Changelog

Version 0.0.14

Ensure Crosstabs Dictionary is Populated with return_dict=True

This resolves the issue where the stacked_crosstab_plot function fails to populate and return the crosstabs dictionary (crosstabs_dict) when return_dict=True and output="plots_only". The fix ensures that crosstabs are always generated when return_dict=True, regardless of the output parameter.

• Always Generate Crosstabs with return_dict=True:

  • Added logic to ensure crosstabs are created and populated in crosstabs_dict whenever return_dict=True, even if the output parameter is set to "plots_only".

• Separation of Crosstabs Display from Generation:

  • The generation of crosstabs is now independent of the output parameter.

  • Crosstabs display (print) occurs only when output includes "both" or "crosstabs_only".

Enhancements and Fixes for scatter_fit_plot Function

This addresses critical issues and introduces key enhancements for the scatter_fit_plot function. These changes aim to improve usability, flexibility, and robustness of the function.

1. Added exclude_combinations Parameter. Users can now exclude specific variable pairs from being plotted by providing a list of tuples with the combinations to omit.

2. Added combinations Parameter to show_plot. Users can also now show just the list of combinations that are part of the selection process when all_vars=True.

3. When plotting a single variable pair with show_plot="both", the function threw an AttributeError. Single-variable pairs are now properly handled.

4. Changed the default value of show_plot to "both" to prevent excessive individual plots when handling large variable sets.

5. Fixed Issues with Legend, xlim, and ylim; inputs were not being used; these have been corrected.

Fix Default Title and Filename Handling in flex_corr_matrix

This resolves issues in the flex_corr_matrix function where:

1. No default title was provided when title=None, resulting in missing titles on plots.

2. Saved plot filenames were incorrect, leading to issues like .png.png when title was not provided.

The fix ensures that a default title (“Correlation Matrix”) is used for both plot display and file saving when no title is explicitly provided. If title is explicitly set to None, the plot will have no title, but the saved filename will still use "correlation_matrix".

1. If no title is provided, "Correlation Matrix" is used as the default for filenames and displayed titles. If title=None is explicitly passed, no title is displayed on the plot.

2. File names are generated based on the title or default to "correlation_matrix" if title is not provided. Spaces in the title are replaced with underscores, and special characters like : are removed to ensure valid filenames.

Version 0.0.13

This version introduces a series of updates and fixes across multiple functions to enhance error handling, improve cross-environment compatibility, streamline usability, and optimize performance. These changes address critical issues, add new features, and ensure consistent behavior in both terminal and notebook environments.

Add ValueError for Insufficient Pool Size in add_ids and Enhance ID Deduplication

This update enhances the add_ids function by adding explicit error handling and improving the uniqueness guarantee for generated IDs.

Key Changes

• New ValueError for Insufficient Pool Size:

  • Calculates the pool size \(9 \times 10^{(\text{d} - 1)}\) and compares it with the number of rows in the DataFrame.

  • Behavior:

    • Throws a ValueError if n_rows > pool_size.

    • Prints a warning if n_rows approaches 90% of the pool size, suggesting an increase in digit length.

• Improved ID Deduplication:

  • Introduced a set (unique_ids) to track generated IDs.

  • IDs are checked against this set to ensure uniqueness before being added to the DataFrame.

  • Prevents collisions by regenerating IDs only for duplicates, minimizing retries and improving performance.

Enhance strip_trailing_period to Support Strings and Mixed Data Types

This update enhances the strip_trailing_period function to handle trailing periods in both numeric and string values. The updated implementation ensures robustness for columns with mixed data types and gracefully handles special cases like NaN.

Key Enhancements

• Support for Strings with Trailing Periods:

  • Removes trailing periods from string values, such as "123." or "test.".

• Mixed Data Types:

  • Handles columns containing both numeric and string values seamlessly.

• Graceful Handling of NaN:

  • Skips processing for NaN values, leaving them unchanged.

• Robust Type Conversion:

  • Converts numeric strings (e.g., "123.") back to float where applicable.

  • Retains strings if conversion to float is not possible.

Changes in stacked_crosstab_plot

Remove IPython Dependency by Replacing display with print

This resolves an issue where the eda_toolkit library required IPython as a dependency due to the use of display(crosstab_df) in the stacked_crosstab_plot function. The dependency caused import failures in environments without IPython, especially in non-Jupyter terminal-based workflows.

