PySpark select() and collect() functions
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PySpark select() and collect() functions

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Questions and Answers

What does the DataFrame.fillna() function do in PySpark?

  • Partition a large dataset into smaller files based on one or multiple columns
  • Execute an input function on each element of an RDD
  • Rotate or transpose data from one column into multiple columns
  • Replace NULL/None values with specified constant literal values (correct)

    • What is the purpose of the PySpark pivot() function?

  • Replace NULL/None values with zero(0) in DataFrame columns
  • Store key-value pairs similarly to Python Dictionary
  • Partition a large dataset into smaller files based on one or multiple columns
  • Rotate/transpose data from one column into multiple DataFrame columns (correct)

    • How is the partitionBy() function in PySpark utilized?

  • Store key-value pairs similarly to Python Dictionary
  • Replace NULL/None values on DataFrame columns with zero(0)
  • Partition a large dataset into smaller files based on one or multiple columns (correct)
  • Rotate/transpose data from one column into multiple DataFrame columns

    • What data type does MapType in PySpark represent?

    <p>Python Dictionary (dict)</p> Signup and view all the answers

    In PySpark, what is the main purpose of DataFrameNaFunctions.fill()?

    <p>To replace NULL/None values on DataFrame columns with constant values</p> Signup and view all the answers

    What action does the foreach() function perform in PySpark?

    <p>Execute the input function on each element of an RDD</p> Signup and view all the answers

    What does the PySpark select() function do?

    <p>Selects specific columns from a DataFrame</p> Signup and view all the answers

    What is the purpose of the PySpark collect() operation?

    <p>Retrieves all elements of the dataset to the driver node</p> Signup and view all the answers

    What happens when retrieving larger datasets with PySpark collect()?

    <p>An OutOfMemory error occurs</p> Signup and view all the answers

    What is the purpose of the PySpark withColumn() function?

    <p>Changes the value or converts the datatype of an existing column</p> Signup and view all the answers

    How can you rename a DataFrame column in PySpark?

    <p>Using the withColumnRenamed() function</p> Signup and view all the answers

    What does the PySpark filter() function do?

    <p>Filters the rows from a DataFrame based on a given condition</p> Signup and view all the answers

    Which PySpark transformation function is used to remove duplicate rows from a DataFrame based on selected columns?

    <p>dropDuplicates()</p> Signup and view all the answers

    Which PySpark function is used to sort DataFrame by ascending or descending order based on single or multiple columns?

    <p>orderBy()</p> Signup and view all the answers

    What is the purpose of PySpark groupBy() function?

    <p>To perform computations on each group of data.</p> Signup and view all the answers

    Which PySpark transformation is used to combine two DataFrames based on a common key similar to SQL JOIN?

    <p>join()</p> Signup and view all the answers

    Which PySpark transformation is used to merge two DataFrames with different schemas based on column names?

    <p>unionByName()</p> Signup and view all the answers

    What is a UDF in PySpark?

    <p>User Defined Function</p> Signup and view all the answers

    Which PySpark function is used to chain custom transformations on a DataFrame?

    <p>transform()</p> Signup and view all the answers

    Which PySpark function is used to apply a transformation function on every element of a DataFrame and returns a new RDD?

    <p>map()</p> Signup and view all the answers

    Which PySpark transformation operation is used to flatten the DataFrame after applying a function on every element?

    <p>flatMap()</p> Signup and view all the answers

    What is the purpose of the PySpark foreach() operation?

    <p>To iterate over each element in the DataFrame.</p> Signup and view all the answers

    In PySpark, the MapType data type is used to represent a Python tuple.

    <p>False</p> Signup and view all the answers

    The PySpark partitionBy() function can partition a large dataset into smaller files based on multiple columns.

    <p>True</p> Signup and view all the answers

    The PySpark pivot() function transposes data from multiple columns into a single column.

    <p>False</p> Signup and view all the answers

    PySpark's foreach() function returns a new RDD after applying a transformation function on each element of the input RDD.

    <p>False</p> Signup and view all the answers

    The PySpark fillna() function can replace NULL/None values with a custom constant literal value.

    <p>True</p> Signup and view all the answers

    PySpark MapType comprises four fields: keyType, valueType, valueContainsNull, and keyContainsNull.

    <p>False</p> Signup and view all the answers

    The PySpark withColumn() function can be used to rename columns in a DataFrame.

    <p>False</p> Signup and view all the answers

    The PySpark select() function can only be used to select a single column from a DataFrame.

    <p>False</p> Signup and view all the answers

    Calling the collect() function in PySpark always results in an OutOfMemoryError for large datasets.

    <p>True</p> Signup and view all the answers

    The PySpark filter() function and where() clause operate differently based on the given condition.

    <p>False</p> Signup and view all the answers

    By default, the PySpark filter() function returns a new DataFrame with all the rows that meet the specified condition.

    <p>True</p> Signup and view all the answers

    The PySpark withColumnRenamed() function can only rename one DataFrame column at a time.

