Time Scale Heatmap Pattern using Python

 

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

days = pd.date_range("2025-05-18", periods=7, freq="D")

hours = np.arange(24)

data = np.random.rand(len(days), len(hours))

df = pd.DataFrame(data, index=days.strftime('%a %d'), columns=hours)

plt.figure(figsize=(8, 6))

plt.imshow(df, aspect='auto', cmap='YlGnBu')

plt.xticks(ticks=np.arange(len(hours)), labels=hours)

plt.yticks(ticks=np.arange(len(days)), labels=df.index)

plt.xlabel("Hour of Day")

plt.ylabel("Date")

plt.title("Timescale Heatmap (Hourly Activity Over a Week)")

plt.colorbar(label='Intensity')

plt.tight_layout()

plt.show()

#source code --> clcoding.com

Code Explanation:

1. Import Required Libraries

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

numpy: For creating numerical data (e.g., rand, arange).

pandas: Used for handling time-series and tabular data (DataFrame, date_range).

matplotlib.pyplot: For creating the actual heatmap plot.

 

2. Define Time Axes: Days & Hours

days = pd.date_range("2025-05-01", periods=7, freq="D")

hours = np.arange(24)

pd.date_range(...): Creates 7 sequential days starting from May 1, 2025.

 

np.arange(24): Creates an array [0, 1, ..., 23] representing each hour in a day.

These will become the y-axis (days) and x-axis (hours) of the heatmap.

 

3. Simulate Random Data

data = np.random.rand(len(days), len(hours))

Generates a 7×24 matrix of random numbers between 0 and 1.

Each value represents some measurement (e.g., activity level, temperature) at a specific hour on a specific day.

 

4. Create a Pandas DataFrame

df = pd.DataFrame(data, index=days.strftime('%a %d'), columns=hours)

Converts the NumPy array into a labeled table.

index=days.strftime('%a %d'): Formats each date like 'Thu 01', 'Fri 02' etc.

columns=hours: Sets hours (0–23) as column headers.

This structure is perfect for feeding into a heatmap.

 

5. Create the Heatmap

plt.figure(figsize=(12, 6))

plt.imshow(df, aspect='auto', cmap='YlGnBu')

plt.figure(...): Sets the size of the figure (12x6 inches).

plt.imshow(...): Plots the DataFrame as a 2D image (the heatmap).

aspect='auto': Automatically scales the plot height.

cmap='YlGnBu': Applies a yellow-green-blue colormap.

Each cell's color intensity represents the magnitude of the data value.

 

6. Add Axes Labels and Ticks

plt.xticks(ticks=np.arange(len(hours)), labels=hours)

plt.yticks(ticks=np.arange(len(days)), labels=df.index)

plt.xlabel("Hour of Day")

plt.ylabel("Date")

plt.title("Timescale Heatmap (Hourly Activity Over a Week)")

xticks/yticks: Places numeric hour labels on the x-axis and day labels on the y-axis.

xlabel, ylabel, title: Adds descriptive text to explain what the axes and plot represent.

 

7. Add Colorbar

plt.colorbar(label='Intensity')

Adds a color legend (colorbar) on the side.

Helps interpret what the color shading corresponds to (e.g., higher activity = darker color).

 

8. Final Touches and Display

plt.tight_layout()

plt.show()

tight_layout(): Adjusts spacing to prevent overlaps.

 show(): Displays the plot window.