不幸的是，azure databricks不支持读取和写入azure文件共享。
azure databricks支持的数据源：https://docs.microsoft.com/en-us/azure/databricks/data/data-sources/
我建议你提供同样的反馈：
https://feedback.azure.com/forums/909463-azure-databricks
您在这些论坛中共享的所有反馈都将由负责构建azure的microsoft工程团队进行监视和审核。
您可以 checkout 解决类似问题的线程：databricks和azure文件
下面是将csv数据直接写入azuredatabricks笔记本中的azureblob存储容器的代码段。

# Configure blob storage account access key globally

spark.conf.set("fs.azure.account.key.chepra.blob.core.windows.net", "gv7nVIXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXdlOiA==")
output_container_path = "wasbs://sampledata@chepra.blob.core.windows.net"
output_blob_folder = "%s/wrangled_data_folder" % output_container_path

# write the dataframe as a single file to blob storage

(dataframe
 .coalesce(1)
 .write
 .mode("overwrite")
 .option("header", "true")
 .format("com.databricks.spark.csv")
 .save(output_blob_folder))

# Get the name of the wrangled-data CSV file that was just saved to Azure blob storage (it starts with 'part-')

files = dbutils.fs.ls(output_blob_folder)
output_file = [x for x in files if x.name.startswith("part-")]

# Move the wrangled-data CSV file from a sub-folder (wrangled_data_folder) to the root of the blob container

# While simultaneously changing the file name

dbutils.fs.mv(output_file[0].path, "%s/predict-transform-output.csv" % output_container_path)