Plotting data using tkinter-based libraries (matplotlib, seaborn)

Python offers a wide range of libraries for data visualization, and two popular choices are matplotlib and seaborn. These libraries allow you to create interactive and visually appealing plots directly within tkinter-based GUI applications. In this article, we will explore how to use these libraries to plot data in a tkinter GUI.

matplotlib

Matplotlib is a powerful library for creating static, animated, and interactive visualizations in Python. It integrates seamlessly with tkinter, making it an excellent choice for plotting data in GUI applications.

To begin plotting with matplotlib in tkinter, you need to import the FigureCanvasTkAgg and NavigationToolbar2Tk classes from the matplotlib.backends.backend_tkagg module. These classes provide the necessary components to embed a matplotlib plot within a tkinter window.

import tkinter as tk
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
import matplotlib.pyplot as plt

# Create tkinter window
window = tk.Tk()
window.title("Matplotlib in tkinter")

# Create matplotlib figure
fig = plt.Figure(figsize=(6, 5), dpi=100)
ax = fig.add_subplot(111)

# Plot some data
x = [1, 2, 3, 4, 5]
y = [10, 5, 7, 4, 8]
ax.plot(x, y)

# Embed matplotlib plot in tkinter window
canvas = FigureCanvasTkAgg(fig, master=window)
canvas.draw()
canvas.get_tk_widget().pack()

# Add matplotlib toolbar to tkinter window
toolbar = NavigationToolbar2Tk(canvas, window)
toolbar.update()
canvas.get_tk_widget().pack()

# Run the tkinter event loop
tk.mainloop()

In the above example, we create a tkinter window and a matplotlib figure. We plot some data on the figure and then embed the plot in the tkinter window using FigureCanvasTkAgg. Additionally, we add a toolbar for navigation and interactiveness to the tkinter window using NavigationToolbar2Tk. Finally, we run the tkinter event loop to display the window and plot.

seaborn

Seaborn is a high-level data visualization library that is built on top of matplotlib. It provides a more visually appealing and concise API for creating statistical graphics. Seaborn integrates seamlessly with matplotlib and tkinter, allowing you to plot data with ease.

To use seaborn in a tkinter-based GUI, you need to import the necessary modules and functions, and then apply seaborn styles to your matplotlib plots.

import tkinter as tk
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
import matplotlib.pyplot as plt
import seaborn as sns

# Set seaborn style
sns.set()

# Create tkinter window
window = tk.Tk()
window.title("Seaborn in tkinter")

# Create matplotlib figure
fig = plt.Figure(figsize=(6, 5), dpi=100)
ax = fig.add_subplot(111)

# Plot some data
x = [1, 2, 3, 4, 5]
y = [10, 5, 7, 4, 8]
ax.plot(x, y)

# Embed matplotlib plot in tkinter window
canvas = FigureCanvasTkAgg(fig, master=window)
canvas.draw()
canvas.get_tk_widget().pack()

# Add matplotlib toolbar to tkinter window
toolbar = NavigationToolbar2Tk(canvas, window)
toolbar.update()
canvas.get_tk_widget().pack()

# Run the tkinter event loop
tk.mainloop()

In the above example, we add the line sns.set() to set the seaborn style for our plots. This line changes the default matplotlib aesthetics to those of seaborn. The rest of the code is similar to the previous example, embedding the plot in a tkinter window and adding a toolbar.

Both matplotlib and seaborn offer a wide range of customization options for plots like changing colors, labels, annotations, and more. You can explore the extensive documentation for these libraries to learn more about these customization options.

In conclusion, plotting data using tkinter-based libraries like matplotlib and seaborn allows you to create interactive and visually appealing plots within tkinter GUI applications. By leveraging the power of these libraries, you can enhance the user experience and provide valuable insights through data visualization within your tkinter-based applications.