常用的ArrayType类型列操作: array(将两个表合并成array)、array_contains、array_distinct、array_except(两个array的差集)、array_intersect(两个array的交集不去重)、array_join、array_max、array_min、array_position(返回指定元素在array中的索引,索引值
To select a specific field or object from the converted JSON, use the [] notation. For example, to select the products field which itself is an array of products:Python Копирај display(df_drugs.select(df_drugs["products"])) ...
withColumn('empty_array_column', F.array([])) # Get element at index – col.getItem(n) df = df.withColumn('first_element', F.col("my_array").getItem(0)) # Array Size/Length – F.size(col) df = df.withColumn('array_length', F.size('my_array')) # Flatten Array – F....
If you have data with mostly regular structure this is better than nesting it in an array. See jsonlines.org df = spark.read.json("data/weblog.jsonl") # Code snippet result: +---+---+---+---+---+---+ | client| country| session| timestamp| uri| user| +---+---+---...
To select a specific field or object from the converted JSON, use the [] notation. For example, to select the products field which itself is an array of products:Python Kopiraj display(df_drugs.select(df_drugs["products"])) You can also chain together method calls to traverse multiple ...
Array Size/Length – F.size(col)df=df.withColumn('array_length',F.size('my_array'))# Flatten Array – F.flatten(col)df=df.withColumn('flattened',F.flatten('my_array'))# Unique/Distinct Elements – F.array_distinct(col)df=df.withColumn('unique_elements',F.array_distinct('my_array')...
If you have data with mostly regular structure this is better than nesting it in an array. See jsonlines.org df = spark.read.json("data/weblog.jsonl") # Code snippet result: +---+---+---+---+---+---+ | client| country| session| timestamp| uri| user| +---+---+---...