Changes Made

1. Replaced display with print: - The line display(crosstab_df) was replaced with print(crosstab_df) to eliminate the need for IPython.

2. Removed ``IPython`` Import: - The from IPython.display import display import statement was removed from the codebase.

Updated Function Behavior:

• Crosstabs are displayed using print, maintaining functionality in all runtime environments.

• The change ensures no loss in usability or user experience.

Root Cause and Fix

The issue arose from reliance on IPython.display.display for rendering crosstab tables in Jupyter notebooks. Environments without IPython experienced a ModuleNotFoundError. To address this, the display(crosstab_df) statement was replaced with print(crosstab_df).

Testing:

• Jupyter Notebook: Crosstabs are displayed as plain text via print(), rendered neatly in notebook outputs.

• Terminal Session: Crosstabs are printed as expected, ensuring seamless use in terminal-based workflows.

Add Environment Detection to dataframe_columns Function

This enhances the dataframe_columns function to dynamically adjust its output based on the runtime environment (Jupyter Notebook or terminal).

Changes Made

1. Environment Detection:

   • Added a check to determine if the function is running in a Jupyter Notebook or terminal:

     ` is_notebook_env = "ipykernel" in sys.modules `

2. Dynamic Output Behavior:

   • Terminal Environment:

     • Returns a plain DataFrame (result_df) when running outside of a notebook or when return_df=True.

   • Jupyter Notebook:

     • Retains the styled DataFrame functionality when running in a notebook and return_df=False.

3. Improved Compatibility:

   • The function now works seamlessly in both terminal and notebook environments without requiring additional dependencies.

Add tqdm Progress Bar to dataframe_columns Function

This enhances the dataframe_columns function by incorporating a tqdm progress bar to track column processing. This is particularly useful for analyzing large DataFrames, providing real-time feedback.

Changes Made:

• Wrapped the column processing loop with a tqdm progress bar:

  for col in tqdm(df.columns, desc="Processing columns"):
  ...
  

Other Enhancements and Fixes

• Improved save_dataframes_to_excel with tqdm integration.

• Fixed plot_2d_pdp plot display logic to adhere strictly to the plot_type parameter.

• Updated project dependencies and added robust environment testing.

Version 0.0.12

New Features

• Added data_doctor function:

  A versatile tool designed to facilitate detailed feature analysis, outlier detection, and data transformation within a DataFrame.

  Key Capabilities:

  • Outlier Detection:

    • Detects and highlights outliers visually using boxplots, histograms, and other visualization options.

    • Allows cutoffs to be applied directly, offering a configurable approach for handling extreme values.

  • Data Transformation:

    • Supports a range of scaling transformations, including absolute, log, square root, min-max, robust, and Box-Cox transformations, among others.

    • Configurable via scale_conversion and scale_conversion_kws parameters to customize transformation approaches based on user needs.

  • Visualization Options:

    • Provides flexible visualization choices, including KDE plots, histograms, and box/violin plots.

    • Allows users to specify multiple plot types in a single call (e.g., plot_type=["hist", "kde"]), facilitating comprehensive visual exploration of feature distributions.

  • Customizable Display:

    • Adds text annotations, such as cutoff values, below plots, and enables users to adjust various styling parameters like label_fontsize, tick_fontsize, and figsize.

  • Output Control:

    • Offers options to save plots directly to PNG or SVG formats, with file names reflecting key transformations and cutoff information for easy identification.

Improvements

• Enhanced add_ids function:

  • Switched to NumPy for Random Seed Handling:

    • Updated from random.seed(seed) (Python’s random module) to np.random.seed(seed), providing better control and reproducibility within NumPy’s ecosystem.

  • Improved ID Generation Method:

    • Replaced the previous approach, which used a while loop and random.choice, with np.random.choice and vectorized operations, significantly improving efficiency.

  • Enhanced Efficiency:

    • Eliminated the while loop for unique ID generation, using a vectorized approach with NumPy arrays.

    • Generated the first digit (non-zero) and the remaining digits in a single step with NumPy, reducing computational overhead and enhancing scalability.

The updated implementation removes custom logic for ID generation, leveraging NumPy’s built-in array operations for a more concise and readable codebase.

