Introduction to Graph Sensor Data Analysis with Python and Matplotlib

As a technical blogger, I often receive questions from readers about data analysis and visualization. One of the most common challenges is working with sensor data, which can be noisy, irregularly spaced, and difficult to interpret. In this article, we’ll explore how to analyze graph sensor data using Python and matplotlib.

Understanding Sensor Data

Sensor data typically consists of a collection of measurements taken from various sensors over time. Each measurement has two essential components: the sensor name and the timestamp (or time). The sensor name represents the type of sensor that recorded the measurement, while the timestamp provides information about when the measurement was taken.

For example, let’s consider a hypothetical scenario where we have three sensors measuring different aspects of a household:

• Bed sensor
• Kitchen sensor
• Living room sensor

Each sensor might provide measurements at regular intervals, such as every hour or minute. The resulting data would look something like this:

In this example, we have three sensors measuring different aspects of the household at regular intervals. However, our primary goal is to analyze graph sensor data, which means identifying and highlighting changes in sensor readings.

Computing a Mask for Sensor Changes

To achieve this, we need to compute a mask that identifies rows where the sensor name changes (i.e., gets updated). We can use the shift function from pandas DataFrame operations to accomplish this. The shift function shifts all values up or down by one row and returns the result.

Here’s how you can calculate the mask:

mask = df['sensor_name'].ne(df['sensor_name'].shift())

In this line of code, we’re comparing each value in the ‘sensor_name’ column with its shifted counterpart. If they are different (ne operator returns True), it means there’s a change in the sensor name.

Applying the Mask to Get Updated Rows

Now that we have our mask, let’s use it to identify and extract the rows where there is indeed a change in the sensor name.

# Filter out rows with no changes or previous rows
df_updated = df[(mask) | (mask.shift(-1))]

In this line of code, we’re applying a logical OR operation (|) between our mask and its shifted version. This ensures that if there is a change in the sensor name, it’s either because the value changed or the previous row had the same value.

We’ll now use this filtered DataFrame df_updated to plot only the sensor readings where changes occur.

Plotting Sensor Readings with Matplotlib

To visualize our updated rows, we can utilize matplotlib, a popular Python library for creating static, animated, and interactive visualizations in python. We will create a line plot for each sensor type using its corresponding ’time’ column to display the timestamps of interest.

Here’s how you can do it:

import matplotlib.pyplot as plt

# Create a figure with 3 subplots, one per sensor type
fig, axs = plt.subplots(1, len(df_updated['sensor_name'].unique()), figsize=(20,6))

for ax,sensor in zip(axs,df_updated['sensor_name'].unique()):
    # Filter rows by this specific sensor and plot 'time' column
    df_sensor = df_updated[df_updated['sensor_name'] == sensor]
    ax.plot(df_sensor['time'], marker='o')
    ax.set_title(sensor)
    ax.set_xlabel('Time')
    ax.set_ylabel('Value')

# Set labels for the subplots to match our sensors (bed, kitchen, living room)
for i, sensor in enumerate(df_updated['sensor_name'].unique()):
    axs[i].set_label(sensor)

# Add a legend with custom labels
plt.legend(title='Sensor Types', bbox_to_anchor=(1.05, 0.5), loc=2, borderaxespad=0.)

plt.tight_layout()

plt.show()

This code snippet creates three subplots corresponding to each sensor type (bed, kitchen, living room) and plots the ’time’ column for that specific sensor on each subplot.

Conclusion

In this article, we explored how to analyze graph sensor data using Python and matplotlib. We computed a mask by checking if the sensor name changes, applied it to filter out rows with no changes or previous rows, and finally plotted only the updated sensor readings where changes occur.

We hope this in-depth guide has provided valuable insights into working with sensor data.

Last modified on 2025-02-09