The map() is a built in function in Python. The map function executes a specified function for each item in an iterable. An iterable can be a list or a set or a dictionary or a tuple.
The syntax of map function with a list iterable is:
The function funcA() is applied to each list element. The resulting output is a new iterator. The process is known as mapping.
Example with a Python code
list_of_states = ['new jersey','new york','texas','california','florida'] # for loop to convert each element into upper case modified_list_of_states =  for idx in range(len(list_of_states)): modified_list_of_states.append(str.upper(list_of_states[idx])) modified_list_of_states ['NEW JERSEY', 'NEW YORK', 'TEXAS', 'CALIFORNIA', 'FLORIDA']
Lambda and Map function
modified_list_of_states = map(lambda list_of_states: str.upper(list_of_states), list_of_states) list(modified_list_of_states) ['NEW JERSEY', 'NEW YORK', 'TEXAS', 'CALIFORNIA', 'FLORIDA']
Map function with a defined function that takes as input each list item and returns the modified value
def convert_case_f(val): return str.upper(val) modified_list_of_states = map(convert_case_f, list_of_states) modified_list_of_states # creates an iterator. To display the items we use the list() <map at 0x7f08a15d6ee0> list(modified_list_of_states) ['NEW JERSEY', 'NEW YORK', 'TEXAS', 'CALIFORNIA', 'FLORIDA']
Map function in Pyspark
We have the movie ratings dataset from Kaggle. The sample data as below:
The third column is the movie ratings data and our use case is to find the total count per rating.
Using the map() function, we can extract the ratings column.
For every record, the lambda function split the columns based on the white space.The third column that is the rating column gets extracted from every data record. This transformation is applied to every row in the dataset with the map() function.
from pyspark import SparkConf, SparkContext import collections # Setting up the SparkContext object conf = SparkConf().setMaster("local").setAppName("MovieRatingsData") sc = SparkContext(conf = conf) # Loading data movie_ratings_data = sc.textFile("/content/u.data") # Extracting the ratings data with the map() function ratings = movie_ratings_data.map(lambda x: x.split()) # Count the total per rating ratings_count = ratings.countByValue() # Display the result collections.OrderedDict(sorted(ratings_count.items()))
Map() function with Apache Beam
With the same use case lets see the working example with Apache Beam.First install apache-beam library. Next create the pipeline p and a PCollection movie_data that stores the results of all the transformations.
Pipeline p applies all the transformations in sequence. It first read the data file using read transform into a collection and then split each row into columns. Next, we make a key value pair of each rating where key is the rating and value assigned is 1. This is then combined and summed up. Displaying the final results.
import apache_beam as beam p = beam.Pipeline() movie_data = ( p | 'Read from data file' >> beam.io.ReadFromText('/content/u.data') | 'Split rows' >> beam.Map(lambda record: record.split('\t')) | 'Fetch ratings data' >> beam.Map(lambda record: (record, 1)) | 'Total count per rating' >> beam.CombinePerKey(sum) | 'Write results for rating' >> beam.io.WriteToText('results') ) p.run() # creates the result output file
Taming Big Data with Apache Spark and Python Udemy
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