使用pyspark从字典中映射数据框中的值

Question

我想知道如何在数据框中的特定列中映射值.

我有一个数据框,看起来像:

df = sc.parallelize([('india','japan'),('usa','uruguay')]).toDF(['col1','col2'])

+-----+-------+
| col1|   col2|
+-----+-------+
|india|  japan|
|  usa|uruguay|
+-----+-------+

我有一个字典,我想要映射值.

dicts = sc.parallelize([('india','ind'), ('usa','us'),('japan','jpn'),('uruguay','urg')])

我想要的输出是:

+-----+-------+--------+--------+
| col1|   col2|col1_map|col2_map|
+-----+-------+--------+--------+
|india|  japan|     ind|     jpn|
|  usa|uruguay|      us|     urg|
+-----+-------+--------+--------+

我尝试过使用lookup function它但不起作用.它抛出错误SPARK-5063.以下是我失败的方法:

def map_val(x):
    return dicts.lookup(x)[0]

myfun = udf(lambda x: map_val(x), StringType())

df = df.withColumn('col1_map', myfun('col1')) # doesn't work
df = df.withColumn('col2_map', myfun('col2')) # doesn't work

Answer 1

我认为,更简单的方法就是使用一个简单的dictionary和df.withColumn.

from itertools import chain
from pyspark.sql.functions import create_map, lit

simple_dict = {'india':'ind', 'usa':'us', 'japan':'jpn', 'uruguay':'urg'}

mapping_expr = create_map([lit(x) for x in chain(*simple_dict.items())])

df = df.withColumn('col1_map', mapping_expr[df['col1']])\
       .withColumn('col2_map', mapping_expr[df['col2']])

df.show(truncate=False)

Answer 2

udf方式

我建议您将元组列表更改为字典并将其广播以在udf中使用

dicts = sc.broadcast(dict([('india','ind'), ('usa','us'),('japan','jpn'),('uruguay','urg')]))

from pyspark.sql import functions as f
from pyspark.sql import types as t
def newCols(x):
    return dicts.value[x]

callnewColsUdf = f.udf(newCols, t.StringType())

df.withColumn('col1_map', callnewColsUdf(f.col('col1')))\
    .withColumn('col2_map', callnewColsUdf(f.col('col2')))\
    .show(truncate=False)

这应该给你

+-----+-------+--------+--------+
|col1 |col2   |col1_map|col2_map|
+-----+-------+--------+--------+
|india|japan  |ind     |jpn     |
|usa  |uruguay|us      |urg     |
+-----+-------+--------+--------+

联接方式（比udf方式慢）

您要做的就是也将rds更改为dataframe，并使用两个具有别名的联接，如下所示

df = sc.parallelize([('india','japan'),('usa','uruguay')]).toDF(['col1','col2'])

dicts = sc.parallelize([('india','ind'), ('usa','us'),('japan','jpn'),('uruguay','urg')]).toDF(['key', 'value'])

from pyspark.sql import functions as f
df.join(dicts, df['col1'] == dicts['key'], 'inner')\
    .select(f.col('col1'), f.col('col2'), f.col('value').alias('col1_map'))\
    .join(dicts, df['col2'] == dicts['key'], 'inner') \
    .select(f.col('col1'), f.col('col2'), f.col('col1_map'), f.col('value').alias('col2_map'))\
    .show(truncate=False)

应该给你同样的结果