pandas.DataFrame.asof#

DataFrame.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)
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": [10.0, 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