pandas.Series.asfreq¶

Series.asfreq(self: ~FrameOrSeries, freq, method=None, how: Union[str, NoneType] = None, normalize: bool = False, fill_value=None) → ~FrameOrSeries[source]¶

Convert TimeSeries to specified frequency.

Optionally provide filling method to pad/backfill missing values.

Returns the original data conformed to a new index with the specified frequency. resample is more appropriate if an operation, such as summarization, is necessary to represent the data at the new frequency.

Parameters

freqDateOffset or str

method{‘backfill’/’bfill’, ‘pad’/’ffill’}, default None

Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present):

‘pad’ / ‘ffill’: propagate last valid observation forward to next valid
‘backfill’ / ‘bfill’: use NEXT valid observation to fill.

how{‘start’, ‘end’}, default end

For PeriodIndex only (see PeriodIndex.asfreq).

normalizebool, default False

Whether to reset output index to midnight.

fill_valuescalar, optional

Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present).

Returns

convertedsame type as caller

See also

reindex

Notes

To learn more about the frequency strings, please see this link.

Examples

Start by creating a series with 4 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s':series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

Upsample the series into 30 second bins.

>>> df.asfreq(freq='30S')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

Upsample again, providing a fill value.

>>> df.asfreq(freq='30S', fill_value=9.0)
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    9.0
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    9.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    9.0
2000-01-01 00:03:00    3.0

Upsample again, providing a method.

>>> df.asfreq(freq='30S', method='bfill')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0