python - 创建一个包含每个文件的架构数据的数据框

Question

我正在尝试创建一个数据框，然后运行一个 for 循环来查看一堆文件。遍历每一个并向文件的数据帧添加一行。包含文件名和架构详细信息？

# Schema    
schema = StructType([
    StructField("filename", StringType(), True),
    StructField("converteddate", StringType(), True),
    StructField("eventdate", StringType(), True)
])


# Create empty dataframe
df = spark.createDataFrame(sc.emptyRDD(), schema)


for files in mvv_list:
    loadName = files
    videoData = spark.read\
                     .format('parquet')\
                     .options(header='true', inferSchema='true')\
                     .load(loadName)
    dataTypeList = videoData.dtypes
    two = dataTypeList[:2]
    print(loadName)
    print(two)

#mnt/master-video/year=2018/month=03/day=24/part-00004-tid-28948428924977-e0fc2-c85b-4296-8a05-94c5af6-2427-c000.snappy.parquet
#[('converteddate', 'timestamp'), ('eventdate', 'timestamp')]

#mnt/master-video/year=2017/month=05/day=12/part-00004-tid-2894842977-e0f21c2-c85b-4296-8a05-94c5af6-2427-c000.snappy.parquet
#[('converteddate', 'timestamp'), ('eventdate', 'date')]

#mnt/master-video/year=2016/month=03/day=24/part-00004-tid-2884924977-e0f2512-c8b-4296-8a05-945a6-2427-c000.snappy.parquet
#[('converteddate', 'timestamp'), ('eventdate', 'string')]

我正在努力创建一行并将其附加到数据框。

想要的输出

+-----------------------------+-----------------+---------------------+
|filename                     |converteddate    |eventdate            |
+-----------------------------+-----------------+---------------------+
|mnt/master-video/year=2018...|timestamp        |timestamp            |
|mnt/master-video/year=2017...|timestamp        |date                 |
|mnt/master-video/year=2016...|timestamp        |string               |
+-----------------------------+-----------------+---------------------+

Answer 1

一种方法是将所需的数据构建为列表，然后创建 DataFrame(而不是尝试追加行)

data = []
for files in mvv_list:
    loadName = files
    videoData = spark.read\
                     .format('parquet')\
                     .options(header='true', inferSchema='true')\
                     .load(loadName)
    dataTypeDict = dict(videoData.dtypes)
    data.append((loadName, dataTypeDict['converteddate'], dataTypeDict['eventdate']))

schema = StructType([
    StructField("filename", StringType(), True),
    StructField("converteddate", StringType(), True),
    StructField("eventdate", StringType(), True)
])

df = spark.createDataFrame(data, schema)