Fixed duplicate neighbouring names and ids

[pushpak-gote-mapup]

User description

Modified script to remove duplicate neighbouring names and IDs

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PR Type

enhancement

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Description

• Enhanced the group_and_aggregate function by adding logic to drop duplicate rows based on specific columns before performing group and aggregate operations.
• Introduced a list drop_duplicates_cols to specify columns for duplicate removal, ensuring unique entries before aggregation.
• Commented out a line for renaming a column in the state_road dataset, indicating a potential future change or consideration.

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Changes walkthrough 📝

	Relevant files
Enhancement	get_neighbouring_roads.py Enhance grouping by removing duplicate rows and update column handling mile-point-approach/get_neighbouring_roads.py Added logic to drop duplicate rows based on specific columns before grouping. Introduced a list drop_duplicates_cols to specify columns for duplicate removal. Commented out a line for renaming a column in the state_road dataset. +9/-2

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[github-actions]

PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Key issues to review Code Redundancy The new code line at 210 seems to be a redundant addition, as it simply re-adds a previously commented-out argument description without any changes. Commented Code The commented code at lines 291-292 might lead to confusion or be accidentally uncommented without proper review. It's generally a good practice to remove or clearly justify commented-out code.

[github-actions]

PR Code Suggestions ✨

Category	Suggestion	Score
Possible issue	Handle potential NaN values in data aggregation to avoid 'nan' in output strings The aggregation functions convert values to strings and join them, which might not handle NaN values gracefully. Consider using dropna() before the join() to ensure no 'nan' strings are included in the output. mile-point-approach/get_neighbouring_roads.py [223-224] -'created_unique_id_1_right': lambda x: ', '.join(x.astype(str)), -'RD_NAME_right': lambda x: ', '.join(x.astype(str)), +'created_unique_id_1_right': lambda x: ', '.join(x.dropna().astype(str)), +'RD_NAME_right': lambda x: ', '.join(x.dropna().astype(str)), Suggestion importance[1-10]: 9 Why: The suggestion effectively handles NaN values during aggregation, preventing 'nan' strings in the output, which is crucial for data integrity.	9
Possible bug	Ensure the column exists before renaming to avoid errors The renaming of columns should be conditional based on the presence of the column to avoid potential errors if the column does not exist. Use if 'NAME' in state_road.columns: before renaming. mile-point-approach/get_neighbouring_roads.py [291-292] -#Change 'NAME' to column name that contains road names from state_road dataset -# state_road.rename(columns={'NAME':'RD_NAME'}, inplace=True) +if 'NAME' in state_road.columns: + state_road.rename(columns={'NAME':'RD_NAME'}, inplace=True) Suggestion importance[1-10]: 8 Why: This suggestion addresses a potential bug by ensuring that the column exists before attempting to rename it, which prevents runtime errors.	8
Maintainability	Separate the concerns of dropping duplicates and grouping to simplify the code The list drop_duplicates_cols includes columns for dropping duplicates and then grouping by some of them. However, the grouping columns are repeated in the list, which is redundant. Simplify the list by separating the concerns of dropping duplicates and grouping. mile-point-approach/get_neighbouring_roads.py [221-225] drop_duplicates_cols=['geometry', 'created_unique_id_1_left', 'bridge_id_left','created_unique_id_1_right','RD_NAME_right'] -grouped = df[drop_duplicates_cols].drop_duplicates().groupby(['geometry', 'created_unique_id_1_left', 'bridge_id_left']).agg({ +grouping_cols=['geometry', 'created_unique_id_1_left', 'bridge_id_left'] +grouped = df.drop_duplicates(subset=drop_duplicates_cols).groupby(grouping_cols).agg({ 'created_unique_id_1_right': lambda x: ', '.join(x.astype(str)), 'RD_NAME_right': lambda x: ', '.join(x.astype(str)), }).reset_index() Suggestion importance[1-10]: 7 Why: The suggestion improves code maintainability by separating the logic for dropping duplicates and grouping, making the code more readable and easier to modify.	7
Performance	Optimize the groupby operation by avoiding an unnecessary reset_index() The groupby operation is followed by a reset_index() which could be optimized by using as_index=False in the groupby method to avoid the extra step. mile-point-approach/get_neighbouring_roads.py [222-225] -grouped = df[drop_duplicates_cols].drop_duplicates().groupby(['geometry', 'created_unique_id_1_left', 'bridge_id_left']).agg({ +grouped = df[drop_duplicates_cols].drop_duplicates().groupby(['geometry', 'created_unique_id_1_left', 'bridge_id_left'], as_index=False).agg({ 'created_unique_id_1_right': lambda x: ', '.join(x.astype(str)), 'RD_NAME_right': lambda x: ', '.join(x.astype(str)), -}).reset_index() +}) Suggestion importance[1-10]: 6 Why: The suggestion offers a minor performance improvement by eliminating an unnecessary step, making the code slightly more efficient.	6

[pushpak-gote-mapup]

Did changes as per necessary bot review:
Commented Code
The commented code at lines 291-292 might lead to confusion or be accidentally uncommented without proper review. It's generally a good practice to remove or clearly justify commented-out code.
Solved
Changed comment