我有一个DataFrame( {
// Other fields of the user
address: {
country : "5b56ecab8cba833c28e0e613"
}
}
),它由50多个列和不同类型的数据类型组成,例如
df
现在我希望可以一次性更改所有一种类型的列,例如
df3.printSchema()
CtpJobId: string (nullable = true)
|-- TransformJobStateId: string (nullable = true)
|-- LastError: string (nullable = true)
|-- PriorityDate: string (nullable = true)
|-- QueuedTime: string (nullable = true)
|-- AccurateAsOf: string (nullable = true)
|-- SentToDevice: string (nullable = true)
|-- StartedAtDevice: string (nullable = true)
|-- ProcessStart: string (nullable = true)
|-- LastProgressAt: string (nullable = true)
|-- ProcessEnd: string (nullable = true)
|-- ClipFirstFrameNumber: string (nullable = true)
|-- ClipLastFrameNumber: double (nullable = true)
|-- SourceNamedLocation: string (nullable = true)
|-- TargetId: string (nullable = true)
|-- TargetNamedLocation: string (nullable = true)
|-- TargetDirectory: string (nullable = true)
|-- TargetFilename: string (nullable = true)
|-- Description: string (nullable = true)
|-- AssignedDeviceId: string (nullable = true)
|-- DeviceResourceId: string (nullable = true)
|-- DeviceName: string (nullable = true)
|-- srcDropFrame: string (nullable = true)
|-- srcDuration: double (nullable = true)
|-- srcFrameRate: double (nullable = true)
|-- srcHeight: double (nullable = true)
|-- srcMediaFormat: string (nullable = true)
|-- srcWidth: double (nullable = true)
我知道如何像现在一样一步一步地做。
timestamp_type = [
'PriorityDate', 'QueuedTime', 'AccurateAsOf', 'SentToDevice',
'StartedAtDevice', 'ProcessStart', 'LastProgressAt', 'ProcessEnd'
]
integer_type = [
'ClipFirstFrameNumber', 'ClipLastFrameNumber', 'TargetId', 'srcHeight',
'srcMediaFormat', 'srcWidth'
]
但是,这看起来很丑陋,很容易错过任何我想更改的列。有什么办法可以写任何函数来处理要更改的相同类型的列列表,因此我可以轻松实现convert_data_type并传递这些列名。 预先感谢
答案 0 :(得分:2)
应该枚举循环,而不是枚举所有值:
for c in timestamp_type:
df3 = df3.withColumn(c, df[c].cast(TimestampType()))
for c in integer_type:
df3 = df3.withColumn(c, df[c].cast(IntegerType()))
或者等效地,您可以使用functools.reduce
:
from functools import reduce # not needed in python 2
df3 = reduce(
lambda df, c: df.withColumn(c, df[c].cast(TimestampType())),
timestamp_type,
df3
)
df3 = reduce(
lambda df, c: df.withColumn(c, df[c].cast(IntegerType())),
integer_type,
df3
)