Live training with Boris Paskhaver starts on April 6th at 1 PM (ET)
Training duration: 4 hours (Hands-on)
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Software Engineer | Stride Consulting
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By the end of the course, participants will be able to:
Apply concepts to a NEW dataset , and watch and practice the concepts.
Have a solid grasp of the capabilities of the pandas library.
Perform various data manipulations - sorting, joining, cleaning, aggregating, deduping, and more.
DIFFICULTY LEVEL: BEGINNER - INTERMEDIATE
Lesson 1. Foundations of pandas:
- Discover the 1-dimensional Series and the 2-dimensional DataFrame, the two core data structures in pandas.
- Learn how to sort values across one or more columns, identify missing values, remove duplicates, count occurrences of values, filter rows based on one or more criteria, and more.
- At the end of this lesson, you'll have knowledge of the most popular features of pandas.
Lesson 2. Working with Data of Different Types
- Data can come in a variety of formats (and be messy to boot)! In this lesson, we'll explore how to convert columns from one data type to another.
- We'll optimize our dataset to reduce memory consumption.
- We'll discover how to clean messy text data and how to extract date-time information from text.
- Finally, we'll have a chance to review all concepts from the previous lesson with new datasets.
Lesson 3. Working with Text Data
- Real-world text data can be riddled with issues -- whitespace, letter casings, inconsistent formats, and more.
- In this lesson, we'll learn how to to clean text data in pandas.
- We'll apply text operations like splitting, replacing, and joining to whole columns of data.
- We'll conclude with a quick discussion on regular expressions, which allow us to define search patterns for text.
Lesson 4. Aggregating and Joining Datasets
- In this section, we'll learn how to merge data across multiple datasets.
- We'll explore the pandas equivalent of common SQL operations like inner joins, outer joins, left joins, and right joins.
- We'll also introduce the GroupBy object for grouping rows by shared values across one or more columns.
- Finally, we'll walk through common aggregation operations like pivoting, melting, stacking, unstacking, and more.
Which knowledge and skills you should have?
Basic/intermediate experience with spreadsheet software (Excel, Google Sheets, etc.)
Basic experience with Python programming language
What is included in your ticket?
Access to live training and QA session with the Instructor
Access to the on-demand recording
Certificate of completion
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