import os
import argparse
import pandas as pd 
import numpy as np 
import apache_beam as beam 
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.io.textio import ReadFromText, WriteToText

root_dir = os.path.dirname(os.path.abspath(os.getcwd()))

class Split(beam.DoFn):
    def process(self, element):
        item_id, item_price, category_1, category_2, category_3, product_type = element.split(",")
        return [{
            "item_id": int(item_id),
            "item_price": float(item_price),
            "category_1": int(category_1),
            "category_2": int(category_2),
            "category_3": int(category_3),
            "product_type": int(product_type)
        }]

class CollectOpen(beam.DoFn):
    def process(self, element):
        """
        return a list of tuples containing Date and Open value
        """
        
        # first parameter of the tuple is fixed since we want to calculate the mean over the whole dataset, 
        # but you can make it dynamic to perform the next transform only on a sub-set defined by that key.
        result = [(element["category_1"], element["item_price"])]  
        
        return result


def main(source_path, destination_path, args):
    """
    defining the whole pipeline
    """
    p = beam.Pipeline(argv = args)

    values = (
        p | "ReadCSV" >> ReadFromText(source_path, skip_header_lines = True)
          | beam.ParDo(Split())
    )

    mean_item_id = (
        values | beam.ParDo(CollectOpen()) |
        "Grouping Keys Open" >> beam.GroupByKey() |
        "Calculating Mean for item price" >> beam.CombineValues(
            beam.combiners.MeanCombineFn()
        )
    )

    output = (
        mean_item_id | "WriteCSV" >> WriteToText(destination_path, file_name_suffix = ".csv")
    )

    p.run()

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description = __doc__,
        formatter_class = argparse.RawDescriptionHelpFormatter
    )

    parser.add_argument("source_path", 
    help = ("path from where file is to be read")
    )

    parser.add_argument("destination_path", 
    help = ("path to where file is to be written")
    )

    args, pipeline_args = parser.parse_known_args()

    main(args.source_path, args.destination_path, args = pipeline_args)
ffbee707ab86ad25fab7f1be78dd1c95f73c9dd8
<mask token>


class Split(beam.DoFn):
    <mask token>


class CollectOpen(beam.DoFn):

    def process(self, element):
        """
        return a list of tuples containing Date and Open value
        """
        result = [(element['category_1'], element['item_price'])]
        return result


<mask token>

<mask token>


class Split(beam.DoFn):

    def process(self, element):
        (item_id, item_price, category_1, category_2, category_3, product_type
            ) = element.split(',')
        return [{'item_id': int(item_id), 'item_price': float(item_price),
            'category_1': int(category_1), 'category_2': int(category_2),
            'category_3': int(category_3), 'product_type': int(product_type)}]


class CollectOpen(beam.DoFn):

    def process(self, element):
        """
        return a list of tuples containing Date and Open value
        """
        result = [(element['category_1'], element['item_price'])]
        return result


def main(source_path, destination_path, args):
    """
    defining the whole pipeline
    """
    p = beam.Pipeline(argv=args)
    values = p | 'ReadCSV' >> ReadFromText(source_path, skip_header_lines=True
        ) | beam.ParDo(Split())
    mean_item_id = values | beam.ParDo(CollectOpen()
        ) | 'Grouping Keys Open' >> beam.GroupByKey(
        ) | 'Calculating Mean for item price' >> beam.CombineValues(beam.
        combiners.MeanCombineFn())
    output = mean_item_id | 'WriteCSV' >> WriteToText(destination_path,
        file_name_suffix='.csv')
    p.run()


<mask token>

<mask token>


class Split(beam.DoFn):

    def process(self, element):
        (item_id, item_price, category_1, category_2, category_3, product_type
            ) = element.split(',')
        return [{'item_id': int(item_id), 'item_price': float(item_price),
            'category_1': int(category_1), 'category_2': int(category_2),
            'category_3': int(category_3), 'product_type': int(product_type)}]


class CollectOpen(beam.DoFn):

    def process(self, element):
        """
        return a list of tuples containing Date and Open value
        """
        result = [(element['category_1'], element['item_price'])]
        return result


