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  • 1

    Using iterators in PythonLand

    Free

    You'll learn all about iterators and iterables, which you have already worked with when writing for loops. You'll learn some handy functions that will allow you to effectively work with iterators. And you’ll finish the chapter with a use case that is pertinent to the world of data science and dealing with large amounts of data—in this case, data from Twitter that you will load in chunks using iterators.

  • 2

    List comprehensions and generators

    In this chapter, you'll build on your knowledge of iterators and be introduced to list comprehensions, which allow you to create complicated lists—and lists of lists—in one line of code! List comprehensions can dramatically simplify your code and make it more efficient, and will become a vital part of your Python toolbox. You'll then learn about generators, which are extremely helpful when working with large sequences of data that you may not want to store in memory, but instead generate on the fly.

  • 3

    Bringing it all together!

    This chapter will allow you to apply your newly acquired skills toward wrangling and extracting meaningful information from a real-world dataset—the World Bank's World Development Indicators. You'll have the chance to write your own functions and list comprehensions as you work with iterators and generators to solidify your Python chops.

Initializing

Exercise

Processing data in chunks (1)

Sometimes, data sources can be so large in size that storing the entire dataset in memory becomes too resource-intensive. In this exercise, you will process the first 1000 rows of a file line by line, to create a dictionary of the counts of how many times each country appears in a column in the dataset.

The csv file 'world_dev_ind.csv' is in your current directory for your use. To begin, you need to open a connection to this file using what is known as a context manager. For example, the command with open('datacamp.csv') as datacamp binds the csv file 'datacamp.csv' as datacamp in the context manager. Here, the with statement is the context manager, and its purpose is to ensure that resources are efficiently allocated when opening a connection to a file.

If you'd like to learn more about context managers, refer to the DataCamp course on Importing Data in Python.

Instructions

100 XP
  • Use open() to bind the csv file 'world_dev_ind.csv' as file in the context manager.
  • Complete the for loop so that it iterates 1000 times to perform the loop body and process only the first 1000 rows of data of the file.