pandas.DataFrame.asof#

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

Return the last row(s) without any NaNs beforewhere.

The last row (for each element inwhere, if list) without anyNaN is taken.In case of aDataFrame, the last row without NaNconsidering only the subset of columns (if notNone)

If there is no good value, NaN is returned for a Series ora 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, defaultNone

For DataFrame, if notNone, only use these columns tocheck for NaNs.

Returns:

scalar, Series, or DataFrame

The return can be:

• scalar : whenself is a Series andwhere is a scalar

• Series: whenself is a Series andwhere is an array-like,or whenself is a DataFrame andwhere is a scalar

• DataFrame : whenself is a DataFrame andwhere is anarray-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 scalarwhere.

>>>s=pd.Series([1,2,np.nan,4],index=[10,20,30,40])>>>s10    1.020    2.030    NaN40    4.0dtype: float64

>>>s.asof(20)2.0

For a sequencewhere, a Series is returned. The first value isNaN, because the first element ofwhere is before the firstindex value.

>>>s.asof([5,20])5     NaN20    2.0dtype: float64

Missing values are not considered. The following is2.0, notNaN, 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   b2018-02-27 09:03:30 NaN NaN2018-02-27 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   b2018-02-27 09:03:30  30.0 NaN2018-02-27 09:04:30  40.0 NaN