fixed empty plots, updated filters

03_quant_report.script.py

@@ -28,8 +28,6 @@ FILTER_CONFIG = {
     'income': 'options_income',
     'consumer': 'options_consumer',
     'business_owner': 'options_business_owner',
-    'employment_status': 'options_employment_status',
-    'personal_products': 'options_personal_products',
     'ai_user': 'options_ai_user',
     'investable_assets': 'options_investable_assets',
     'industry': 'options_industry',
@@ -257,17 +255,17 @@ mo.md(r"""
 """)
 
 # %%
-_pairwise_df, _meta = S.compute_ranking_significance(char_rank)
+# _pairwise_df, _meta = S.compute_ranking_significance(char_rank)
 
-# print(_pairwise_df.columns)
+# # print(_pairwise_df.columns)
 
-mo.md(f"""
+# mo.md(f"""
 
 
-{mo.ui.altair_chart(S.plot_significance_heatmap(_pairwise_df, metadata=_meta))}
+# {mo.ui.altair_chart(S.plot_significance_heatmap(_pairwise_df, metadata=_meta))}
 
-{mo.ui.altair_chart(S.plot_significance_summary(_pairwise_df, metadata=_meta))}
-""")
+# {mo.ui.altair_chart(S.plot_significance_summary(_pairwise_df, metadata=_meta))}
+# """)
 
 # %%
 mo.md(r"""
@@ -463,34 +461,34 @@ mo.md(r"""
 
 # %%
 # Compute pairwise significance tests
-pairwise_df, metadata = S.compute_pairwise_significance(
-    voice_1_10,
-    test_type="mannwhitney",  # or "ttest", "chi2", "auto"
-    alpha=0.05,
-    correction="bonferroni"   # or "holm", "none"
-)
+# pairwise_df, metadata = S.compute_pairwise_significance(
+#     voice_1_10,
+#     test_type="mannwhitney",  # or "ttest", "chi2", "auto"
+#     alpha=0.05,
+#     correction="bonferroni"   # or "holm", "none"
+# )
 
-# View significant pairs
-# print(pairwise_df.filter(pl.col('significant') == True))
+# # View significant pairs
+# # print(pairwise_df.filter(pl.col('significant') == True))
 
-# Create heatmap visualization
-_heatmap = S.plot_significance_heatmap(
-    pairwise_df, 
-    metadata=metadata,
-    title="Voice Rating Significance<br>(Pairwise Comparisons)"
-)
+# # Create heatmap visualization
+# _heatmap = S.plot_significance_heatmap(
+#     pairwise_df, 
+#     metadata=metadata,
+#     title="Voice Rating Significance<br>(Pairwise Comparisons)"
+# )
 
-# Create summary bar chart
-_summary = S.plot_significance_summary(
-    pairwise_df,
-    metadata=metadata
-)
+# # Create summary bar chart
+# _summary = S.plot_significance_summary(
+#     pairwise_df,
+#     metadata=metadata
+# )
 
-mo.md(f"""
-{mo.ui.altair_chart(_heatmap)}
+# mo.md(f"""
+# {mo.ui.altair_chart(_heatmap)}
 
-{mo.ui.altair_chart(_summary)}
-""")
+# {mo.ui.altair_chart(_summary)}
+# """)
 
 # %%
 

plots.py

@@ -1305,6 +1305,16 @@ class QualtricsPlotsMixin:
         # Create frequency dictionary
         trait_freq = Counter(traits_list)
         
+        # Handle empty data gracefully - return empty figure with message
+        if not trait_freq:
+            fig, ax = plt.subplots(figsize=(width/100, height/100), dpi=100)
+            ax.text(0.5, 0.5, "No trait data available for current filter", 
+                    ha='center', va='center', fontsize=14, color='gray',
+                    transform=ax.transAxes)
+            ax.axis('off')
+            ax.set_title(title, fontsize=16, pad=20, color=ColorPalette.TEXT)
+            return fig
+        
         # Set random seed for color selection
         random.seed(random_state)
         
