Question

我通过执行以下代码创建了数据框。

from pyspark.sql import Row
l = [('Ankit',25,'Ankit','Ankit'),('Jalfaizy',22,'Jalfaizy',"aa"),('saurabh',20,'saurabh',"bb"),('Bala',26,"aa","bb")]
rdd = sc.parallelize(l)
people = rdd.map(lambda x: Row(name=x[0], age=int(x[1]),lname=x[2],mname=x[3]))
schemaPeople = sqlContext.createDataFrame(people)
schemaPeople.show()

执行上述代码后，我的结果如下所示。

+---+--------+-----+--------+
|age|   lname|mname|    name|
+---+--------+-----+--------+
| 25|   Ankit|Ankit|   Ankit|
| 22|Jalfaizy|   aa|Jalfaizy|
| 20| saurabh|   bb| saurabh|
| 26|      aa|   bb|    Bala|
+---+--------+-----+--------+

但是我想映射每一行中的每一列值，并且基于age列，哪些列是相同的，我的预期结果如下所示。

+---+----------------+-------------------+------------------+
|age| lname_map_same | mname_map_same    |    name_map_same |
+---+----------------+-------------------+------------------+
| 25|  mname,name    |   lname,name      |   lname,mname    |
| 22|    name        |  none             |   lname          |
| 20|    name        |  none             |   lname          |
| 26|    none        |  none             |   none           |
+---+----------------+-------------------+------------------+

Answer 1

您可以使用地图功能解决您的问题。看下面的代码：

df_new = spark.createDataFrame([
( 25,"Ankit","Ankit","Ankit"),( 22,"Jalfaizy","aa","Jalfaizy"),( 26,"aa","bb","Bala")
], ("age", "lname","mname","name"))
#only 3 records added to dataset

def find_identical(row):
    labels = ["lname","mname","name"]
    result = [row[0],]                 #save the age for final result
    row = row[1:]                      #drop the age from row
    for i in range(3):
        s = []
        field = row[i]
        if field == row[(i+1)%3]:     #check whether field is identical with next field
            s.append(labels[(i+1)%3])
        if field == row[(i-1)%3]:     #check whether field is identical with previous field
            s.append(labels[(i-1)%3])
        if not s:                     #if no identical values found return None
            s = None     
        result.append(s)
    return result

df_new.rdd.map(find_identical).toDF(["age","lname_map_same","mname_map_same","name_map_same"]).show()

输出：

+---+--------------+--------------+--------------+
|age|lname_map_same|mname_map_same| name_map_same|
+---+--------------+--------------+--------------+
| 25| [mname, name]| [name, lname]|[lname, mname]|
| 22|        [name]|          null|       [lname]|
| 26|          null|          null|          null|
+---+--------------+--------------+--------------+

如果要考虑5列，则可以按照注释中的说明进行操作。因此，您必须修改标签列表并添加其他if语句。此外，必须对所有模运算进行调整以与5相匹配，并且for循环应迭代5个元素。然后，您最终得到如下代码：

df_new = spark.createDataFrame([
( 25,"Ankit","Ankit","Ankit","Ankit","Ankit"),( 22,"Jalfaizy","aa","Jalfaizy","Jalfaizy","aa"),( 26,"aa","bb","Bala","cc","dd")
], ("age", "lname","mname","name","n1","n2"))

def find_identical(row):
    labels = ["lname","mname","name","n1","n2"]
    result = [row[0],]
    row = row[1:]
        for i in range(5):
            s = []
            field = row[i]
            if field == row[(i+1)%5]:
                s.append(labels[(i+1)%5])
            if field == row[(i-1)%5]:
                s.append(labels[(i-1)%5])
            if field == row[(i+2)%5]:
                s.append(labels[(i+2)%5])
            if field == row[(i+3)%5]:
                s.append(labels[(i+3)%5])
            if not s:
                s = None
            result.append(s)
        return result

df_new.rdd.map(find_identical).toDF(["age","lname_map_same","mname_map_same","name_map_same","n1_map_same","n2_map_same"]).show(truncate=False)

输出：

    +---+---------------------+---------------------+----------------------+------------------------+------------------------+
|age|lname_map_same       |mname_map_same       |name_map_same         |n1_map_same             |n2_map_same             |
+---+---------------------+---------------------+----------------------+------------------------+------------------------+
|25 |[mname, n2, name, n1]|[name, lname, n1, n2]|[n1, mname, n2, lname]|[n2, name, lname, mname]|[lname, n1, mname, name]|
|22 |[name, n1]           |[n2]                 |[n1, lname]           |[name, lname]           |[mname]                 |
|26 |null                 |null                 |null                  |null                    |null                    |
+---+---------------------+---------------------+----------------------+------------------------+------------------------+

动态方法将列数作为参数。但在我的情况下，该数字应在1到5之间，因为创建的数据集最多包含5个属性。 IT可能看起来像这样：

df_new = spark.createDataFrame([
( 25,"Ankit","Ankit","Ankit","Ankit","Ankit"),( 22,"Jalfaizy","aa","Jalfaizy","Jalfaizy","aa"),( 26,"aa","bb","Bala","cc","dd")
], ("age", "n1","n2","n3","n4","n5"))


def find_identical(row,number):
    labels = []
    for n in range(1,number+1):
        labels.append("n"+str(n))   #create labels dynamically
    result = [row[0],]
    row = row[1:]
    for i in range(number):
        s = []
        field = row[i]
        for x in range(1,number):
            if field == row[(i+x)%number]:
                s.append(labels[(i+x)%number]) #check for similarity in all the other fields
        if not s:
            s = None
        result.append(s)
    return result

number=4
colNames=["age",]
for x in range(1,number+1):
    colNames.append("n"+str(x)+"_same") #create the 'nX_same' column names
df_new.rdd.map(lambda r: find_identical(r,number)).toDF(colNames).show(truncate=False)

根据number参数，输出会有所不同，我将age列静态保留为第一列。

输出：

+---+------------+------------+------------+------------+
|age|n1_same     |n2_same     |n3_same     |n4_same     |
+---+------------+------------+------------+------------+
|25 |[n2, n3, n4]|[n3, n4, n1]|[n4, n1, n2]|[n1, n2, n3]|
|22 |[n3, n4]    |null        |[n4, n1]    |[n1, n3]    |
|26 |null        |null        |null        |null        |
+---+------------+------------+------------+------------+

如何将每个列映射到pyspark数据框中的其他列？

1 个答案: