Details
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New Feature
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Status: Resolved
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Minor
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Resolution: Fixed
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0.15.1
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Windows, python 3.6.7,
Description
Sorry in advance if I mess anything up. This is my first issue.
I have hourly data for 3 years using a Pandas datetime as the index. Pandas allows me load/save .csv with the following code (only one month with 2 variables shown):
`
Write data to .csv
jan90.to_csv('PEC fine course 1 grid 199001.csv', index=True)
Load data from .csv
jan90 = pd.read_csv('PEC fine course 1 grid 199001.csv', index_col=0, parse_dates=True)
`
Using .csv works, but is slow when I get to the full dataset of 26k+ rows and 21.6k+ columns (and more columns may be coming if I have to add lags to my data). So, a more efficient load/save routine is very desirable. I was excited when I found feather, but the lost index is a no-go for my use.
Thanks for your consideration.
Attachments
Attachments
Issue Links
- duplicates
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ARROW-15018 [Python] DataFrame Index modified during Feather serialization round trip
- Closed
- links to