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Data-Simply / pyretailscience

pyretailscience - A data analysis and science toolkit for detail data
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docs: updated analysis module docs #80

Closed mvanwyk closed 2 months ago

mvanwyk commented 2 months ago

PR Type

Documentation

Description

Changes walkthrough πŸ“

Relevant files
Documentation
standard_graphs.py
Enhance `index_plot` function docstring with detailed explanation
pyretailscience/standard_graphs.py
  • Enhanced the docstring for the index_plot function.
  • Provided detailed explanation of index plots in retail analytics.
    		•  +20/-1   
    analysis_modules.md
    Expand documentation with index plots and analysis modules
    docs/analysis_modules.md
  • Added detailed documentation for index plots.
  • Included examples and explanations for various analysis modules.
  • Introduced new sections for different analytical tools.
    		•  +239/-2 

    πŸ’‘ PR-Agent usage: Comment /help on the PR to get a list of all available PR-Agent tools and their descriptions

    Summary by CodeRabbit

    coderabbitai[bot] commented 2 months ago

    Walkthrough

    The pull request introduces enhancements to the documentation and functionality of the pyretailscience library, specifically focusing on retail analytics tools. It expands the docs/analysis_modules.md file with new sections detailing various analytical tools, including Index Plots and Customer Decision Hierarchy, along with examples and future content placeholders. Additionally, the index_plot function in standard_graphs.py has received an expanded docstring that clarifies its purpose and applications in retail analytics.

    Changes

    Files Change Summary
    docs/analysis_modules.md Added new sections on various analytical tools, including Index Plots and Customer Decision Hierarchy, with descriptions, examples, and placeholders for future content.
    pyretailscience/standard_graphs.py Expanded docstring for the index_plot function to elaborate on its purpose, utility, and applications in retail analytics.

    Poem

    In fields of data, hopping bright,
    New plots and charts bring pure delight.
    With index lines and choices clear,
    Retail insights draw us near.
    So let us leap, with joy we say,
    Analytics blooms in a vibrant way! πŸ‡βœ¨

    Recent review details **Configuration used: CodeRabbit UI** **Review profile: CHILL**
    Commits Files that changed from the base of the PR and between 7f7828a8f9e0cc4b7640fc57d4e70408386ace8c and 364c1cf5295e80bc74fefa3e9b8d0c4d971743aa.
    Files ignored due to path filters (2) * `docs/assets/images/analysis_modules/customer_decision_hierarchy.svg` is excluded by `!**/*.svg` * `docs/assets/images/analysis_modules/index_plot.svg` is excluded by `!**/*.svg`
    Files selected for processing (2) * docs/analysis_modules.md (3 hunks) * pyretailscience/standard_graphs.py (1 hunks)
    Additional comments not posted (6)
    docs/analysis_modules.md (5)
    `46-90`: **Excellent addition to the documentation!** The new section on Index Plots provides a clear and comprehensive explanation of their purpose and utility in retail analytics. The example image and code snippet effectively demonstrate how to create an index plot using the `pyretailscience` library. --- `122-127`: **LGTM!** The modifications to the example code for creating a sample DataFrame are minor and do not affect the overall functionality. The changes create a more realistic sample dataset with multiple groups and varying values, which is helpful for demonstrating the Timeline Plot. --- `202-217`: **Skipping review of placeholder content.** The Cross Shop section contains placeholder text and code, which do not require a thorough review at this stage. The inclusion of the placeholder image and comments helps maintain consistency in the document structure. --- `235-276`: **Great addition to the documentation!** The new section on Customer Decision Hierarchy (CDH) provides a clear and comprehensive explanation of their purpose and utility in retail analytics. The example image and code snippet effectively demonstrate how to create a CDH plot using the `pyretailscience` library. --- `278-388`: **Skipping review of placeholder content.** The various analysis module sections (Revenue Tree, HML Segmentation, Threshold Segmentation, Segmentation Stats, Purchases Per Customer, Days Between Purchases, and Transaction Churn) contain placeholder text and code, which do not require a thorough review at this stage. The inclusion of the placeholder images and comments helps maintain consistency in the document structure.
    pyretailscience/standard_graphs.py (1)
    `188-207`: **Excellent job on expanding the docstring for the `index_plot` function!** The updated docstring provides a comprehensive explanation of index plots and their applications in retail analytics. The content is well-structured, clear, and informative, making it easier for users to understand the purpose and benefits of using index plots. Some key highlights: - Clearly explains what index plots are and how they compare categories or segments against a baseline - Emphasizes the benefits of using index plots, such as identifying performance discrepancies and facilitating standardized comparisons - Provides specific examples of how index plots can be used in retail contexts - Uses bullet points to organize the information, making it easy to read and understand - Follows the existing format and style of the codebase Great work on enhancing the documentation for this function!
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    codiumai-pr-agent-pro[bot] commented 2 months ago

    PR Reviewer Guide πŸ”

