Support Series and DataFrame with DatetimeIndex about pyxirr

[Anexen]

Hi @Peque,
I can support the Series with DatetimeIndex and the case with DataFrame will be simply df.apply(xirr)

You can use the following code with the current implementation:

# for Series
xirr(s.reset_index())
xirr(s.index, s)

# for DataFrame
df.apply(lambda col: xirr(col.reset_index()))
df.apply(lambda col: xirr(col.index, col)

[Anexen]

@Peque , I don't think so. I would like a clear separation between "Invalid input" case and "does not converge" case.

You can use something like this:

def suppress(func):
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except InvalidPaymentsError:
            return None
    return wrapper
    
df.apply(suppress(xirr))

[Peque]

@Anexen Do you think it would make sense to return NaN instead of raising exceptions?

This would allow for df.apply(xirr) to work fine when possible.

In example:

from pandas import DataFrame
from pandas import date_range
from pyxirr import xirr

timezone = "Europe/Madrid"
index = date_range("2000-01-01", periods=10, freq="Y", closed="left", tz=timezone)
df = DataFrame(index=index, data={"a": 10, "b": 10})
df.iloc[0]["a"] = -25
df.apply(xirr)

Would return 0.3774630471754315, NaN.