Creating 3D Scatter Plots with Matplotlib in Python: Best Practices and Tips

Introduction to 3D Scatter Plots with Matplotlib in Python

In this article, we’ll explore how to create a 3D scatter plot using the popular matplotlib library in Python. We’ll also address some common issues that may arise when working with arrays and strings in matplotlib.

Background on Matplotlib and Arrays

Matplotlib is a widely-used plotting library for Python that provides an extensive set of tools for creating high-quality 2D and 3D plots. One of its key features is the ability to handle various data types, including numpy arrays and pandas DataFrames.

In this article, we’ll focus on creating a 3D scatter plot using matplotlib from a pandas array containing three columns: x, y, and z values. We’ll explore how to convert these columns to numpy arrays and use them as input for the scatter plot function.

The Challenge of Working with Strings

The problem presented in the Stack Overflow question arises when trying to create a 3D scatter plot using matplotlib from a pandas array containing strings instead of numerical values. Specifically, the code attempts to pass string values (B, C, and D) as arguments to the plt.scatter function along with the x and y coordinates.

However, the error message indicates that s must be a scalar or float array-like with the same size as x and y. This is because matplotlib’s scatter plot function expects numerical values for the color (c) parameter, not strings.

Converting Arrays to Strings

To create a 3D scatter plot using matplotlib, we need to convert the string arrays into something that can be used with the library. In this case, we’ll use the np.array2string function from numpy to convert the arrays to string representations.

Here’s an example of how you might modify the original code:

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

# Importing csv data via pd
A = pd.read_csv('input.csv') # import file for current master list
Diagnosis_Des = A["Diagnosis Code"]
Discharge_Date = A["Discharge Date"]
Patient_ID = A["Patient ID"]

B = Diagnosis_Des.to_numpy()
C = Discharge_Date.to_numpy()
D = Patient_ID.to_numpy()

from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D

sequence_containing_x_vals = D
sequence_containing_y_vals = B
sequence_containing_z_vals = C

# Convert arrays to string representations
B_str = np.array2string(B)
C_str = np.array2string(C)

print(type(sequence_containing_y_vals))  # Should print <class 'numpy.str_'>
print(type(sequence_containing_z_vals))  # Should print <class 'numpy.str_'>

plt.scatter(sequence_containing_x_vals, sequence_containing_y_vals, s=B_str)
pyplot.show()

In this modified version, we use np.array2string to convert the string arrays into something that can be used with the scatter plot function.

Understanding the Issues

There are a few issues at play here:

Type checking: In Python, strings and numerical values have different types. When working with matplotlib, it expects numerical values for s.
Data type conversion: To work around this issue, we need to convert the string arrays into something that can be used with the library.
Missing context: The original code snippet lacked sufficient context about the data being plotted.

Best Practices and Advice

When working with matplotlib, here are some best practices to keep in mind:

Use numerical values: Whenever possible, use numerical values instead of strings for plotting purposes.
Convert arrays as needed: If you need to work with string arrays, use functions like np.array2string to convert them into something that can be used with the library.
Provide sufficient context: Make sure your code snippet includes enough context about the data being plotted.

Creating a 3D Scatter Plot

To create a 3D scatter plot using matplotlib, follow these steps:

Import the necessary libraries (matplotlib.pyplot, numpy, and pandas).
Load the data into pandas DataFrames or numpy arrays.
Prepare the data for plotting by converting it to numerical values where necessary.
Create a scatter plot using plt.scatter with the appropriate arguments.

Here’s an updated version of the original code that addresses these points:

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

# Importing csv data via pd
A = pd.read_csv('input.csv') # import file for current master list
Diagnosis_Des = A["Diagnosis Code"]
Discharge_Date = A["Discharge Date"]
Patient_ID = A["Patient ID"]

B = Diagnosis_Des.to_numpy()
C = Discharge_Date.to_numpy()
D = Patient_ID.to_numpy()

from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D

sequence_containing_x_vals = D
sequence_containing_y_vals = B
sequence_containing_z_vals = C

# Convert arrays to string representations (optional)
B_str = np.array2string(B)
C_str = np.array2string(C)

plt.scatter(sequence_containing_x_vals, sequence_containing_y_vals,
            s=10)  # Use numerical values for the size parameter

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(sequence_containing_x_vals, sequence_containing_y_vals,
            s=10)
ax.set_xlabel('Patient ID')  # Set labels and titles
ax.set_ylabel('Diagnosis Code')
ax.set_zlabel('Discharge Date')

plt.show()

In this updated version, we use numerical values for the size parameter of plt.scatter and add labels and titles to the plot using ax.set_xlabel, ax.set_ylabel, and ax.set_zlabel.

Last modified on 2024-02-13