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https://issues.apache.org/jira/browse/ARROW-7914?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17554200#comment-17554200
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Mike commented on ARROW-7914:
-----------------------------
Thoughts on reopening this task? [~salonijain27] [~jorisvandenbossche]
This is currently blocking a change I have to [support all index types in
Pandas' Feather calls|https://github.com/pandas-dev/pandas/issues/28208]
> [Python] Allow pandas datetime as index for feather
> ---------------------------------------------------
>
> Key: ARROW-7914
> URL: https://issues.apache.org/jira/browse/ARROW-7914
> Project: Apache Arrow
> Issue Type: New Feature
> Components: Python
> Affects Versions: 0.15.1
> Environment: Windows, python 3.6.7,
> Reporter: Samuel Jones
> Assignee: saloni jain
> Priority: Minor
> Labels: arrow, datetime, feather, pull-request-available, python
> Fix For: 8.0.0
>
> Attachments: PEC fine course 1 grid 199001.csv, PEC fine course 1
> grid 199001.feather
>
> Time Spent: 1h 10m
> Remaining Estimate: 0h
>
> 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):
> `
> h1. Write data to .csv
> jan90.to_csv('PEC fine course 1 grid 199001.csv', index=True)
> h1. 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.
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