pandas.Series.asof — pandas 3.0.0.dev0+2097.gcdc5b7418e documentation (original) (raw)

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:

wheredate or array-like of dates

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

subsetstr 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) np.float64(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) np.float64(2.0)

Take all columns into consideration

df = pd.DataFrame( ... { ... "a": [10.0, 20.0, 30.0, 40.0, 50.0], ... "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