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analysis/generators/time_estimate_spread_by_occupation.py

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+import seaborn as sns
+import matplotlib.pyplot as plt
+from pathlib import Path
+import tempfile
+import logging
+import pandas as pd
+
+# Based on O*NET SOC 2018 structure, this mapping helps translate
+# the 2-digit major group codes into human-readable labels.
+OCCUPATION_MAJOR_CODES = {
+    '11': 'Management',
+    '13': 'Business & Financial',
+    '15': 'Computer & Mathematical',
+    '17': 'Architecture & Engineering',
+    '19': 'Life, Physical, & Social Science',
+    '21': 'Community & Social Service',
+    '23': 'Legal',
+    '25': 'Education, Training, & Library',
+    '27': 'Arts, Design, & Media',
+    '29': 'Healthcare Practitioners',
+    '31': 'Healthcare Support',
+    '33': 'Protective Service',
+    '35': 'Food Preparation & Serving',
+    '37': 'Building & Grounds Maintenance',
+    '39': 'Personal Care & Service',
+    '41': 'Sales & Related',
+    '43': 'Office & Admin Support',
+    '45': 'Farming, Fishing, & Forestry',
+    '47': 'Construction & Extraction',
+    '49': 'Installation, Maintenance, & Repair',
+    '51': 'Production',
+    '53': 'Transportation & Material Moving',
+    '55': 'Military Specific',
+}
+
+
+def generate(processed_df: pd.DataFrame):
+    """
+    Generates a box plot showing the spread of time-range estimates per occupation.
+
+    This corresponds to 'cell2' from the original analysis notebook. It visualizes
+    the distribution of the difference between upper and lower time estimates for
+    each major occupational group.
+
+    Args:
+        processed_df (pd.DataFrame): The preprocessed data. Expected columns:
+                                     'lb_estimate_in_minutes',
+                                     'ub_estimate_in_minutes', 'onetsoc_major'.
+
+    Returns:
+        Path: The path to the generated temporary image file, or None on failure.
+    """
+    logging.info("Generating plot of time estimate spread by occupation...")
+
+    # --- Data Validation and Preparation ---
+    required_cols = ['lb_estimate_in_minutes', 'ub_estimate_in_minutes', 'onetsoc_major']
+    if not all(col in processed_df.columns for col in required_cols):
+        logging.error(f"Missing one or more required columns: {required_cols}. Cannot generate plot.")
+        return None
+
+    df = processed_df.copy()
+
+    # Calculate the estimate range.
+    df['estimate_range'] = df['ub_estimate_in_minutes'] - df['lb_estimate_in_minutes']
+
+    # For log scaling, we need positive values. Filter out any non-positive ranges.
+    df = df[df['estimate_range'] > 0]
+    if df.empty:
+        logging.warning("No data with a positive estimate range available to plot.")
+        return None
+
+    # Sort by the major code to ensure a consistent plot order
+    df = df.sort_values('onetsoc_major')
+
+    # --- Plotting ---
+    try:
+        plt.figure(figsize=(14, 10))
+
+        ax = sns.boxplot(
+            data=df,
+            x='onetsoc_major',
+            y='estimate_range',
+            showfliers=False  # Outliers are excluded for a clearer view of the main distribution
+        )
+
+        plt.yscale('log')  # The long tail of the data makes a log scale more readable
+        plt.xlabel('Occupation Major Group', fontsize=12)
+        plt.ylabel('Time Estimate Range (upper - lower, in minutes, log scale)', fontsize=12)
+        plt.title('Spread of Time-Range Estimates by Occupation', fontsize=16)
+
+        # Replace numeric x-tick labels (e.g., '11', '15') with meaningful text labels
+        ax.set_xticklabels(
+            [OCCUPATION_MAJOR_CODES.get(code.get_text(), code.get_text()) for code in ax.get_xticklabels()],
+            rotation=60,
+            ha='right' # Align rotated labels correctly
+        )
+
+        plt.tight_layout()
+
+        # --- File Saving ---
+        temp_dir = tempfile.gettempdir()
+        temp_path = Path(temp_dir) / "time_estimate_spread_by_occupation.png"
+        plt.savefig(temp_path, dpi=300, bbox_inches='tight')
+        logging.info(f"Successfully saved plot to temporary file: {temp_path}")
+
+        return temp_path
+
+    except Exception as e:
+        logging.error(f"An error occurred while generating the plot: {e}", exc_info=True)
+        return None
+    finally:
+        plt.close()