Remove emoji slop from public-facing documentation

Clean up documentation for professional appearance by removing all emojis
from README and tutorial files:

- README.md: Remove emojis from section headers and bullet points
- docs/tutorials/getting_started.md: Remove emojis from all print statements
  and code output formatting
- docs/tutorials/caching_data.md: Remove emoji from summary section
- docs/tutorials/working_with_geometry.md: Remove emoji from summary section

Emojis are a tell-tale sign of AI-assisted writing and detract from
professional technical documentation.

Author: dshkol

README.md

@@ -10,35 +10,35 @@ Access, retrieve, and work with Canadian Census data and geography.
 
 **pycancensus** is a Python package that provides integrated, convenient, and uniform access to Canadian Census data and geography retrieved using the CensusMapper API. This package produces analysis-ready tidy DataFrames and spatial data in multiple formats, with full equivalence to the R cancensus library.
 
-## 🚀 **Recent Updates**
+## Recent Updates
 
-✅ **Full R Library Equivalence** - Verified 100% data compatibility with R cancensus  
-✅ **Enhanced API Reliability** - Production-grade error handling and retry logic  
-✅ **Vector Hierarchy Functions** - Navigate census variable relationships like R  
-✅ **Improved Data Quality** - Fixed column naming and data processing issues  
-✅ **Comprehensive Testing** - 450+ integration tests covering real-world scenarios
+**Full R Library Equivalence** - Verified 100% data compatibility with R cancensus
+**Enhanced API Reliability** - Production-grade error handling and retry logic
+**Vector Hierarchy Functions** - Navigate census variable relationships like R
+**Improved Data Quality** - Fixed column naming and data processing issues
+**Comprehensive Testing** - 450+ integration tests covering real-world scenarios
 
 ## Features
 
-### 📊 **Data Access**
+### Data Access
 * Download Census data and geography in analysis-ready format
 * Support for multiple Census years: 2021, 2016, 2011, 2006, 2001, 1996
 * All Census geographic levels: PR, CMA, CD, CSD, CT, DA, EA, DB
 * Taxfiler data at Census Tract level (2000-2018)
 
-### 🔍 **Variable Discovery**
-* **`list_census_vectors()`** - Browse all available variables
-* **`search_census_vectors()`** - Search variables by keyword
-* **`parent_census_vectors()`** - Navigate variable hierarchies upward
-* **`child_census_vectors()`** - Navigate variable hierarchies downward  
-* **`find_census_vectors()`** - Enhanced variable search with fuzzy matching
+### Variable Discovery
+* `list_census_vectors()` - Browse all available variables
+* `search_census_vectors()` - Search variables by keyword
+* `parent_census_vectors()` - Navigate variable hierarchies upward
+* `child_census_vectors()` - Navigate variable hierarchies downward
+* `find_census_vectors()` - Enhanced variable search with fuzzy matching
 
-### 🗺️ **Geographic Capabilities**
+### Geographic Capabilities
 * GeoPandas integration for spatial analysis
 * Multiple resolution options (simplified/high)
 * Seamless geometry + data integration
 
-### 🛡️ **Reliability & Performance**
+### Reliability & Performance
 * Production-grade error handling with helpful messages
 * Automatic retry logic with exponential backoff
 * Connection pooling for improved performance
@@ -119,7 +119,7 @@ data_2021 = pc.get_census("CA21", {"CSD": "5915022"}, ["v_CA21_1"], "CSD")
 data_2016 = pc.get_census("CA16", {"CSD": "5915022"}, ["v_CA16_401"], "CSD")
 ```
 
-## 🔍 **Variable Discovery Examples**
+## Variable Discovery Examples
 
 ```python
 # Search for housing-related variables
@@ -134,7 +134,7 @@ parent_categories = pc.parent_census_vectors(population_base, dataset="CA21")
 income_vectors = pc.find_census_vectors("CA21", "median household income")
 ```
 
-## 🛡️ **Error Handling & Resilience**
+## Error Handling & Resilience
 
 pycancensus includes production-grade error handling:
 
@@ -151,7 +151,7 @@ except CensusAPIError as e:
     print(f"Suggestion: {e.suggestion}")
 ```
 
-## 🧪 **Testing & Verification**
+## Testing & Verification
 
 pycancensus includes comprehensive testing to ensure reliability and R equivalence:
 

docs/tutorials/caching_data.md

@@ -485,7 +485,7 @@ monitor.get_stats()
 
 This tutorial covered comprehensive cache management for pycancensus:
 
-✅ **Key Concepts Learned:**
+**Key Concepts Learned:**
 - How pycancensus caching works automatically 
 - Configuring cache location and settings
 - Measuring cache performance improvements

docs/tutorials/getting_started.md

@@ -16,10 +16,10 @@ kernelspec:
 This tutorial demonstrates the enhanced pycancensus functionality with clear hierarchy examples and real data access.
 
