Average of two timestamps in python

Question

I have two timestamps (pandas.tslib.Timestamp) ts1 and ts2 and I want to calculate the average of them.

(ts1+ts2)/2

However, I am getting this error:

TypeError: unsupported operand type(s) for +: 'Timestamp' and 'Timestamp'

Thank you for any help.

Simply fetch their ticks, calculate the average and convert it back to a timestamp. — Willem Van Onsem, Jan 18 '17 at 15:23
@KarelMacek: I don't know how to do this with Timestamp since I don't know the library from which you have imported that object. Please edit your question and make your code reproducable. — Willem Van Onsem, Jan 18 '17 at 15:25
Is `'Timestamp'` a custom class or what ? It simply does not support `+` operations, you need to call a method to convert it — Seif, Jan 18 '17 at 15:25
datetime. E.g. import datetime and then datetime.datetime.now() — Karel Macek, Jan 18 '17 at 15:25
Sorry but the error says 'Timestamp' and not datetime (which is standard) — Seif, Jan 18 '17 at 15:26
if you were using `datetime`, a different error message would be given. — Haleemur Ali, Jan 18 '17 at 15:27
In fact, it is a record in a pandas dataframe. The type is 'pandas.tslib.Timestamp' — Karel Macek, Jan 18 '17 at 15:28
anyway, the general solution would involve calculating the interval, halving the interval and then adding the halved interval to the earlier timestamp or subtracting from the later timestamp — Haleemur Ali, Jan 18 '17 at 15:28
You might use the the approach you have or getting the ticks converting it first to datetime. Check: http://stackoverflow.com/questions/25852044/converting-pandas-tslib-timestamp-to-datetime-python — Rafael Aguilar, Jan 18 '17 at 15:33

score 13 · Accepted Answer · answered Jan 18 '17 at 15:34

subtracting a timestamp from another generates an interval, which may then be divided.

as the error says, adding timestamps is not allowed.

the solution involves calculating the interval, halving the interval and then adding the halved interval to the earlier timestamp or subtracting from the later timestamp.

from pandas.tslib import Timestamp
d1 = Timestamp.now()
# wait a few seconds
d2 = Timestamp.now()
d3 = d1 + (d2 - d1) / 2
# d3 will be the timestamp exactly in between d1 & d2

Seif · Answer 2 · 2017-01-18T15:46:21.203

10

Objects similar to datetime.datetime objects do not support addition as well, because there is no meaning in adding dates. You should use datetime.timedelta to get the average time.

How ? This way:

average_delta = (ts2 - ts1) / 2
average_ts = ts1 + average_delta

edited Jan 18 '17 at 15:46

answered Jan 18 '17 at 15:34

Seif

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score 4 · Answer 3 · answered Jan 18 '17 at 15:55

4

This method gave the same result as others:

t1 = Timestamp('2017-01-18 10:00:00.0000000')
t2 = Timestamp('2017-01-20 10:00:00.0000000')
average = Timestamp((t1.value + t2.value)/2.0)

answered Jan 18 '17 at 15:55

niraj

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score 3 · Answer 4 · answered Jan 18 '17 at 15:35

This is how I take the median of 2 timestamps:

ts1 = pd.Timestamp('2016-1-18 10:00')

ts2 = pd.Timestamp('2016-1-18 10:20')

ts1+(ts2-ts1)/2
Out[11]: Timestamp('2016-01-18 10:10:00')

No need to test if ts2 is greater than ts2 as the equation is symmetrical.

score 3 · Answer 5 · answered Jun 29 '20 at 12:48

Preparing a sample dataframe (2 or more timestamps to average on):

# Initiate dataframe
date_var = "date"
df = pd.DataFrame(data=[['A', '2018-08-05 17:06:01'],
                        ['A', '2018-08-05 17:06:02'],
                        ['A', '2018-08-05 17:06:03'],
                        ['B', '2018-08-05 17:06:07'],
                        ['B', '2018-08-05 17:06:09'],
                        ['B', '2018-08-05 17:06:11']],
                  columns=['column', date_var])

# Convert date-column to proper pandas Datetime-values/pd.Timestamps
df[date_var] = pd.to_datetime(df[date_var])

Extraction of the desired average Timestamp-value:

# Extract the numeric value associated to each timestamp (epoch time)
# NOTE: this is being accomplished via accessing the .value - attribute of each Timestamp in the column
In:
[tsp.value for tsp in df[date_var]]
Out:
[
    1533488761000000000, 1533488762000000000, 1533488763000000000,
    1533488767000000000, 1533488769000000000, 1533488771000000000
]

# Use this to calculate the mean, then convert the result back to a timestamp
In:
pd.Timestamp(np.nanmean([tsp.value for tsp in df[date_var]]))
Out:
Timestamp('2018-08-05 17:06:05.500000')

Average of two timestamps in python

5 Answers5