Hello guys i need help i have a piece of code that adds a list of horizontal lines to a Plotly figure in python. i want to change the code to were we will use a slider to add the horizontal lines to the figure and relayout it. As the slider moves right more horizontal lines will be added to the figure when the slider moves left the horizontal lines will be removed from the figure. Below is the bit of my code so far
for v in range(len(sortadlist)):
fig.add_hline(y=sortadlist[v][0], line_color='brown', line_width=1.5, row=1, col=1)
fig.add_shape(type="rect",
y0=round(sortadlist[v][0],2)-.3, y1=round(sortadlist[v][0],2)+.3, x0=-1, x1=len(df),
fillcolor="darkcyan",
opacity=0.15)
All the code above does is loop through a list of numbers and uses the fig.add_hline to add the horizontal line to the figure. I need help creating a slider that will add the horizontal lines to the figure
This is how the figure currently looks i want a slider to help with adding more horizontal lines to the figure and remove them also
Since the entire code is not available, the sample data was handled by obtaining the company's stock price. First of all, horizontal lines and shapes do not have a show/hide attribute, so they are not compatible with sliders. So I have created a code to draw a line according to the appropriate price list in the line chart of the scatter chart. Once the line chart is hidden, the first line chart is made visible.
The structure of the graph is a candlestick with 6 lines and the candlestick is always displayed. A loop process is used to create a list of lines to be shown or hidden.
import yfinance as yf
import plotly.graph_objects as go
import numpy as np
df = yf.download("AAPL", start="2022-01-01", end="2023-01-01", progress=False)
df.reset_index(inplace=True)
pricelist = np.arange(130,190,10)
fig = go.Figure()
fig.add_trace(go.Candlestick(x=df['Date'],
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'],
name='AAPL'
)
)
fig.add_hrect(y0=df['Close'].median()-10,
y1=df['Close'].median()+10,
annotation_text="Median+-10",
annotation_position="top right",
fillcolor="darkcyan",
opacity=0.25,
line_width=0)
for p in pricelist:
fig.add_trace(go.Scatter(x=df['Date'],
y=[p]*len(df['Date']),
line_color='blue',
name=str(p),
showlegend=False,
visible=False,
)
)
fig.data[1].visible = True
steps = []
for i in np.arange(1,len(fig.data)):
step = dict(
method="update",
args=[{"visible": [False] * len(fig.data)},
{"title": "Price lines: " + str(pricelist[i-1])}],
label=str(pricelist[i-1])
)
step["args"][0]["visible"][0] = True
step["args"][0]["visible"][i] = True
steps.append(step)
sliders = [dict(
active=10,
currentvalue={"prefix": "Price: "},
pad={"t": 50},
steps=steps
)]
fig.update_layout(
sliders=sliders
)
fig.update_layout(height=600, xaxis_rangeslider_visible=False)
fig.show()
Here is CDF visualization I have:
fig_cdf = px.ecdf(df['Timespan'], color_discrete_sequence=['blue'],ecdfnorm='probability', orientation='h')
fig_cdf.add_hline(y=90, line_width=2, line_color="red", name='90%', visible=True)
fig_cdf.add_hline(y=30, line_width=2, line_color="red", name='75%', visible=True)
fig_cdf.update_layout(width=500, height=500)
The problem here is that i want horizontal lines' names to be visible and appear as 2nd and 3rd legends. For this, I tried to add visible=True. However, it seems not to work. What's wrong?
This is one way of doing it...
Add the two lines to the dataframe as new columns
Use color_discrete_sequence to identify the colors you want
I am using some random dummy data, which you can replace with your data
import plotly.express as px
df = pd.DataFrame({'firstline': random.sample(range(1, 500), 20),'myX' : range(20)}) #My dummy data
#Add the two lines to dataframe
df['90%'] = [90] * 20
df['75%'] = [75] * 20
fig = px.line(df,
y = ['firstline', '90%', '75%'], x= 'myX', color_discrete_sequence=["blue", "red", "red"])
fig.update_layout(legend_title_text='Legend Heading') #Update Legend header if you dont like 'variable'
fig.show()
Output graph
This is my first experience with this graph, but to add it to the legend, you can use the line mode of the scatter plot. So I took the maximum x-axis value used in the first graph and set the legend name Average using the appropriate y-axis value. This example is taken from the official reference.
import plotly.express as px
import plotly.graph_objects as go
df = px.data.tips()
fig = px.ecdf(df, x=["total_bill", "tip"])
xmax = max(fig.data[0]['x'])
#print(xmax)
fig.add_trace(go.Scatter(
x=[0,xmax],
y=[0.6,0.6],
mode='lines',
line_color='red',
name='mean',
showlegend=True
))
fig.show()
I am graphing a line graph that I'd like to add text comments to some data points. The text is long and causes overlaps on the screen so I'd like to position the text better.
Suggestions on how to do this best? It seems like I may need to use annotations but I do not want to need to absolutely position each one. Is there a way to do this?
trace1 = go.Scatter(
x=df[('Data1')],
y=df[('Data2')],
name='Name',
text=df[('DataComment')],
textposition='top right',
mode = "lines+text",
)
Although a reproducible example would help, I have created a DataFrame with a DataComment column containing the lengthy comment "this is a really long comment that has an overlap" which exceeds the boundaries of the plot and gets cut off.
import numpy as np
import pandas as pd
import plotly.graph_objects as go
np.random.seed(42)
df = pd.DataFrame({
'Data1':[20,40,60,80,100],
'Data2':np.random.randint(100, size=5),
'DataComment':["short comment"] + [float("nan")]*3 + ["this is a really long comment that has an overlap"]
})
fig = go.Figure()
trace1 = go.Scatter(
x=df[('Data1')],
y=df[('Data2')],
name='Name',
text=df[('DataComment')],
textposition='top right',
mode = "lines+text"
)
fig.add_trace(trace1)
fig.show()
One thing you can do to fix this is make the text wrap at a specified length. Before you pass df['DataComment'] to go.Scatter, you can modify the DataFrame with the following code, which will insert a <br> tag every 10 characters so that the text will wrap when Plotly renders it using html.
