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 add a secondary Y axis to a Plotly Express bar plot?

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()

How to Insert numerical information at plotly chart legend

I'm trying to added some data at my chart legends but i don't know how. I did search at plotly docs at https://plotly.com/python/legend/ but none of those examples available bring this feature. In the figure below is showed what i want to do. As you can see there is a legend of my chart and i want insert the data corresponded to the name of legend, i.g: UCL - 100, ICL - 50 and so on.
Here is what i have:
Here is a real example of what i really aim to:
A piece of the code i'm using is below, I can't share the rest:
fig.add_trace(go.Scatter(
x=df_mean_control_chart['Samples'],
y=df_mean_control_chart['UCL'],
mode='lines',
name='UCL',
line=dict(color='black', width=2)))
Description of the variables:
df_mean_control_chart['Samples'] and df_mean_control_chart['UCL'] = it's a column of a data from a dataframe which only contains numerical data.

You can add numerical values to the legend by using f-string to add the numerical value you wish to add to the legend.
import plotly.express as px
import plotly.graph_objects as go
df = px.data.stocks()
goog_max = df['GOOG'].max()
goog_mean = df['GOOG'].mean()
goog_min = df['GOOG'].min()
fig = go.Figure()
fig.add_trace(go.Scatter(x=df.index, y=df['GOOG'], name='GOOG'))
fig.add_trace(go.Scatter(mode='lines',
x=df.index,
y=[goog_mean]*len(df),
name=f'GOOG {round(goog_mean,2)}'))
fig.add_trace(go.Scatter(mode='lines',
x=df.index,
y=[goog_max]*len(df),
name=f'GOOG {round(goog_max,2)}'))
fig.add_trace(go.Scatter(mode='lines',
x=df.index,
y=[goog_min]*len(df),
name=f'GOOG {round(goog_min,2)}'))
fig.show()

Huge proglem to load Plotly plots in Jupyter Notebook Python

I have a huge problem with Jupyter Notebook.
When he builds graphs using the plot packet, to display the next graph I have to reload the dataset, otherwise an error pops up: TypeError: list indices must be integers or slices, not str. When I reopen the project, all the graphs created using Plotly are not visible, just a white background and I have to reload them if I want to see them, and everything after reloading the dataset, that is:
1) I open the project in Jupyte Notebook, all graphs created by Plotly are not visible (others from Seaborn for example are visible.
2) I load the Plotly charts, but I can load them one by one, because first I have to load the dataset, then the chart, and so an error occurs to all Plotly charts differently: TypeError: list indices must be integers or slices
what can I do about it how to fix it? it seriously hinders the work
this is my libraries:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import plotly.offline as py
import plotly.graph_objs as go
import plotly.tools as tls
This is an example of this error, but if I load the dataset again and again load this plot everything will be ok:
and the example of the code of plot:
#Distribution of credit risk in dataset (target variable)
#Size of the plot
figsize=(10,5)
#Sums of good and bad credits in the dataset
goodCount = data[data["Risk"]== 'good']["Risk"].value_counts().values
badCount = data[data["Risk"]== 'bad']["Risk"].value_counts().values
#Bar fo good credit
trace0 = go.Bar(x = data[data["Risk"]== 'good']["Risk"].value_counts().index.values,
y = data[data["Risk"]== 'good']["Risk"].value_counts().values,
name='Good credit',
text= goodCount,
textposition="auto",
marker = dict(color = "green", line=dict(color="black", width=1),),opacity=1)
#Bar of bad credit
trace1 = go.Bar(x = data[data["Risk"]== 'bad']["Risk"].value_counts().index.values,
y = data[data["Risk"]== 'bad']["Risk"].value_counts().values,
name='Bad credit',
text= badCount,
textposition="auto",
marker = dict(color = "red", line=dict(color="black", width=1),),opacity=1)
#Creation of bar plot
data = [trace0, trace1]
layout = go.Layout()
layout = go.Layout(yaxis=dict(title='Count'),
xaxis=dict(title='Risk variable'),
title='Distribution of target variable in the dataset')
fig = go.Figure(data=data, layout=layout)
fig.show()

Plotly: How to make line charts colored by a variable using plotly.graph_objects?

I'm making a line chart below. I want to make the lines colored by a variable Continent. I know it can be done easily using plotly.express
Does anyone know how I can do that with plotly.graph_objects? I tried to add color=gapminder['Continent'], but it did not work.
Thanks a lot for help in advance.
import plotly.express as px
gapminder = px.data.gapminder()
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=gapminder['year'], y=gapminder['lifeExp'],
mode='lines+markers'))
fig.show()

Using an approach like color=gapminder['Continent'] normally applies to scatterplots where you define categories to existing points using a third variable. You're trying to make a line plot here. This means that not only will you have a color per continent, but also a line per continent. If that is in fact what you're aiming to do, here's one approach:
Plot:
Code:
import plotly.graph_objects as go
import plotly.express as px
# get data
df_gapminder = px.data.gapminder()
# manage data
df_gapminder_continent = df_gapminder.groupby(['continent', 'year']).mean().reset_index()
df = df_gapminder_continent.pivot(index='year', columns='continent', values = 'lifeExp')
df.tail()
# plotly setup and traces
fig = go.Figure()
for col in df.columns:
fig.add_trace(go.Scatter(x=df.index, y=df[col].values,
name = col,
mode = 'lines'))
# format and show figure
fig.update_layout(height=800, width=1000)
fig.show()