🔍 Extract Field Names Containing 'type' (Integer Fields Without Domain) from GDB Using ArcPy

 

⚙️ How the Script Works

🗂️ Geodatabase Setup

The script starts by pointing to a target File Geodatabase (.gdb) and initializing a CSV file for output.

🔍 Dataset & Feature Class Loop

It loops through all feature datasets and standalone feature classes, capturing relevant metadata about each field.

🧠 Filtering Logic

Only fields that meet the following criteria are exported:

• Their name contains "type" (case-insensitive)

• They are of type "Integer" (which is ArcPy's internal label for Long)

• They do not have a domain assigned

📝 CSV Output

Matching fields are written into a structured CSV file, showing:

• GDB name

• Dataset name

• Feature class

• Field name

• Field type

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🧾 The Code

python
import arcpy
import csv

# === INPUTS ===
gdb_path = r"C:\Work\Projects\MPDA\DataModel\Schema\Buildings.gdb"
output_csv = r"C:\Work\Projects\MPDA\DataModel\Schema\Buildings_Fields.csv"

# === SETUP CSV ===
headers = ['GDBName', 'Feature dataset', 'Feature class', 'Field', 'Datatype']

with open(output_csv, 'w', newline='', encoding='utf-8') as csvfile:
    writer = csv.writer(csvfile)
    writer.writerow(headers)

    gdb_name = arcpy.Describe(gdb_path).baseName
    arcpy.env.workspace = gdb_path
    datasets = arcpy.ListDatasets(feature_type='feature') or ['']  # Includes standalone FCs

    for dataset in datasets:
        feature_classes = arcpy.ListFeatureClasses(feature_dataset=dataset)

        for feature_class in feature_classes:
            fc_path = f"{gdb_path}\\{dataset}\\{feature_class}" if dataset else f"{gdb_path}\\{feature_class}"
            fields = arcpy.ListFields(fc_path)

            for field in fields:
                if (
                    "type" in field.name.lower() and
                    field.type == "Integer" and
                    not field.domain
                ):
                    writer.writerow([gdb_name, dataset or "Standalone", feature_class, field.name, field.type])

print(f"✅ CSV file created successfully at: {output_csv}")

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🔎 Use Cases

• 🔍 Auditing field structure across a GDB

• 🛠️ Schema cleanup: identifying unstandardized or unused fields

• 📤 Exporting metadata for documentation or review

• 🚫 Detecting missing domains on critical integer fields

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✅ Benefits

• Automates what would be a tedious manual inspection

• Works for both dataset-bound and standalone feature classes

• Helps track down inconsistencies in schema structure

• Outputs a clean CSV for further use in Excel or other tools