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Questions and Answers
What is the primary purpose of using PySpark SQL functions lit() and typedLit()?
What is the return type of both lit() and typedLit() functions?
What is the main advantage of using typedLit() function over lit() function?
How can you add a new column to a DataFrame with a constant value using lit() function?
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What is the difference between lit() and typedLit() functions?
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What is the recommended approach when possible?
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What is the benefit of using typedLit() function?
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How can you ensure data consistency and type correctness in PySpark workflows?
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What is the primary function of the split() function in PySpark?
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What is the return type of the split() function?
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How can you use the split() function to create a new array column?
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What is an alternative way to achieve the same result as using the split() function?
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What is the purpose of the createOrReplaceTempView() function?
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What is the delimiter used in the example to split the string column?
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What is the data type of the 'NameArray' column in the resulting DataFrame?
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What is the purpose of the drop() method?
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Study Notes
PySpark SQL Functions: lit() and typedLit()
-
lit()andtypedLit()functions are used to add a new column to a DataFrame by assigning a literal or constant value. - Both functions return a Column type as their return type.
- They are available in PySpark by importing
pyspark.sql.functions.
lit() Function
-
lit()function is used to add a constant or literal value as a new column to a DataFrame. - It can be used to add a simple constant value to a DataFrame, but this may not be useful in real-time scenarios.
-
lit()function can be used withwithColumnto derive a new column based on some conditions.
typedLit() Function
-
typedLit()function is similar tolit()function but provides a way to be explicit about the data type of the constant value being added to a DataFrame. - It can handle collection types such as Array, Dictionary (map), etc.
-
typedLit()function can be used to add a column with a specific data type, such as a string type flag.
Key Differences
- The main difference between
lit()andtypedLit()functions is thattypedLit()can handle collection types.
Best Practices
- When possible, try to use predefined PySpark functions as they provide compile-time safety and perform better than user-defined functions.
- Avoid using custom UDF functions in critical applications as they are not guaranteed to perform well.
Converting String to Array Column in PySpark
- The
split()function from thepyspark.sql.functionsmodule is used to convert a string column (StringType) to an array column (ArrayType) in PySpark. - The
split()function splits a string on a specified delimiter (e.g. space, comma, pipe) and returns an array. - The
split()function takes two arguments: the DataFrame column of type String as the first argument and the string delimiter as the second argument. - The
split()function returns apyspark.sql.Columnof type Array.
Using split() with select()
- The
select()method can be used with thesplit()function to split the string column and create an array. - The
select()method returns the array column.
Using split() with withColumn()
- The
split()function can be used within thewithColumn()method to create a new column with an array on the DataFrame. - If the original column is not needed, use the
drop()method to remove the column.
Converting String to Array Column using SQL Query
- The
split()function can be used with SQL queries to convert a string column to an array column. - Create a table using
createOrReplaceTempView()and usespark.sql()to run the SQL query.
Key Use Cases
- The
split()function is useful for transforming comma-separated values or other delimited strings into array structures for further processing. - The
split()function is used with thewithColumn()orselect()methods to create a new array column where each string element is separated into an array based on the delimiter.
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Description
Learn about PySpark SQL functions lit() and typedLit() used to add a new column to a DataFrame by assigning a literal or constant value. Understand the benefits of using typedLit() for data type consistency.
