Pandas Series: asfreq() function
Convert Pandas TimeSeries to specified frequency
The asfreq() function is used to 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.
Syntax:
Series.asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None)
Parameters:
Name | Description | Type/Default Value | Required / Optional |
---|---|---|---|
freq | DateOffset object, or string | Required | |
method | Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present):
|
{'backfill'/'bfill', 'pad'/'ffill'} Default Value: None |
Required |
how | For PeriodIndex only, see PeriodIndex.asfreq | {'start', 'end'} Default Value: end |
Required |
normalize | Whether to reset output index to midnight | bool Default Value: False |
Required |
fill_value | Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present). | scalar | Optional |
Returns: converted - same type as caller
Example:
Download the Pandas Series Notebooks from here.
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