    <p>False</p> Signup and view all the answers

    In PySpark, the distinct() transformation function is used to sort a DataFrame by ascending or descending order based on single or multiple columns.

    <p>False</p> Signup and view all the answers

    PySpark Joins support all basic join types available in traditional SQL, such as INNER, LEFT OUTER, RIGHT OUTER, LEFT ANTI, LEFT SEMI, CROSS, and SELF JOIN.

    <p>True</p> Signup and view all the answers

    In PySpark, the DataFrameNaFunctions.fill() function replaces null values in DataFrame columns with specified scalar values.

    <p>True</p> Signup and view all the answers

    PySpark map() is an action operation that returns a new RDD by applying a transformation function on every element of the RDD.

    <p>False</p> Signup and view all the answers

    PySpark's distinct() function returns the first occurrence of a duplicate row, thus preserving the original order of the DataFrame.

    <p>False</p> Signup and view all the answers

    PySpark's unionByName() transformation can be used to merge two DataFrames with a different number of columns, given that allowMissingColumns parameter is set to True.

    <p>True</p> Signup and view all the answers

    PySpark's flatMap() transformation operation performs a function and returns a new RDD/DataFrame without flattening the array or map-type DataFrame columns.

    <p>False</p> Signup and view all the answers

    PySpark's groupBy() function is used to perform count, sum, average, minimum, and maximum functions on aggregated data.

    <p>True</p> Signup and view all the answers

    PySpark's transform() function is an action operation that chains custom transformations and returns a new DataFrame.

    <p>False</p> Signup and view all the answers

    PySpark's UDF feature is used to extend the built-in capabilities of Spark SQL & DataFrame and allows users to create their own custom functions for specific use-cases.

    <p>True</p> Signup and view all the answers

    Study Notes

    PySpark Functions

    foreach()

    • An action operation that iterates over each element in a DataFrame or RDD
    • Executes a function on each element without returning a value
    • Similar to a for loop with advanced concepts

    Data Manipulation

    fillna()

    • Replaces NULL/None values in DataFrame columns with specified values (e.g., zero, empty string, space)
    • Can be used with multiple columns

    pivot()

    • Rotates data from one column into multiple columns and back using unpivot()
    • An aggregation function that transposes values from one column into distinct columns

    partitionBy()

    • Divides a large dataset (DataFrame) into smaller files based on one or multiple columns
    • Used when writing to disk

    MapType

    • A data type to represent Python dictionaries (dict) and store key-value pairs
    • Comprises three fields: keyType (DataType), valueType (DataType), and valueContainsNull (BooleanType)

    select()

    • Selects single, multiple, or all columns from a DataFrame
    • Returns a new DataFrame with selected columns
    • Can be used with column indices or nested columns

    collect()

    • An action operation that retrieves all elements of a dataset (from all nodes) to the driver node
    • Should be used with smaller datasets after filtering or grouping to avoid OutOfMemory errors

    withColumn()

    • Changes values, converts data types, creates new columns, and more
    • Examples include renaming columns, creating new columns, and applying functions

    Filtering and Sorting

    filter()

    • Filters rows from RDD/DataFrame based on a condition or SQL expression
    • Returns a new DataFrame or RDD with only the rows that meet the condition
    • Can be used with the where() clause

    distinct() and dropDuplicates()

    • Remove duplicate rows (all columns) from DataFrame or drop rows based on selected columns
    • Return a new DataFrame

    sort() and orderBy()

    • Sorts DataFrame by ascending or descending order based on single or multiple columns
    • Can also be done using PySpark SQL sorting functions

    Grouping and Joining

    groupBy()

    • Collects identical data into groups on DataFrame and performs count, sum, avg, min, max functions on the grouped data
    • Similar to SQL GROUP BY clause

    join()

    • Combines two DataFrames and supports various join types (e.g., INNER, LEFT OUTER, RIGHT OUTER)
    • Involves data shuffling across the network

    union() and unionAll()

    • Merge two or more DataFrames of the same schema or structure
    • Can be used with PySpark's unionByName() function, which takes an allowMissingColumns parameter

    User-Defined Functions (UDF)

    • Extend PySpark's built-in capabilities
    • Can be created and used with DataFrame select(), withColumn(), and SQL
    • Allow custom functions to be applied to columns

    Transformations

    transform()

    • Chains custom transformations and returns a new DataFrame
    • Used to apply functions to columns

    map()

    • Applies a transformation function (lambda) to every element of RDD/DataFrame
    • Returns a new RDD

    flatMap()

    • Flattens RDD/DataFrame (array/map DataFrame columns) after applying a function to every element
    • Returns a new PySpark RDD/DataFrame

    sample()

    • Retrieves a random sampling subset from a large dataset
    • Offers multiple methods (e.g., DataFrame.sample(), RDD.sample(), RDD.takeSample())

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    Description

    Learn about PySpark's select() function used for selecting single, multiple, or nested columns from a DataFrame and how collect() is used to retrieve all elements from the dataset to the driver node. Use collect() on smaller datasets typically after operations like filter(), group(), etc.

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