熟悉pandas的pythoner 應(yīng)該知道給dataframe增加一列很容易，直接以字典形式指定就好了，pyspark中就不同了，摸索了一下，可以使用如下方式增加

from pyspark import SparkContext
from pyspark import SparkConf
from pypsark.sql import SparkSession
from pyspark.sql import functions

spark = SparkSession.builder.config(conf=SparkConf()).getOrCreate()

data =   [['Alice', 19, 'blue', '["Alice", 19, "blue"]'],
  ['Jane', 20, 'green', '["Jane", 20, "green"]'],
   ['Mary', 21, 'blue', '["Mary", 21, "blue"]'], ]
frame = spark.createDataFrame(data, schema=["name", "age", "eye_color", "detail"])

frame.cache()
frame.show()

+-----+---+---------+--------------------+
| name|age|eye_color|              detail|
+-----+---+---------+--------------------+
|Alice| 19|     blue|["Alice", 19, "bl...|
| Jane| 20|    green|["Jane", 20, "gre...|
| Mary| 21|     blue|["Mary", 21, "blue"]|
+-----+---+---------+--------------------+

1、 增加常數(shù)項(xiàng)

frame2 = frame.withColumn("contant", functions.lit(10))
frame2.show()

+-----+---+---------+--------------------+-------+
| name|age|eye_color|              detail|contant|
+-----+---+---------+--------------------+-------+
|Alice| 19|     blue|["Alice", 19, "bl...|     10|
| Jane| 20|    green|["Jane", 20, "gre...|     10|
| Mary| 21|     blue|["Mary", 21, "blue"]|     10|
+-----+---+---------+--------------------+-------+

2、簡(jiǎn)單根據(jù)某列進(jìn)行計(jì)算

2.1 使用 withColumn

frame3_1 = frame.withColumn("name_length", functions.length(frame.name))
frame3_1.show()

+-----+---+---------+--------------------+-----------+
| name|age|eye_color|              detail|name_length|
+-----+---+---------+--------------------+-----------+
|Alice| 19|     blue|["Alice", 19, "bl...|          5|
| Jane| 20|    green|["Jane", 20, "gre...|          4|
| Mary| 21|     blue|["Mary", 21, "blue"]|          4|
+-----+---+---------+--------------------+-----------+

2.2 使用 select

frame3_2 = frame.select(["name", functions.length(frame.name).alias("name_length")])
frame3_2.show()

+-----+-----------+
| name|name_length|
+-----+-----------+
|Alice|          5|
| Jane|          4|
| Mary|          4|
+-----+-----------+

2.3 使用 selectExpr

frame3_3 = frame.selectExpr(["name", "length(name) as name_length"])
frame3_3.show()

+-----+-----------+
| name|name_length|
+-----+-----------+
|Alice|          5|
| Jane|          4|
| Mary|          4|
+-----+-----------+

3、定制化根據(jù)某列進(jìn)行計(jì)算

比如我想對(duì)某列做指定操作，但是對(duì)應(yīng)的函數(shù)沒得咋辦，造，自己造~

frame4 = frame.withColumn("detail_length", functions.UserDefinedFunction(lambda obj: len(json.loads(obj)))(frame.detail))

# or
def length_detail(obj):
 return len(json.loads(obj))
frame4 = frame.withColumn("detail_length", functions.UserDefinedFunction(length_detail)(frame.detail))
frame4.show()

+-----+---+---------+--------------------+-------------+
| name|age|eye_color|              detail|detail_length|
+-----+---+---------+--------------------+-------------+
|Alice| 19|     blue|["Alice", 19, "bl...|            3|
| Jane| 20|    green|["Jane", 20, "gre...|            3|
| Mary| 21|     blue|["Mary", 21, "blue"]|            3|
+-----+---+---------+--------------------+-------------+

到此這篇關(guān)于pyspark給dataframe增加新的一列的實(shí)現(xiàn)示例的文章就介紹到這了,更多相關(guān)pyspark dataframe增加列內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

最新評(píng)論