pandas.Series.asof

Series.asof(where, subset=None)[source]

Return the last row(s) without any NaNs before where.

The last row (for each element in where, if list) without any NaN is taken. In case of a DataFrame, the last row without NaN considering only the subset of columns (if not None)

If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame

Parameters:

where:date or array-like of dates

Date(s) before which the last row(s) are returned.

subset:str or array-like of str, default None

For DataFrame, if not None, only use these columns to check for NaNs.

Returns:

scalar, Series, or DataFrame

The return can be:

• scalar : when self is a Series and where is a scalar

• Series: when self is a Series and where is an array-like, or when self is a DataFrame and where is a scalar

• DataFrame : when self is a DataFrame and where is an array-like

See also

merge_asof

Perform an asof merge. Similar to left join.

Notes

Dates are assumed to be sorted. Raises if this is not the case.

Examples

A Series and a scalar where.

>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10    1.0
20    2.0
30    NaN
40    4.0
dtype: float64

>>> s.asof(20)
2.0

For a sequence where, a Series is returned. The first value is NaN, because the first element of where is before the first index value.

>>> s.asof([5, 20])
5     NaN
20    2.0
dtype: float64

Missing values are not considered. The following is 2.0, not NaN, even though NaN is at the index location for 30.

>>> s.asof(30)
2.0

Take all columns into consideration

>>> df = pd.DataFrame({'a': [10., 20., 30., 40., 50.],
...                    'b': [None, None, None, None, 500]},
...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
...                                           '2018-02-27 09:02:00',
...                                           '2018-02-27 09:03:00',
...                                           '2018-02-27 09:04:00',
...                                           '2018-02-27 09:05:00']))
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']))
                      a   b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN

Take a single column into consideration

>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']),
...         subset=['a'])
                        a   b
2018-02-27 09:03:30  30.0 NaN
2018-02-27 09:04:30  40.0 NaN