5 Best Ways to Dynamically Update a Plot in a Loop in IPython Notebook

💡 Problem Formulation: Visualizing data dynamically in an IPython Notebook, often used within the Jupyter Notebook environment, is a common requirement. You may want to monitor the progress of a long-running process or interact with your data in real time. This article shows how you can update plots within a loop, using different methods, to reflect changing data, without the need to manually regenerate the entire plot. Imagine looping over a dataset to compute a mathematical function and wanting to see a graph that updates with each iteration.

Method 1: Using IPython.display with clear_output

The IPython.display module contains the clear_output function which can be used to clear the output of a cell. By using it with the display function, we can create and update plots inside a loop by first clearing the previous plot and then displaying the new plot in each iteration.

Here’s an example:

import matplotlib.pyplot as plt
from IPython.display import clear_output
import time

for i in range(10):
    plt.plot([i], [i**2], 'bo')
    plt.xlim(0, 10)
    plt.ylim(0, 100)
    clear_output(wait=True)
    display(plt.gcf())
    time.sleep(1)
plt.close()

Output: A dynamic plot in the IPython Notebook that updates every second with a new point on a graph.

This code snippet executes a for loop that plots a single point (x=i, y=i^2) and updates the figure in each iteration. The clear_output(wait=True) function ensures that the output is cleared only just before the new plot is ready to display to avoid flickering.

Method 2: Using FuncAnimation from matplotlib.animation

The FuncAnimation function is a tool provided by Matplotlib’s animation module. It’s designed to create animations by repeatedly calling a function to generate a sequence of plots at scheduled intervals.

Here’s an example:

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

fig, ax = plt.subplots()
x, y = [], []

def animate(i):
    x.append(i)
    y.append(i**2)
    ax.clear()
    ax.plot(x, y)

ani = FuncAnimation(fig, animate, frames=10, interval=1000)
plt.show()

Output: An animated plot that shows data points being added every second for 10 seconds.

This snippet makes use of the FuncAnimation function to animate the plotting process. The animate function defines how the plot should update in each frame of the animation; by adding a new point to the x and y lists and then clearing and re-plotting the axis.

Method 3: Using Plotly for Interactive and Real-Time Updates

Plotly is an interactive plotting library that supports real-time data visualization. It can be used to update a plot in real-time within an IPython Notebook, letting users engage with the plot through zooming and panning, even as it updates.

Here’s an example:

import plotly.graph_objs as go
from IPython.display import display
from plotly.subplots import make_subplots
import time

fig = make_subplots(1, 1)
graph = go.Scatter(x=[], y=[])
fig.add_trace(graph)
fig.show()

for i in range(10):
    fig.data[0].x += (i,)
    fig.data[0].y += (i**2,)
    time.sleep(1)

Output: An interactive and continuously updating Plotly scatter plot in the cell output.

This example uses Plotly’s make_subplots to create a real-time updating scatter plot. The graph’s data is modified in each iteration to include a new point, and Plotly takes care of the rest, rendering the updated chart live.

Method 4: Using Interactive Widgets with ipywidgets

ipywidgets provide interactive HTML widgets for Jupyter notebooks. By coupling these widgets with a plotting library like Matplotlib, it is possible to dynamically update plots in reaction to widget interactions.

Here’s an example:

import matplotlib.pyplot as plt
import ipywidgets as widgets
from IPython.display import display

def update_plot(change):
    ax.clear()
    ax.plot(range(change.new), [x**2 for x in range(change.new)])
    display(fig)

slider = widgets.IntSlider(value=1, min=1, max=10)
slider.observe(update_plot, 'value')

fig, ax = plt.subplots()
display(slider)

Output: A Matplotlib plot that updates dynamically in response to changing the value of an interactive slider widget.

This code defines a callback function update_plot that updates and displays the plot according to the slider’s current value. The observe method of the slider widget is used to call the update_plot function whenever the slider’s value changes.

Bonus One-Liner Method 5: Using List Comprehension Inside the Plot Call

For very simple updates, sometimes a one-liner using list comprehension inside the plot call is sufficient.

Here’s an example:

import matplotlib.pyplot as plt
from IPython.display import display, clear_output

[plt.plot([i], [i**2], 'bo') or display(plt.gcf()) or clear_output(wait=True) for i in range(10)]
plt.close()

Output: A dynamically updated plot appearing as a series of individual frames shown one after the other.

The list comprehension runs through the numbers from 0 to 9, updates the plot, displays the current figure, and clears the output for the next iteration in one line. This approach is quick and dirty but not recommended for complex plotting updates as it lacks clarity and control.

Summary/Discussion

Method 1: IPython.display with clear_output. Strengths: Simple and straightforward. Weaknesses: Can cause flickering and might not handle more complex interactive elements well.

Method 2: FuncAnimation from matplotlib.animation. Strengths: Integrates seamlessly with Matplotlib, creates smooth animations. Weaknesses: Overkill for simple updates, can be complicated for beginners.

Method 3: Using Plotly. Strengths: Highly interactive and polished output. Weaknesses: Requires familiarity with a different plotting library and is heavier on browser resources.

Method 4: Interactive Widgets with ipywidgets. Strengths: Highly customizable and interactive. Weaknesses: Requires additional code to set up widgets and callbacks.

Method 5: One-Liner with List Comprehension. Strengths: Concise for simple cases. Weaknesses: Not very readable or maintainable, not suitable for complex updates.