feat: add total row to SegTransactionStats calculation

[mvanwyk]

PR Type

Enhancement, Tests

---

Description

• Added a total row calculation to the segment statistics in SegTransactionStats.
• Refactored the segment statistics calculation into a new _calc_seg_stats method.
• Introduced a hide_total parameter in the plot method to optionally hide the total row.
• Removed validation against TransactionItemLevelContract and TransactionLevelContract.
• Added comprehensive unit tests for the new _calc_seg_stats method.

---

Changes walkthrough 📝

	Relevant files
Enhancement	segmentation.py Add total row and refactor segment statistics calculation pyretailscience/segmentation.py Added a total row calculation to segment statistics. Refactored the segment statistics calculation into a new _calc_seg_stats method. Introduced a hide_total parameter in the plot method to optionally hide the total row. Removed validation against TransactionItemLevelContract and TransactionLevelContract. +44/-26
Tests	test_segmentation.py Add unit tests for segment statistics calculation                tests/test_segmentation.py Added tests for the _calc_seg_stats method. Verified correct calculation of revenue, transactions, and customers per segment. Ensured the original DataFrame remains unaltered. Tested handling of DataFrame with only one segment. Checked for errors when DataFrame is missing required columns. +101/-0

---

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Summary by CodeRabbit

• New Features

  • Added hide_total parameter to control the visibility of the total row in plots.
  • Enhanced segment statistics calculations, including new metrics like revenue and transactions.

• Bug Fixes

  • Improved validation and error handling in segmentation initialization.

• Tests

  • Introduced test cases to validate revenue, transactions, and customer calculations per segment, ensuring proper handling of various DataFrame scenarios.

[coderabbitai]

Walkthrough

The segmentation.py module in pyretailscience has seen substantial updates, including enhanced validation logic using CustomContract, a refactored method _calc_seg_stats for calculating various segment statistics, and an updated plot method with a new hide_total parameter. Testing for these enhancements has been added in tests/test_segmentation.py.

Changes

File Path	Change Summary
pyretailscience/segmentation.py	Significant updates including refactored _calc_seg_stats, enhanced __init__ validation, and new hide_total parameter in plot.
tests/test_segmentation.py	Added new file with test cases for validating segment statistics calculations and error handling in pyretailscience.segmentation.

Poem

In the code's enchanted warren,
Segments dance without a bar,
Stats arise, with logic's paren,
From hutch to code, we've come so far.
With plots anew and errors clear,
Our module sings—give it a cheer! 🎉🐇

---

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[qodo-merge-pro]

PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 PR contains tests

🔒 No security concerns identified

⚡ Key issues to review Possible Bug: The removal of TransactionItemLevelContract and TransactionLevelContract validations and their replacement with a custom contract might introduce issues if the custom contract does not cover all the scenarios previously handled. Ensure that the new contract validations are comprehensive. Data Integrity: The method _calc_seg_stats adds a total row directly to the DataFrame. This could lead to unexpected behavior or errors in downstream processes that do not expect a 'total' row. Consider returning the total calculations separately or ensuring that all consuming methods handle this appropriately.

[mvanwyk]

/describe
--pr_description.extra_instructions="
For the title, use the format [type]: [summary]
"
--pr_description.publish_labels=true
--pr_description.publish_description_as_comment=true
--pr_description.generate_ai_title=true

[qodo-merge-pro]

Title

Enhancement: Add total row calculation and refactor segment statistics

---

PR Type

Enhancement, Tests

---

Description

• Added a total row calculation to the segment statistics in SegTransactionStats.
• Refactored the segment statistics calculation into a new _calc_seg_stats method.
• Introduced a hide_total parameter in the plot method to optionally hide the total row.
• Removed the dependency on TransactionItemLevelContract and TransactionLevelContract.
• Added comprehensive unit tests for the new _calc_seg_stats method and SegTransactionStats class.

---

Changes walkthrough 📝

	Relevant files
Enhancement	segmentation.py Add total row calculation and refactor segment statistics method pyretailscience/segmentation.py Added a total row calculation to segment statistics. Refactored the method to calculate segment statistics. Introduced a new parameter hide_total in the plot method. Removed dependency on TransactionItemLevelContract and TransactionLevelContract. +44/-26
Tests	test_segmentation.py Add unit tests for SegTransactionStats and _calc_seg_stats tests/test_segmentation.py Added tests for the _calc_seg_stats method. Added tests for the SegTransactionStats class. Verified calculations and ensured original DataFrame is unaltered. +101/-0

---

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

[qodo-merge-pro]

