CODEWITHSUNDEEP
pythonpandasdatetimetime-series
Remind
Remind

Reputation: 23

How to convert a pandas time series with hour (h) as index unit into pandas datetime format?

I am working on time-series data, where my pandas dataframe has indices specified in hours, like this:

[0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, ...]

This goes on for a few thousand hours. I know that the first measurement was taken on, let's say, May 1, 2017 12:00. How do I use this information to turn my indices into pandas datetime format?

Upvotes: 2

Views: 303

Answers (2)

jezrael
jezrael

Reputation: 862641

You can add hours to index by parameter origin in to_datetime for DatetimeIndex:

idx = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4]
df = pd.DataFrame({'a':range(13)}, index=idx)

start = 'May 1, 2017 12:00'
df.index = pd.to_datetime(df.index, origin=start, unit='h')
print (df)
                      a
2017-05-01 12:00:00   0
2017-05-01 12:12:00   1
2017-05-01 12:24:00   2
2017-05-01 12:36:00   3
2017-05-01 12:48:00   4
2017-05-01 13:00:00   5
2017-05-01 13:12:00   6
2017-05-01 13:24:00   7
2017-05-01 13:36:00   8
2017-05-01 13:48:00   9
2017-05-01 14:00:00  10
2017-05-01 14:12:00  11
2017-05-01 14:24:00  12

Upvotes: 2

Erfan
Erfan

Reputation: 42916

You can use pandas.date_range to specify the amount of periods based on the length of your index (in this case list) and specify the frequency, which is in this case 12min or 1/5 H:

l = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4]

data = {'Num':np.random.randint(1, 10, size=len(l))}

idx = pd.date_range(start=pd.Timestamp(2017, 5, 1, 12), periods=len(l), freq='12T')

df = pd.DataFrame(data = data, index= idx)

print(df)
                     Num
2017-05-01 12:00:00    8
2017-05-01 12:12:00    3
2017-05-01 12:24:00    3
2017-05-01 12:36:00    4
2017-05-01 12:48:00    8
2017-05-01 13:00:00    3
2017-05-01 13:12:00    6
2017-05-01 13:24:00    3
2017-05-01 13:36:00    4
2017-05-01 13:48:00    9
2017-05-01 14:00:00    5
2017-05-01 14:12:00    2
2017-05-01 14:24:00    6

Upvotes: 1

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