🔍 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

---

🧾 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}")

---

🔎 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

---

✅ 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

📸 Batch Extraction of Photo Filenames from Fields in Multiple GDBs Using ArcPy

🖼️ Batch Filename Extraction for Photo Fields Across Multiple GDBs (ArcPy)

In many GIS workflows, feature classes often store file paths to images or documents—such as photos captured in the field. However, storing the full file path may not always be ideal. Sometimes you only need the filename (e.g., for linking, display, or standardization purposes).

This ArcPy script automates the process of extracting filenames from path strings for specified fields across multiple File Geodatabases (GDBs). It's a powerful way to clean and standardize your photo field data with minimal manual effort.

---

🔄 How the Script Works

📂 Directory Setup

The script starts by defining the folder containing all your GDBs. It automatically loops through each .gdb file inside that folder.

🧾 Fields & Expressions

You define a list of fields to update (Photo, Photo1, Photo2, etc.) and a corresponding list of Python expressions that extract just the filename from each path using:

python
!Photo!.split('\\\\')[-1]

This expression splits the full file path by double backslashes (\\) and retrieves the last part (i.e., the filename).

🔍 GDB Traversal

The script processes:

• Standalone feature classes

• Feature classes inside datasets

Each feature class is scanned, and if the field exists, it gets updated using arcpy.management.CalculateField() with the specified Python expression.

🛡️ Smart Checks

If a field doesn’t exist in a feature class, the script skips it gracefully with a message.

---

🧠 The Code

python
import arcpy
import os

# === USER INPUT ===
gdb_folder = r'C:\Path\To\Your\GDBs'

fields_to_update = ["Photo", "Photo1", "Photo2"]
field_values_to_update = [
    "!Photo!.split('\\\\')[-1]",
    "!Photo1!.split('\\\\')[-1]",
    "!Photo2!.split('\\\\')[-1]"
]

for folder in os.listdir(gdb_folder):
    if folder.endswith(".gdb"):
        gdb_path = os.path.join(gdb_folder, folder)
        arcpy.env.workspace = gdb_path

        # --- 1. Standalone Feature Classes ---
        feature_classes = arcpy.ListFeatureClasses()
        for fc in feature_classes:
            print(f"Working on standalone Feature Class: {fc}")
            for field_name, update_expression in zip(fields_to_update, field_values_to_update):
                if arcpy.ListFields(fc, field_name):
                    arcpy.management.CalculateField(fc, field_name, update_expression, "PYTHON3")
                    print(f"✅ Updated {field_name} in {fc}")
                else:
                    print(f"⚠️ Field {field_name} not found in {fc}, skipping.")

        # --- 2. Feature Classes in Datasets ---
        datasets = arcpy.ListDatasets() or []
        for ds in datasets:
            arcpy.env.workspace = os.path.join(gdb_path, ds)
            dataset_feature_classes = arcpy.ListFeatureClasses()
            for fc in dataset_feature_classes:
                print(f"Working on Feature Class: {fc} in Dataset: {ds}")
                for field_name, update_expression in zip(fields_to_update, field_values_to_update):
                    if arcpy.ListFields(fc, field_name):
                        arcpy.management.CalculateField(fc, field_name, update_expression, "PYTHON3")
                        print(f"✅ Updated {field_name} in {fc}")
                    else:
                        print(f"⚠️ Field {field_name} not found in {fc}, skipping.")

print("\n✅ Field update process completed.")

---

💡 Use Case Scenarios

• Removing long or broken directory paths from image/document fields

• Extracting filenames for linking in web maps or reporting systems

• Normalizing photo fields across merged or imported datasets

• Automating repetitive clean-up tasks across dozens of GDBs

---

✅ Key Benefits

• Works seamlessly across multiple geodatabases

• Applies to both standalone and nested feature classes

• Uses Python 3 expressions, ensuring compatibility with ArcGIS Pro

• Gracefully skips missing fields to avoid errors

 

🔄 Copy Field Values Between Columns Across Multiple GDBs Using ArcPy

In many GIS workflows, especially in large-scale data preparation or standardization tasks, you might need to duplicate the value of one field into other fields—for example, copying a unique identifier or standardized code from one field into localized or secondary fields for display or downstream processing.

This ArcPy script simplifies that process by automatically copying values from a source field (PORTID) to one or more target fields (PORTNAMEEG, PORTNAMEAR) across all feature classes in multiple File Geodatabases (GDBs). It handles both standalone feature classes and those nested within datasets.

