PYTHON: Pandas datetime index range to change column values

Question

I have a dataframe indexed using a 12hr frequency datetime:

                    id  mm ls
date            
2007-09-27 00:00:00 1   0   0
2007-09-27 12:00:00 1   0   0
2007-09-28 00:00:00 1   15  0
2007-09-28 12:00:00 NaN NaN 0
2007-09-29 00:00:00 NaN NaN 0
Timestamp('2007-09-27 00:00:00', offset='12H')

I use column 'ls' as a binary variable with default value '0' using:

data['ls'] = 0

I have a list of days in the form '2007-09-28' from which I wish to update all 'ls' values from 0 to 1.

                    id  mm ls
date            
2007-09-27 00:00:00 1   0   0
2007-09-27 12:00:00 1   0   0
2007-09-28 00:00:00 1   15  1
2007-09-28 12:00:00 NaN NaN 1
2007-09-29 00:00:00 NaN NaN 0
Timestamp('2007-09-27 00:00:00', offset='12H')

I understand how this can be done using another column variable ie:

data.ix[data.id == '1'], ['ls'] = 1

yet this does not work using datetime index. Could you let me know what the method for datetime index is?

IIUC doesn't data.loc['2007-09-28','ls'] = 1 work?

EdChum
– EdChum

2015-11-13 15:07:22 +00:00
Commented Nov 13, 2015 at 15:07 — EdChum
– EdChum, Commented Nov 13, 2015 at 15:07

eumiro · Accepted Answer · 2015-11-13 15:01:12Z

2

You have a list of days in the form '2007-09-28':

days = ['2007-09-28', ...]

then you can modify your df using:

df['ls'][pd.DatetimeIndex(df.index.date).isin(days)] = 1

answered Nov 13, 2015 at 15:01

eumiro

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1 Comment

BenP Over a year ago

This works, as does "EdChum" comment, yet this answer creates a copy of the df, rather than updating the existing df. I didn't specify in the question however.

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