@@ -3997,6 +3997,10 @@ def asfreq(self, freq, method=None, how=None, normalize=False,
39973997
39983998 Optionally provide filling method to pad/backfill missing values.
39993999
4000+ Returns the original data conformed to a new index with the specified
4001+ frequency. ``resample`` is more appropriate if an operation, such as
4002+ summarization, is necessary to represent the data at the new frequency.
4003+
40004004 Parameters
40014005 ----------
40024006 freq : DateOffset object, or string
@@ -4012,7 +4016,8 @@ def asfreq(self, freq, method=None, how=None, normalize=False,
40124016 normalize : bool, default False
40134017 Whether to reset output index to midnight
40144018 fill_value: scalar, optional
4015- value to use for missing values, applied during upsampling
4019+ Value to use for missing values, applied during upsampling (note
4020+ this does not fill NaNs that already were present).
40164021
40174022 .. version added:: 0.20.0
40184023
@@ -4059,6 +4064,18 @@ def asfreq(self, freq, method=None, how=None, normalize=False,
40594064 2000-01-01 00:02:30 9.0
40604065 2000-01-01 00:03:00 3.0
40614066
4067+ Upsample again, providing a ``method``.
4068+
4069+ >>> df.asfreq(freq='30S', method='bfill')
4070+ s
4071+ 2000-01-01 00:00:00 0.0
4072+ 2000-01-01 00:00:30 NaN
4073+ 2000-01-01 00:01:00 NaN
4074+ 2000-01-01 00:01:30 2.0
4075+ 2000-01-01 00:02:00 2.0
4076+ 2000-01-01 00:02:30 3.0
4077+ 2000-01-01 00:03:00 3.0
4078+
40624079 Notes
40634080 -----
40644081 To learn more about the frequency strings, please see `this link
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