---

⚙️ How the Script Works

🗂️ Folder Setup

You define the folder path containing all the GDBs you want to process. The script scans this folder and processes each GDB found within it.

🔁 Field Mapping

You specify:

• A source field (e.g., PORTID)

• One or more target fields (e.g., PORTNAMEEG, PORTNAMEAR)

The script copies the value from the source field into each target field using a simple Python 3 field calculator expression.

🔍 Traversing GDB Structure

The script:

• Handles standalone feature classes

• Handles feature classes inside datasets

• Skips no steps—every feature class is checked and updated

🧠 Smart Execution

For each feature class, arcpy.CalculateField is used to copy the data, keeping things clean, fast, and scriptable.

---

🧾 The Code

python
import arcpy
import os

# === USER INPUT ===
gdb_folder = r'C:\Path\To\Your\GDBs'
source_field = "PORTID"
target_fields = ["PORTNAMEEG", "PORTNAMEAR"]

for folder in os.listdir(gdb_folder):
    if folder.endswith(".gdb"):
        gdb_path = os.path.join(gdb_folder, folder)
        arcpy.env.workspace = gdb_path

        # --- 1. Standalone Feature Classes ---
        feature_classes = arcpy.ListFeatureClasses()
        for fc in feature_classes:
            print(f"Working on standalone Feature Class: {fc}")
            for target_field in target_fields:
                expression = f'!{source_field}!'
                arcpy.management.CalculateField(fc, target_field, expression, "PYTHON3")
                print(f"✅ Updated {target_field} with values from {source_field} in {fc}")

        # --- 2. Feature Classes in Datasets ---
        datasets = arcpy.ListDatasets() or []
        for ds in datasets:
            arcpy.env.workspace = os.path.join(gdb_path, ds)
            dataset_feature_classes = arcpy.ListFeatureClasses()
            for fc in dataset_feature_classes:
                print(f"Working on Feature Class: {fc} in Dataset: {ds}")
                for target_field in target_fields:
                    expression = f'!{source_field}!'
                    arcpy.management.CalculateField(fc, target_field, expression, "PYTHON3")
                    print(f"✅ Updated {target_field} with values from {source_field} in {fc}")

print("\n✅ Field update process completed.")

---

🔎 Use Cases

• 🏷️ Label localization: Copy a unique identifier into translated name fields for multilingual mapping.

• 🛠️ Schema normalization: Standardize data values across fields before publishing or merging.

• 🧹 Data cleanup: Replace blank or placeholder target fields with valid values from trusted fields.

• 📦 Bulk processing: Apply the same rule across dozens of geodatabases without manual editing.

---

✅ Key Benefits

• Fully automated, no need for manual field edits

• Works on both top-level and dataset-level feature classes

• Uses Python 3 syntax, ensuring compatibility with ArcGIS Pro

• Lightweight and fast—great for batch updates across large projects

🔁 Batch Field Updates in Multiple GDBs Using Arcade Expressions (ArcPy)

 

🔍 How the Script Works

🗂 Directory Setup

The script begins by defining the main folder that contains all your .gdb files. It automatically loops through every GDB inside.

🧠 Arcade Expressions

You define a list of fields to update and the corresponding Arcade expressions that will be applied to each. Arcade allows you to build dynamic values based on feature attributes, domain values, or calculations.

In this example:

arcade
'UG/' + DomainName($feature, 'PortNameEg') + '/Photos/' + $feature.Photo

This constructs a folder path or URL-like string using the domain name of a field and another attribute.

🔁 Iterating Through GDBs

The script scans both:

• Standalone feature classes (outside of datasets)

• Feature classes inside feature datasets

🧮 Field Updates

It applies the Arcade expression to each field using arcpy.management.CalculateField(). If the field does not exist in a particular feature class, it simply skips it.

