Overcoming the Limitation of Plotly When Working with Multiple Data Frames

Understanding the Issue with Plotly and Multiple Data Frames

In this article, we will delve into a common issue encountered when working with multiple data frames using the popular Python library, Plotly. The problem arises when trying to plot all the data frames in one graph, but instead of displaying all the plots, only two are shown. We’ll explore the reasons behind this behavior and provide solutions to overcome it.

Introduction to Data Frames and Plotly

Before we dive into the issue, let’s briefly discuss what data frames are and how they relate to plotting with Plotly.

Data frames are a fundamental concept in pandas, which is a powerful library for data manipulation and analysis in Python. A data frame is essentially a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet. Each column represents a variable, while each row represents an observation.

Plotly is a popular data visualization library that allows us to create interactive plots, charts, and graphs. It’s particularly useful for creating complex visualizations with multiple layers.

The Problem: Only Two Data Frames Are Shown

The issue we’re facing can be summarized as follows:

We have four data frames, each representing a different dataset.
Each data frame has only one column and is indexed by Datetime.
We want to plot all four data frames in one graph using Plotly.

However, instead of displaying all four plots, only two are shown. This can be frustrating when trying to visualize multiple datasets.

Possible Causes

After exploring the code provided in the Stack Overflow question, we found that there’s a subtle issue with how the data frames are being plotted.

One possible cause is due to the way Plotly handles duplicate values or very similar values across different data frames. In this case, it seems that Plotly is only plotting two of the data frames because the values in those data frames are almost identical, with differences less than 0.01.

Solution: Using Different Colors for Each Data Frame

To overcome this issue, we can try using different colors for each data frame to make them more distinguishable from one another. This approach will help Plotly to correctly identify and plot all four data frames.

Here’s the modified code:

plt.figure(figsize=(16,8))
plt.title("Monthly Load Prediction" ,fontsize=25)
plt.plot(predictedbyXGB, label='Proposed Model', color='blue')
plt.plot(test_target, label='Actual Data', color='red')
plt.plot(predictedbynb, label='Naive Bayes', color='green')
plt.plot(predictedbylinear, label='Linear Regression', color='orange')
plt.grid(True)
plt.legend(loc='best',fontsize=18)
plt.show()

By assigning different colors to each data frame, we can help Plotly to differentiate between them and display all four plots correctly.

Solution: Using Different Line Styles for Each Data Frame

Another approach is to use different line styles for each data frame. This will not only make the plots more distinguishable but also provide additional information about the type of regression or model being used.

Here’s an example:

plt.figure(figsize=(16,8))
plt.title("Monthly Load Prediction" ,fontsize=25)
plt.plot(predictedbyXGB, label='Proposed Model', linestyle='--', color='blue')
plt.plot(test_target, label='Actual Data', linestyle='-', color='red')
plt.plot(predictedbynb, label='Naive Bayes', linestyle=':', color='green')
plt.plot(predictedbylinear, label='Linear Regression', linestyle='-.', color='orange')
plt.grid(True)
plt.legend(loc='best',fontsize=18)
plt.show()

In this example, we’ve added different line styles (dashed, solid, dotted) to each data frame. This will not only make the plots more distinguishable but also provide additional visual cues about the type of regression or model being used.

Solution: Using Separate Subplots for Each Data Frame

If using a single graph is not feasible, we can consider using separate subplots for each data frame. This approach allows us to create multiple graphs with different settings and styles without cluttering the original plot.

Here’s an example:

import matplotlib.pyplot as plt

fig, axs = plt.subplots(2, 2)

axs[0, 0].plot(predictedbyXGB, label='Proposed Model')
axs[0, 0].set_title('Proposed Model')

axs[0, 1].plot(test_target, label='Actual Data')
axs[0, 1].set_title('Actual Data')

axs[1, 0].plot(predictedbynb, label='Naive Bayes')
axs[1, 0].set_title('Naive Bayes')

axs[1, 1].plot(predictedbylinear, label='Linear Regression')
axs[1, 1].set_title('Linear Regression')

plt.tight_layout()
plt.show()

In this example, we’ve used the subplots function to create a 2x2 grid of separate subplots for each data frame. We can then customize each subplot independently without affecting the others.

Conclusion

Plotly is a powerful library for creating interactive visualizations, but it can be challenging when dealing with multiple data frames. By understanding the reasons behind this behavior and using different approaches to overcome it, we can create complex visualizations that showcase all the relevant information from our datasets.

Last modified on 2024-07-13