def main(source_path, destination_path, args):
    """
    defining the whole pipeline
    """
    p = beam.Pipeline(argv=args)
    values = p | 'ReadCSV' >> ReadFromText(source_path, skip_header_lines=True
        ) | beam.ParDo(Split())
    mean_item_id = values | beam.ParDo(CollectOpen()
        ) | 'Grouping Keys Open' >> beam.GroupByKey(
        ) | 'Calculating Mean for item price' >> beam.CombineValues(beam.
        combiners.MeanCombineFn())
    output = mean_item_id | 'WriteCSV' >> WriteToText(destination_path,
        file_name_suffix='.csv')
    p.run()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=__doc__, formatter_class=
        argparse.RawDescriptionHelpFormatter)
    parser.add_argument('source_path', help=
        'path from where file is to be read')
    parser.add_argument('destination_path', help=
        'path to where file is to be written')
    args, pipeline_args = parser.parse_known_args()
    main(args.source_path, args.destination_path, args=pipeline_args)

import os
import argparse
import pandas as pd
import numpy as np
import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.io.textio import ReadFromText, WriteToText
root_dir = os.path.dirname(os.path.abspath(os.getcwd()))


class Split(beam.DoFn):

    def process(self, element):
        (item_id, item_price, category_1, category_2, category_3, product_type
            ) = element.split(',')
        return [{'item_id': int(item_id), 'item_price': float(item_price),
            'category_1': int(category_1), 'category_2': int(category_2),
            'category_3': int(category_3), 'product_type': int(product_type)}]


class CollectOpen(beam.DoFn):

    def process(self, element):
        """
        return a list of tuples containing Date and Open value
        """
        result = [(element['category_1'], element['item_price'])]
        return result


def main(source_path, destination_path, args):
    """
    defining the whole pipeline
    """
    p = beam.Pipeline(argv=args)
    values = p | 'ReadCSV' >> ReadFromText(source_path, skip_header_lines=True
        ) | beam.ParDo(Split())
    mean_item_id = values | beam.ParDo(CollectOpen()
        ) | 'Grouping Keys Open' >> beam.GroupByKey(
        ) | 'Calculating Mean for item price' >> beam.CombineValues(beam.
        combiners.MeanCombineFn())
    output = mean_item_id | 'WriteCSV' >> WriteToText(destination_path,
        file_name_suffix='.csv')
    p.run()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=__doc__, formatter_class=
        argparse.RawDescriptionHelpFormatter)
    parser.add_argument('source_path', help=
        'path from where file is to be read')
    parser.add_argument('destination_path', help=
        'path to where file is to be written')
    args, pipeline_args = parser.parse_known_args()
    main(args.source_path, args.destination_path, args=pipeline_args)

import os
import argparse
import pandas as pd 
import numpy as np 
import apache_beam as beam 
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.io.textio import ReadFromText, WriteToText

root_dir = os.path.dirname(os.path.abspath(os.getcwd()))

class Split(beam.DoFn):
    def process(self, element):
        item_id, item_price, category_1, category_2, category_3, product_type = element.split(",")
        return [{
            "item_id": int(item_id),
            "item_price": float(item_price),
            "category_1": int(category_1),
            "category_2": int(category_2),
            "category_3": int(category_3),
            "product_type": int(product_type)
        }]

class CollectOpen(beam.DoFn):
    def process(self, element):
        """
        return a list of tuples containing Date and Open value
        """
        
        # first parameter of the tuple is fixed since we want to calculate the mean over the whole dataset, 
        # but you can make it dynamic to perform the next transform only on a sub-set defined by that key.
        result = [(element["category_1"], element["item_price"])]  
        
        return result


def main(source_path, destination_path, args):
    """
    defining the whole pipeline
    """
    p = beam.Pipeline(argv = args)

    values = (
        p | "ReadCSV" >> ReadFromText(source_path, skip_header_lines = True)
          | beam.ParDo(Split())
    )

    mean_item_id = (
        values | beam.ParDo(CollectOpen()) |
        "Grouping Keys Open" >> beam.GroupByKey() |
        "Calculating Mean for item price" >> beam.CombineValues(
            beam.combiners.MeanCombineFn()
        )
    )

    output = (
        mean_item_id | "WriteCSV" >> WriteToText(destination_path, file_name_suffix = ".csv")
    )

    p.run()

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description = __doc__,
        formatter_class = argparse.RawDescriptionHelpFormatter
    )

    parser.add_argument("source_path", 
    help = ("path from where file is to be read")
    )

    parser.add_argument("destination_path", 
    help = ("path to where file is to be written")
    )

    args, pipeline_args = parser.parse_known_args()

    main(args.source_path, args.destination_path, args = pipeline_args)