Unable to convert row 1, column 2 to type class java.lang.integer

Ignition
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1

Perspective 8.1.47

Working on doing some subtraction of daily meter readings for a report using a script data source. Raw data source is from a tag historian query (‘METER’).

I am getting an error message (subject of this post) and the data type I’m performing the operation on should be a floating point value. I believe the code I referenced might be generically adding an integer column, not sure? Do I need to use system.dataset.addColumn in lieu of headers = list(pyDataset.getColumnNames()) + [‘Difference’]? If so, how would I use system.dataset.addColumn here?

Another question is, I actually have a query and datakey where I have used a tag historian query to get the previous value. How would I reference that instead of initializing previousValue to 0? It is a query with a fixed sample size of 1.

Thank you for any help!

def updateData(data, sample):
    #data['myKey']='Example'
    
    # Access the raw data from an existing data key
    rawDataset = data['METER'].getCoreResults()
    
    #Convert the dataset to a pydataset for easier raw iteration
    pyDataset = system.dataset.toPyDataSet(rawDataset)
    
    # Initialize a list to hold the new data, including a new difference column
    newData = []
    headers = list(pyDataset.getColumnNames()) + ['Difference']
    
    # Subtract the value in the current row from the value in the previous row for a 'Value' column
    previousValue = 0 # Initialize the 'previous' value
    
    for i, row in enumerate(pyDataset):
        currentValue = row['ty_total']
        if i == 0:
            difference = 0 # No previous row for the first entry
        else:
            difference = currentValue - previousValue
        
        # Append all existing values and the new difference value to the list
        rowList = list(row) + [difference]
        newData.append(rowList)
        
        previousValue = currentValue # Update the previous value for the next iteration
        
    #Convert the list of new data back into a standard dataset
    resultDataset = system.dataset.toDataSet(headers, newData)
    
    # Assign the new dataset to a new data key
    data['METERDIFFERENCE'] = resultDataset
2

The system functions that create new datasets, or add columns, use the values in the first row to infer the data types to use for the columns.

If that doesn't yield correct behavior (due to mixed types), you need to instead use the DatasetBuilder utility, where you explicitly set the column types.