I started with:
DISCH_PRESS stage
timestamp
2017-11-17 07:10:09 6241.483887 14.0
2017-11-17 07:10:12 6353.102051 14.0
2017-11-17 07:10:15 6673.465820 14.0
2017-11-17 07:10:17 7089.970215 14.0
2017-11-17 07:10:20 7449.266113 14.0
I performed the following operation to resample the timestamp after each stage was grouped:
df = df.groupby(['stage']).resample('1S').asfreq()
Now my data looks like this:
DISCH_PRESS stage
stage timestamp
1.0 2017-11-18 23:18:20 4728.775879 1.0
2017-11-18 23:18:21 NaN NaN
2017-11-18 23:18:22 NaN NaN
2017-11-18 23:18:23 5109.784180 1.0
2017-11-18 23:18:24 NaN NaN
I want to remove the stage row index. My data should look like:
DISCH_PRESS stage
timestamp
2017-11-18 23:18:20 4728.775879 1.0
2017-11-18 23:18:21 NaN NaN
2017-11-18 23:18:22 NaN NaN
2017-11-18 23:18:23 5109.784180 1.0
2017-11-18 23:18:24 NaN NaN
When I tried searching for solutions I mostly found solutions to multi column indexing, I tried adapting those solutions for my application, see the following inputs and resulting errors:
df.MultiIndex.droplevel(level=0)
AttributeError: 'DataFrame' object has no attribute 'MultiIndex'
df.droplevel(level=0)
AttributeError: 'DataFrame' object has no attribute 'droplevel'
dframe.reset_index()
ValueError: cannot insert timestamp, already exists
df.rows.droplevel()
AttributeError: 'DataFrame' object has no attribute 'row'
Obviously I am using these methods incorrectly. Any help is appreciated.