Version 0.0.11

Description

Fixes a TypeError in the stacked_crosstab_plot function when save_formats is None. The update ensures that save_formats defaults to an empty list, preventing iteration over a NoneType object.

Changes

• Initializes save_formats as an empty list if not provided.

• Adds handling for string and tuple input types for save_formats.

Issue Fixed

• Resolves TypeError when save_formats is None.

Version 0.0.10

Legend Handling

• The legend is now displayed only if there are valid legend handles (len(handles) > 0) and if show_legend is set to True.

• The check ax.get_legend().remove() ensures that unnecessary legends are removed if they are empty or if show_legend is set to False.

Error Handling - Error handling in the except block has been enhanced to ensure that any exceptions related to legends or labels are managed properly. The legend handling logic still respects the show_legend flag even in cases where exceptions occur.

This update prevents empty legend squares from appearing and maintains the intended default behavior of showing legends only when they contain relevant content.

Version 0.0.9

Bug Fixes and Minor Improvements

Improved error messages and validation checks across multiple functions to prevent common pitfalls and ensure smoother user experience.

Visualization Enhancements

DataFrame Columns: Added a background_color variable to dataframe_columns`, allowing the user to enter a string representing a color name, or hex value. Try/Except on the output, in case the end user has a deprecated version of Pandas, where the styler would use hide() instead of hide_index(). The highlighted columns allow for easier null versus unique value analysis.

The docstring now clearly describes the purpose of the function—analyzing DataFrame columns to provide summary statistics.

Args:

• The df argument is specified as a pandas.DataFrame.

• The background_color argument is marked as optional, with a brief description of its role.

• The return_df argument is also marked as optional, explaining what it controls.

Returns: The return type is specified as pandas.DataFrame, with a clear explanation of the difference based on the return_df flag.

KDE Distribution Plots: Improved kde_distributions() with enhanced options for log scaling, mean/median plotting, custom standard deviation lines, and better handling of legends and scientific notation.

Scatter Plots: Enhanced scatter_fit_plot() with support for hue-based coloring, best fit lines, correlation display, and flexible grid plotting options.

Version 0.0.8

stacked_crosstab_plot

• Flexible `save_formats` Input: - save_formats now accepts a string, tuple, or list for specifying formats (e.g., “png”, (“png”, “svg”), or [“png”, “svg”]). - Single strings or tuples are automatically converted to lists for consistent processing.

• Dynamic Error Handling: - Added checks to ensure a valid path is provided for each format in save_formats. - Raises a ValueError if a format is specified without a corresponding path, with a clear, dynamic error message.

• Improved Plot Saving Logic: - Updated logic allows saving plots in one format (e.g., only “png” or “svg”) without requiring the other. - Simplified and more intuitive path handling for saving plots.

plot_3d_pdp

This update introduces several key changes to the plot_3d_pdp function, simplifying the function’s interface and improving usability, while maintaining the flexibility needed for diverse visualization needs.

1. Parameter Changes

• Removed Parameters:

  • The parameters x_label_plotly, y_label_plotly, and z_label_plotly have been removed. These parameters previously allowed custom axis labels specifically for the Plotly plot, defaulting to the general x_label, y_label, and z_label. Removing these parameters simplifies the function signature while maintaining flexibility.

• Default Values for Labels:

  • The parameters x_label, y_label, and z_label are now optional, with None as the default. If not provided, these labels will automatically default to the names of the features in the feature_names_list. This change makes the function more user-friendly, particularly for cases where default labels are sufficient.

• Changes in Default Values for View Angles:

  • The default values for camera positioning parameters have been updated: horizontal is now -1.25, depth is now 1.25, and vertical is now 1.25. These adjustments refine the default 3D view perspective for the Plotly plot, providing a more intuitive starting view.

2. Plot Generation Logic

• Conditionally Checking Labels:

  • The function now checks whether x_label, y_label, and z_label are provided. If these are None, the function will automatically assign default labels based on the feature_names_list. This enhancement reduces the need for users to manually specify labels, making the function more adaptive.

• Camera Position Adjustments:

  • The camera positions for the Plotly plot are now adjusted by multiplying horizontal, depth, and vertical by zoom_out_factor. This change allows for more granular control over the 3D view, enhancing the interactivity and flexibility of the Plotly visualizations.

