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Changelog

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.

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

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

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

  4. 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