我想在databricks中读取一个带有pyspark的csv文件。marketingstartdate是这种格式 yyyyMMdd 以及 lastweek = marketingStartDate -7days ```
readFactToDataFrame('Facts',
'Fact.csv',startDate=str(dateBefore),
endDate=str(marketingStartDate),
inferSchema=False)

我收到这个错误信息。你知道问题出在哪里吗？
![](https://i.stack.imgur.com/PX8xZ.png)

df = spark.read.format(fileFormat).options(header=True, inferSchema=
inferSchema, delimiter = columnDelimiter).load(URL).filter("Year *
10000 + Month * 100 + Day = "+str(startDate) + " AND Year * 10000 +
Month * 100 + Day <=" + str(endDate)) 37 elif fileFormat == "json": 38
df =
spark.read.format(fileFormat).options(multiline=True).load(URL).filter("Year

• 10000 + Month * 100 + Day = "+str(startDate) + " AND Year * 10000 + Month * 100 + Day <=" + str(endDate))

/databricks/spark/python/pyspark/sql/readwriter.py in load(self, path,
format, schema,**options) 164 self.options(**options) 165 if
isinstance(path, basestring): -- 166 return
self._df(self._jreader.load(path)) 167 elif path is not None: 168 if
type(path) != list:
/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py
in call(self, *args) 1255 answer =
self.gateway_client.send_command(command) 1256 return_value =
get_return_value( - 1257 answer, self.gateway_client, self.target_id,
self.name) 1258 1259 for temp_arg in temp_args:
/databricks/spark/python/pyspark/sql/utils.py in deco(*a,**kw) 61 def
deco(*a,**kw): 62 try: --- 63 return f(*a,**kw) 64 except
py4j.protocol.Py4JJavaError as e: 65 s = e.java_exception.toString()
/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in
get_return_value(answer, gateway_client, target_id, name) 326 raise
Py4JJavaError( 327 "An error occurred while calling {0}{1}{2}.\n". --
328 format(target_id, ".", name), value) 329 else: 330 raise
Py4JError( Py4JJavaError: An error occurred while calling o2301.load.
: java.lang.AssertionError: assertion failed: Conflicting partition
column names detected: Partition column name list #0: Year, Month, Day
Partition column name list #1: Year, Month For partitioned table
directories, data files should only live in leaf directories. And
directories at the same level should have the same partition column
name. Please check the following directories for unexpected files or
inconsistent partition column names:
dbfs:/mnt/DL/Facts/Year=2020/Month=08
dbfs:/mnt/DL/Facts/Year=2020/Month=03/Day=16
dbfs:/mnt/DL/Facts/Year=2019/Month=09/Day=27
dbfs:/mnt/DL/Facts/Year=2019/Month=09/Day=02
dbfs:/mnt/DL/Facts/Year=2020/Month=08/Day=01
dbfs:/mnt/DL/Facts/Year=2020/Month=03/Day=09
dbfs:/mnt/DL/Facts/Year=2020/Month=02/Day=26
dbfs:/mnt/DL/Facts/Year=2020/Month=08/Day=10
dbfs:/mnt/DL/Facts/Year=2019/Month=09/Day=12
dbfs:/mnt/DL/Facts/Year=2019/Month=10/Day=12
dbfs:/mnt/DL/Facts/Year=2020/Month=03/Day=24
dbfs:/mnt/DL/Facts/Year=2019/Month=09/Day=05
dbfs:/mnt/DL/Facts/Year=2020/Month=03/Day=13
dbfs:/mnt/DL/Facts/Year=2019/Month=10/Day=27
dbfs:/mnt/DL/Facts/Year=2019/Month=09/Day=16
dbfs:/mnt/DL/Facts/Year=2020/Month=02/Day=20
dbfs:/mnt/DL/Facts/Year=2020/Month=03/Day=31 at
scala.Predef$.assert(Predef.scala:170) at
org.apache.spark.sql.execution.datasources.PartitioningUtils$.resolvePartitions(PartitioningUtils.scala:396)
at
org.apache.spark.sql.execution.datasources.PartitioningUtils$.parsePartitions(PartitioningUtils.scala:197)
at
org.apache.spark.sql.execution.datasources.PartitioningUtils$.parsePartitions(PartitioningUtils.scala:132)