Data Types for Data Science in Python
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.
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Course Description
Have you got your basic Python programming chops down for Data Science but are yearning for more? Then this is the course for you. Herein, you'll consolidate and practice your knowledge of lists, dictionaries, tuples, sets, and date times. You'll see their relevance in working with lots of real data and how to leverage several of them in concert to solve multistep problems, including an extended case study using Chicago metropolitan area transit data. You'll also learn how to use many of the objects in the Python Collections module, which will allow you to store and manipulate your data for a variety of Data Scientific purposes. After taking this course, you'll be ready to tackle many Data Science challenges Pythonically.
- 1
Fundamental data types
FreeThis chapter will introduce you to the fundamental Python data types - lists, sets, and tuples. These data containers are critical as they provide the basis for storing and looping over ordered data. To make things interesting, you'll apply what you learn about these types to answer questions about the New York Baby Names dataset!
Introduction and lists50 xpManipulating lists for fun and profit100 xpLooping over lists100 xpMeet the Tuples50 xpData type usage50 xpUsing and unpacking tuples100 xpMaking tuples by accident100 xpSets for unordered and unique data50 xpFinding all the data and the overlapping data between sets100 xpDetermining set differences100 xp - 2
Dictionaries - the root of Python
At the root of all things Python is a dictionary. Herein, you'll learn how to use them to safely handle data that can viewed in a variety of ways to answer even more questions about the New York Baby Names dataset. You'll explore how to loop through data in a dictionary, access nested data, add new data, and come to appreciate all of the wonderful capabilities of Python dictionaries.
Using dictionaries50 xpCreating and looping through dictionaries100 xpSafely finding by key100 xpDealing with nested data100 xpAltering dictionaries50 xpAdding and extending dictionaries100 xpPopping and deleting from dictionaries100 xpPythonically using dictionaries50 xpWorking with dictionaries more pythonically100 xpChecking dictionaries for data100 xpWorking with CSV files50 xpReading from a file using CSV reader100 xpCreating a dictionary from a file100 xp - 3
Meet the collections module
The collections module is part of Python's standard library and holds some more advanced data containers. You'll learn how to use the Counter, defaultdict, OrderedDict and namedtuple in the context of answering questions about the Chicago transit dataset.
Counting made easy50 xpUsing Counter on lists100 xpFinding most common elements100 xpDictionaries of unknown structure - Defaultdict50 xpCreating dictionaries of an unknown structure100 xpSafely appending to a key's value list100 xpMaintaining Dictionary Order with OrderedDict50 xpWorking with OrderedDictionaries100 xpPowerful Ordered popping100 xpWhat do you mean I don't have any class? Namedtuple50 xpCreating namedtuples for storing data100 xpLeveraging attributes on namedtuples100 xp - 4
Handling Dates and Times
Handling times can seem daunting at time, but here, you'll dig in and learn how to create datetime objects, print them, look to the past and to the future. Additionally, you'll learn about some third party modules that can make all of this easier. You'll continue to use the Chicago Transit dataset to answer questions about transit times.
There and Back Again a DateTime Journey50 xpStrings to DateTimes100 xpConverting to a String100 xpWorking with Datetime Components and current time50 xpPieces of Time100 xpCreating DateTime Objects... Now100 xpTimezones100 xpTime Travel (Adding and Subtracting Time)50 xpFinding a time in the future and from the past100 xpFinding differences in DateTimes100 xpHELP! Libraries to make it easier50 xpLocalizing time with pendulum100 xpHumanizing Differences with Pendulum100 xp - 5
Answering Data Science Questions
Time for a case study to reinforce all of your learning so far! You'll use all the containers and data types you've learned about to answer several real world questions about a dataset containing information about crime in Chicago. Have fun!
Counting within Date Ranges50 xpReading your data with CSV Reader and Establishing your Data Containers100 xpFind the Months with the Highest Number of Crimes100 xpTransforming your Data Containers to Month and Location100 xpFind the Most Common Crimes by Location Type by Month in 2016100 xpDictionaries with Time Windows for Keys50 xpReading your Data with DictReader and Establishing your Data Containers100 xpDetermine the Arrests by District by Year100 xpUnique Crimes by City Block100 xpFinal thoughts50 xp
In the following tracks
Python ProgrammerCollaborators
Prerequisites
Intermediate Python
Jason Myers
Co-Author of Essential SQLAlchemy and Software Engineer
Jason Myers is a software engineer and author. His area of expertise is in developing data analytics platforms. He has also written the Essential SQLAlchemy book, co-authored with Rick Copeland, that introduces you to working with relational databases in Python.
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