Open
Description
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')
tdFeature 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