A pipeline run fails because of an error in a function.
An analysis stopped message is displayed on the Data tab of the device type.
Review the message that is displayed in the pipeline log file and identify the function that caused the error. Some common Python errors are listed in Table 1. The most common errors are key errors and index errors.
Table 1. Probable cause by error type
| Error type | Probable cause | |
|---|---|---|
| Key error | A function tries to access a key in a dictionary or in a data frame that does not exist. The missing key is typically the name of a data item. The error indicates that something went wrong in the calculation of this data item. | |
| Index error | A function tries to access an index that is outside the bounds of a list. A common index error results from an operation in a custom function cutting up a data frame. For tips on fixing index errors, see Function catalog tips. | |
| Name error | A function cannot find the identifier, for example, a device ID is not found. If you are referring to data items in a pandas expression, refer to the data item name by using a quoted string, for example, 'pressure'. | |
| Attribute error | A function tries to access an attribute of a device that does not exist. | |
| Type error | An argument to the function is not of the right type. For example, in your code, you provide a string instead of an integer. | |
| Syntax error | An incorrect statement, for example, adding an extra bracket in the code. | |
| Value error | An argument to a function is of the right type, but the value is invalid, for example, an empty string. |
) and click Data.
) and click Show log. You can download the log to view it offline. The log shows the function that the pipeline
was executing when an error occurred, for example, “The pipeline failed at the “PythonExpression” stage.”.