Understanding the scatter_matrix Function in pandas

Introduction to Scatter Matrices

In data analysis, a scatter plot is a graphical representation of the relationship between two variables. A scatter matrix, on the other hand, is an extension of this concept that displays multiple pairs of variables simultaneously. In this article, we’ll delve into how to extract the 2D array from the scatter matrix in pandas and understand which AxesSubplot corresponds to which matrix.

The pandas.scatter_matrix Function

The scatter_matrix function in pandas is a convenience function that creates a scatter plot matrix for multiple variables. It’s part of the pandas.plotting submodule. This function allows you to visualize the relationships between multiple pairs of variables simultaneously, making it easier to identify patterns or correlations.

Creating a Scatter Matrix with pandas

To create a scatter matrix using pandas, you can use the following code:

import numpy as np
import pandas as pd
from pandas.plotting import scatter_matrix

# Create a random DataFrame with multiple variables
df = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])

# Create a scatter matrix
axs = scatter_matrix(df, alpha=0.2, figsize=(6,6), diagonal='kde')

# Display the figure
axs[0,0].get_figure().show()

In this example, we create a random DataFrame df with four variables (a, b, c, and d). We then create a scatter matrix using the scatter_matrix function, specifying the alpha transparency level, figure size, and diagonal option.

Understanding the AxesSubplot Objects

When creating a scatter matrix, pandas returns an AxesSubplot object for each subplot. These objects contain information about the plot, including the data, labels, and limits.

Identifying the Order of Subplots

To understand which AxesSubplot corresponds to which matrix, you can examine the label on the bottom and left axes. The labels indicate which columns of the input DataFrame are plotted against one another in each row and column. For example, if the first column is labeled ‘a’ on the x-axis, it means that df.a is used as the x-data for the first subplot.

Accessing Data from AxesSubplot Objects

One way to access data from individual AxesSubplot objects is to use the respective columns of the input DataFrame directly. For example:

import matplotlib.pyplot as plt

# ... (rest of the code remains the same)

for i in range(axs.shape[0]):
    for j in range(axs.shape[1]):
        if i == j:
            continue
        axs[i,j].set_title('x: {}, y: {}'.format(df.columns[j],df.columns[i]),
                           position=(0.5,0.5))

This code adds a title to each subplot that indicates the column names used for x and y-data.

Extracting Data from Diagonal AxesSubplot Objects

When creating a scatter matrix with a diagonal option (e.g., diagonal='kde'), you don’t have direct access to the underlying data. However, you can extract the lines from the diagonal AxesSubplot objects using the get_lines() method:

import matplotlib.pyplot as plt

index = 0
xdat,ydat = axs[index,index].get_lines()[0].get_data() # example for diagonal [0,0]

plt.figure()
plt.plot(xdat,ydat,'-')
plt.xlabel(df.columns[index])
plt.ylabel('density')

This code extracts the x and y data from the first diagonal AxesSubplot object (axs[0,0]) and plots them as a line using matplotlib.

Conclusion

In this article, we explored how to extract the 2D array from a scatter matrix in pandas and understand which AxesSubplot corresponds to which matrix. We also discussed the use of the diagonal option when creating a scatter matrix and how to access data from diagonal AxesSubplot objects. By following these tips and techniques, you can create informative and insightful scatter plots for your data analysis tasks.

Additional Tips

• Use the alpha transparency level to customize the plot appearance.
• Experiment with different diagonal options (e.g., 'kde', 'hist') to visualize different aspects of your data.
• Consider using other visualization tools, such as matplotlib’s subplots() function, for more customized plots.

Example Code

Here’s an updated example code that includes additional features and customization:

import numpy as np
import pandas as pd
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt

# Create a random DataFrame with multiple variables
df = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])

# Create a scatter matrix with customization options
axs = scatter_matrix(df, alpha=0.2, figsize=(8,6), diagonal='kde')

# Display the figure
for i in range(axs.shape[0]):
    for j in range(axs.shape[1]):
        if i == j:
            continue
        axs[i,j].set_title('x: {}, y: {}'.format(df.columns[j],df.columns[i]),
                           position=(0.5,0.5))

# Extract data from diagonal AxesSubplot objects
index = 0
xdat,ydat = axs[index,index].get_lines()[0].get_data() # example for diagonal [0,0]

plt.figure()
plt.plot(xdat,ydat,'-')
plt.xlabel(df.columns[index])
plt.ylabel('density')

plt.show()

This code creates a scatter matrix with customization options and extracts data from the first diagonal AxesSubplot object using matplotlib.

Last modified on 2023-12-09