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Pandas provides fast, flexible, and expressive data structures that have been designed to make working with relational or “labeled” data not only easy, but also intuitive. It’s the fundamental high-level building block for doing practical and real world data analysis in Python.
What you'll learn
- Create and explore the fundamental data structures of the pandas library
- Use labels and indices to narrow data
- Use common DataFrame exploration techniques
- Recognize the importance of performing vectorized operations in place
Overview
Syllabus
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Meet pandas
Let's explore the main data structures that pandas introduces
Chevron 16 steps-
Welcome
5:18
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Meet Series
1:14
- instruction
About This Course
- instruction
Creating a Series
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Review Series Creation
3 questions
- instruction
Accessing a Series
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Review Series Access
5 questions
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Vectorization and Broadcasting Review
2:00
- instruction
Series Vectorization and Broadcasting
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Review Series Vectorization and Broadcasting
3 questions
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Meet DataFrames
1:33
- instruction
Creating a DataFrame
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Review DataFrame Creation
3 questions
- instruction
Accessing a DataFrame
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Review DataFrame Access
3 questions
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Onwards
0:31
Exploring pandas
Let's see what we can do with this library!
Chevron 19 steps-
Importing Data
1:29
- instruction
Exploration Methods
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Review Exploration Methods
3 questions
- instruction
Selecting Data
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Review Selecting Data
2 questions
- instruction
Optional Challenge #1 - Top Referrers
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Manipulation
0:50
- instruction
Manipulation Techniques
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Review Manipulation Techniques
4 questions
- instruction
Optional Challenge #2 - Update Users
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Combining DataFrames
0:55
- instruction
Combining DataFrames
- instruction
Handling Duplicated and Missing Data
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Review Merging and Handling Missing Data
3 questions
- instruction
Manipulating Text
- instruction
Optional Challenge #3 - Verified Email List
- instruction
Grouping
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Review Text Manipulation and Grouping
2 questions
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Until Next Time
1:15
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