@@ -4061,6 +4061,10 @@ def asfreq(self, freq, method=None, how=None, normalize=False,
40614061
40624062 Optionally provide filling method to pad/backfill missing values.
40634063
4064+ Returns the original data conformed to a new index with the specified
4065+ frequency. ``resample`` is more appropriate if an operation, such as
4066+ summarization, is necessary to represent the data at the new frequency.
4067+
40644068 Parameters
40654069 ----------
40664070 freq : DateOffset object, or string
@@ -4076,7 +4080,8 @@ def asfreq(self, freq, method=None, how=None, normalize=False,
40764080 normalize : bool, default False
40774081 Whether to reset output index to midnight
40784082 fill_value: scalar, optional
4079- value to use for missing values, applied during upsampling
4083+ Value to use for missing values, applied during upsampling (note
4084+ this does not fill NaNs that already were present).
40804085
40814086 .. version added:: 0.20.0
40824087
@@ -4123,6 +4128,18 @@ def asfreq(self, freq, method=None, how=None, normalize=False,
41234128 2000-01-01 00:02:30 9.0
41244129 2000-01-01 00:03:00 3.0
41254130
4131+ Upsample again, providing a ``method``.
4132+
4133+ >>> df.asfreq(freq='30S', method='bfill')
4134+ s
4135+ 2000-01-01 00:00:00 0.0
4136+ 2000-01-01 00:00:30 NaN
4137+ 2000-01-01 00:01:00 NaN
4138+ 2000-01-01 00:01:30 2.0
4139+ 2000-01-01 00:02:00 2.0
4140+ 2000-01-01 00:02:30 3.0
4141+ 2000-01-01 00:03:00 3.0
4142+
41264143 Notes
41274144 -----
41284145 To learn more about the frequency strings, please see `this link
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