    ⏱️ Estimated effort to review: 2 πŸ”΅πŸ”΅βšͺβšͺβšͺ
    πŸ§ͺ No relevant tests
    πŸ”’ No security concerns identified
    ⚑ Key issues to review

    Incomplete Documentation
    Several sections in the analysis_modules.md file have placeholder text ("PASTE TEXT HERE") and code ("PASTE CODE HERE") that need to be filled with actual content.
    codiumai-pr-agent-pro[bot] commented 2 months ago

    PR Code Suggestions ✨

    CategorySuggestion                                                                                                                                    Score
    Enhancement
    Add actual code examples for each analysis module ___ **Replace the placeholder code "PASTE CODE HERE" with actual code examples for each
    analysis module. This will provide practical guidance for users on how to implement
    and use these modules.** [docs/analysis_modules.md [213-217]](https://github.com/Data-Simply/pyretailscience/pull/80/files#diff-3dc6510be3b4cf4266ad054e6ce79b1e63a4a65c6199c3e3b5eb62fc2c457419R213-R217) ```diff Example: ```python -PASTE CODE HERE +from pyretailscience.analysis import CrossShopAnalysis + +# Assuming 'df' is your transaction dataframe +cross_shop = CrossShopAnalysis(df) +result = cross_shop.analyze(category_column='product_category', customer_id_column='customer_id') +cross_shop.plot_heatmap(result, title='Cross-Shop Analysis Heatmap') ``` ``` - [ ] **Apply this suggestion**
    Suggestion importance[1-10]: 9 Why: Providing actual code examples offers practical guidance for users on implementing and using the modules, which is crucial for effective utilization of the documentation.
    		9
    Replace placeholder text with actual descriptions for each analysis module ___ **Replace the placeholder text "PASTE TEXT HERE" with actual descriptions for each
    analysis module. This will provide valuable information to users about the purpose
    and benefits of each module.** [docs/analysis_modules.md [202-211]](https://github.com/Data-Simply/pyretailscience/pull/80/files#diff-3dc6510be3b4cf4266ad054e6ce79b1e63a4a65c6199c3e3b5eb62fc2c457419R202-R211) ```diff ### Cross Shop
    ![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} -PASTE TEXT HERE +Cross Shop analysis examines customer purchasing behavior across different product categories or stores. This analysis helps identify complementary products, understand customer preferences, and develop targeted marketing strategies to increase cross-selling opportunities.
    ``` - [ ] **Apply this suggestion**
    Suggestion importance[1-10]: 8 Why: Replacing placeholder text with actual descriptions significantly improves the documentation by providing users with valuable information about the purpose and benefits of each module.
    		8
    Add explanations for interpreting the output of each analysis module ___ **Consider adding a brief explanation of the expected output or interpretation
    guidelines for each analysis module. This will help users understand how to
    interpret and apply the results of these analyses.** [docs/analysis_modules.md [202-211]](https://github.com/Data-Simply/pyretailscience/pull/80/files#diff-3dc6510be3b4cf4266ad054e6ce79b1e63a4a65c6199c3e3b5eb62fc2c457419R202-R211) ```diff ### Cross Shop
    ![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} -PASTE TEXT HERE +Cross Shop analysis examines customer purchasing behavior across different product categories or stores. This analysis helps identify complementary products, understand customer preferences, and develop targeted marketing strategies to increase cross-selling opportunities. + +The output typically includes a matrix or heatmap showing the likelihood of customers purchasing from different categories together. Higher values indicate stronger cross-shopping behavior between categories.
    ``` - [ ] **Apply this suggestion**
    Suggestion importance[1-10]: 8 Why: Including interpretation guidelines helps users understand how to apply the results of the analyses, enhancing the utility and effectiveness of the documentation.
    		8
    βœ… Add an introductory section to provide context for the new analysis modules ___ **Consider adding a brief introduction or overview at the beginning of the new
    sections to provide context for the reader. This will help users understand the
    purpose and importance of these analysis modules before diving into the details.** [docs/analysis_modules.md [202-211]](https://github.com/Data-Simply/pyretailscience/pull/80/files#diff-3dc6510be3b4cf4266ad054e6ce79b1e63a4a65c6199c3e3b5eb62fc2c457419R202-R211) ```diff +## Advanced Analysis Modules + +This section covers a range of advanced analysis modules used in retail analytics. Each module provides unique insights into customer behavior, product performance, and market trends. + ### Cross Shop
    ![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} PASTE TEXT HERE
    ``` `[Suggestion has been applied]`
    Suggestion importance[1-10]: 7 Why: Adding an introductory section enhances the readability and context for the new analysis modules, helping users understand their purpose and importance before diving into specifics.
    		7

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    codecov[bot] commented 2 months ago

    Codecov Report

    All modified and coverable lines are covered by tests :white_check_mark:

    Files with missing lines Coverage Ξ”
    pyretailscience/standard_graphs.py 40.00% <ΓΈ> (ΓΈ)