python – 在pyspark中指定多个列数据类型更改为不同的数据类型

我有一个DataFrame(df),它包含50多列和不同类型的数据类型,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)

现在我希望所有类型的列都可以一次更改,例如

timestamp_type = [
    'PriorityDate', 'QueuedTime', 'AccurateAsOf', 'SentToDevice', 
    'StartedAtDevice', 'ProcessStart', 'LastProgressAt', 'ProcessEnd'
]


integer_type = [
    'ClipFirstFrameNumber', 'ClipLastFrameNumber', 'TargetId', 'srcHeight',
    'srcMediaFormat', 'srcWidth'
]

我知道如何一个接一个地做,就像我现在做的那样.

df3 = df3.withColumn("PriorityDate", df3["PriorityDate"].cast(TimestampType()))
df3 = df3.withColumn("QueuedTime", df3["QueuedTime"].cast(TimestampType()))
df3 = df3.withColumn("AccurateAsOf", df3["AccurateAsOf"].cast(TimestampType())

df3= df3.withColumn("srcMediaFormat", df3["srcMediaFormat"].cast(IntegerType()))
df3= df3.withColumn("DeviceResourceId", df3["DeviceResourceId"].cast(IntegerType()))
df3= df3.withColumn("AssignedDeviceId", df3["AssignedDeviceId"].cast(IntegerType()))

但这看起来很丑陋,我很容易错过任何我想改变的专栏.有没有什么办法可以编写任何能够处理相同类型的列列表来更改的函数.所以我可以轻松实现convert_data_type并传递这些列名.
提前致谢

解决方法:

您应该使用循环来代替枚举所有值：

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
)

标签：python,apache-spark,pandas,pyspark,databricks
来源： https://codeday.me/bug/20191007/1865970.html