5 Best Ways to Place a Plot on Tkinter Main Window in Python

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πŸ’‘ Problem Formulation: When working with Python’s Tkinter library for building GUI applications, you might come across the need to display a graphical plot within the main window. Whether you’re visualizing data in a desktop app or just showcasing results, knowing how to embed plots directly into a Tkinter interface can be invaluable. This article will explain and demonstrate five popular methods to display plots within the Tkinter window, starting with a basic Tkinter setup and ending with the produced plot visualization.

Method 1: Using the matplotlib’s FigureCanvasTkAgg

This method involves using the matplotlib library’s backend_agg FigureCanvasTkAgg which makes it easy to draw plots on the Tkinter canvas. It acts as a bridge between matplotlib’s plot rendering and Tkinter’s Canvas widget. This is one of the most common ways to display plots in a Tkinter GUI application.

Here’s an example:

import tkinter as tk
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg

root = tk.Tk()

fig = Figure(figsize=(5, 4), dpi=100)
t = [0, 1, 2, 3, 4]
s = [1, 3, 2, 3, 5]

fig.add_subplot(111).plot(t, s)
canvas = FigureCanvasTkAgg(fig, master=root)  
canvas.draw()
canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=1)

root.mainloop()

Output: A Tkinter window with a matplotlib plot.

This code snippet creates a Tkinter window and attaches a matplotlib figure to it using the FigureCanvasTkAgg class. It packs the canvas containing the plot into the Tkinter window, which upon running will display the window with the graph plotted from the list t as the x-values and s as the y-values.

Method 2: Using the Canvas widget and Figure class

Instead of using the backend-specific class FigureCanvasTkAgg, you can use the general matplotlib Figure class along with the Tkinter Canvas widget to embed a plot. This allows you to have more control over how the plot is displayed within the Tkinter interface.

Here’s an example:

import tkinter as tk
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg

root = tk.Tk()

fig = Figure()
ax = fig.add_subplot(111)

x = [0, 1, 2, 3, 4]
y = [10, 20, 10, 20, 30]
ax.plot(x, y)

canvas = FigureCanvasTkAgg(fig, root)
canvas_widget = canvas.get_tk_widget()
canvas_widget.pack()

root.mainloop()

Output: A Tkinter window displaying a simple line plot.

This code creates a new instance of the Figure class and plots a simple line graph within it. The figure is then passed to FigureCanvasTkAgg to create a canvas widget that can be packed into the Tkinter main window. This results in the plot being displayed when the code is run.

Method 3: Embedding plots via the toolbar

For more interactive plots that offer tools like zooming and panning, you can embed the navigation toolbar from matplotlib into your Tkinter application. This not only displays the plot but also allows the user to interact with it using the toolbar’s features.

Here’s an example:

import tkinter as tk
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk

root = tk.Tk()

fig = Figure(figsize=(5, 4), dpi=100)
ax = fig.add_subplot(111)

ax.plot([1, 2, 3], [1, 2, 3])

canvas = FigureCanvasTkAgg(fig, root)
canvas.draw()

toolbar = NavigationToolbar2Tk(canvas, root)
toolbar.update()

canvas.get_tk_widget().pack()

root.mainloop()

Output: A Tkinter window with an interactive matplotlib plot and a navigation toolbar.

This code integrates a navigation toolbar into the GUI which is useful for providing plot interaction options such as zoom and pan. The toolbar and canvas widget are packed into the main Tkinter window, offering an enhanced plotting experience to users.

Method 4: Updating plots in real-time

To visualize dynamic data that changes over time, you might want to update the plot displayed in your Tkinter window. This can be done by periodically updating the data for the plot and redrawing it.

Here’s an example:

import tkinter as tk
import numpy as np
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg

root = tk.Tk()
fig = Figure()
ax = fig.add_subplot(111)
canvas = FigureCanvasTkAgg(fig, root)
canvas_widget = canvas.get_tk_widget()
canvas_widget.pack()

def update_plot():
    ax.clear()
    x = np.random.rand(10)
    y = np.random.rand(10)
    ax.scatter(x, y)
    canvas.draw_idle()
    root.after(1000, update_plot)  # schedule the next update in 1 sec.

root.after(1000, update_plot)
root.mainloop()

Output: A Tkinter window with a plot that updates every second.

This snippet demonstrates how to update a plot on the fly inside a Tkinter window. A scatter plot is dynamically updated every second to reflect new randomly generated data points. This is achieved with the after method of the Tkinter root window, which schedules the update_plot function to run periodically.

Bonus One-Liner Method 5: Using Pyplot’s show()

While not directly placing a plot on the Tkinter main window, for quick and dirty visualizations, one can rely on matplotlib’s pyplot show() method. This is more of a quick visualization hack where the plot opens in a separate window, not embedded within the Tkinter application.

Here’s an example:

import tkinter as tk
import matplotlib.pyplot as plt

root = tk.Tk()

# Assume some event or callback triggers the plotting:
def plot():
    x = [1, 2, 3, 4]
    y = [10, 20, 25, 30]
    plt.figure()
    plt.plot(x, y)
    plt.show()

plot_button = tk.Button(root, text="Plot", command=plot)
plot_button.pack()

root.mainloop()

Output: A separate matplotlib window with a plot, triggered from a Tkinter interface.

This simple code example shows how a press of a button in the Tkinter main window can trigger the opening of a separate matplotlib window with the plotted graph. Though the graph is not actually on the Tkinter window, this is a quick way to visualize data without embedding the plot into the GUI framework.

Summary/Discussion

  • Method 1: Using FigureCanvasTkAgg. Provides a seamless integration of matplotlib plots into Tkinter. Strengths include ease of implementation and a stable, tested solution. Weaknesses involve additional overhead for setting up matplotlib.
  • Method 2: Using Canvas widget and Figure class. Gives more manual control and potentially finer customization of the plot’s presentation in the GUI. Strengths are flexibility and control. The downside is a slightly more complex setup process.
  • Method 3: Embedding the toolbar for interactive plots. Strengths include enhanced interactivity and user engagement with the plot. Weaknesses might be the additional complexity and increased learning curve for users unfamiliar with the toolbar.
  • Method 4: Updating plots in real-time. Ideal for applications requiring dynamic data visualization. Strengths cover the capacity to represent changing data effectively. A weakness is the added complexity of managing periodic updates and performance considerations.
  • Bonus Method 5: Quick visualization with pyplot’s show(). It’s simple and quick for displaying plots. A major strength is immediate gratification with limited code. The downside is that it does not embed the plot within the Tkinter window and lacks professionalism in a final product.