scatterd

Common errors

• ModuleNotFoundError: No module named 'scatterd'

  cause The 'scatterd' package is not installed in the current Python environment.
  fix Run `pip install scatterd` to install the package.

• TypeError: 'numpy.ndarray' object is not callable (or similar for non-numeric data)

  cause Plotting functions expect numeric data for X and Y axes. If non-numeric data (e.g., strings) are passed, a TypeError will occur.
  fix Ensure that the data arrays passed to the `x` and `y` parameters are numeric (integers or floats). Convert data types using `df['column'].astype(float)` or similar methods if necessary.

Warnings

• gotcha Be mindful of overplotting when dealing with large datasets or discrete variables. Multiple observations at the same coordinates may appear as a single point, misleading visual interpretation. Consider density plots or alpha blending for such cases.
  Fix: For very large datasets, use alternative visualization techniques like 2D histograms or density plots. For discrete data, consider jittering points or adjusting marker transparency (alpha).
• gotcha Correlation shown in a scatter plot does not imply causation. A strong visual correlation between two variables does not mean one causes the other.
  Fix: Always consider confounding factors and domain knowledge. Do not assume causal relationships solely based on visual correlation in scatter plots.
• gotcha Ignoring outliers or misinterpreting the scale of axes can lead to incorrect conclusions about data trends and patterns. Discrepancies in scale or non-uniform scaling can distort the perceived relationships.
  Fix: Carefully examine outliers and their impact on the overall trend. Always check axis scales to ensure they are appropriate and not exaggerating or minimizing relationships.

Install

• pip install scatterd Install with pip

Imports

• scatterd
  wrong:

  import scatterd

  correct:

  from scatterd import scatterd

  The primary plotting function/class is directly exposed as 'scatterd' within the 'scatterd' package.

Quickstart

This example demonstrates how to create a simple scatter plot using `scatterd` with a Pandas DataFrame. It generates random data for X, Y, point size, and a categorical variable, then calls the `scatterd` function with appropriate arguments to visualize the data. It's assumed that `scatterd` internally uses or is compatible with Matplotlib for rendering, hence `matplotlib.pyplot.show()` is included to display the plot.

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

# Sample data
np.random.seed(42)
data = {
    'X': np.random.rand(100) * 10,
    'Y': np.random.rand(100) * 10 + np.random.rand(100) * 2,
    'Size': np.random.rand(100) * 50 + 10, # Example for variable point sizes
    'Category': np.random.choice(['A', 'B', 'C'], 100)
}
df = pd.DataFrame(data)

# Create a basic scatter plot using the scatterd function
# Assuming scatterd function accepts common plotting arguments like Matplotlib's scatter
scatterd(x=df['X'], y=df['Y'], title='My First Scatter Plot',
         xlabel='Feature X', ylabel='Feature Y', s=df['Size'], c=df['Category'],
         colorbar_title='Category' # Example for categorical coloring
        )

# Display the plot. scatterd often leverages Matplotlib internally.
plt.show()