Azure / Azure/MachineLearningNotebooks
TypeError: argument of type 'azureml.dataprep.rslex.StreamInfo' is not iterable
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Descripción
Hi, I encountered below error, I have azureml-opendatasets==1.55.0
Calling to_spark_dataframe()
Traceback (most recent call last):
File "/mnt/c/users/cruiseli/OneDrive - Microsoft/Desktop/workspace/SynapseML-Utils/test_aml.py", line 30, in
nyc_tlc_df2 = nyc_tlc.to_spark_dataframe()
File "/home/cruise/mambaforge/lib/python3.10/site-packages/azureml/opendatasets/accessories/_loggerfactory.py", line 139, in wrapper
return func(*args, **kwargs)
File "/home/cruise/mambaforge/lib/python3.10/site-packages/azureml/opendatasets/accessories/open_dataset_base.py", line 164, in to_spark_dataframe
return self._to_spark_dataframe()
File "/home/cruise/mambaforge/lib/python3.10/site-packages/azureml/opendatasets/accessories/open_dataset_base.py", line 305, in _to_spark_dataframe
return self._blob_accessor.get_spark_dataframe(
File "/home/cruise/mambaforge/lib/python3.10/site-packages/azureml/opendatasets/dataaccess/_blob_accessor.py", line 303, in get_spark_dataframe
paths = [wasab_format % (self._blob_container_name, self._blob_account_name,
File "/home/cruise/mambaforge/lib/python3.10/site-packages/azureml/opendatasets/dataaccess/_blob_accessor.py", line 304, in
self._get_relative_path(path)) for path in target_paths]
File "/home/cruise/mambaforge/lib/python3.10/site-packages/azureml/opendatasets/dataaccess/_blob_accessor.py", line 470, in _get_relative_path
if "blob.core.windows.net" in url:
TypeError: argument of type 'azureml.dataprep.rslex.StreamInfo' is not iterable
Code to reproduce:
```
import azureml.core
from azureml.core import Datastore, Dataset
from azureml.core.workspace import Workspace
from azureml.core.experiment import Experiment
from azureml.core.authentication import InteractiveLoginAuthentication
import logging
import pandas as pd
import time
import sys
print("Testing opendatasets -- start")
from azureml.opendatasets import NycTlcYellow
from datetime import datetime
from dateutil import parser
end_date = parser.parse('2018-05-30')
start_date = parser.parse('2018-05-28')
nyc_tlc = NycTlcYellow(start_date=start_date, end_date=end_date)
print("Calling to_pandas_dataframe()")
ts = time.time()
nyc_tlc_df = nyc_tlc.to_pandas_dataframe()
te = time.time()
print("Time taken to perform to_pandas_dataframe():" + str(te-ts))
print("Calling to_spark_dataframe()")
ts2 = time.time()
nyc_tlc_df2 = nyc_tlc.to_spark_dataframe()
te2 = time.time()
nyc_tlc_df2.show(2, truncate = False)
print("Time taken to perform to_spark_dataframe():" + str(te2-ts2))
print("Testing opendatasets -- end")
```
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Línea de trabajo
Comienza ejecutando la reproducción proporcionada de NycTlcYellow con azureml-opendatasets==1.55.0 y, después, inspecciona azureml/opendatasets/dataaccess/_blob_accessor.py, especialmente get_spark_dataframe y _get_relative_path. Rastrea por qué target_paths contiene un StreamInfo y compara las rutas de pandas y Spark; se considera terminado cuando to_spark_dataframe se completa sin el TypeError indicado.
Escrito por el modelo de indexación a partir del texto del issue.
Evaluación
- Stack tecnológico
- azure, python, spark
- Área
- data, machine-learning
- Tipo de issue
- Error
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Estado de actividad
- Estancado
- Claridad
- Bastante claro
- Aptitud para principiantes
- 38/100