ArcPy Update Cursor vs Insert Cursor
When working with ArcPy for GIS data manipulation, understanding the differences between Update Cursors and Insert Cursors is crucial for efficient data management. This guide provides a comprehensive comparison of these two essential tools.
Overview of ArcPy Cursors
A search cursor can be used to retrieve rows. An update cursor can be used to update and delete rows, while an insert cursor is used to insert rows into a table or feature class. Both cursors are part of the arcpy.da (data access) module, which provides enhanced performance and functionality compared to legacy cursor implementations.
Update Cursor (arcpy.da.UpdateCursor)
Purpose and Functionality
The Update Cursor is designed for modifying existing records in feature classes and tables. It provides read-write access to data, allowing you to:
- Update field values in existing rows
- Delete existing rows
- Iterate through and modify records based on specific criteria
Key Characteristics
- Read-Write Access: Can both read existing values and write new ones
- Row-by-Row Processing: Iterates through existing records
- In-Place Modification: Changes are made to existing data structure
- Deletion Capability: Can remove rows using the
deleteRow()method
Syntax
python
arcpy.da.UpdateCursor(in_table, field_names, {where_clause}, {spatial_reference}, {explode_to_points}, {sql_clause}, {datum_transformation}, {explicit})Common Use Cases
- Updating attribute values based on calculations
- Modifying geometry properties
- Conditional field updates
- Data cleanup and standardization
- Deleting records that meet specific criteria
Example Implementation
import arcpy
# Update cursor example - modify existing records
with arcpy.da.UpdateCursor("roads.shp", ["SPEED_LIMIT", "ROAD_TYPE"]) as cursor:
for row in cursor:
if row[1] == "HIGHWAY":
row[0] = 65 # Update speed limit for highways
cursor.updateRow(row)
elif row[1] == "RESIDENTIAL":
cursor.deleteRow() # Remove residential roads
Insert Cursor (arcpy.da.InsertCursor)
Purpose and Functionality
The Insert Cursor is specifically designed for adding new records to feature classes and tables. It provides write-only access for creating new data entries.
Key Characteristics
- Write-Only Access: Only creates new records, cannot read existing ones
- New Record Creation: Adds entirely new rows to the dataset
- Append Operation: Data is appended to existing structure
- No Deletion Capability: Cannot remove existing records
Syntax
arcpy.da.InsertCursor(in_table, field_names, {datum_transformation}, {explicit})Common Use Cases
- Adding new features from external data sources
- Creating records from calculations or analysis
- Batch data import operations
- Programmatic feature creation
- Data migration and loading
Example Implementation
import arcpy
# Insert cursor example - add new records
with arcpy.da.InsertCursor("points.shp", ["NAME", "X_COORD", "Y_COORD", "SHAPE@XY"]) as cursor:
# Add new point features
cursor.insertRow(["Point 1", 100.5, 200.3, (100.5, 200.3)])
cursor.insertRow(["Point 2", 150.7, 180.9, (150.7, 180.9)])
Key Differences Comparison
| Aspect | Update Cursor | Insert Cursor |
|---|---|---|
| Primary Purpose | Modify existing records | Create new records |
| Data Access | Read-Write | Write-Only |
| Record Interaction | Existing rows only | New rows only |
| Performance Focus | Row-by-row updates | Batch insertions |
| Memory Usage | Moderate (reads existing data) | Lower (no read operations) |
| Typical Operations | UPDATE, DELETE | INSERT |
| Iteration Behavior | Through existing records | N/A (creates records) |
Performance Considerations
Esri has estimated that SearchCursors are up to 30 times faster, while InsertCursors are up to 12 times faster. Both cursors benefit from the arcpy.da module’s performance improvements:
Update Cursor Performance
- Most efficient when using WHERE clauses to limit processed records
- Performance decreases with complex field calculations
- Memory usage increases with large datasets due to read operations
Insert Cursor Performance
- Highly efficient for batch operations
- Minimal memory overhead
- Performance scales well with record volume
- Fastest when inserting multiple records in single session
Edit Session Requirements
Opening simultaneous insert or update operations on the same workspace using different cursors requires the start of an edit session. This is particularly important when:
- Working with geodatabase feature classes
- Performing simultaneous operations
- Working with versioned data
- Ensuring data integrity
Edit Session Example
import arcpy
# Start edit session for complex operations
workspace = r"C:\Data\MyGeodatabase.gdb"
edit = arcpy.da.Editor(workspace)
edit.startEditing(False, True)
edit.startOperation()
try:
# Perform cursor operations
with arcpy.da.UpdateCursor("features", ["FIELD1"]) as cursor:
for row in cursor:
row[0] = "Updated"
cursor.updateRow(row)
edit.stopOperation()
edit.stopEditing(True)
except Exception as e:
edit.stopOperation()
edit.stopEditing(False)
print(f"Error: {e}")
Best Practices
When to Use Update Cursor
- Data Quality Improvements: Standardizing values, correcting errors
- Conditional Updates: Modifying records based on specific criteria
- Geometry Modifications: Updating spatial properties
- Record Cleanup: Removing unwanted or duplicate entries
When to Use Insert Cursor
- Data Loading: Importing from external sources
- Batch Creation: Generating multiple new features
- Analysis Results: Creating outputs from geoprocessing
- Data Migration: Moving data between systems
General Optimization Tips
- Use Field Lists: Specify only required fields to improve performance
- Apply WHERE Clauses: Limit processing to necessary records
- Context Managers: Always use
withstatements for proper resource management - Batch Operations: Group multiple operations when possible
- Error Handling: Implement try-except blocks for robust processing
Common Pitfalls to Avoid
Update Cursor Issues
- Modifying fields not included in field list
- Attempting to update locked or protected fields
- Memory issues with large datasets without WHERE clauses
Insert Cursor Issues
- Using InsertCursor on a layer with a joined table is not supported
- Incorrect field order in insertRow values
- Missing required fields or constraint violations
Advanced Usage Scenarios
Combining Cursors for Data Transfer
# Read from source, write to destination
with arcpy.da.SearchCursor("source.shp", ["NAME", "VALUE"]) as search_cursor:
with arcpy.da.InsertCursor("destination.shp", ["NAME", "VALUE"]) as insert_cursor:
for row in search_cursor:
insert_cursor.insertRow(row)
Conditional Data Processing
# Update existing records and add new ones based on conditions
with arcpy.da.UpdateCursor("data.shp", ["STATUS", "LAST_UPDATED"]) as update_cursor:
for row in update_cursor:
if row[0] == "INACTIVE":
update_cursor.deleteRow()
else:
row[1] = datetime.datetime.now()
update_cursor.updateRow(row)
# Add replacement records
with arcpy.da.InsertCursor("data.shp", ["STATUS", "LAST_UPDATED"]) as insert_cursor:
insert_cursor.insertRow(["ACTIVE", datetime.datetime.now()])
Understanding when and how to use Update Cursors versus Insert Cursors is fundamental for effective ArcPy programming. Update Cursors excel at modifying existing data, while Insert Cursors are optimized for creating new records. Choose the appropriate cursor based on your specific data manipulation needs, and always consider performance implications and edit session requirements for your workflows.
Both cursors are powerful tools in the ArcPy ecosystem, and mastering their differences will significantly improve your GIS automation and data management capabilities.
