Feature Type

• Adding new functionality to pandas
• Changing existing functionality in pandas
• Removing existing functionality in pandas

Problem Description

It's hard to read a column of timedeltas with fractional seconds of different precisions because it's not aligned, for example:

0 0 days 00:00:01 1 0 days 00:00:00.500000 2 0 days 00:00:00.333333333 dtype: timedelta64[ns] 

It'd be easier to read like this:

0 0 days 00:00:01 1 0 days 00:00:00.500000 2 0 days 00:00:00.333333333 dtype: timedelta64[ns] 

or

0 0 days 00:00:01.000000000 1 0 days 00:00:00.500000000 2 0 days 00:00:00.333333333 dtype: timedelta64[ns] 

To reproduce:

s = 1 / pd.Series(np.arange(3)+1) td = pd.to_timedelta(s, unit='s') td

Feature Description

Add padding on the right.

In this case, it can be done by left-justifying to the largest size:

n = td.astype(str).apply(len).max() td.astype(str).str.ljust(n)

0 0 days 00:00:01 1 0 days 00:00:00.500000 2 0 days 00:00:00.333333333 dtype: object 

But of course, this wouldn't work for different numbers of days. For example:

td1 = pd.concat([td, pd.Series(pd.Timedelta(days=4000))])

0 0 days 00:00:01 1 0 days 00:00:00.500000 2 0 days 00:00:00.333333333 0 4000 days 00:00:00 dtype: object 

If timedeltas had a .replace() method so you could zero out the days, then it would work, but AFAIK such a method doesn't exist.

BTW, the same thing is already implemented for floats and datetimes.

>>> s*10 0 10.000000 1 5.000000 2 3.333333 dtype: float64 >>> pd.Timestamp(0) + td 0 1970-01-01 00:00:01.000000000 1 1970-01-01 00:00:00.500000000 2 1970-01-01 00:00:00.333333333 dtype: datetime64[ns]

Alternative Solutions

n/a

Additional Context

No response