pyautoplot 1.1.0

PyAutoPlot is an open-source Python library designed to make dataset analysis much easier by generating helpful detailed plots using matplotlib based on your dataset.

PyAutoPlot

PyAutoPlot is an open-source Python library designed to make dataset analysis much easier by generating helpful detailed plots using matplotlib. It automatically generates appropriate plots based on the dataset you feed it.

Changes in version 1.1.0:

1. New Visualizations: Added Correlation Heatmaps and Violin Plots.
2. Data Cleanup: New cleanup() method allows filling or dropping missing values with multiple strategies.
3. Advanced Reporting:
   • export_json(): Saves all stats and metadata to JSON.
   • export_report(): Generates professional HTML (self-contained) or PDF reports.
4. Improved Intelligence: Automatically detects and parses date strings into datetime objects.
5. Optimizations: Better memory management and resource warnings for large datasets.

[!TIP] For more information and Python package API references, view PyAutoPlot's documentation: https://infinitode-docs.gitbook.io/documentation/package-documentation/pyautoplot-package-documentation.

Changes in version 1.0.2:

Bug Fixes and Robustness:

• Corrected calculation of missing values in _generate_analysis.
• Added error handling for statistical functions (skewness, kurtosis, autocorrelation) to prevent crashes with empty or zero-variance data, returning NaN instead.
• Added warnings for columns not classified by _detect_column_types.
• Included input validation in the plot() method to check for valid column names and required arguments, raising ValueErrors for invalid input.

Performance Enhancements:

• Modified auto_plot() to display and close plot figures section by section (or individually for looped plots like pie/line charts). This significantly reduces memory consumption when generating many plots.
• Added a warning in the auto_plot() docstring regarding potential performance issues of pairwise scatter plots with many numeric columns.

Code Cleanup:

• Refined static method definitions (_calculate_skewness, _calculate_kurtosis, etc.) by removing unnecessary self arguments and ensuring calls are updated.

Testing:

• Replaced the existing test.py with a new script that:
  • Creates a test_output directory for plot outputs.
  • Uses energy_consumption_dataset.csv (with a dummy fallback).
  • Demonstrates AutoPlot initialization.
  • Runs auto_plot() with default and custom configurations.
  • Runs manual plot() for scatter, distribution, boxplot, and bar types.
  • Shows usage of the customize() method.
  • Includes a test with a small, inline dataset.
  • Saves all generated plots for visual inspection.

Changes in version 1.0.1:

• Added package dependencies to PyAutoPlot: matplotlib>=3.0.0, pandas>=1.0.0, and numpy>=1.18.0.

Changes in version 1.0.0:

• AutoPlot class, along with auto_plot and plot functions. auto_plot automatically generates suitable plots based on values in your dataset.

[!IMPORTANT] The AutoPlot object needs to be initialized with a CSV dataset before any plots can be generated using either plot or auto_plot.

Installation

You can install PyAutoPlot using pip:

pip install pyautoplot

Supported Python Versions

PyAutoPlot supports the following Python versions:

• Python 3.6
• Python 3.7
• Python 3.8
• Python 3.9
• Python 3.10
• Python 3.11/Later (Preferred)

Please ensure that you have one of these Python versions installed before using PyAutoPlot. PyAutoPlot may not work as expected on lower versions of Python than the supported.

Features

• Automatic Plotting: PyAutoPlot automatically generates appropriate plots based on values present in your dataset. Currently, PyAutoPlot supports the following types of data: numeric, categorical, and time-series.
• Customization: Due to matplotlib's high customizability, you can create and use custom themes for PyAutoPlot, just pass in your theme as a dictionary of RCParams (See plot.rcParams.keys() for a list of valid parameters) or choose from our predefined themes. You can also pass in additional parameters to the function that matplotlib can recognize (e.g. color='#5d17eb').

Usage

AutoPlot

from pyautoplot import AutoPlot

# Initialize with a CSV file
plotter = AutoPlot("path/to/dataset.csv")

# Automatically analyze and plot
plotter.auto_plot(output_file='test', theme="dark", color='orange', excludes=['detailed_analysis'])

[!NOTE] PyAutoPlot may not work well with certain datasets. If you find any issues please open an issue.

Customization

from pyautoplot import AutoPlot

# Define your custom theme
custom_theme = {
    "axes.facecolor": "#ffffff",
    "axes.edgecolor": "#000000",
    "axes.labelcolor": "#000000",
    "figure.facecolor": "#ffffff",
    "grid.color": "#dddddd",
    "text.color": "#000000",
    "xtick.color": "#000000",
    "ytick.color": "#000000",
    "legend.frameon": True,
}

# Initialize with a CSV file
plotter = AutoPlot("path/to/dataset.csv")

# Automatically analyze and plot using the custom theme
plotter.auto_plot(output_file='test', theme=custom_theme, color='orange', excludes=['detailed_analysis'])

Contributing

Contributions are welcome! If you encounter any issues, have suggestions, or want to contribute to PyAutoPlot, please open an issue or submit a pull request on GitHub.

License

PyAutoPlot is released under the terms of the MIT License (Modified). Please see the LICENSE file for the full text.

Modified License Clause

The modified license clause grants users the permission to make derivative works based on the PyAutoPlot software. However, it requires any substantial changes to the software to be clearly distinguished from the original work and distributed under a different name.