---

🧾 The Code

python
import arcpy
import os

# === USER INPUT ===
# Folder containing GDBs
gdb_folder = r'C:\Path\To\Your\GDBs'

# Fields and corresponding Arcade expressions
fields_to_update = ["PORTNAMEEG"]
field_values_to_update = ["'UG/' + DomainName($feature, 'PortNameEg') + '/Photos/' + $feature.Photo"]

# Loop through each GDB in the directory
for folder in os.listdir(gdb_folder):
    if folder.endswith(".gdb"):
        gdb_path = os.path.join(gdb_folder, folder)
        arcpy.env.workspace = gdb_path

        # --- 1. Process standalone feature classes ---
        feature_classes = arcpy.ListFeatureClasses()
        for fc in feature_classes:
            print(f"Working on standalone Feature Class: {fc}")
            for field_name, arcade_expression in zip(fields_to_update, field_values_to_update):
                if arcpy.ListFields(fc, field_name):
                    arcpy.management.CalculateField(fc, field_name, arcade_expression, "ARCADE")
                    print(f"✅ Updated {field_name} in {fc}")
                else:
                    print(f"⚠️ Field {field_name} not found in {fc}, skipping.")

        # --- 2. Process feature classes inside datasets ---
        datasets = arcpy.ListDatasets() or []
        for ds in datasets:
            arcpy.env.workspace = os.path.join(gdb_path, ds)
            dataset_feature_classes = arcpy.ListFeatureClasses()
            for fc in dataset_feature_classes:
                print(f"Working on Feature Class: {fc} in Dataset: {ds}")
                for field_name, arcade_expression in zip(fields_to_update, field_values_to_update):
                    if arcpy.ListFields(fc, field_name):
                        arcpy.management.CalculateField(fc, field_name, arcade_expression, "ARCADE")
                        print(f"✅ Updated {field_name} in {fc}")
                    else:
                        print(f"⚠️ Field {field_name} not found in {fc}, skipping.")

print("\n✅ Field update process completed.")

---

💡 Key Highlights

• Uses Arcade expressions for dynamic, attribute-based field updates.

• Supports both standalone and dataset-based feature classes.

• Automatically scans and updates multiple geodatabases.

• Safely skips fields that do not exist—no interruptions in the workflow.

---

✅ Use Case Scenarios

• Generating dynamic paths or URLs for photos, reports, or documents.

• Populating fields based on domain values (e.g., DomainName($feature, 'FieldName')).

• Cleaning up and standardizing attribute fields after merges or imports.

• Repeating field updates across multiple geodatabases with minimal effort.

---

✨ Pro Tip

You can expand this script by:

• Adding logging to a .txt file.

• Accepting folder paths and field info via a simple GUI or argparse CLI.

• Converting it to a custom toolbox tool (.tbx) in ArcGIS Pro for team use.

 

📊 Count Features in All Feature Classes Across Multiple GDBs (ArcPy)

In geospatial data management, it is often necessary to get a quick overview of the number of features across various layers and geodatabases. This ArcPy script automates the process of counting features in each feature class—both within and outside feature datasets—across multiple File Geodatabases (GDBs).

This solution is especially helpful for large-scale GIS projects where understanding the data volume is essential for quality control, reporting, and performance tuning.

---

🔍 How the Script Works

🗂 Directory Setup

The script begins by pointing to a parent folder that contains one or more .gdb files. It will recursively scan all subdirectories to find geodatabases.

📋 CSV Output

It outputs the results into a CSV file, listing each feature class’s name, the dataset it belongs to (if any), its shape type (Point, Polyline, Polygon, etc.), and the feature count.

🔁 GDB Iteration

It checks each folder for .gdb extensions and sets the ArcPy workspace to the current geodatabase being processed.

🧾 Feature Class & Dataset Traversal

The script counts features in:

• Standalone feature classes

• Feature classes inside feature datasets

✅ Summary in CSV

For each feature class found, it logs:

• GDB Path

• GDB Name

• Dataset Name (or None)

• Feature Class Name

• Geometry Type

• Count of features

---

🧠 Why Use This Script?

• Quickly summarize large collections of geospatial data

• Useful for data validation, inventory, and reporting

• Automates a task that would otherwise require repetitive clicks in ArcGIS Pro or Catalog

• Clean and exportable format (CSV) ready for Excel or reporting tools

---

🧾 The Code

python
import arcpy
import os
import csv

# === USER INPUT ===
# Folder containing GDBs
folder_path = r"C:\Path\To\Your\GDBs"  # Update this path
# Output CSV path
csv_file = os.path.join(folder_path, "FeatureClass_Counts.csv")

# Define CSV headers
csv_headers = ['GDB Path', 'GDB Name', 'Dataset', 'Featureclass Name', 'Shape Type', 'Count']

# Open the CSV file for writing
with open(csv_file, mode='w', newline='', encoding='utf-8') as file:
    writer = csv.writer(file)
    writer.writerow(csv_headers)

    # Walk through the directory tree
    for root, dirs, files in os.walk(folder_path):
        for dir_name in dirs:
            if dir_name.endswith('.gdb'):
                gdb_path = os.path.join(root, dir_name)
                arcpy.env.workspace = gdb_path
                print(f"Processing GDB: {dir_name}")

                # 1. Standalone feature classes
                for fc in arcpy.ListFeatureClasses():
                    fc_path = os.path.join(gdb_path, fc)
                    desc = arcpy.Describe(fc)
                    shape_type = desc.shapeType
                    count = int(arcpy.GetCount_management(fc)[0])
                    writer.writerow([gdb_path, dir_name, 'None', fc, shape_type, count])

                # 2. Feature classes inside datasets
                datasets = arcpy.ListDatasets('', 'Feature') or []
                for dataset in datasets:
                    for fc in arcpy.ListFeatureClasses(feature_dataset=dataset):
                        fc_path = os.path.join(gdb_path, dataset, fc)
                        desc = arcpy.Describe(fc_path)
                        shape_type = desc.shapeType
                        count = int(arcpy.GetCount_management(fc_path)[0])
                        writer.writerow([gdb_path, dir_name, dataset, fc, shape_type, count])

print(f"\n✅ Feature counts exported to CSV:\n{csv_file}")

---

💡 Key Points to Remember

• The script automatically scans all .gdb files in the given directory and subdirectories.

• It distinguishes between standalone and dataset-based feature classes.

• Each feature class's geometry type and feature count are captured.

• Results are written to a CSV file—easy to share or import into Excel.

---

✅ Use Case Scenarios

• Preparing for data migration

• Performing a QA/QC audit

• Generating summary reports

• Checking for unexpected empty layers

• Monitoring data growth across projects

 

Automating Feature Class Count and Metadata Export with Python and ArcPy

If you are working with large geospatial datasets in Esri’s ArcGIS, keeping track of the features within each geodatabase is essential. For example, you may need a quick overview of feature class types, their counts, and the shape types across all geodatabases in a directory. In this blog post, I’ll show you how to automate the process of gathering feature class metadata and exporting it to a CSV file using Python and ArcPy.

The solution I’ll demonstrate scans all geodatabases in a specified directory, collects metadata (such as the feature class name, dataset, shape type, and feature count), and exports it to a CSV file. This approach helps you quickly summarize key attributes of all feature classes in your project.

Use Case

You might need this kind of automation when:

• You want to document all feature classes in your geodatabases.

• You need to review the shape types and feature counts of all geospatial data in a project.

• You’re preparing reports or verifying data integrity across large geodatabases.

The Python script below automates this process by walking through a directory of geodatabases and saving the results in a CSV file for later use.

Code:

python
import arcpy
import os
import csv

# Define the folder path containing the geodatabases
folder_path = r'PATH_TO_YOUR_GEODATABASES_FOLDER'  # Update with your geodatabase folder path

# Define the output CSV file path
csv_file = r'PATH_TO_YOUR_OUTPUT_CSV'  # Update with your desired CSV file path

# Define the CSV headers
csv_headers = ['GDB Name', 'Dataset', 'Featureclass Name', 'Shape Type', 'Count']

# Open the CSV file for writing with utf-8 encoding
with open(csv_file, mode='w', newline='', encoding='utf-8') as file:
    writer = csv.writer(file)
    writer.writerow(csv_headers)

    # Walk through the directory tree using os.walk()
    for root, dirs, files in os.walk(folder_path):
        for dir_name in dirs:
            if dir_name.endswith('.gdb'):  # Check if the directory is a geodatabase
                gdb_path = os.path.join(root, dir_name)

                # Set the workspace to the current GDB
                arcpy.env.workspace = gdb_path
                print(f"Processing {dir_name}")

                # List all standalone feature classes (those not in datasets)
                standalone_featureclasses = arcpy.ListFeatureClasses()

                for fc in standalone_featureclasses:
                    fc_path = os.path.join(arcpy.env.workspace, fc)
                    desc = arcpy.Describe(fc_path)

                    # Get the shape type and feature count
                    shape_type = desc.shapeType
                    count = arcpy.GetCount_management(fc_path)[0]

