How to compute average of pandas pd.Timestamp

Problem

If you have an array of pd.Timestamp objects, you can’t directly compute the average since they can’t be summed directly:

import pandas as pd

# Creating an array of five fixed pd.Timestamp objects
timestamps = [
    pd.Timestamp('2023-01-01 12:00:00'),
    pd.Timestamp('2023-01-02 12:00:00'),
    pd.Timestamp('2023-01-03 12:00:00'),
    pd.Timestamp('2023-01-04 12:00:00'),
    pd.Timestamp('2023-01-05 12:00:00')
]

# FAIL: This will raise a TypeError
average = sum(timestamps) / len(timestamps)

This will raise a TypeError:

TypeError                                 Traceback (most recent call last)
Cell In[1], line 13
      4 timestamps = [
      5     pd.Timestamp('2023-01-01 12:00:00'),
      6     pd.Timestamp('2023-01-02 12:00:00'),
   (...)
      9     pd.Timestamp('2023-01-05 12:00:00')
     10 ]
     12 # FAIL: This will raise a TypeError
---> 13 average = sum(timestamps) / len(timestamps)

File timestamps.pyx:483, in pandas._libs.tslibs.timestamps._Timestamp.__radd__()

File timestamps.pyx:465, in pandas._libs.tslibs.timestamps._Timestamp.__add__()

TypeError: Addition/subtraction of integers and integer-arrays with Timestamp is no longer supported.  Instead of adding/subtracting `n`, use `n * obj.freq`

Solution

You can sum/average ts.value instead of summing ts directly, and after the averaging, convert it back to a timestamp:

average = pd.Timestamp(sum(ts.value for ts in timestamps) / len(timestamps))

Full example:

import pandas as pd

# Creating an array of five fixed pd.Timestamp objects
timestamps = [
    pd.Timestamp('2023-01-01 12:00:00'),
    pd.Timestamp('2023-01-02 12:00:00'),
    pd.Timestamp('2023-01-03 12:00:00'),
    pd.Timestamp('2023-01-04 12:00:00'),
    pd.Timestamp('2023-01-05 12:00:00')
]

# Result: Timestamp('2023-01-03 12:00:00')
average = sum(timestamps) / len(timestamps)