Python Pandas: TypeError: unsupported operand type(s) for +: 'datetime.time' and 'Timedelta'

Question

I am attempting to add two series in a dataframe in pandas with the first series being a 24-hr time value (e.g. 17:30) exported from an excel file and the second series being a series of the same length in Timedelta format converted from floats with the 'pd.Timedelta' command.

The desired resulting third column would be a 24-hr time regardless of day change (e.g. 22:00 + 4 hours = 02:00).

I created the Delta series like this:

delta = pd.Series(0 for x in range(0, len(df.Time_In_Hours)))

for j in range(0, len(df.Time_In_Hours)):
    delta[j] = pd.Timedelta(df.Time_In_Hours[j], 'h')
df = df.assign(Delta = delta)   
print ("Delta dtype = %s" % (df.Delta.dtype))
print ("Start_Time dtype = %s" % (df.Start_Time.dtype))

#Output
Delta dtype = object
Start_Time dtype = object

My goal is:

df["end_Time"] = df["Start_Time"] + df["Delta"]  

The error I am receiving is: TypeError: unsupported operand type(s) for +: 'datetime.time' and 'Timedelta'

It seems this datetime.time format is immutable. Am I missing something?

Answer 1

The cause

The error is pretty clear. If you check the types of the elements, you will find out that at some point you are tying to add datetime.time object and pandas.Timedelta.

There are 2 kinds of dates, times and timedeltas:

• python's builtin from datetime module i.e. datetime.time, datetime.date, datetime.timedelta, ...
• pandas / numpy i.e pandas.Timestamp, pandas.Timedelta

these two stacks are incompatible for basic operations as addition or comparison.

Solution 1

Convert everything to pandas type and extract the times in the end

You should make sure, that dtypes of your columns are something like datetime64[ns] and timedelta64[ns]. For that, try converting them explicitly using pd.to_datetime and pd.to_timedelta.

Solution 2

Another approach would be just converting the Delta column to datetime.timedelta you could try

df["end_Time"] = df["Start_Time"] + df["Delta"].map(pd.Timedelta.to_pytimedelta)

But you may run into some more errors depending on what is in your df["Delta"] and df["Start_Time"]