• Surface Plot Coordinates Adjustments:

  • The order of the coordinates for the Plotly plot’s surface has been changed from ZZ, XX, YY[::-1] to ZZ, XX, YY. This adjustment ensures the proper alignment of axes and grids, resulting in more accurate visual representations.

3. Code Simplifications

• Removed Complexity:

  • By removing the x_label_plotly, y_label_plotly, and z_label_plotly parameters, the code is now simpler and easier to maintain. This change reduces potential confusion and streamlines the function for users who do not need distinct labels for Matplotlib and Plotly plots.

• Fallback Mechanism for Grid Values:

  • The function continues to implement a fallback mechanism when extracting grid values, ensuring compatibility with various versions of scikit-learn. This makes the function robust across different environments.

4. Style Adjustments

• Label Formatting:

  • The new version consistently uses y_label, x_label, and z_label for axis labels in the Matplotlib plot, aligning the formatting across different plot types.

• Color Bar Adjustments:

  • The color bar configuration in the Matplotlib plot has been slightly adjusted with a shrink value of 0.6 and a pad value of 0.02. These adjustments result in a more refined visual appearance, particularly in cases where space is limited.

5. Potential Use Case Differences

• Simplified Interface:

  • The updated function is more streamlined for users who prefer a simplified interface without the need for separate label customizations for Plotly and Matplotlib plots. This makes it easier to use in common scenarios.

• Less Granular Control:

  • Users who need more granular control, particularly for presentations or specific formatting, may find the older version more suitable. The removal of the *_plotly label parameters means that all plots now use the same labels across Matplotlib and Plotly.

6. Matplotlib Plot Adjustments

• Wireframe and Surface Plot Enhancements:

  • The logic for plotting wireframes and surface plots in Matplotlib remains consistent with previous versions, with subtle enhancements to color and layout management to improve overall aesthetics.

Summary

• Version 0.0.8d of the plot_3d_pdp function introduces simplifications that reduce the number of parameters and streamline the plotting process. While some customizability has been removed, the function remains flexible enough for most use cases and is easier to use.

• Key updates include adjusted default camera views for 3D plots, removal of Plotly-specific label parameters, and improved automatic labeling and plotting logic.

Decision Point

• This update may be especially useful for users who prefer a cleaner and more straightforward interface. However, those requiring detailed customizations may want to continue using the older version, depending on their specific needs.

Version 0.0.8c

Version 0.0.8c is a follow-up release to version 0.0.8b. This update includes minor enhancements and refinements based on feedback and additional testing. It serves as an incremental step towards improving the stability and functionality of the toolkit.

Key Updates in 0.0.8c:

• Bug Fixes: Addressed minor issues identified in version 0.0.8b to ensure smoother performance and better user experience.

• Additional Testing: Incorporated further tests to validate the changes introduced in previous versions and to prepare for future stable releases.

• Refinements: Made small enhancements to existing features based on user feedback and internal testing results.

Summary of Changes

1. New Features & Enhancements

• plot_3d_pdp Function:

  • Added show_modebar Parameter: Introduced a new boolean parameter, show_modebar, to allow users to toggle the visibility of the mode bar in Plotly interactive plots.

  • Custom Margins and Layout Adjustments:

    • Added parameters for left_margin, right_margin, and top_margin to provide users with more control over the plot layout in Plotly.

    • Adjusted default values and added options for better customization of the Plotly color bar (cbar_x, cbar_thickness) and title positioning (title_x, title_y).

  • Plotly Configuration:

    • Enhanced the configuration options to allow users to enable or disable zoom functionality (enable_zoom) in the interactive Plotly plots.

    • Updated the code to reflect these new parameters, allowing for greater flexibility in the appearance and interaction with the Plotly plots.

  • Error Handling:

    • Added input validation for html_file_path and html_file_name to ensure these are provided when necessary based on the selected plot_type.

• plot_2d_pdp Function:

  • Introduced file_prefix Parameter:

    • Added a new file_prefix parameter to allow users to specify a prefix for filenames when saving grid plots. This change streamlines the naming process for saved plots and improves file organization.

  • Enhanced Plot Type Flexibility:

    • The plot_type parameter now includes an option to generate both grid and individual plots (both). This feature allows users to create a combination of both layout styles in one function call.

    • Updated input validation and logic to handle this new option effectively.

