从文本pysp中提取字符串

Question

我有一个Pypark数据帧：

示例：

text <String>                 |   name <String>  |   original_name <String>
----------------------------------------------------------------------------
HELLOWORLD2019THISISGOOGLE    |   WORLD2019      |   WORLD_2019
----------------------------------------------------------------------------
NATUREISVERYGOODFORHEALTH     |   null           |   null  
----------------------------------------------------------------------------
THESUNCONTAINVITAMIND         |   VITAMIND       |   VITAMIN_D
----------------------------------------------------------------------------
BECARETOOURHEALTHISVITAMIND   |   OURHEALTH      |   OUR_/HEALTH
----------------------------------------------------------------------------

我想循环name列并查看name值是否存在于text中，如果存在，我创建一个new_column，将包含在text中找到的name值的original_name值。知道有时数据帧列是null。你知道吗

示例：

• 在dataframe示例的第4行中，text包含来自name列的2个值：[OURHEALTH, VITAMIND]，我应该获取它的original_name值并将它们存储在new_column。

• 在第2行中，text包含来自name列的OURHEALTH，我应该在new_column中存储找到的原始name值==>；[OUR_/HEALTH]

预期结果：

text <String>                 |   name <String>  |   original_name <String>  | new_column <Array>
------------------------------|------------------|---------------------------|----------------------------
HELLOWORLD2019THISISGOOGLE    |   WORLD2019      |   WORLD_2019              |  [WORLD_2019]
------------------------------|------------------|---------------------------|----------------------------
NATUREISVERYGOODFOROURHEALTH  |   null           |   null                    |  [OUR_/HEALTH]
------------------------------|------------------|---------------------------|----------------------------
THESUNCONTAINVITAMIND         |   VITAMIND       |   VITAMIN_D               |  [VITAMIN_D]
------------------------------|------------------|---------------------------|----------------------------
BECARETOOURHEALTHISVITAMIND   |   OURHEALTH      |   OUR_/HEALTH             |  [OUR_/HEALTH, VITAMIN_D ]
-----------------------------------------------------------------------------|----------------------------

我希望我的解释清楚。你知道吗

我按以下代码进行了尝试：

df = df.select("text", "name", "original_name").agg(collect_set("name").alias("name_array"))
for name_item in name_array:    
    df.withColumn("new_column", F.when(df.text.contains(name_item), "original_name").otherwise(None))

有人能帮我吗？ 谢谢

Answer 1

一个简单的解决方案是在原始数据帧和只有name列的派生数据帧之间使用join。由于多行可以满足连接条件，因此在连接之后必须按原始列分组。你知道吗

下面是一个详细的输入示例：

data = [
    ("HELLOWORLD2019THISISGOOGLE", "WORLD2019", "WORLD_2019"),
    ("NATUREISVERYGOODFOROURHEALTH", None, None),
    ("THESUNCONTAINVITAMIND", "VITAMIND", "VITAMIN_D"),
    ("BECARETOOURHEALTHISVITAMIND", "OURHEALTH", "OUR_ / HEALTH")
]
df = spark.createDataFrame(data, ["text", "name", "original_name"])

# create new DF with search words
# as it's the originl_name which interests us for the final list so we select it too
search_df = df.select(struct(col("name"), col("original_name")).alias("search_match"))

# join on df.text contains search_df.name
df_join = df.join(search_df, df.text.contains(search_df["search_match.name"]), "left")

# group by original columns and collect matches in a list
df_join.groupBy("text", "name", "original_name")\
    .agg(collect_list(col("search_match.original_name")).alias("new_column"))\
    .show(truncate=False)

输出：

+              +    -+      -+             +
|text                        |name     |original_name|new_column                |
+              +    -+      -+             +
|HELLOWORLD2019THISISGOOGLE  |WORLD2019|WORLD_2019   |[WORLD_2019]              |
|THESUNCONTAINVITAMIND       |VITAMIND |VITAMIN_D    |[VITAMIN_D]               |
|NATUREISVERYGOODFOROURHEALTH|null     |null         |[OUR_ / HEALTH]           |
|BECARETOOURHEALTHISVITAMIND |OURHEALTH|OUR_ / HEALTH|[VITAMIN_D, OUR_ / HEALTH]|
+              +    -+      -+             +