指定多列数据类型更改为pyspark中不同的数据类型

Question

我有一个 DataFrame (df)，它由 50 多列和不同类型的数据类型组成，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)

现在我希望所有一种类型的列都可以一次性更改，例如

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


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

我知道如何像现在这样一个接一个地做。

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()))

但这看起来很难看，而且我很容易错过任何我想更改的列。有什么方法可以编写任何函数来处理相同类型的列列表 change.So 我可以轻松实现 convert_data_type 并传递这些列名称。提前致谢

Answer 1

与其枚举所有值，不如使用循环：

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
)

指定多列数据类型更改为pyspark中不同的数据类型

Specify multiple columns data type changes to different data types in pyspark

python

pandas

apache-spark

pyspark

databricks