pyspark在没有 pandas 的情况下将一列拆分为多列 [英] pyspark split a column to multiple columns without pandas

问题描述

我的问题是如何将一列拆分为多列.我不知道为什么 df.toPandas() 不起作用.

my question is how to split a column to multiple columns. I don't know why df.toPandas() does not work.

例如，我想将df_test"更改为df_test2".我看到了很多使用 pandas 模块的例子.还有其他方法吗?提前致谢.

For example, I would like to change 'df_test' to 'df_test2'. I saw many examples using the pandas module. Is there another way? Thank you in advance.

df_test = sqlContext.createDataFrame([
(1, '14-Jul-15'),
(2, '14-Jun-15'),
(3, '11-Oct-15'),
], ('id', 'date'))

df_test2

id     day    month    year
1       14     Jul      15
2       14     Jun      15
1       11     Oct      15

推荐答案

Spark >= 2.2

您可以跳过unix_timestamp 并转换和使用to_date 或to_timestamp:

You can skip unix_timestamp and cast and use to_date or to_timestamp:

from pyspark.sql.functions import to_date, to_timestamp

df_test.withColumn("date", to_date("date", "dd-MMM-yy")).show()
## +---+----------+
## | id|      date|
## +---+----------+
## |  1|2015-07-14|
## |  2|2015-06-14|
## |  3|2015-10-11|
## +---+----------+


df_test.withColumn("date", to_timestamp("date", "dd-MMM-yy")).show()
## +---+-------------------+
## | id|               date|
## +---+-------------------+
## |  1|2015-07-14 00:00:00|
## |  2|2015-06-14 00:00:00|
## |  3|2015-10-11 00:00:00|
## +---+-------------------+

然后应用下面显示的其他日期时间函数.

and then apply other datetime functions shown below.

火花<2.2

不可能在一次访问中派生多个顶级列.您可以像这样将结构或集合类型与 UDF 一起使用:

It is not possible to derive multiple top level columns in a single access. You can use structs or collection types with an UDF like this:

from pyspark.sql.types import StringType, StructType, StructField
from pyspark.sql import Row
from pyspark.sql.functions import udf, col

schema = StructType([
  StructField("day", StringType(), True),
  StructField("month", StringType(), True),
  StructField("year", StringType(), True)
])

def split_date_(s):
    try:
        d, m, y = s.split("-")
        return d, m, y
    except:
        return None

split_date = udf(split_date_, schema)

transformed = df_test.withColumn("date", split_date(col("date")))
transformed.printSchema()

## root
##  |-- id: long (nullable = true)
##  |-- date: struct (nullable = true)
##  |    |-- day: string (nullable = true)
##  |    |-- month: string (nullable = true)
##  |    |-- year: string (nullable = true)

但它在 PySpark 中不仅非常冗长，而且成本也很高.

but it is not only quite verbose in PySpark, but also expensive.

对于基于日期的转换，您可以简单地使用内置函数:

For date based transformations you can simply use built-in functions:

from pyspark.sql.functions import unix_timestamp, dayofmonth, year, date_format

transformed = (df_test
    .withColumn("ts",
        unix_timestamp(col("date"), "dd-MMM-yy").cast("timestamp"))
    .withColumn("day", dayofmonth(col("ts")).cast("string"))
    .withColumn("month", date_format(col("ts"), "MMM"))
    .withColumn("year", year(col("ts")).cast("string"))
    .drop("ts"))

同样，您可以使用 regexp_extract 来分割日期字符串.

Similarly you could use regexp_extract to split date string.

另请参见从 Spark 数据帧中的单个列派生多个列

注意:

如果您使用未针对 SPARK-11724 打补丁的版本，这将需要unix_timestamp(...) 之后和 cast("timestamp") 之前的更正.

If you use version not patched against SPARK-11724 this will require correction after unix_timestamp(...) and before cast("timestamp").

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