Step 1: Prepare Time Series Dataset

Generate a sample time-stamped measurement dataset for visualization:

import pandas as pd
time_series_dataset = pd.DataFrame({
    "timestamp": pd.date_range(start="2023-01-01", periods=10),
    "sensor_value": [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
})

Step 2: Import Required Libraries

Import Matplotlib and FuncAnimation to create dynamic visualizations:

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

Step 3: Buildd Dynamic Line Chart Animation

Create a animated line plot that updates with each frame to display growing data:

# Initialize plot figure and axis
plot_figure, plot_axis = plt.subplots()
x_points, y_points = [], []
plot_line, = plot_axis.plot([], [], linewidth=2)

# Set initial plot bounds
def init_frame():
    plot_axis.set_xlim(0, 10)
    plot_axis.set_ylim(0, 110)
    return plot_line,

# Update plot data for each animation frame
def update_frame(frame_index):
    x_points.append(frame_index)
    y_points.append(time_series_dataset["sensor_value"][frame_index])
    plot_line.set_data(x_points, y_points)
    return plot_line,

# Create animation instance
live_plot = FuncAnimation(
    plot_figure,
    update_frame,
    frames=range(10),
    init_func=init_frame,
    blit=True
)

Step 4: Display the Animation

Run this comand to launch the interactive plot window showing the dynamic visualization:

plt.show()