# operate calculation of date time in a column on data frame

Supposed I have two column in a data frame, consist of probability and remaining time to an event.

``````      prob          time
0   0.975909   0 days 00:00:00
1   0.957819   0 days 01:00:00
2   0.937498   0 days 02:00:00
3   0.912779   0 days 03:00:00
4   0.894139   0 days 04:00:00
5   0.873184   0 days 05:00:00
6   0.847748   0 days 06:00:00
7   0.828572   0 days 07:00:00
8   0.807029   0 days 08:00:00
9   0.780847   0 days 09:00:00
10  0.761082   0 days 10:00:00
11  0.738855   0 days 11:00:00
12  0.711733   0 days 12:00:00
``````

I want to calculate exact time and date, with some additional input is date and time, and probability, for example I will put this:

``````# Type the date of input data
i = datetime.datetime.now() #e.g. 2018-01-01 00:00:00

# Type the expected probability
exprob = 0.80
``````

And what I need for the output is the result of: Find the nearest probability with 'exprob' (0.80) --> 0.80709, then calculate 'i' + time related to 0.80709 = 2018-01-01 08:00:00

You can use `idxmin` to find the index for the minimum difference between `df['prob']` and `exprob` and then find the `Timedelta` and add it to the date `i` like:

``````i = datetime.datetime.now()
exprob = 0.80

df.loc[((df['prob'] - exprob).abs().idxmin()),'time'] + i
Timestamp('2018-11-08 18:36:11.529609')
``````

using `argsort()` we can get it like below.

``````input = 0.80
i = datetime.now()
next_time = i + df.ix[(df['prob']-input).abs().argsort()[:1]]['time']
``````

Complete example is

``````import pandas as pd
from datetime import datetime, timedelta

df = pd.DataFrame(columns = ['prob', 'time'])
df.loc[len(df)] = [0.975909, timedelta(hours=0, minutes=0, seconds=0)]
df.loc[len(df)] = [0.957819, timedelta(hours=1, minutes=0, seconds=0)]
df.loc[len(df)] = [0.937498, timedelta(hours=2, minutes=0, seconds=0)]
df.loc[len(df)] = [0.912779, timedelta(hours=3, minutes=0, seconds=0)]
df.loc[len(df)] = [0.894139, timedelta(hours=4, minutes=0, seconds=0)]
df.loc[len(df)] = [0.873184, timedelta(hours=5, minutes=0, seconds=0)]
df.loc[len(df)] = [0.847748, timedelta(hours=6, minutes=0, seconds=0)]
df.loc[len(df)] = [0.828572, timedelta(hours=7, minutes=0, seconds=0)]
df.loc[len(df)] = [0.807029, timedelta(hours=8, minutes=0, seconds=0)]
df.loc[len(df)] = [0.780847, timedelta(hours=9, minutes=0, seconds=0)]
df.loc[len(df)] = [0.761082, timedelta(hours=10, minutes=0, seconds=0)]
df.loc[len(df)] = [0.738855, timedelta(hours=11, minutes=0, seconds=0)]
df.loc[len(df)] = [0.711733, timedelta(hours=12, minutes=0, seconds=0)]

input = 0.80
i = datetime.now()
next_time = i + df.ix[(df['prob']-input).abs().argsort()[:1]]['time']

print(i)
print(next_time)
``````