Remove "seconds" and "minutes" from Pandas DataFrame column
For data like:
import numpy as np
import pandas as pd
df = pd.DataFrame(
{'Date' : pd.date_range('1/1/2011', periods=5, freq='3675S'),
'Num' : np.random.rand(5)})
Date Num
0 2011-01-01 00:00:00 0.580997
1 2011-01-01 01:01:15 0.407332
2 2011-01-01 02:02:30 0.786035
3 2011-01-01 03:03:45 0.821792
4 2011-01-01 04:05:00 0.807869
I would like to remove the "minutes" and "seconds" information.
The following (mostly stolen from: How do I delete the Pandas data index "seconds" data? ) Work fine,
df = df.assign(Date = lambda x: pd.to_datetime(x['Date'].dt.strftime('%Y-%m-%d %H')))
Date Num
0 2011-01-01 00:00:00 0.580997
1 2011-01-01 01:00:00 0.407332
2 2011-01-01 02:00:00 0.786035
3 2011-01-01 03:00:00 0.821792
4 2011-01-01 04:00:00 0.807869
but it seems weird to convert date and time to string and then back to date. Is there a way to do this more directly?
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1 answer
dt.round
This is how it should be done ... use dt.round
df.assign(Date=df.Date.dt.round('H'))
Date Num
0 2011-01-01 00:00:00 0.577957
1 2011-01-01 01:00:00 0.995748
2 2011-01-01 02:00:00 0.864013
3 2011-01-01 03:00:00 0.468762
4 2011-01-01 04:00:00 0.866827
OLD ANSWER
One approach is to set the index and use resample
df.set_index('Date').resample('H').last().reset_index()
Date Num
0 2011-01-01 00:00:00 0.577957
1 2011-01-01 01:00:00 0.995748
2 2011-01-01 02:00:00 0.864013
3 2011-01-01 03:00:00 0.468762
4 2011-01-01 04:00:00 0.866827
Another alternative is to separate the components date
andhour
df.assign(
Date=pd.to_datetime(df.Date.dt.date) +
pd.to_timedelta(df.Date.dt.hour, unit='H'))
Date Num
0 2011-01-01 00:00:00 0.577957
1 2011-01-01 01:00:00 0.995748
2 2011-01-01 02:00:00 0.864013
3 2011-01-01 03:00:00 0.468762
4 2011-01-01 04:00:00 0.866827
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