  • Added save_plots Parameter:

    • Introduced a new parameter, save_plots, to control the saving of plots. Users can specify whether to save all plots, only individual plots, only grid plots, or none.

  • Custom Margins and Layout Adjustments:

    • Included the save_plots parameter in the validation process to ensure paths are provided when needed for saving the plots.

2. Documentation Updates

• Docstrings:

  • Updated docstrings for both functions to reflect the new parameters and enhancements, providing clearer and more comprehensive guidance for users.

  • Detailed the use of new parameters such as show_modebar, file_prefix, save_plots, and others, ensuring that the function documentation is up-to-date with the latest changes.

3. Refactoring & Code Cleanup

• Code Structure:

  • Improved the code structure to maintain clarity and readability, particularly around the new functionality.

  • Consolidated the layout configuration settings for the Plotly plots into a more flexible and user-friendly format, making it easier for users to customize their plots.

Version 0.0.8b

Version 0.0.8b is an exact replica of version 0.0.8a. The purpose of this beta release was to test whether releasing it as the latest version would update its status on PyPI to reflect it as the latest release. However, it continues to be identified as a pre-release on PyPI.

Version 0.0.8a

Version 0.0.8a introduces significant enhancements and new features to improve the usability and functionality of the EDA Toolkit.

New Features:

1. Optional file_prefix in stacked_crosstab_plot Function

   • The stacked_crosstab_plot function has been updated to make the file_prefix argument optional. If the user does not provide a file_prefix, the function will now automatically generate a default prefix based on the col and func_col parameters. This change streamlines the process of generating plots by reducing the number of required arguments.

   • Key Improvement:

     • Users can now omit the file_prefix argument, and the function will still produce appropriately named plot files, enhancing ease of use.

     • Backward compatibility is maintained, allowing users who prefer to specify a custom file_prefix to continue doing so without any issues.

2. Introduction of 3D and 2D Partial Dependence Plot Functions

   • Two new functions, plot_3d_pdp and plot_2d_pdp, have been added to the toolkit, expanding the visualization capabilities for machine learning models.

     • plot_3d_pdp: Generates 3D partial dependence plots for two features, supporting both static visualizations (using Matplotlib) and interactive plots (using Plotly). The function offers extensive customization options, including labels, color maps, and saving formats.

     • plot_2d_pdp: Creates 2D partial dependence plots for specified features with flexible layout options (grid or individual plots) and customization of figure size, font size, and saving formats.

   • Key Features:

     • Compatibility: Both functions are compatible with various versions of scikit-learn, ensuring broad usability.

     • Customization: Extensive options for customizing visual elements, including figure size, font size, and color maps.

     • Interactive 3D Plots: The plot_3d_pdp function supports interactive visualizations, providing an enhanced user experience for exploring model predictions in 3D space.

Impact:

• These updates improve the user experience by reducing the complexity of function calls and introducing powerful new tools for model interpretation.

• The optional file_prefix enhancement simplifies plot generation while maintaining the flexibility to define custom filenames.

• The new partial dependence plot functions offer robust visualization options, making it easier to analyze and interpret the influence of specific features in machine learning models.

Version 0.0.7

Added Function for Customizable Correlation Matrix Visualization

This release introduces a new function, flex_corr_matrix, which allows users to generate both full and upper triangular correlation heatmaps with a high degree of customization. The function includes options to annotate the heatmap, save the plots, and pass additional parameters to seaborn.heatmap().

Summary of Changes

• New Function: flex_corr_matrix.

  • Functionality: - Generates a correlation heatmap for a given DataFrame. - Supports both full and upper triangular correlation matrices based on the triangular parameter. - Allows users to customize various aspects of the plot, including colormap, figure size, axis label rotation, and more. - Accepts additional keyword arguments via **kwargs to pass directly to seaborn.heatmap(). - Includes validation to ensure the triangular, annot, and save_plots parameters are boolean values. - Raises an exception if save_plots=True but neither image_path_png nor image_path_svg is specified.

