Fix: skip NA values in intensities in transform_ibaq

[yueqixuan]

PR Type

Bug fix

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Description

• Add NA value handling in transform_ibaq function

• Drop rows with NaN intensities before transformation

• Add warning log for NA detection

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Diagram Walkthrough

flowchart LR
  A["Input DataFrame"] --> B["Check for NA values"]
  B --> C["Log warning if NA found"]
  C --> D["Drop NA rows"]
  D --> E["Continue transformation"]

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File Walkthrough

	Relevant files
Bug fix	tools.py Add NA handling in transform_ibaq                                                quantmsio/operate/tools.py Add NA value detection and warning logging Drop rows with NaN values in intensities column Prevent errors during DataFrame transformation +8/-0

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[qodo-merge-pro]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

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

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Logging Grammar The warning message has a minor grammatical issue; consider "contains" instead of "contain" to improve clarity. logging.warning( "[transform_ibaq]: The 'intensities' column contain NaN values." ) df.dropna(subset=["intensities"], inplace=True) Inplace Mutation Using inplace=True on dropna mutates the original DataFrame; verify this function is expected to modify the input or work on a copy to avoid side effects. df.dropna(subset=["intensities"], inplace=True) Logging Import Ensure that the logging module is imported/configured in this module; otherwise the warning may fail or be silenced depending on project setup. logging.warning( "[transform_ibaq]: The 'intensities' column contain NaN values." )

[qodo-merge-pro]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
General	Improve performance by avoiding row-wise apply Improve performance by replacing the row-wise DataFrame.apply with a vectorized approach using the .str accessor to extract dictionary values into new columns. quantmsio/operate/tools.py [292-294] -df[["sample_accession", "channel", "intensity"]] = df[["intensities"]].apply( - transform, axis=1, result_type="expand" -) +df["sample_accession"] = df["intensities"].str["sample_accession"] +df["channel"] = df["intensities"].str["channel"] +df["intensity"] = df["intensities"].str["intensity"] Apply / Chat Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies a performance bottleneck with apply(axis=1) and proposes a significantly more efficient and idiomatic vectorized solution using the .str accessor.	Medium
	Avoid inplace operations and simplify logic Avoid using inplace=True by reassigning the DataFrame after dropping NaNs. Also, simplify the logic by checking for dropped rows after the operation to decide whether to log a warning. quantmsio/operate/tools.py [284-289] -# Check for NA in the "intensities" column -if df["intensities"].isna().any(): +# Check for NA in the "intensities" column and drop them +rows_before_drop = len(df) +df = df.dropna(subset=["intensities"]) +if len(df) < rows_before_drop: logging.warning( - "[transform_ibaq]: The 'intensities' column contain NaN values." + "[transform_ibaq]: The 'intensities' column contained NaN values, which were removed." ) - df.dropna(subset=["intensities"], inplace=True) Apply / Chat Suggestion importance[1-10]: 5 __ Why: The suggestion correctly advises against using inplace=True, which is a pandas best practice to avoid side effects and SettingWithCopyWarning, and proposes a cleaner implementation.	Low
Update

[Copilot]

Pull Request Overview

This PR fixes a bug in the transform_ibaq function by adding proper handling for NA/NaN values in the intensities column to prevent transformation errors.

• Adds NA value detection with warning logging
• Removes rows containing NaN intensities before transformation
• Prevents downstream errors during DataFrame processing

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