pandas.DataFrame.asfreq — pandas 0.24.2 documentation (original) (raw)
DataFrame. asfreq(freq, method=None, how=None, normalize=False, fill_value=None)[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: | freq : DateOffset object, 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 normalize : bool, default False Whether to reset output index to midnight fill_value : scalar, optional Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present). New in version 0.20.0. |
|---|---|
| Returns: | converted : same type as caller |
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