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這篇文章主要講解了pyspark如何給dataframe增加新的一列,內(nèi)容清晰明了,對(duì)此有興趣的小伙伴可以學(xué)習(xí)一下,相信大家閱讀完之后會(huì)有幫助。
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|
+-----+---+---------+--------------------+-------------+
看完上述內(nèi)容,是不是對(duì)pyspark如何給dataframe增加新的一列有進(jìn)一步的了解,如果還想學(xué)習(xí)更多內(nèi)容,歡迎關(guān)注億速云行業(yè)資訊頻道。
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