2
)
fig.update_layout(height=600, width=800, title_text="Side By Side Subplots")
fig.show()
Is there a work around for px.ecdf subplots?
Thank you in advance!
ECDF plots follow the Plotly Express pattern of having face_col parameter https://plotly.com/python-api-reference/generated/plotly.express.ecdf.html
simplest way to achieve this is to prepare the dataframe for this capability. In this example have created two copies of the data with each copy having a column for this capability
alternative is far more complex, both make_subplots() and px.ecdf() create multiple x and y axis. It would be necessary to manage all of these yourself
import plotly.express as px
import pandas as pd
df = px.data.tips()
df = pd.concat([px.data.tips().assign(col=c) for c in ["left","right"] ])
fig = px.ecdf(df, x="total_bill", color="sex", markers=True, lines=False, marginal="histogram", facet_col="col")
fig.update_layout(height=600, width=800, title_text="Side By Side Subplots")
Related
I am trying to add the total at the top of the each stacked bar along with the individual bar values in Plotly Express in Python.
import plotly.express as px
df = px.data.medals_long()
fig = px.bar(df, x="medal", y="count", color="nation", text_auto=True)
fig.show()
This gives the below result
However I want the chart as below:
Although it can be annotated as a string, the easiest way is to add a graph in the text mode of a scatter plot.
import plotly.express as px
import plotly.graph_objects as go
df = px.data.medals_long()
dfs = df.groupby('medal').sum()
fig = px.bar(df, x="medal", y="count", color="nation", text_auto=True)
fig.add_trace(go.Scatter(
x=dfs.index,
y=dfs['count'],
text=dfs['count'],
mode='text',
textposition='top center',
textfont=dict(
size=18,
),
showlegend=False
))
fig.update_yaxes(range=[0,50])
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 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()
I want to plot this data to evaluate data availability. I used the following plotting code in Plotly.
import datetime
import plotly.express as px
fig = px.bar(df, x=df.index, y="variable", color='value', orientation="h",
hover_data=[df.index],
height=350,
color_continuous_scale=['firebrick', '#2ca02c'],
title='',
template='plotly_white',
)
The result is just like what I want below.
But, the x-index show numbers. I want a timestamp (month+year) on the x-axis, instead.
Edit
Adding the fllowing
fig.update_layout(yaxis=dict(title=''),
xaxis=dict(
title='Timestamp',
tickformat = '%Y-%b',
)
)
Gives
which seems that the x-axis is not read from the data index.
If you want to use bars it seems to me that you need to find a nice workaround. Have you considered to use Heatmap?
import pandas as pd
import plotly.graph_objs as go
df = pd.read_csv("availability3.txt",
parse_dates=["Timestamp"])\
.drop("Unnamed: 0", axis=1)
# you want to have variable as columns
df = pd.pivot_table(df,
index="Timestamp",
columns="variable",
values="value")
fig = go.Figure()
fig.add_trace(
go.Heatmap(
z=df.values.T,
x=df.index,
y=df.columns,
colorscale='RdYlGn',
xgap=1,
ygap=2)
)
fig.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.