Best Practices For Handling Field Data Types In Arcpy

Understanding Field Data Types

When working with geospatial data in ArcGIS, choosing appropriate data types for fields is critical for ensuring data quality and enabling desired analyses. The main data types used for fields in the ArcGIS geodatabase are:

  • Numeric – For quantitative data like measurements. Stored as double precision floating point numbers by default.
  • String – For textual qualitative data. Stored as Unicode text by default with a max length set by the field size.
  • Date – For temporal date and time data. Stored in a date/time format.
  • Blob – For binary large object data like images or files. Stored as blobs.
  • Raster – For raster dataset storage. Stored as raster data.

Numeric fields should contain only quantitative data like amounts, ratios, or measurements. Text and qualitative categories should not be placed in numeric fields. Strings are appropriate for codes, names, descriptions and other textual attributes. Date fields should store properly formatted temporal values, while blob and raster fields store related multimedia and imagery data.

Mismatched data types can lead to errors during analysis tools, cause truncated or rounded values, or prevent certain functions like sorting properly. Setting field types accurately for the intended data is crucial for maintaining data integrity.

Setting Field Data Types

In arcpy, new field types are set using the .type property on field objects. This allows choice of text, float, double, date, blob, raster, and other types. Some common examples:

new_field = arcpy.AddField_management(fc, 'Temperature', 'DOUBLE') 
new_field.type = 'INTEGER'

new_field = arcpy.AddField_management(fc, 'Comments', 'TEXT', field_length=250)
new_field.type = 'STRING' 

new_field = arcpy.AddField_management(fc, 'Event_Date', 'DATE')

For existing fields, alter field tool can modify properties like data type. This allows changing to compatible types to clean or validate data.

arcpy.AlterField_management(fc, 'Old_Field', new_data_type='LONG')

Care should be taken to only switch between compatible types where data loss will not occur. Additionally, field length may need adjusted to prevent truncation of values during the conversion.

Handling Field Values

Frequently field data needs cleaned and converted between types during workflows. The Field Calculator in ArcGIS provides built-in functions to cast values to various data types:


Similar casting can be achieved in arcpy by retrieving field values as certain data types:

row[0].getValue('MyField') # Retrieved as a string
row.Temperature # Retrieved as float
row.Event_Date # Retrieved as date 

For more complex parsing and conversion, Python’s type casting functions can transform field values:


Validating that values match expected formats helps identify issues early on:

if not str.isdigit(row.TextData):
    # Handle error
if not dateparser.parse(row.StringDate): 
   # Handle invalid date  

Optimizing Field Size

For string fields, setting an appropriate length for expected data is important to minimize storage needs while preventing truncation issues. Excessive field sizes can bloat geodatabases and hurt performance.

Numeric fields should be given enough precision to capture the full resolution of data without rounding values prematurely. Float type is appropriate for measurements expected to have decimal fractions. Double type supports higher precision for very large or very small values if needed.

Some guidelines for setting field properties:

new_field = arcpy.AddField_management(fc, field_name, field_type, field_precision, field_scale, field_length)  
  • field_precision – Maximum number of digits to store (applies to numeric types)
  • field_scale – Number of digits after decimal point (applies to numeric types)
  • field_length – Maximum number of characters to store (applies to string, date, and blob types)

Example usage:

# Short text string
arcpy.AddField_management(fc, 'Sample_Code', 'TEXT', field_length=25) 

# High precision decimal number  
arcpy.AddField_management(fc, 'Measurement', 'DOUBLE', field_precision=12, field_scale=6)

# Date without time
arcpy.AddField_management(fc, 'Date_Received', 'DATE', field_length=8) 

Troubleshooting Common Issues

During data validation or analysis operations, mismatches between data types and field formats frequently cause errors and unexpected values. Some common issues include:

  • Truncation – Text data gets cut off when longer than the field size
  • Rounding – Fractional numeric values become rounded over numerous computations
  • Failed casting – Unable to convert data types due to invalid formats
  • Inappropriate characters – Certain symbols and codes cause issues for analysis functions

Strategies for avoiding and identifying these problems include:

  • Check values against field size and precision constraints
  • Use try/except blocks to catch failed type casting
  • Print length of string values vs field size to test for truncation
  • Disable automated number rounding in analysis environments
  • Replace unsupported special characters before parsing strings

Catching data issues early allows problems to be corrected at the source before cascading across analyses and data products down the line.

Best Practices Summary

Properly handling field data types is crucial for maintaining high quality geospatial data in ArcGIS. To summarize key points:

  • Set field types based on the specific data attributes planned for storage.
  • Use numeric types for quantitative data, string for textual data, date for temporal data, etc.
  • Cast and convert data to match field data types, validating format compliance.
  • Set field size, precision and scale to optimize storage efficiency.
  • Troubleshoot truncation, rounding issues and handle unsupported characters.

Matching data formats closely to field type definitions, then cleaning and validating values, ensures expected analytic performance while providing stability and consistency through operations. Additional ArcGIS resources are available to provide guidance managing specific field data scenarios as needed.

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