Usage

# Full correlation matrix example
flex_corr_matrix(df=my_dataframe, triangular=False, cmap="coolwarm", annot=True)

# Upper triangular correlation matrix example
flex_corr_matrix(df=my_dataframe, triangular=True, cmap="coolwarm", annot=True)

Contingency table df to object type

Convert all columns in the DataFrame to object type to prevent issues with numerical columns.

df = df.astype(str).fillna("")

Version 0.0.6

Added validation for Plot Type Parameter in KDE Distributions Function

This release adds a validation step for the plot_type parameter in the kde_distributions function. The allowed values for plot_type are "hist", "kde", and "both". If an invalid value is provided, the function will now raise a ValueError with a clear message indicating the accepted values. This change improves the robustness of the function and helps prevent potential errors due to incorrect parameter values.

# Validate plot_type parameter
valid_plot_types = ["hist", "kde", "both"]
if plot_type.lower() not in valid_plot_types:
    raise ValueError(
        f"Invalid plot_type value. Expected one of {valid_plot_types}, "
        f"got '{plot_type}' instead."
    )

Version 0.0.5

Ensure Consistent Font Size and Text Wrapping Across Plot Elements

This PR addresses inconsistencies in font sizes and text wrapping across various plot elements in the stacked_crosstab_plot function. The following updates have been implemented to ensure uniformity and improve the readability of plots:

1. Title Font Size and Text Wrapping: - Added a text_wrap parameter to control the wrapping of plot titles. - Ensured that title font sizes are consistent with axis label font sizes by explicitly setting the font size using ax.set_title() after plot generation.

2. Legend Font Size Consistency: - Incorporated label_fontsize into the legend font size by directly setting the font size of the legend text using plt.setp(legend.get_texts(), fontsize=label_fontsize). - This ensures that the legend labels are consistent with the title and axis labels.

Testing

• Verified that titles now wrap correctly and match the specified label_fontsize.

• Confirmed that legend text scales according to label_fontsize, ensuring consistent font sizes across all plot elements.

Version 0.0.4

• Stable release

  • No new updates to the codebase.

  • Updated the project description variable in setup.py to re-emphasize key elements of the library.

  • Minor README cleanup:

    • Added icons for sections that did not have them.

Version 0.0.3

• Stable release

  • Updated logo size, fixed citation title, and made minor README cleanup:

    • Added an additional section for documentation, cleaned up verbiage, moved acknowledgments section before licensing and support.

Version 0.0.2

• First stable release

  • No new updates to the codebase; minimal documentation updates to README and setup.py files.

  • Added logo, badges, and Zenodo-certified citation to README.

Version 0.0.1rc0

• No new updates to the codebase; minimal documentation updates to README and setup.py files.

Version 0.0.1b0

New Scatter Fit Plot and Additional Updates

• Added new scatter_fit_plot(), removed unused data_types(), and added comment section headers.

Added xlim and ylim Inputs to KDE Distribution

• kde_distribution():

  • Added xlim and ylim inputs to allow users to customize axes limits in kde_distribution().

Added xlim and ylim Params to Stacked Crosstab Plot

• stacked_crosstab_plot():

  • Added xlim and ylim input parameters to stacked_crosstab_plot() to give users more flexibility in controlling axes limits.

Added x and y Limits to Box and Violin Plots

• box_violin_plot():

  • Changed function name from metrics_box_violin() to box_violin_plot().

  • Added xlim and ylim inputs to control x and y-axis limits of box_violin_plot() (formerly metrics_box_violin).

Added Ability to Remove Stacks from Plots, Plot All or One at a Time

Key Changes

1. Plot Type Parameter - plot_type: This parameter allows the user to choose between "regular", "normalized", or "both" plot types.

2. Remove Stacks Parameter - remove_stacks: This parameter, when set to True, generates a regular bar plot using only the col parameter instead of a stacked bar plot. It only works when plot_type is set to “regular”. If remove_stacks is set to True while plot_type is anything other than “regular”, the function will raise an exception.

Explanation of Changes

• Plot Type Parameter

  • Provides flexibility to the user, allowing specification of the type of plot to generate:

    • "regular": Standard bar plot.

    • "normalized": Normalized bar plot.

    • "both": Both regular and normalized bar plots.

• Remove Stacks Parameter - remove_stacks: Generates a regular bar plot using only the col parameter, removing the stacking of the bars. Applicable only when plot_type is set to “regular”. An exception is raised if used with any other plot_type.

These changes enhance the flexibility and functionality of the stacked_crosstab_plot function, allowing for more customizable and specific plot generation based on user requirements.

