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Convert a String of Words Separated by '|' and ',' Characters into a PySpark DataFrame

Question: How can you convert a string of words separated by '|' and ',' characters into a PySpark DataFrame with each word on a separate row?


In this blog post, we'll explore how to use PySpark's built-in functions to process string data and convert it into a DataFrame. Using a sample input of 'abc | cde, efg | hij, jkl' in a text file, we'll walk through the steps to transform the input into the desired output:


+-------+

| words|

+-------+

| abc |

| cde |

| efg |

| hij |

| jkl |

+------+



Readers will learn how to use PySpark's string manipulation functions and DataFrame API to transform string data into structured, tabular data that can be used for further analysis and processing. This post will be helpful for anyone working with text data in PySpark, and looking to gain a deeper understanding of how to process and manipulate it using PySpark's powerful APIs.



Code :



Explanation:


This PySpark code is used to convert a string of words separated by '|' and ',' characters into a PySpark DataFrame with each word on a separate row. Let's break down the code line by line to understand it step by step:

# create a SparkSession
spark = SparkSession.builder.appName("delimiter_example").getOrCreate()

This line creates a new SparkSession with the given name ("delimiter_example") or retrieves an existing one if available.


# read the text file into an RDD
rdd = spark.sparkContext.textFile("path/to/textfile.txt")

This line reads the text file containing the input data into a Spark RDD (Resilient Distributed Dataset). The file path should be replaced with the actual path to the file.


['abc | cde, efg | hij, jkl']

This is the sample input data.


# split each line using "|"
split_rdd = rdd.map(lambda x: x.split("|"))

This line splits each line of the RDD using the '|' character and returns a new RDD containing a list of lists, where each inner list contains the words separated by the '|' character.


[  ['abc ', ' cde, efg ', ' hij, jkl'] ]

This is the result of the previous line, a list containing a single list of words.


# flatten the resulting list of lists
flatten_rdd = split_rdd.flatMap(lambda x: x)

This line flattens the list of lists from the previous step into a single RDD by using the flatMap() function.


['abc ', ' cde, efg ', ' hij, jkl']

This is the resulting RDD containing a list of words separated by '|' and ',' characters.


# split each element using ","
split_rdd = flatten_rdd.map(lambda x: x.split(","))

This line splits each element in the RDD using the ',' character, returning a new RDD containing a list of lists where each inner list contains the individual words.


[  ['abc '],
  [' cde', ' efg '],
  [' hij', ' jkl']
]

This is the resulting RDD containing a list of lists with each word separated by ',' character.


# transform each element in the split RDD into a separate row
flatmap_rdd = split_rdd.flatMap(lambda x: x)

This line uses the flatMap() function to transform each element in the RDD into a separate row, so that each word is on its own line.


['abc ', ' cde', ' efg ', ' hij', ' jkl']

This is the resulting RDD with each word on its own line.


# convert the RDD to a DataFrame with column name "name"
df = flatmap_rdd.toDF(["name"])

This line converts the RDD to a PySpark DataFrame with a single column named "name".


# show only the "name" column
df.select("name").show()

This line selects only the "name" column from the DataFrame and displays it on the console using the show() function.


The output is the desired DataFrame with each word on a separate row:

+-----+
| name|
+-----+
| abc |
| cde |
| efg |
| hij |
| jkl |
+-----+

Overall, this PySpark code shows how to manipulate string data and convert it into a structured DataFrame using PySpark's built-in functions.




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