pandas.Series.asfreq#
- Series.asfreq(freq, method=None, how=None, normalize=False, fill_value=None)[source]#
Convert time series to specified frequency.
Returns the original data conformed to a new index with the specified frequency.
If the index of this Series is a
PeriodIndex
, the new index is the result of transforming the original index withPeriodIndex.asfreq
(so the original index will map one-to-one to the new index).Otherwise, the new index will be equivalent to
pd.date_range(start, end, freq=freq)
wherestart
andend
are, respectively, the first and last entries in the original index (seepandas.date_range()
). The values corresponding to any timesteps in the new index which were not present in the original index will be null (NaN
), unless a method for filling such unknowns is provided (see themethod
parameter below).The
resample()
method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency.- Parameters
- freqDateOffset or str
Frequency DateOffset or string.
- 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
- Series
Series object reindexed to the specified frequency.
See also
reindex
Conform DataFrame to new index with optional filling logic.
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