[Doc enhancement]: Nan explanation
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Assessment
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Newbie friendliness
- 76/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Active
- Tech stack
- pandas, python
- Domain
- documentation
Research direction
Start with the linked “Starting With Data” lesson at 02-starting-with-data.html and review where surveys.csv and the weight column are introduced. Add a clear explanation of NaNs, how missing values appear in the lesson data, and simple ways to identify them, with links or references to the later visualization and multiplication examples; the lesson should make the affected examples understandable to learners using their own data.
Written by the indexing model from the issue text.
Description
How could the content be improved?
Query / Context:
When in the "Starting With Data" section, a surveys.csv is used but the file is incomplete so when read in using pandas the dataframe fills in the blanks with Nans as expected. Nans don't look to be discussed on this episode or prior episodes in series and can be seen in screenshots for the episode. Additionally, the weight column which is used to show pandas functions for metrics has prominent number of Nans in starting rows.
Suggested improvement:
Include documentation and explanation of what Nans are. This would be good as links into the challenges for visualisation later why 1 group doesn't have a bar and later a column with Nans in the first rows is used for multiplication example. When discussing csv structure or when first load in data could be a good point to introduce concept (and if time few simple commands to check for nans) as can show the lack of values in raw/table display and then in pandas preview.
Additional context:
As people will want to use what have learnt for own data, explaining what nans are and maybe how to identify them would help as a lot of analysis can be wrong or break if include nans and plenty of errors relating to Nans.
Screenshots from lesson:
Example of column weight usage which has high number of nans so not true representation of all records:
Which part of the content does your suggestion apply to?
https://datacarpentry.github.io/python-ecology-lesson/02-starting-with-data.html
- Dominant language
- Jupyter Notebook
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