@@ -6371,7 +6371,7 @@ def dropna(
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Data variables:
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temperature (time, location) float64 64B 23.4 24.1 nan ... 24.2 20.5 25.3
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- # Drop NaN values from the dataset
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+ Drop NaN values from the dataset
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>>> dataset.dropna(dim="time")
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<xarray.Dataset> Size: 80B
@@ -6382,7 +6382,7 @@ def dropna(
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Data variables:
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temperature (time, location) float64 48B 23.4 24.1 21.8 24.2 20.5 25.3
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- # Drop labels with any NAN values
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+ Drop labels with any NaN values
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>>> dataset.dropna(dim="time", how="any")
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<xarray.Dataset> Size: 80B
@@ -6393,7 +6393,7 @@ def dropna(
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Data variables:
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temperature (time, location) float64 48B 23.4 24.1 21.8 24.2 20.5 25.3
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- # Drop labels with all NAN values
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+ Drop labels with all NAN values
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>>> dataset.dropna(dim="time", how="all")
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<xarray.Dataset> Size: 104B
@@ -6404,7 +6404,7 @@ def dropna(
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Data variables:
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temperature (time, location) float64 64B 23.4 24.1 nan ... 24.2 20.5 25.3
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- # Drop labels with less than 2 non-NA values
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+ Drop labels with less than 2 non-NA values
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>>> dataset.dropna(dim="time", thresh=2)
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<xarray.Dataset> Size: 80B
@@ -6418,6 +6418,11 @@ def dropna(
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Returns
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-------
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Dataset
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+
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+ See Also
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+ --------
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+ DataArray.dropna
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+ pandas.DataFrame.dropna
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"""
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# TODO: consider supporting multiple dimensions? Or not, given that
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# there are some ugly edge cases, e.g., pandas's dropna differs
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