@@ -131,7 +131,7 @@ def __iter__(self):
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--------
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- GroupBy.__iter__
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+ GroupBy.__iter__ : Generator yielding sequence of (name, subset object) for each group
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"""
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self ._set_binner ()
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return super ().__iter__ ()
@@ -235,9 +235,9 @@ def pipe(self, func, *args, **kwargs):
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"""
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See Also
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--------
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- DataFrame.groupby.aggregate
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- DataFrame.resample.transform
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- DataFrame.aggregate
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+ DataFrame.groupby.aggregate : Aggregate using callable, string, dict, or list of string/callables
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+ DataFrame.resample.transform : Transforms the Series on each group based on the given function.
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+ DataFrame.aggregate: Aggregate using one or more operations over the specified axis.
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"""
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)
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@@ -453,8 +453,8 @@ def pad(self, limit=None):
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--------
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- Series.fillna
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- DataFrame.fillna
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+ Series.fillna: Fill NA/NaN values using the specified method.
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+ DataFrame.fillna: Fill NA/NaN values using the specified method.
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"""
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return self ._upsample ("pad" , limit = limit )
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@@ -828,8 +828,8 @@ def asfreq(self, fill_value=None):
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See Also
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--------
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- Series.asfreq
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- DataFrame.asfreq
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+ Series.asfreq: Convert TimeSeries to specified frequency.
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+ DataFrame.asfreq: Convert TimeSeries to specified frequency.
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"""
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return self ._upsample ("asfreq" , fill_value = fill_value )
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@@ -915,8 +915,11 @@ def quantile(self, q=0.5, **kwargs):
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--------
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Series.quantile
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+ Return a series, where the index is q and the values are the quantiles
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DataFrame.quantile
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+ Return a DataFrame, where the columns are the columns of self, and the values are the quantiles.
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DataFrameGroupBy.quantile
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+ Return a DataFrame, where the coulmns are groupby columns, and the values are its quantiles.
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"""
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return self ._downsample ("quantile" , q = q , ** kwargs )
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@@ -1072,7 +1075,7 @@ def _upsample(self, method, limit=None, fill_value=None):
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--------
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- .fillna
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+ .fillna: Fill NA/NaN values using the specified method.
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"""
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self ._set_binner ()
@@ -1208,7 +1211,7 @@ def _upsample(self, method, limit=None, fill_value=None):
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See Also
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--------
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- .fillna
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+ .fillna: Fill NA/NaN values using the specified method.
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"""
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# we may need to actually resample as if we are timestamps
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