Fix pandas attrs comparison bug and add labels='short' to tutorial

## Issues Fixed

### 1. Pandas attrs comparison bug causing nlargest() to fail
- **Bug**: Storing DataFrame in attrs["census_vectors"] caused pandas to fail
  when comparing attrs during operations like nlargest()
- **Error**: "ValueError: The truth value of a DataFrame is ambiguous"
- **Fix**: Store metadata as list of dicts instead of DataFrame in core.py:358
- **Fix**: Update label_vectors() to convert list back to DataFrame in vectors.py:53

### 2. Missing labels='short' in getting_started tutorial
- **Bug**: Tutorial code tried to access 'v_CA21_923' but columns had full labels
- **Error**: "KeyError: 'v_CA21_923'"
- **Fix**: Added labels='short' parameter in getting_started.md:160

## Build Results
- All tutorials now execute successfully ✅
- No execution errors in documentation build ✅
- Population density calculations work correctly ✅

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: dshkol

docs/tutorials/getting_started.md

@@ -157,6 +157,7 @@ try:
         regions={'CMA': '35535'},  # Toronto CMA
         vectors=['v_CA21_923', 'v_CA21_939', 'v_CA21_942', 'v_CA21_943'],  # Income categories
         level='CMA',
+        labels='short',
         use_cache=False
     )
     

pycancensus/core.py

@@ -353,8 +353,9 @@ def _extract_vector_metadata(df, vectors, labels):
         if rename_dict:
             df = df.rename(columns=rename_dict)
 
-        # Store metadata as attribute (always store, but mainly useful with short labels)
-        df.attrs["census_vectors"] = metadata_df
+        # Store metadata as dict to avoid pandas attrs comparison bug
+        # Convert DataFrame to list of dicts for storage
+        df.attrs["census_vectors"] = metadata_df.to_dict(orient='records')
 
     return df
 

pycancensus/vectors.py

@@ -48,7 +48,14 @@ def label_vectors(x):
     import warnings
 
     if hasattr(x, "attrs") and "census_vectors" in x.attrs:
-        return x.attrs["census_vectors"]
+        # Convert stored dict back to DataFrame
+        metadata = x.attrs["census_vectors"]
+        if isinstance(metadata, list):
+            # Stored as list of dicts
+            return pd.DataFrame(metadata)
+        else:
+            # Already a DataFrame (legacy)
+            return metadata
     else:
         warnings.warn(
             "Data does not have variables to labels. No Census variables selected "