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[英]add a new column in pyspark dataframe based on matching values from a list
[英]Add a new column in dataframe with user defined values. (pyspark)
数组A1的三个值来自某个函数-
A1 = [1,2,3,4]
A1 = [5,6,7,8]
A1 = [1,3,4,1]
我想在其中添加带有数组值的新列的数据框-
+---+---+-----+
| x1| x2| x3|
+---+---+-----+
| 1| A| 3.0|
| 2| B|-23.0|
| 3| C| -4.0|
+---+---+-----+
我这样尝试过(假设'df'是我的数据框)-
for i in range(0, 2):
df = df.withColumn("x4", array(lit(A1[0]), lit(A1[1]), lit(A1[2]))
但是此代码的问题是它正在使用数组'A1'的最后一个值更新列,如下所示:
+---+---+-----+---------+
| x1| x2| x3| x4|
+---+---+-----+---------+
| 1| A| 3.0|[1,3,4,1]|
| 2| B|-23.0|[1,3,4,1]|
| 3| C| -4.0|[1,3,4,1]|
+---+---+-----+---------+
但是我想要这样-
+---+---+-----+---------+
| x1| x2| x3| x4|
+---+---+-----+---------+
| 1| A| 3.0|[1,2,3,4]|
| 2| B|-23.0|[5,6,7,8]|
| 3| C| -4.0|[1,3,4,1]|
+---+---+-----+---------+
我需要在代码中添加些什么?
怎么样:
from pyspark.sql import SparkSession
import pandas as pd
spark = SparkSession.builder.appName('test').getOrCreate()
df=spark.createDataFrame(data=[(1,'A',3),(2,'B',-23),(3,'C',-4)],schema=['x1','x2','x3'])
+---+---+---+
| x1| x2| x3|
+---+---+---+
| 1| A| 3|
| 2| B|-23|
| 3| C| -4|
+---+---+---+
mydict = {1:[1,2,3,4] , 2:[5,6,7,8], 3:[1,3,4,1]}
def addExtraColumn(df,mydict):
names = df.schema.names
count=1
mylst=[]
for row in df.rdd.collect():
RW=row.asDict()
rowLst=[]
for name in names:
rowLst.append(RW[name])
rowLst.append(mydict[count])
count=count+1
mylst.append(rowLst)
return mylst
newlst = addExtraColumn(df,mydict)
df1 = spark.sparkContext.parallelize(newlst).toDF(['x1','x2','x3','x4'])
df1.show()
+---+---+---+------------+
| x1| x2| x3| x4|
+---+---+---+------------+
| 1| A| 3|[1, 2, 3, 4]|
| 2| B|-23|[5, 6, 7, 8]|
| 3| C| -4|[1, 3, 4, 1]|
+---+---+---+------------+
查看您的代码,我认为A1值取决于x1,x2或x3列中的至少一个。
因此,您不能使用A1定义新列,而是使用将需要定义A1的列作为参数的函数。
这只是一个假设,但也许您只需要字典, A = {1:[1,2,3,4] , 2:[5,6,7,8], 3:[1,3,4,1],}
然后将其与withColumn
一起用于UDF。
因此,在大惊小怪之后,我发现使用pyspark的withColumn函数无法完成此操作,因为它将创建一列,但所有行都相同。 而且我不能使用udf
因为我的新列不依赖于现有数据框的任何先前列。
所以我做了这样的事情-假设您在for循环中获得了数组A1的不同值(在我的情况下就是这种情况)
f_array = []
for i in range(0,10):
f_array.extend([(i, A1)])
# Creating a new df for my array.
df1 = spark.createDataFrame(data = f_array, schema = ["id", "x4"])
df1.show()
+---+---------+
| id| x4|
+---+---------+
| 0|[1,2,3,4]|
| 1|[5,6,7,8]|
| 2|[1,3,4,1]|
+---+---------+
# Suppose no columns matches to our df then creating one extra column named `id` as present in our `df1`. This is used for joining both the dataframes.
df = df.withColumn('id', monotonically_increasing_id())
df.show()
+---+---+---+-----+
| id| x1| x2| x3|
+---+---+---+-----+
| 0| 1| A| 3.0|
| 1| 2| B|-23.0|
| 2| 3| C| -4.0|
+---+---+---+-----+
# Now join both the dataframes using common column `id`.
df = df.join(df1, df.id == df1.id).drop(df.id).drop(df1.id)
df.show()
+---+---+---+------------+
| x1| x2| x3| x4|
+---+---+---+------------+
| 1| A| 3|[1, 2, 3, 4]|
| 2| B|-23|[5, 6, 7, 8]|
| 3| C| -4|[1, 3, 4, 1]|
+---+---+---+------------+
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