Projection, Filtering, and Aggregation of Data Streams
Focusing on data manipulation operations, this course teaches how to perform data projection, filtering, and aggregation using Python without needing advanced external libraries. You will learn how to use both procedural and functional programming techniques when applying filtering and aggregation on the data stream.
Lessons and practices
Lesson 1: Understanding Data Streams: Basics and Operations
Add Stream Index Method
DataStream Class Revision
Add Method to Get DataStream Elements
Enhance the DataStream Class with Slice String Conversion Method
DataStream Class Slice to String Method
Lesson 2: Implementing Data Filtering in Python
Implement a DataFilter Method Threshold
Filtering Out Strings Below a Certain Length
Add a Method to Filter Even Numbers
Filter Out Even Positive Numbers
Filter Data Stream by a Custom Condition
Lesson 3: Data Projection Techniques in Python
Integrate Data Projection with Custom Attributes
Enhancing the DataProjector Class Operations
Add Horsepower Conversion to DataProjector
Enhance DataProjector with Combined Filter and Map Operations
Enhance Data Projection with Temperature Unit Conversion
Lesson 4: Aggregation Methods for Summarizing Data Streams
Aggregating Exam Scores
Aggregating Data to Compute Standard Deviation
Compute Average Temperature with a While Loop
Aggregating Data to Find the Most Common Element
Aggregating Adult Participants in a Group
Lesson 5: Scenario-based Exercises for Data Manipulation
Add Projection and Aggregation to Calculate Department Budget Share
Add Method Chaining to Compute Average Sales in the Sales Department
Add Specific Data Manipulation Methods to DataStream Class
Calculating Sales Department's High Earners' Average Salary
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