A Python toolkit that streamlines the creation of Plotly visualizations for exploratory data analysis (EDA). Built to simplify the visualization workflow, Exploralytics provides an intuitive interface for creating common EDA plots like histograms, correlation matrices, and bar charts with consistent styling and formatting.
I created this to simplify my own workflow, but other data professionals might find it useful too.
Create sophisticated data visualizations with minimal code. Key features include:
- Histogram Grid: Analyze distributions of multiple numerical variables
- Correlation Analysis:
- Full correlation matrix heatmap
- Target-focused correlation analysis
- Bar Charts:
- Horizontal bar plots with customizable highlighting
- Dot plots with connecting lines
- Consistent Styling: Unified look across all visualizations
- Customization Options: Colors, dimensions, templates, and more
Requires Python 3.9 or newer.
Using pip:
pip install exploralyticsOr install from source:
git clone https://github.com/jpcurada/exploralytics.git
cd exploralytics
pip install -e .from exploralytics.visualize import Visualizer
import pandas as pd
# Initialize visualizer with custom styling
viz = Visualizer(
color="#94C973", # Custom color
height=768, # Plot height
width=1366, # Plot width
template="simple_white" # Plotly template
)
# Create histogram grid
fig = viz.plot_histograms(
df,
title='Distribution Analysis',
subtitle='Histogram of numerical variables',
num_cols=2,
show_mean=True,
show_median=True
)
fig.show()
# Create correlation heatmap
fig = viz.plot_correlation_map(
df,
title='Correlation Analysis',
subtitle='Relationship between variables'
)
fig.show()# Target-specific correlation analysis
fig = viz.plot_correlation_with_target(
df,
target_column='sales',
title='Feature Importance',
subtitle='Correlation with sales'
)
# Horizontal bar plot with highlights
fig = viz.plot_hbar(
df,
x_col='category',
y_col='value',
highlight_top_n=(3, '#2E75B6'), # Highlight top 3 in blue
highlight_low_n=(2, '#FF9999') # Highlight bottom 2 in red
)
# Dot plot with reference line
fig = viz.plot_dot(
df,
x_col='category',
y_col='metric',
add_hline_at=('Average', 75.5),
top_n=10
)The Visualizer class accepts several parameters for customization:
viz = Visualizer(
color="#94C973", # Default color for plot elements
height=768, # Plot height in pixels
width=1366, # Plot width in pixels
template="simple_white", # Plotly template
colorscale=px.colors.diverging.Earth, # Color scale for heatmaps
texts_font_style="Arial", # Font family
title_bold=True # Bold titles
)- pandas >= 1.3.0
- plotly >= 5.0.0
- numpy >= 1.20.0
Want to contribute? Here's how:
- Fork the repository
- Create a feature branch
git checkout -b feature/new-feature- Make your changes
- Submit a pull request
BSD License
For bugs, questions, or suggestions, please open an issue on GitHub.
Created and maintained by John Paul Curada. Contributions welcome!