                    # Write to CSV (No dataset for standalone feature classes)
                    writer.writerow([dir_name, 'None', fc, shape_type, count])

                # List all feature datasets in the geodatabase
                datasets = arcpy.ListDatasets('', 'Feature')

                # If datasets exist, iterate through them
                if datasets:
                    for dataset in datasets:
                        # List feature classes in each dataset
                        dataset_featureclasses = arcpy.ListFeatureClasses(feature_dataset=dataset)

                        for fc in dataset_featureclasses:
                            fc_path = os.path.join(arcpy.env.workspace, dataset, fc)
                            desc = arcpy.Describe(fc_path)

                            # Get the shape type and feature count
                            shape_type = desc.shapeType
                            count = arcpy.GetCount_management(fc_path)[0]

                            # Write to CSV
                            writer.writerow([dir_name, dataset, fc, shape_type, count])

print(f"CSV created successfully at {csv_file}")

How the Script Works:

1. Set Folder and CSV Paths:

   • You need to define the path where your geodatabases are stored (folder_path) and the location of the output CSV file (csv_file).

2. CSV Headers:

   • The script writes a header row into the CSV that includes:

     • GDB Name: The name of the geodatabase.

     • Dataset: The name of the feature dataset (if applicable).

     • Featureclass Name: The name of the feature class.

     • Shape Type: The geometry type (e.g., point, line, polygon).

     • Count: The number of features in the feature class.

3. Directory Traversal:

   • The os.walk() function walks through the folder that contains the geodatabases. If a directory ends with .gdb, it processes that geodatabase.

4. Standalone Feature Classes:

   • The script first lists standalone feature classes (those not in any dataset) within each geodatabase and gets their shape type and feature count.

5. Feature Datasets:

   • If feature datasets exist in the geodatabase, the script processes each feature class within the dataset, retrieves the metadata, and writes the information to the CSV.

6. Writing to CSV:

   • For each feature class, the script writes a new row in the CSV with the collected metadata, including the shape type and count.

Benefits of Using This Script:

• Automation: It automatically collects and exports metadata for all feature classes in a geodatabase, eliminating the need for manual tracking.

• Documentation: The script generates a well-structured CSV that can be used for documentation or reports.

• Batch Processing: Whether you have a few geodatabases or hundreds, this script handles them all in a batch process, saving you valuable time.

• Versatility: You can easily modify the script to capture other metadata or make it work with different data sources.

Conclusion:

Managing geospatial data across multiple geodatabases can become a daunting task without the right tools. By automating the process of collecting feature class metadata and exporting it into a CSV, you can gain deeper insights into your datasets with minimal effort. This script, leveraging ArcPy and Python, ensures that you can process large amounts of data efficiently and keep track of important details such as feature counts, shape types, and more.

Feel free to adjust the file paths and adapt the script to fit your specific project needs. This tool is perfect for data management, quality assurance, and reporting tasks!

 

Automating Field Updates in Geodatabase Feature Classes with Python and ArcPy

In this blog post, I’ll walk you through a Python script that automates the process of updating field types within feature classes in Esri file geodatabases. This solution uses ArcPy, which is part of the ArcGIS API for Python, to streamline data management tasks—perfect for anyone managing large geodatabases and needing to update multiple field types efficiently.

Use Case

Imagine you have several feature classes within your geodatabase, and some of the field types need to be changed—perhaps from integer to string, or altering field length to meet new specifications. Manually updating these fields could take a lot of time, especially if there are many geodatabases and feature classes. That's where automation comes in!

This script reads a CSV file containing the required field updates and applies them across all geodatabases in a specified directory. It ensures that the updates are logged so you can track what was modified or if any errors occurred.

Script Breakdown

Below is the Python script that automates the field update process. It updates field types based on data in a CSV file and provides a log of changes.