PR Code Suggestions ✨

Category	Suggestion	Score
Possible issue	✅ Include the "quantity" column in the required columns list if it exists in the DataFrame Suggestion Impact: The suggestion was implemented by adding a check for the "quantity" column in the DataFrame and appending it to the required columns list if it exists. code diff: + if "quantity" in df.columns: + required_cols.append("quantity") The required_cols list should include the "quantity" column if it exists in the DataFrame to ensure consistency in validation. pyretailscience/segmentation.py [147] required_cols = ["customer_id", "total_price", "transaction_id", segment_col] +if "quantity" in df.columns: + required_cols.append("quantity") Apply this suggestion Suggestion importance[1-10]: 8 Why: This suggestion is crucial for ensuring data consistency and validation completeness, especially since "quantity" is used later in the calculations.	8
	Ensure the segment_col exists in the DataFrame before proceeding with calculations Add a check to ensure that the segment_col exists in the DataFrame before proceeding with calculations to avoid potential errors. pyretailscience/segmentation.py [160] +if segment_col not in df.columns: + raise ValueError(f"The segment column '{segment_col}' does not exist in the DataFrame") self.df = self._calc_seg_stats(df, segment_col) Apply this suggestion Suggestion importance[1-10]: 8 Why: This suggestion is critical to avoid runtime errors when the specified segment column does not exist in the DataFrame, which can lead to misleading or incorrect analysis results.	8
	Ensure the DataFrame is not empty before proceeding with calculations Add a check to ensure that the DataFrame is not empty before proceeding with calculations to avoid potential errors. pyretailscience/segmentation.py [154-156] -if contract.validate() is False: - msg = f"The dataframe requires the columns {required_cols} and they must be non-null" +if contract.validate() is False or df.empty: + msg = f"The dataframe requires the columns {required_cols}, they must be non-null, and the dataframe must not be empty" raise ValueError(msg) Apply this suggestion Suggestion importance[1-10]: 7 Why: This suggestion is important to prevent runtime errors from operations on an empty DataFrame, which is a common source of bugs in data processing.	7
Enhancement	Use the agg method directly with a dictionary for better readability and maintainability Use agg method directly with a dictionary instead of unpacking it to improve readability and maintainability. pyretailscience/segmentation.py [178-182] stats_df = pd.concat( [ - df.groupby(segment_col).agg(**aggs), + df.groupby(segment_col).agg(aggs), pd.DataFrame(total_aggs, index=["total"]), ], ) Apply this suggestion Suggestion importance[1-10]: 5 Why: The suggestion improves readability but does not significantly impact functionality as the existing code is already correct.	5

[coderabbitai]

Actionable comments posted: 0

Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between 9a0d672 and 030e7ed.

Files selected for processing (2)

• pyretailscience/segmentation.py (5 hunks)
• tests/test_segmentation.py (1 hunks)

Additional comments not posted (10)

tests/test_segmentation.py (6)

12-23: LGTM!

The base_df fixture is well-defined and provides a comprehensive DataFrame for testing.

---

25-40: LGTM!

The test comprehensively checks multiple metrics calculated by the _calc_seg_stats method and includes a 'total' row in the expected output.

---

42-63: LGTM!

The test ensures the _calc_seg_stats method works correctly without the 'quantity' column.

---

65-70: LGTM!

The test ensures the _calc_seg_stats method does not alter the original DataFrame.

---

72-90: LGTM!

The test ensures the _calc_seg_stats method works correctly with a single-segment DataFrame and includes a 'total' row in the expected output.

---

96-101: LGTM!

The test ensures proper error handling when the DataFrame is missing a required column.

pyretailscience/segmentation.py (4)

Line range hint 35-50:
LGTM!

The ExistingSegmentation class is well-defined and the validation logic ensures the required columns are present and non-null.

---

Line range hint 53-98:
LGTM!

The HMLSegmentation class is well-defined and the segmentation logic is clear and concise.

---

145-189: LGTM!

The SegTransactionStats class is well-defined and the refactored _calc_seg_stats method provides detailed segment statistics. The plot method update allows for more flexibility in plotting.

---

Line range hint 191-243:
LGTM!

The plot method update provides more flexibility in plotting and is well-defined.

[qodo-merge-pro]

Suggestion: Use the agg method directly with a dictionary for better readability and maintainability [Enhancement, importance: 5]

Suggested change

	total_aggs["total_quantity"] = [df["quantity"].sum()]
	stats_df = pd.concat(
	[
	df.groupby(segment_col).agg(**aggs),
	stats_df = pd.concat(
	[
	df.groupby(segment_col).agg(aggs),
	pd.DataFrame(total_aggs, index=["total"]),
	],
	)

[coderabbitai]

Actionable comments posted: 0

Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between 030e7ed and 6dd0289.

Files selected for processing (1)

• pyretailscience/segmentation.py (5 hunks)

Files skipped from review as they are similar to previous changes (1)

• pyretailscience/segmentation.py