Learn how to treat missing values. This video covers dropping values, approximation, and filtering.
- [Voiceover] Let's talk about treating missing values. … By default, missing values are represented in Python … with NaN, which stands for not a number. … I want to give you a warning here … that if your dataset has 0s, 99s, … or 999s, … be sure to either drop or approximate them as you would … with a missing value. … Let me give you an example … of where treating missing values is useful. … Imagine you work in a marketing department … of a local car dealership. … You've been tasked with summarizing recent results … from a customer satisfaction survey. … You get this data set and you can see that most … of the records have been completed, … but Sally and Jim didn't respond with information … about their opinion of quality of work. … You can see that here with the missing values. … Nevertheless, Sally and Jim have responded to 75% … of the request for information. … So we wouldn't want to drop them from the survey altogether. … That said, the other respondents Rob, Sam, … and Jane did give information about what they thought …
AuthorLillian Pierson, P.E.
- Why use Python for working with data
- Filtering and selecting data
- Concatenating and transforming data
- Data visualization best practices
- Visualizing data
- Creating a plot
- Creating statistical data graphics
- Performing basic math and linear algebra
- Correlation analysis
- Multivariate analysis
- Data sourcing via web scraping
- Introduction to natural language processing
- Collaborative analytics with Plotly
Skill Level Intermediate
Python Statistics Essential Trainingwith Michele Vallisneri2h 58m Intermediate
1. Introduction to the Data Professions
High-level course road map1m 28s
2. Data Preparation Basics
3. Data Visualization 101
4. Practical Data Visualization
5. Basic Math and Statistics
6. Data Sourcing via Web Scraping
7. Collaborative Analytics with Plotly
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