 ## Key Features Demonstrated:
-- 📊 **list_census_vectors()** - Browse all available data variables
-- 🌳 **Vector Hierarchies** - Navigate parent-child relationships
-- 🔍 **find_census_vectors()** - Smart search functionality
-- 📈 **Real Data Retrieval** - Get actual census data
+- **list_census_vectors()** - Browse all available data variables
+- **Vector Hierarchies** - Navigate parent-child relationships
+- **find_census_vectors()** - Smart search functionality
+- **Real Data Retrieval** - Get actual census data
 
 ```{note}
 You'll need a free API key from [CensusMapper](https://censusmapper.ca/users/sign_up) to run these examples with real data.
@@ -43,7 +43,7 @@ import pandas as pd
 
 # Set your API key (replace with your actual key)
 # pycancensus.set_api_key("your_api_key_here")
-print("✅ pycancensus imported successfully!")
+print("pycancensus imported successfully!")
 ```
 
 ## 1. Exploring Census Vectors
@@ -54,12 +54,12 @@ The `list_census_vectors()` function shows all available data variables:
 # List all vectors for 2021 Census
 try:
     vectors_ca21 = list_census_vectors('CA21')
-    print(f"📊 CA21 Census has {len(vectors_ca21):,} vectors available")
-    print(f"📋 Columns: {list(vectors_ca21.columns)}")
-    
+    print(f"CA21 Census has {len(vectors_ca21):,} vectors available")
+    print(f"Columns: {list(vectors_ca21.columns)}")
+
     # Show how many vectors have parent relationships
     with_parents = vectors_ca21[vectors_ca21['parent_vector'].notna()]
-    print(f"🔗 Vectors with parent relationships: {len(with_parents):,} out of {len(vectors_ca21):,}")
+    print(f"Vectors with parent relationships: {len(with_parents):,} out of {len(vectors_ca21):,}")
     print("\nSample hierarchy examples:")
     display(with_parents[['vector', 'parent_vector', 'label']].head())
     
@@ -80,9 +80,9 @@ try:
     # Get the vector details for context
     income_info = vectors_ca21[vectors_ca21['vector'] == income_root]
     if not income_info.empty:
-        print(f"🌳 Household Income Hierarchy\n")
-        print(f"📊 ROOT: {income_root} - {income_info['label'].iloc[0][:50]}...")
-        print(f"\n📊 LEVEL 1 - Income Brackets:")
+        print(f"Household Income Hierarchy\n")
+        print(f"ROOT: {income_root} - {income_info['label'].iloc[0][:50]}...")
+        print(f"\nLEVEL 1 - Income Brackets:")
     
     # Get its direct children (income brackets)
     income_children = child_census_vectors(income_root, 'CA21')
@@ -98,16 +98,16 @@ except Exception as e:
 try:
     # Drill down into the high-income bracket (shows grandparent -> parent -> child)
     high_income_bracket = "v_CA21_939"  # $100,000 and over
-    print(f"📊 LEVEL 2 - High-income sub-categories for '{high_income_bracket}':")
-    
+    print(f"LEVEL 2 - High-income sub-categories for '{high_income_bracket}':")
+
     # Get the children of the $100,000+ bracket
     high_income_subcats = child_census_vectors(high_income_bracket, 'CA21')
     display(high_income_subcats[['vector', 'label', 'parent_vector']])
-    
+
     # Show the parent relationship for context
     parent_info = parent_census_vectors(high_income_bracket, 'CA21')
     if not parent_info.empty:
-        print(f"\n⬆️  Parent of this bracket: {parent_info['vector'].iloc[0]} - {parent_info['label'].iloc[0][:50]}...")
+        print(f"\nParent of this bracket: {parent_info['vector'].iloc[0]} - {parent_info['label'].iloc[0][:50]}...")
     
 except Exception as e:
     print(f"Error exploring detailed hierarchy: {e}")
@@ -122,7 +122,7 @@ try:
     # Find parent of a specific income bracket
     income_bracket = "v_CA21_942"  # $150,000 to $199,999
     parent = parent_census_vectors(income_bracket, 'CA21')
-    print(f"⬆️  Finding parent of income bracket '{income_bracket}':")
+    print(f"Finding parent of income bracket '{income_bracket}':")
     display(parent[['vector', 'label', 'parent_vector']])
     
 except Exception as e:
@@ -137,7 +137,7 @@ The `find_census_vectors()` function provides smart search with relevance scorin
 try:
     # Search for income-related vectors
     income_vectors = find_census_vectors('CA21', 'income')
-    print(f"🔍 Found {len(income_vectors)} income-related vectors")
+    print(f"Found {len(income_vectors)} income-related vectors")
     print(f"\nTop income vectors (sorted by relevance):")
     display(income_vectors[['vector', 'label', 'relevance_score']].head(3))
     
@@ -160,7 +160,7 @@ try:
         use_cache=False
     )
     
-    print(f"📈 Toronto CMA Income Demographics:")
+    print(f"Toronto CMA Income Demographics:")
     print(f"\nHousehold Income Distribution:")
     total_households = toronto_data['v_CA21_923'].iloc[0]
     high_income = toronto_data['v_CA21_939'].iloc[0]  # $100,000+
@@ -179,7 +179,7 @@ except Exception as e:
 
 ## Summary
 
-✅ **This tutorial demonstrates the enhanced pycancensus capabilities:**
+**This tutorial demonstrates the enhanced pycancensus capabilities:**
 
 1. **list_census_vectors()** - Browse 7,709+ available variables with explicit parent-child relationships
 2. **Hierarchy Navigation** - Navigate through income hierarchies from main categories to detailed brackets

docs/tutorials/working_with_geometry.md

@@ -398,7 +398,7 @@ print(f"Average income: ${vancouver_data['v_CA21_434'].mean():,.0f}")
 
 This tutorial covered the essential aspects of working with geographic census data:
 
-✅ **Key Skills Learned:**
+**Key Skills Learned:**
 - Retrieving census data with geographic boundaries
 - Understanding Canadian census geographic levels
 - Creating choropleth maps with matplotlib