在pyspark中将字符串更改为时间戳

flseospp 于 2021-07-13 发布在 Spark

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我正在尝试将字符串列转换为时间戳列，格式如下：
c1c22019-12-10 10:07:54.0002019-12-13 10:07:54.0002020-06-08 15:14:49.0002020-06-18 10:07:54.000

from pyspark.sql.functions import col, udf, to_timestamp

joined_df.select(to_timestamp(joined_df.c1, '%Y-%m-%d %H:%M:%S.%SSSS').alias('dt')).collect()
joined_df.select(to_timestamp(joined_df.c2, '%Y-%m-%d %H:%M:%S.%SSSS').alias('dt')).collect()

当日期改变时，我想通过减去c2-c1得到一个新的列日期差
在python中，我正在这样做：

df['c1']        = df['c1'].fillna('0000-01-01').apply(lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f'))

df['c2'] = df['c2'].fillna('0000-01-01').apply(lambda x:  datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f'))

df['days']     = (df['c2'] - df['c1']).apply(lambda x: x.days)

有人能帮我转换成Pypark吗？

apache-spark pyspark apache-spark-sql pyspark-dataframes timestamp

来源：https://stackoverflow.com/questions/66108064/changing-string-to-timestamp-in-pyspark

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按热度按时间

2g32fytz1#

如果你想得到日期差异，你可以使用 datediff :

import pyspark.sql.functions as F

df = df.withColumn('c1', F.col('c1').cast('timestamp')).withColumn('c2', F.col('c2').cast('timestamp'))
result = df.withColumn('days', F.datediff(F.col('c2'), F.col('c1')))
result.show(truncate=False)
+-----------------------+-----------------------+----+
|c1                     |c2                     |days|
+-----------------------+-----------------------+----+
|2019-12-10 10:07:54.000|2019-12-13 10:07:54.000|3   |
|2020-06-08 15:14:49.000|2020-06-18 10:07:54.000|10  |
+-----------------------+-----------------------+----+

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在pyspark中将字符串更改为时间戳

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