@@ -1335,6 +1345,14 @@ class QualtricsPlotsMixin:
         fig, ax = plt.subplots(figsize=(width/100, height/100), dpi=100)
         ax.imshow(wordcloud, interpolation='bilinear')
         ax.axis('off')
+        
+        # Add title with filter subtitle (similar to _add_filter_footnote for Altair charts)
+        filter_text = self._get_filter_description()
+        if filter_text:
+            # Title on top, filter subtitle below in light grey
+            fig.suptitle(title, fontsize=16, y=0.98, color=ColorPalette.TEXT)
+            ax.set_title(filter_text, fontsize=10, pad=10, color='lightgrey', loc='left')
+        else:
             ax.set_title(title, fontsize=16, pad=20, color=ColorPalette.TEXT)
         
         plt.tight_layout(pad=0)

run_filter_combinations.py

@@ -87,7 +87,7 @@ def get_filter_combinations(survey: QualtricsSurvey) -> list[dict]:
             'filters': {'income': [income]}
         })
     
-    # Consumer segments - combine _A and _B options
+    # Consumer segments - combine _A and _B options, and also include standalone
     # Group options by base name (removing _A/_B suffix)
     consumer_groups = {}
     for consumer in survey.options_consumer:
@@ -101,12 +101,21 @@ def get_filter_combinations(survey: QualtricsSurvey) -> list[dict]:
             # Not an _A/_B option, keep as-is
             consumer_groups[consumer] = [consumer]
     
+    # Add combined _A+_B options
     for base_name, options in consumer_groups.items():
+        if len(options) > 1:  # Only combine if there are multiple (_A and _B)
             combinations.append({
                 'name': f'Consumer-{base_name}',
                 'filters': {'consumer': options}
             })
     
+    # Add standalone options (including individual _A and _B)
+    for consumer in survey.options_consumer:
+        combinations.append({
+            'name': f'Consumer-{consumer}',
+            'filters': {'consumer': [consumer]}
+        })
+    
     # Business Owner - one at a time
     for business_owner in survey.options_business_owner:
         combinations.append({
@@ -114,20 +123,6 @@ def get_filter_combinations(survey: QualtricsSurvey) -> list[dict]:
             'filters': {'business_owner': [business_owner]}
         })
     
-    # Employment Status - one at a time
-    for employment_status in survey.options_employment_status:
-        combinations.append({
-            'name': f'Employment-{employment_status}',
-            'filters': {'employment_status': [employment_status]}
-        })
-    
-    # Personal Products - one at a time
-    for personal_products in survey.options_personal_products:
-        combinations.append({
-            'name': f'Products-{personal_products}',
-            'filters': {'personal_products': [personal_products]}
-        })
-    
     # AI User - one at a time
     for ai_user in survey.options_ai_user:
         combinations.append({

utils.py

@@ -751,8 +751,6 @@ class QualtricsSurvey(QualtricsPlotsMixin):
         self.filter_ethnicity:list = None
         self.filter_income:list = None
         self.filter_business_owner:list = None      # QID4
-        self.filter_employment_status:list = None   # QID13
-        self.filter_personal_products:list = None   # QID14
         self.filter_ai_user:list = None             # QID22
         self.filter_investable_assets:list = None   # QID16
         self.filter_industry:list = None            # QID17
@@ -844,8 +842,6 @@ class QualtricsSurvey(QualtricsPlotsMixin):
         self.options_ethnicity = sorted(df['QID3'].drop_nulls().unique().to_list()) if 'QID3' in df.columns else []
         self.options_income = sorted(df['QID15'].drop_nulls().unique().to_list()) if 'QID15' in df.columns else []
         self.options_business_owner = sorted(df['QID4'].drop_nulls().unique().to_list()) if 'QID4' in df.columns else []
-        self.options_employment_status = sorted(df['QID13'].drop_nulls().unique().to_list()) if 'QID13' in df.columns else []
-        self.options_personal_products = sorted(df['QID14'].drop_nulls().unique().to_list()) if 'QID14' in df.columns else []
         self.options_ai_user = sorted(df['QID22'].drop_nulls().unique().to_list()) if 'QID22' in df.columns else []
         self.options_investable_assets = sorted(df['QID16'].drop_nulls().unique().to_list()) if 'QID16' in df.columns else []
         self.options_industry = sorted(df['QID17'].drop_nulls().unique().to_list()) if 'QID17' in df.columns else []
@@ -865,7 +861,7 @@ class QualtricsSurvey(QualtricsPlotsMixin):
         