Code:

python
import arcpy
import csv
import os

# Input folder containing geodatabases
folder_path = r"PATH_TO_YOUR_GEODATABASES_FOLDER"  # Example: r"C:\path\to\your\geodatabases"
# Input CSV file with updated field information
input_csv = r"PATH_TO_YOUR_CSV_FILE"  # Example: r"C:\path\to\your\fields_to_update.csv"
# Temporary output CSV file
temp_csv = r"PATH_TO_TEMP_CSV_FILE"  # Example: r"C:\path\to\temp_updated_fields.csv"

# Read the CSV file into a list
csv_data = []
with open(input_csv, 'r', newline='', encoding='utf-8') as csvfile:
    reader = csv.DictReader(csvfile)
    csv_headers = reader.fieldnames + ['Update Status']  # Add a new column for update status
    for row in reader:
        row['Update Status'] = 'Not Processed'  # Initialize with default status
        csv_data.append(row)

# Iterate through all items in the folder
for item in os.listdir(folder_path):
    gdb_path = os.path.join(folder_path, item)

    # Check if the item is a geodatabase
    if os.path.isdir(gdb_path) and gdb_path.endswith(".gdb"):
        # Set the workspace to the current geodatabase
        arcpy.env.workspace = gdb_path
        datasets = arcpy.ListDatasets(feature_type='feature') or ['']  # Include standalone feature classes

        for dataset in datasets:
            # List feature classes within each dataset
            feature_classes = arcpy.ListFeatureClasses(feature_dataset=dataset)

            for feature_class in feature_classes:
                # Get full path to feature class
                feature_class_path = f"{gdb_path}\\{dataset}\\{feature_class}" if dataset else f"{gdb_path}\\{feature_class}"

                # Describe fields in the feature class
                fields = arcpy.ListFields(feature_class_path)

                for field in fields:
                    # Check if field exists in the CSV data
                    for csv_row in csv_data:
                        csv_dataset = csv_row['Feature dataset']
                        csv_feature_class = csv_row['Feature class']
                        csv_field = csv_row['Field']

                        # Match dataset, feature class, and field
                        if (
                            (csv_dataset == dataset if dataset else "Standalone") and
                            csv_feature_class == feature_class and
                            csv_field == field.name
                        ):
                            # Attempt to alter the field data type
                            try:
                                arcpy.management.AlterField(
                                    in_table=feature_class_path,
                                    field=field.name,
                                    field_type="TEXT",
                                    field_length=150
                                )
                                csv_row['Update Status'] = 'Updated'
                                print(f"Field {field.name} updated to String (150 characters).")
                            except Exception as e:
                                csv_row['Update Status'] = f"Failed: {str(e)}"
                                print(f"Failed to modify field {field.name}: {e}")

# Write updated CSV data back to a new file
with open(temp_csv, 'w', newline='', encoding='utf-8') as csvfile:
    writer = csv.DictWriter(csvfile, fieldnames=csv_headers)
    writer.writeheader()
    writer.writerows(csv_data)

# Replace original CSV with updated one
os.replace(temp_csv, input_csv)

print(f"Field modification process completed. Updates logged in {input_csv}.")

How the Script Works:

1. Input Files:

   • folder_path: The path to the folder that contains your geodatabases.

   • input_csv: The CSV file that holds the required updates (i.e., dataset name, feature class, field name, and new field type).

   • temp_csv: A temporary CSV file where the updated data will be written.

2. Reading the CSV: The script reads the input CSV file and adds an "Update Status" column. This helps keep track of which fields were successfully updated and which ones encountered issues.

3. Iterating Through Geodatabases: The script checks the folder for .gdb files and sets the workspace for each geodatabase. It processes all datasets and feature classes within each geodatabase.

4. Updating Fields:

   • The script matches each field in the feature class against the data from the CSV file.

   • For any matching fields, the script attempts to update the field data type to TEXT with a length of 150.

   • If the update is successful, it updates the status in the CSV to "Updated." If it fails, it logs the error and updates the status to "Failed."

5. Writing Back the CSV: After processing all geodatabases, the script writes the updated CSV data to a new temporary CSV file and replaces the original CSV with the updated one.

Benefits of Using This Script:

• Automation: This saves a tremendous amount of time if you need to make the same changes across multiple geodatabases.

• Logging: The script maintains a log of updates, so you can easily track which fields were updated successfully and which ones failed.

• Scalability: Whether you have 10 or 100 geodatabases, this script can handle large datasets and perform bulk updates without manual intervention.

Conclusion:

Managing geospatial data can be a complex task, especially when working with large geodatabases. Automating repetitive tasks like field type updates not only improves efficiency but also reduces the risk of human error. Using ArcPy and Python, this script simplifies a process that would otherwise take hours and allows you to focus on more critical tasks.

Feel free to customize the script for your specific needs. You can easily modify the field types, field lengths, or the fields you wish to update.