quicksea 🌊

quicksea is a lightweight Python library for fast, interactive 2D and 3D exploratory data visualization powered by Plotly.

Features

• 2D Multi-Pair Scatter Plots: Batch-plot multiple feature pairs in interactive Plotly figures with a single line of code.
• 3D Feature Exploration: Generate interactive 3D scatter plots across triplets of features with color encoding.
• EDA Utilities: Quick data profiling for missing values and summary statistics.
• Jupyter & Script Compatible: Works out-of-the-box in Jupyter Notebooks, Google Colab, and standard Python scripts.

Installation

Install quicksea from PyPI:

pip install quicksea

To include Jupyter notebook dependencies:

pip install "quicksea[jupyter]"

Quickstart

import pandas as pd
from quicksea import Visualizer

# Sample data
df = pd.DataFrame({
    "age": [22, 38, 26, 35, 54],
    "fare": [7.25, 71.28, 7.92, 53.10, 51.86],
    "pclass": [3, 1, 3, 1, 1],
    "survived": [0, 1, 1, 1, 0]
})

# Initialize visualizer with a color column (e.g. classification target)
viz = Visualizer(color_column="survived")

# 1. Quick EDA: missing values and descriptive statistics
viz.check_nan_describe(df)

# 2. 2D Scatter plots
pairs = [("age", "fare"), ("pclass", "fare")]
viz.scatter_2d(df, pairs)

# 3. 3D Scatter plot
triplets = [("age", "fare", "pclass")]
viz.scatter_3d(df, triplets)

API Reference

Visualizer(color_column=None)

• color_column (str, optional): DataFrame column name used to group/color markers.

Methods

scatter_2d(X, two_d_plot_cols, show=True)

• X (pd.DataFrame): Input dataset.
• two_d_plot_cols (list of (str, str)): List of (x_column, y_column) tuples.
• show (bool, default=True): Whether to display each figure with fig.show().
• Returns: List[plotly.graph_objects.Figure]

scatter_3d(X, three_d_plot_cols, show=True)

• X (pd.DataFrame): Input dataset.
• three_d_plot_cols (list of (str, str, str)): List of (x_column, y_column, z_column) tuples.
• show (bool, default=True): Whether to display each figure with fig.show().
• Returns: List[plotly.graph_objects.Figure]

check_nan_describe(df)

• df (pd.DataFrame): Displays NaN count and descriptive summary.

License

MIT License. See LICENSE for details.