## insert html breaks <br> into the string text
df['DataComment'] = df['DataComment'].fillna("")
df['DataComment'] = df['DataComment'].str.wrap(10)
df['DataComment']= df['DataComment'].apply(lambda x: x.replace('\n', '<br>'))
fig = go.Figure()
trace1 = go.Scatter(
x=df[('Data1')],
y=df[('Data2')],
name='Name',
text=df[('DataComment')],
textposition='top right',
mode = "lines+text"
)
fig.add_trace(trace1)
fig.show()
I use plotly package to show dynamic finance chart at python. However I didn't manage to put my all key points lines on one chart with for loop. Here is my code:
fig.update_layout(
for i in range(0,len(data)):
shapes=[
go.layout.Shape(
type="rect",
x0=data['Date'][i],
y0=data['Max_alt'][i],
x1='2019-12-31',
y1=data['Max_ust'][i],
fillcolor="LightSkyBlue",
opacity=0.5,
layer="below",
line_width=0)])
fig.show()
I have a data like below one. It is time series based EURUSD parity financial dataset. I calculated two constraits for both Local Min and Max. I wanted to draw rectangule shape to based on for each Min_alt / Min_ust and Max_alt / Max_range. I can draw for just one date like below image however I didn't manage to show all ranges in same plotly graph.
Here is the sample data set.
Here is the solution for added lines:
import datetime
colors = ["LightSkyBlue", "RoyalBlue", "forestgreen", "lightseagreen"]
ply_shapes = {}
for i in range(0, len(data1)):
ply_shapes['shape_' + str(i)]=go.layout.Shape(type="rect",
x0=data1['Date'][i].strftime('%Y-%m-%d'),
y0=data1['Max_alt'][i],
x1='2019-12-31',
y1=data1['Max_ust'][i],
fillcolor="LightSkyBlue",
opacity=0.5,
layer="below"
)
lst_shapes=list(ply_shapes.values())
fig1.update_layout(shapes=lst_shapes)
fig1.show()
However I have still problems to add traces to those lines. I mean text attribute.
Here is my code:
add_trace = {}
for i in range(0, len(data1)):
add_trace['scatter_' + str(i)] = go.Scatter(
x=['2019-12-31'],
y=[data1['Max_ust'][i]],
text=[str(data['Max_Label'][i])],
mode="text")
lst_trace = list(add_trace.values())
fig2=go.Figure(lst_trace)
fig2.show()
The answer:
For full control of each and every shape you insert, you could follow this logic:
fig = go.Figure()
#[...] data, traces and such
ply_shapes = {}
for i in range(1, len(df)):
ply_shapes['shape_' + str(i)]=go.layout.Shape()
lst_shapes=list(ply_shapes.values())
fig.update_layout(shapes=lst_shapes)
fig.show()
The details:
I'm not 100% sure what you're aimin to do here, but the following suggestion will answer your question quite literally regarding:
How to add more than one shape with loop in plotly?
Then you'll have to figure out the details regarding:
manage to put my all key points lines on one chart
Plot:
The plot itself is most likely not what you're looking for, but since you for some reason are adding a plot by the length of your data for i in range(0,len(data), I've made this:
Code:
This snippet will show how to handle all desired traces and shapes with for loops:
# Imports
import pandas as pd
#import matplotlib.pyplot as plt
import numpy as np
import plotly.graph_objects as go
#from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
# data, random sample to illustrate stocks
np.random.seed(12345)
rows = 20
x = pd.Series(np.random.randn(rows),index=pd.date_range('1/1/2020', periods=rows)).cumsum()
y = pd.Series(x-np.random.randn(rows)*5,index=pd.date_range('1/1/2020', periods=rows))
df = pd.concat([y,x], axis = 1)
df.columns = ['StockA', 'StockB']
# lines
df['keyPoints1']=np.random.randint(-5,5,len(df))
df['keyPoints2']=df['keyPoints1']*-1
# plotly traces
fig = go.Figure()
stocks = ['StockA', 'StockB']
df[stocks].tail()
traces = {}
for i in range(0, len(stocks)):
traces['trace_' + str(i)]=go.Scatter(x=df.index,
y=df[stocks[i]].values,
name=stocks[i])
data=list(traces.values())
fig=go.Figure(data)
# shapes update
colors = ["LightSkyBlue", "RoyalBlue", "forestgreen", "lightseagreen"]
ply_shapes = {}
for i in range(1, len(df)):
ply_shapes['shape_' + str(i)]=go.layout.Shape(type="line",
x0=df.index[i-1],
y0=df['keyPoints1'].iloc[i-1],
x1=df.index[i],
y1=df['keyPoints2'].iloc[i-1],
line=dict(
color=np.random.choice(colors,1)[0],
width=30),
opacity=0.5,
layer="below"
)
lst_shapes=list(ply_shapes.values())
fig.update_layout(shapes=lst_shapes)
fig.show()
Also you can use fig.add_{shape}:
fig = go.Figure()
fig.add_trace(
go.Scatter( ...)
for i in range( 1, len( vrect)):
fig.add_vrect(
x0=vrect.start.iloc[ i-1],
x1=vrect.finish.iloc[ i-1],
fillcolor=vrect.color.iloc[ i-1]],
opacity=0.25,
line_width=0)
fig.show()
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')