[英]map values in a dataframe from a dictionary using pyspark
我想知道如何映射數據框中特定列中的值。
我有一個數據框,它看起來像:
df = sc.parallelize([('india','japan'),('usa','uruguay')]).toDF(['col1','col2'])
+-----+-------+
| col1| col2|
+-----+-------+
|india| japan|
| usa|uruguay|
+-----+-------+
我有一本字典,我想從中映射值。
dicts = sc.parallelize([('india','ind'), ('usa','us'),('japan','jpn'),('uruguay','urg')])
我想要的輸出是:
+-----+-------+--------+--------+
| col1| col2|col1_map|col2_map|
+-----+-------+--------+--------+
|india| japan| ind| jpn|
| usa|uruguay| us| urg|
+-----+-------+--------+--------+
我曾嘗試使用lookup function
但它不起作用。 它拋出錯誤 SPARK-5063。 以下是我失敗的方法:
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
我認為更簡單的方法就是使用簡單的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)
我建議你將元組列表更改為dicts並廣播它以在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)
哪個應該給你
+-----+-------+--------+--------+
|col1 |col2 |col1_map|col2_map|
+-----+-------+--------+--------+
|india|japan |ind |jpn |
|usa |uruguay|us |urg |
+-----+-------+--------+--------+
您所要做的就是將dicts rdd更改為dataframe,並使用兩個帶有別名的 連接 ,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)
哪個應該給你相同的結果
與 Ali AzG 類似,但如果有人覺得有用,可以將其全部提取為一個方便的小方法
from itertools import chain
from pyspark.sql import DataFrame
from pyspark.sql import functions as F
from typing import Dict
def map_column_values(df:DataFrame, map_dict:Dict, column:str, new_column:str="")->DataFrame:
"""Handy method for mapping column values from one value to another
Args:
df (DataFrame): Dataframe to operate on
map_dict (Dict): Dictionary containing the values to map from and to
column (str): The column containing the values to be mapped
new_column (str, optional): The name of the column to store the mapped values in.
If not specified the values will be stored in the original column
Returns:
DataFrame
"""
spark_map = F.create_map([F.lit(x) for x in chain(*map_dict.items())])
return df.withColumn(new_column or column, spark_map[df[column]])
這可以如下使用
from pyspark.sql import Row, SparkSession
spark = SparkSession.builder.master("local[3]").getOrCreate()
df = spark.createDataFrame([Row(A=0), Row(A=1)])
df = map_column_values(df, map_dict={0:"foo", 1:"bar"}, column="A", new_column="B")
df.show()
#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
#+---+---+
#| A| B|
#+---+---+
#| 0|foo|
#| 1|bar|
#+---+---+
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