Creating a Multi-Plot with Overlapping Subplots using Python and Matplotlib

In this tutorial, we’ll explore how to create a multi-plot with overlapping subplots in Python using the popular Matplotlib library. Specifically, we’ll use two dataframes (df1 and df2) to create multiple subplots for each ticker symbol, while also overlaying plots from both dataframes on top of one another.

Introduction

When working with multiple data sources or time series data, it’s often necessary to visualize the relationships between different datasets. One common approach is to use overlapping subplots to display multiple variables or estimates in a single plot. In this tutorial, we’ll demonstrate how to achieve this using Python and Matplotlib.

Required Libraries and Importing

To get started, ensure you have the following libraries installed:

*   matplotlib: For creating plots and subplots.
*   pandas: For data manipulation and analysis (although not strictly necessary in this example).

You can install these libraries using pip:

pip install matplotlib pandas

Now, let’s import the required libraries into your Python script:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Create sample dataframes for demonstration purposes
df1 = pd.DataFrame({
    "date": ["1/1", "1/2"],
    "ticker": ["A", "B"],
    "y_true": [10, 8]
})

df2 = pd.DataFrame({
    "date": ["8/1", "8/2"],
    "ticker": ["A", "B"],
    "y_predict": [10, 8],
    "y_predict_upper": [13, 9],
    "y_predict_lower": [7, 1]
})

# Define the ticker list for plotting
ticker_list = ['A', 'B']

Creating the Multi-Plot with Overlapping Subplots

To create a multi-plot with overlapping subplots, we’ll use subplots to generate multiple axes. We’ll then loop through each ticker symbol and filter the dataframes accordingly.

Here’s how you can do it:

## Create a figure with multiple subplots
fig, axs = plt.subplots(nrows=2, ncols=len(ticker_list), figsize=(15 * len(ticker_list), 8), dpi=150)

# Loop through tickers and axes
for i, ticker in enumerate(ticker_list):
    ax = axs[i]  

    # Filter df1 for the current ticker
    df1_filtered = df1[df1["ticker"] == ticker]
    
    # Plot y_true on the current axis
    df1_filtered['y_true'].plot(ax=ax, color='blue')  
    
    # Get the dates from df2 where the ticker symbol matches
    dates_in_sample = df2[df2['ticker'] == ticker]['date']
    
    # Filter df2 for the current ticker
    df2_filtered = df2[(df2['ticker'] == ticker) & (df2['date'].isin(dates_in_sample))]
    
    # Plot y_predict, y_predict_upper and y_predict_lower on the current axis
    df2_filtered[['y_predict', 'y_predict_upper', 'y_predict_lower']].plot(ax=ax, marker='o', linestyle='--')
    
    # Chart formatting
    ax.set_title(ticker.upper())
    ax.get_legend().remove()
    ax.set_xlabel("")

Finalizing the Plot and Displaying

To display the plot:

plt.tight_layout()
plt.show()

By using overlapping subplots, you can effectively visualize multiple variables or estimates in a single plot. This technique is especially useful when working with time series data or other datasets where there are multiple related metrics that need to be displayed.

Conclusion

In this tutorial, we created a multi-plot with overlapping subplots using Python and Matplotlib. By looping through each ticker symbol and filtering the corresponding dataframes, we were able to overlay plots from two different data sources on top of one another. The resulting plot provides valuable insights into the relationships between different variables or estimates in a single visual representation.

Feel free to experiment with different configurations and data sources to enhance your understanding of this technique.

Additional Tips

To further improve your plotting skills, explore other Matplotlib features, such as customizing colors, line styles, and labels. Additionally, consider using other visualization libraries like Seaborn or Plotly for more advanced plots.

Last modified on 2023-08-22