Timeline for Gather data by year and also by industry
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Sep 20, 2022 at 19:27 | vote | accept | Marie-Pier St-Vincent | ||
| Sep 20, 2022 at 19:27 | vote | accept | Marie-Pier St-Vincent | ||
| Sep 20, 2022 at 19:27 | |||||
| Apr 9, 2022 at 21:16 | comment | added | guardian | @Marie-PierSt-Vincent please accept this answer, in order to recognize the author and mark the question as answered. | |
| Feb 8, 2022 at 22:17 | comment | added | Marie-Pier St-Vincent | Thank you so much it worked!! | |
| Feb 5, 2022 at 22:39 | comment | added | jsmart |
I created a function to assign multiple industry codes to a single industry group. You could also use pd.cut() or join to a series that links industry codes to industry groups. Then I grouped by three fields, instead of two fields. You can do look-ups like this: gdf.loc[(2017, 'A'), :]
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| Feb 5, 2022 at 22:37 | history | edited | jsmart | CC BY-SA 4.0 |
Add industry group.
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| Feb 4, 2022 at 20:26 | comment | added | Marie-Pier St-Vincent | How would this differ if I would be working with grouped industry codes? For example if my Industry #1 includes industry codes 111 to 222 (can take any value between those two numbers) ? | |
| Feb 4, 2022 at 19:41 | history | answered | jsmart | CC BY-SA 4.0 |