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

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 theplot() method to check for valid column names and required arguments, raising ValueErrors for invalid input.

Performance Enhancements:

• Modifiedauto_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 theauto_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 unnecessaryself arguments and ensuring calls are updated.

Testing:

• Replaced the existingtest.py with a new script that:
  • Creates atest_output directory for plot outputs.
  • Usesenergy_consumption_dataset.csv (with a dummy fallback).
  • DemonstratesAutoPlot initialization.
  • Runsauto_plot() with default and custom configurations.
  • Runs manualplot() for scatter, distribution, boxplot, and bar types.
  • Shows usage of thecustomize() method.
  • Includes a test with a small, inline dataset.
  • Saves all generated plots for visual inspection.

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

• AutoPlot class, along withauto_plot andplot functions.auto_plot automatically generates suitable plots based on values in your dataset.

You can install PyAutoPlot using pip:

pip install pyautoplot

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.

• Automatic Plotting: PyAutoPlot automatically generates appropriate plots based on values present in your dataset. Currently, PyAutoPlot supports the following types of data:numeric,categorical, andtime-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 (Seeplot.rcParams.keys() for a list of valid parameters) or choose from our predefined themes. You can also pass in additional parameters to the function thatmatplotlib can recognize (e.g.color='#5d17eb').

frompyautoplotimportAutoPlot# Initialize with a CSV fileplotter=AutoPlot("path/to/dataset.csv")# Automatically analyze and plotplotter.auto_plot(output_file='test',theme="dark",color='orange',excludes=['detailed_analysis'])

frompyautoplotimportAutoPlot# Define your custom themecustom_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 fileplotter=AutoPlot("path/to/dataset.csv")# Automatically analyze and plot using the custom themeplotter.auto_plot(output_file='test',theme=custom_theme,color='orange',excludes=['detailed_analysis'])

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 onGitHub.

PyAutoPlot is released under the terms of theMIT License (Modified). Please see theLICENSE 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.