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A very huge DataFrame with schema:
root
|-- id: string (nullable = true)
|-- ext: array (nullable = true)
| |-- element: integer (containsNull = true)
So far I try to explode data, then collect_list:
select
id,
collect_list(cast(item as string))
from default.dual
lateral view explode(ext) t as item
group by
id
But this way is too expansive.
488
In this article, we will learn how to convert comma-separated string to array in pyspark dataframe. In pyspark SQL, the split () function converts the delimiter separated String to an Array. It is done by splitting the string based on delimiters like spaces, commas, and stack them into an array.
In pyspark SQL, the split () function converts the delimiter separated String to an Array. It is done by splitting the string based on delimiters like spaces, commas, and stack them into an array. This function returns pyspark.sql.Column of type Array. str:- The string to be split.
The PySpark array syntax isn’t similar to the list comprehension syntax that’s normally used in Python. This post covers the important PySpark array operations and highlights the pitfalls you should watch out for. Create a DataFrame with an array column.
Add a first_number column to the DataFrame that returns the first element in the numbers array. The PySpark array indexing syntax is similar to list indexing in vanilla Python.
You can simply cast the ext column to a string array
df = source.withColumn("ext", source.ext.cast("array<string>"))
df.printSchema()
df.show()
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