CODEWITHSUNDEEP
pythonpandasdatetimenumpy
Andrew
Andrew

Reputation: 970

convert float to hours and minutes in pandas/numpy

I have several columns in a pd.DataFrame in which decimal separates hours and minutes (e.g., 3.15 = 3 hours, 15 minutes). Is there a quick way to convert this so that the data are recognized as h.m ? The pandas Time Series documentation doesn't seem to apply to my case. I don't have or want to attach any dates.

I tried:

# create df
hour_min = pd.DataFrame({'a': [4.5, 2.3, 3.17],
             'b': [2.12, 1.13, 9.13],
             'c': [8.23, 9.14, 7.45]})
# convert to hours   
hour_min.astype('timedelta64[h]')

which gives

         a        b        c
0 04:00:00 02:00:00 08:00:00
1 02:00:00 01:00:00 09:00:00
2 03:00:00 09:00:00 07:00:00

but I want

    a     b     c
0 04:50 02:12 08:23
1 02:30 01:13 09:14
2 03:17 09:13 07:45

I also need the following type of result from adding/subtracting column values 1.32 + 1.32 = 3.04

Upvotes: 2

Views: 5086

Answers (2)

Eric Ed Lohmar
Eric Ed Lohmar

Reputation: 1922

You're going to want to use pd.to_timedelta in a function and applymap it to get the math you want. Looks something like this:

import pandas as pd
import math


def to_t_delt(number):
    return pd.to_timedelta(f'{math.floor(number)}hours {(number - math.floor(number)) * 100}min')


hour_min = pd.DataFrame({'a': [4.5, 2.3, 3.17],
                         'b': [2.12, 1.13, 9.13],
                         'c': [8.23, 9.14, 7.45]})

hour_min = hour_min.applymap(to_t_delt)
print(hour_min)
print()
print(hour_min['a'] + hour_min['b'])

and yields this result:

         a        b        c
0 04:50:00 02:12:00 08:23:00
1 02:30:00 01:13:00 09:14:00
2 03:17:00 09:13:00 07:45:00

0   07:02:00
1   03:43:00
2   12:30:00
dtype: timedelta64[ns]

Upvotes: 2

Vinícius Figueiredo
Vinícius Figueiredo

Reputation: 6518

I'm pretty sure there should be a more efficient solution, but since no one answered yet, here is a hacky workaround:

import pandas as pd

hour_min = pd.DataFrame({'a': [4.5, 2.3, 3.17],
             'b': [2.12, 1.13, 9.13],
             'c': [8.23, 9.14, 7.45]})

def convert(number):
    hour = ('%.2f' % number).split(sep='.')[0]
    minute = ('%.2f' % number).split(sep='.')[1]
    return pd.to_datetime(hour+":"+minute, format='%H:%M')

Then you just need to use applymap():

>>> hour_min = hour_min.applymap(convert)
>>> hour_min
                    a                   b                   c
0 1900-01-01 04:50:00 1900-01-01 02:12:00 1900-01-01 08:23:00
1 1900-01-01 02:30:00 1900-01-01 01:13:00 1900-01-01 09:14:00
2 1900-01-01 03:17:00 1900-01-01 09:13:00 1900-01-01 07:45:00

You can show the time only with:

>>> for i in hour_min:
    hour_min[i] = hour_min[i].dt.time    

>>> hour_min
          a         b         c
0  04:50:00  02:12:00  08:23:00
1  02:30:00  01:13:00  09:14:00
2  03:17:00  09:13:00  07:45:00

Upvotes: 5

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