         return q.select(QIDs).rename(rename_dict)
 
-    def filter_data(self, q: pl.LazyFrame, age:list=None, gender:list=None, consumer:list=None, ethnicity:list=None, income:list=None, business_owner:list=None, employment_status:list=None, personal_products:list=None, ai_user:list=None, investable_assets:list=None, industry:list=None) -> pl.LazyFrame:
+    def filter_data(self, q: pl.LazyFrame, age:list=None, gender:list=None, consumer:list=None, ethnicity:list=None, income:list=None, business_owner:list=None, ai_user:list=None, investable_assets:list=None, industry:list=None) -> pl.LazyFrame:
         """Filter data based on provided parameters
         
         Possible parameters:
@@ -875,8 +871,6 @@ class QualtricsSurvey(QualtricsPlotsMixin):
         - ethnicity: list (QID3)
         - income: list (QID15)
         - business_owner: list (QID4)
-        - employment_status: list (QID13)
-        - personal_products: list (QID14)
         - ai_user: list (QID22)
         - investable_assets: list (QID16)
         - industry: list (QID17)
@@ -884,49 +878,41 @@ class QualtricsSurvey(QualtricsPlotsMixin):
         Also saves the result to self.data_filtered.
         """
         
-        # Apply filters
+        # Apply filters - skip if empty list (columns with all NULLs produce empty options)
         self.filter_age = age
-        if age is not None:
+        if age is not None and len(age) > 0:
             q = q.filter(pl.col('QID1').is_in(age))
         
         self.filter_gender = gender
-        if gender is not None:
+        if gender is not None and len(gender) > 0:
             q = q.filter(pl.col('QID2').is_in(gender))
         
         self.filter_consumer = consumer
-        if consumer is not None:
+        if consumer is not None and len(consumer) > 0:
             q = q.filter(pl.col('Consumer').is_in(consumer))
         
         self.filter_ethnicity = ethnicity
-        if ethnicity is not None:
+        if ethnicity is not None and len(ethnicity) > 0:
             q = q.filter(pl.col('QID3').is_in(ethnicity))
         
         self.filter_income = income
-        if income is not None:
+        if income is not None and len(income) > 0:
             q = q.filter(pl.col('QID15').is_in(income))
         
         self.filter_business_owner = business_owner
-        if business_owner is not None:
+        if business_owner is not None and len(business_owner) > 0:
             q = q.filter(pl.col('QID4').is_in(business_owner))
         
-        self.filter_employment_status = employment_status
-        if employment_status is not None:
-            q = q.filter(pl.col('QID13').is_in(employment_status))
-        
-        self.filter_personal_products = personal_products
-        if personal_products is not None:
-            q = q.filter(pl.col('QID14').is_in(personal_products))
-        
         self.filter_ai_user = ai_user
-        if ai_user is not None:
+        if ai_user is not None and len(ai_user) > 0:
             q = q.filter(pl.col('QID22').is_in(ai_user))
         
         self.filter_investable_assets = investable_assets
-        if investable_assets is not None:
+        if investable_assets is not None and len(investable_assets) > 0:
             q = q.filter(pl.col('QID16').is_in(investable_assets))
         
         self.filter_industry = industry
-        if industry is not None:
+        if industry is not None and len(industry) > 0:
             q = q.filter(pl.col('QID17').is_in(industry))
         
         self.data_filtered = q