Version 0.0.1b0

Refined KDE Distributions

Key Changes

1. Alpha Transparency for Histogram Fill - Added a fill_alpha parameter to control the transparency of the histogram bars’ fill color. - Default value is 0.6. An exception is raised if fill=False and fill_alpha is specified.

2. Custom Font Sizes - Introduced label_fontsize and tick_fontsize parameters to control font size of axis labels and tick marks independently.

3. Scientific Notation Toggle - Added a disable_sci_notation parameter to enable or disable scientific notation on axes.

4. Improved Error Handling - Added validation for the stat parameter to ensure valid options are accepted. - Added checks for proper usage of fill_alpha and hist_edgecolor when fill is set to False.

5. General Enhancements - Updated the function’s docstring to reflect new parameters and provide comprehensive guidance on usage.

Version 0.0.1b0

Enhanced KDE Distributions Function

Added Parameters

1. Grid Figsize and Single Figsize - Control the size of the overall grid figure and individual figures separately.

2. Hist Color and KDE Color` - Allow customization of histogram and KDE plot colors.

3. Edge Color - Allows customization of histogram bar edges.

4. Hue - Allows grouping data by a column.

5. Fill - Controls whether to fill histogram bars with color.

6. Y-axis Label` - Customizable y-axis label.

7. Log-Scaling - Specifies which variables to apply log scale.

8. Bins and Bin Width - Control the number and width of bins.

9. ``stat``: - Allows different statistics for the histogram (count, density, frequency, probability, proportion, percent).

Improvements

1. Validation and Error Handling - Checks for invalid log_scale_vars and throws a ValueError if any are found. - Throws a ValueError if edgecolor is changed while fill is set to False. - Issues a PerformanceWarning if both bins and binwidth are specified, warning of potential performance impacts.

2. Customizable Y-Axis Label - Allows users to specify custom y-axis labels.

3. Warning for KDE with Count - Issues a warning if KDE is used with stat='count', as it may produce misleading plots.

Updated Function to Ensure Unique IDs and Index Check

• Ensured that each generated ID in add_ids starts with a non-zero digit.

• Added a check to verify that the DataFrame index is unique.

• Printed a warning message if duplicate index entries are found.

These changes improve the robustness of the function, ensuring that the IDs generated are always unique and valid, and provide necessary feedback when the DataFrame index is not unique.

Check for Unique Indices - Before generating IDs, the function now checks if the DataFrame index is unique. - If duplicates are found, a warning is printed along with the list of duplicate index entries.

Generate Non-Zero Starting IDs

• The ID generation process is updated to ensure that the first digit of each ID is always non-zero.

Ensure Unique IDs

• A set is used to store the generated IDs, ensuring all IDs are unique before adding them to the DataFrame.

Fix Int Conversion for Numeric Columns, Reset Decimal Places

• Fixed integer conversion issue for numeric columns when decimal_places=0 in the save_dataframes_to_excel function.

• Reset decimal_places default value to 0.

These changes ensure correct formatting and avoid errors during conversion.

Contingency Table Updates

1. Error Handling for Columns - Added a check to ensure at least one column is specified. - Updated the function to accept a single column as a string or multiple columns as a list. - Raised a ValueError if no columns are provided or if cols is not correctly specified.

2. Function Parameters - Changed parameters from col1 and col2 to a single parameter cols which can be either a string or a list.

3. Error Handling - Renamed SortBy to sort_by to standardize nomenclature. - Added a check to ensure sort_by is either 0 or 1. - Raised a ValueError if sort_by is not 0 or 1.

5. Sorting Logic - Updated the sorting logic to handle the new cols parameter structure.

6. Handling Categorical Data - Modified code to convert categorical columns to strings to avoid issues with fillna("").

7. Handling Missing Values - Added df = df.fillna('') to fill NA values within the function to account for missing data.

8. Improved Function Documentation - Updated function documentation to reflect new parameters and error handling.

Version 0.0.1b0

Contingency Table Updates

• fillna('') added to output so that null values come through, removed 'All' column name from output, sort options 0 and 1, updated docstring documentation. Tested successfully on Python 3.7.3.

Compatibility Enhancement

1. Added a version check for Python 3.7 and above.

   • Conditional import of datetime to handle different Python versions.

if sys.version_info >= (3, 7):
    from datetime import datetime
else:
    import datetime