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
- 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
Return scalar, Series, or DataFrame.
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 for30
.>>> 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 NaN 2018-02-27 09:04:30 40 NaN