pandas.DataFrame.asof — pandas 0.24.0rc1 documentation (original) (raw)
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)
New in version 0.19.0: For DataFrame
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 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
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
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.
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