asfreq(self, freq: 'Frequency', method=None, how: 'str | None' = None, normalize: 'bool' = False, fill_value=None) -> 'DataFrame'
Returns the original data conformed to a new index with the specified frequency.
If the index of this DataFrame is a ~pandas.PeriodIndex
, the new index is the result of transforming the original index with PeriodIndex.asfreq <pandas.PeriodIndex.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)
where start
and end
are, respectively, the first and last entries in the original index (see pandas.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 the method
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.
To learn more about the frequency strings, please see :None:None:`this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`.
Frequency DateOffset or string.
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.
For PeriodIndex only (see PeriodIndex.asfreq).
Whether to reset output index to midnight.
Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present).
DataFrame object reindexed to the specified frequency.
Convert time series to specified frequency.
reindex
Conform DataFrame to new index with optional filling logic.
Start by creating a series with 4 one minute timestamps.
This example is valid syntax, but we were not able to check execution>>> 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.
This example is valid syntax, but we were not able to check execution>>> 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 3.0See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.frame.DataFrame.fillna
pandas.core.frame.DataFrame.resample
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