ENH: Improve Code Quality in pandas/core/reshape Module

[Koookadooo]

Summary

Refactor the pandas/core/reshape module to improve code quality by reducing duplication, replacing hard-coded values, and simplifying complex conditionals.

Problem Description

The pandas/core/reshape module implements key reshaping functions (pivot, melt, and unstack) used in data manipulation workflows. A review of pivot.py and melt.py reveals a couple of areas where code quality could be improved:

Nested Conditionals:

• In melt.py, nested conditionals add complexity, making the code harder to read and maintain.
• Suggestion: Refactor these conditionals into smaller, more modular functions.

Hard-Coded Values:

• In pivot.py, hard-coded strings (e.g., "All" for margins) reduce flexibility.
• Suggestion: Replace hard-coded values with constants for maintainability.

Relevant File

• melt.py
• pivot.py

Proposed Solution

Refactor Nested Conditionals in melt.py

• Nested Conditional in ensure_list_vars()
  • Before:

  def ensure_list_vars(arg_vars, variable: str, columns) -> list:
    if arg_vars is not None:
        if not is_list_like(arg_vars):
            return [arg_vars]
        elif isinstance(columns, MultiIndex) and not isinstance(arg_vars, list):
            raise ValueError(
                f"{variable} must be a list of tuples when columns are a MultiIndex"
            )
        else:
            return list(arg_vars)
    else:
        return []

  • After:

  def ensure_list_vars(arg_vars, variable: str, columns) -> list:
  if arg_vars is None:
      return []
  
  if not is_list_like(arg_vars):
      return [arg_vars]
  
  if isinstance(columns, MultiIndex) and not isinstance(arg_vars, list):
      raise ValueError(
          f"{variable} must be a list of tuples when columns are a MultiIndex"
      )
  
  return list(arg_vars)

• Nested Conditional in melt() for id_vars:
  • Before:

  if id_vars or value_vars:
    if col_level is not None:
        level = frame.columns.get_level_values(col_level)
    else:
        level = frame.columns
    labels = id_vars + value_vars
    idx = level.get_indexer_for(labels)
    missing = idx == -1
    if missing.any():
        missing_labels = [
            lab for lab, not_found in zip(labels, missing) if not_found
        ]
        raise KeyError(
            "The following id_vars or value_vars are not present in "
            f"the DataFrame: {missing_labels}"
        )
    if value_vars_was_not_none:
      frame = frame.iloc[:, algos.unique(idx)]
    else:
      frame = frame.copy(deep=False)
  else:
    frame = frame.copy(deep=False)

  • After:

  def validate_and_get_level(frame, id_vars, value_vars, col_level):
    level = frame.columns.get_level_values(col_level) if col_level is not None else frame.columns
    labels = id_vars + value_vars
    idx = level.get_indexer_for(labels)
    missing = idx == -1
    if missing.any():
        missing_labels = [lab for lab, not_found in zip(labels, missing) if not_found]
        raise KeyError(
            "The following id_vars or value_vars are not present in "
            f"the DataFrame: {missing_labels}"
        )
    return idx
  
  if id_vars or value_vars:
      idx = validate_and_get_level(frame, id_vars, value_vars, col_level)
      if value_vars_was_not_none:
          frame = frame.iloc[:, algos.unique(idx)]
  else:
      frame = frame.copy(deep=False)

• Nested Conditionals for Setting var_name in melt():
  • Before:

  if var_name is None:
    if isinstance(frame.columns, MultiIndex):
        if len(frame.columns.names) == len(set(frame.columns.names)):
            var_name = frame.columns.names
        else:
            var_name = [f"variable_{i}" for i in range(len(frame.columns.names))]
    else:
        var_name = [
            frame.columns.name if frame.columns.name is not None else "variable"
        ]
  elif is_list_like(var_name):
    if isinstance(frame.columns, MultiIndex):
        if is_iterator(var_name):
            var_name = list(var_name)
        if len(var_name) > len(frame.columns):
            raise ValueError(
                f"{var_name=} has {len(var_name)} items, "
                f"but the dataframe columns only have {len(frame.columns)} levels."
            )
    else:
        raise ValueError(f"{var_name=} must be a scalar.")
  else:
    var_name = [var_name]

  After:

  def determine_var_name(frame, var_name):
    if var_name is None:
        return _default_var_name(frame)
    if is_list_like(var_name):
        _validate_list_var_name(var_name, frame)
        return list(var_name)
    return [var_name]
  
  def _default_var_name(frame):
    if isinstance(frame.columns, MultiIndex):
        if len(frame.columns.names) == len(set(frame.columns.names)):
            return frame.columns.names
        return [f"variable_{i}" for i in range(len(frame.columns.names))]
    return [frame.columns.name or "variable"]
  
  def _validate_list_var_name(var_name, frame):
    if isinstance(frame.columns, MultiIndex):
        if is_iterator(var_name):
            var_name = list(var_name)
        if len(var_name) > len(frame.columns):
            raise ValueError(
                f"{var_name=} has {len(var_name)} items, "
                f"but the dataframe columns only have {len(frame.columns)} levels."
            )
    else:
        raise ValueError(f"{var_name=} must be a scalar.")
  
  var_name = determine_var_name(frame, var_name)

• Benefits:
  • Improves readability:
    Simplifies the main function, making the logic clearer and easier to follow.
  • Makes the logic easier to test and maintain:
    Enables independent testing of each helper function, ensuring robust behavior.
  • Separation of concerns:
    Each helper function is now responsible for a single, well-defined task, aligning with the principle of single responsibility.

Replace Hard-Coded Values in pivot.py

• Before:

# Hard-coded string for margins
margins_name: Hashable = "All"

• After:

# Define a constant for the hard-coded value
MARGIN_NAME = "All"

# Use the constant in the code
margins_name: Hashable = MARGIN_NAME:

• Benefits:
  • Makes the code more readable and maintainable.
  • Centralizes the value so it can be reused or modified easily.

Testing

Unit Testing Helper Functions:
Write focused tests for each new helper function to validate their behavior under expected, edge, and erroneous inputs. For example:

• Ensure validate_and_get_level() correctly identifies missing variables and raises KeyError.
• Test determine_var_name() with var_name=None, scalar inputs, and multi-level columns.

Regression Testing Parent Functions:
Run all pre-existing tests for the parent functions (e.g., melt()) to confirm they maintain their functionality after the refactor.

Edge Cases:
Include additional tests for edge scenarios, such as:

• Empty id_vars or value_vars.
• DataFrames with unusual column configurations like MultiIndex or missing names.

Labels

• ENH
• Code Quality

Compliance with Contributing Guide

• Focus: The issue is specific and addresses code quality improvements without scope creep.
• Clarity: Includes actionable suggestions and a clear implementation path.

Please provide feedback and let me know if you would like further refinements!