How can Python be used for long-term visualization of progress by continuously updating a plot online?

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Python has several libraries such as Plotly, Bokeh, and Matplotlib which can be used for long-term visualization of progress by continuously updating a plot online.

One way to achieve this is to use Plotly, an open-source graphing library for Python. It provides an easy-to-use API and supports various chart types like line, scatter, bar, heatmap, and more. With Plotly, a graph can be easily created and updated in real-time.

Here is an example code snippet that uses Plotly to create a scatter plot and update it with new data points:

import plotly.graph_objs as go
import plotly.offline as pyo
from random import randint
from time import sleep

pyo.init_notebook_mode(connected=True)

# Create initial scatter plot with 10 random data points
data = [go.Scatter(x=[randint(0, 100)], y=[randint(0, 100)], mode='markers')]
layout = go.Layout(title='Real-time Plot')
figure = go.Figure(data=data, layout=layout)
pyo.iplot(figure)

# Update scatter plot with new data points every 5 seconds
while True:
    new_data = go.Scatter(x=[randint(0, 100)], y=[randint(0, 100)], mode='markers')
    figure.add_trace(new_data)
    pyo.iplot(figure)
    sleep(5)

In this example, we create an initial scatter plot with 10 random data points and then continuously update it every 5 seconds with a new data point generated randomly using the randint function from the random module.

The pyo.iplot function displays the scatter plot online and can be accessed through a web browser, making it possible to visualize progress in real-time. With this, it is possible to monitor the progress of a process or system and detect changes and anomalies as they occur.