scatterd

library1.4.2pypiunverified

scatterd is a Python package designed for the easy and fast creation of beautiful scatter plots. It simplifies the process of data visualization, currently at version 1.4.2, with an active release cadence.

data

pip install scatterd

INSTALL

IMPORT

SIG · SCATTERD

scatterd

dataenv1.4.2

Install

15.4s avg

Import

5726ms

Disk

398MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.4.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

installs and imports cleanly · install 0.0s · import 5.910s · 395.5MB

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 15.4s · import 5.541s · 379MB

398MB installed

● package 398MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

scatterd

✓ from scatterd import scatterd

✗ import scatterd

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

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()

Debug

Known issues

gotchaBe 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).

affects: All

gotchaCorrelation 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.

affects: All

gotchaIgnoring 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.

affects: All

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'scatterd'

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)

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.

Upgrade

Version history

1.4.2latest on PyPI · released Feb 21, 2026

Audit

Dependencies

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Agent activity

11 hits · last 30 days

node

OpenAI (training)

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