DOC: Adding examples to DataFrameGroupBy.rank #38972 #42402
There are no files selected for viewing
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -2641,7 +2641,6 @@ def cumcount(self, ascending: bool = True): | |
|
|
||
| @final | ||
| @Substitution(name="groupby") | ||
| def rank( | ||
| self, | ||
| method: str = "average", | ||
|
|
@@ -2675,6 +2674,51 @@ def rank( | |
| Returns | ||
| ------- | ||
| DataFrame with ranking of values within each group | ||
|
|
||
| See Also | ||
| -------- | ||
| Series.groupby : Apply a function groupby to a Series. | ||
| DataFrame.groupby : Apply a function groupby | ||
| to each row or column of a DataFrame. | ||
| Series.rank : Apply a function rank to a Series. | ||
| DataFrame.rank : Apply a function rank | ||
|
There was a problem hiding this comment. Based on the screenshot you posted, looks like this doesn't render as a link, so not that useful in current form. I think best to keep scope small and remove changes to the See Also (which could then be tackled as part of #42406 if you're interested!). There was a problem hiding this comment. reverted the change on |
||
| to each row or column of a DataFrame. | ||
|
|
||
| Examples | ||
| -------- | ||
| >>> df = pd.DataFrame({'group': ['a', 'a', 'a', 'b', | ||
| ... 'a', 'b', 'b', 'b', 'b', 'a'], | ||
| ... 'value': [.2, .4, .2, 0.01, | ||
| ... .3, .11, .21, .4, .01, 0.2]}) | ||
| >>> df | ||
| group value | ||
| 0 a 0.20 | ||
| 1 a 0.40 | ||
| 2 a 0.20 | ||
| 3 b 0.01 | ||
| 4 a 0.30 | ||
| 5 b 0.11 | ||
| 6 b 0.21 | ||
| 7 b 0.40 | ||
| 8 b 0.01 | ||
| 9 a 0.20 | ||
|
There was a problem hiding this comment. I think the example would be easier to see how different groups are treated if groups are contiguous, eg a, a, a, a...b, b, b, b There was a problem hiding this comment. Also think it would be clearer to have fewer distinct values (and maybe use ints instead of floats, with values that are easy to tell at a glance what is smallest, largest, etc |
||
| >>> df['average_rank'] = df.groupby('group')['value'].rank('average') | ||
| >>> df['min_rank'] = df.groupby('group')['value'].rank('min') | ||
| >>> df['max_rank'] = df.groupby('group')['value'].rank('max') | ||
| >>> df['dense_rank'] = df.groupby('group')['value'].rank('dense') | ||
| >>> df['first_rank'] = df.groupby('group')['value'].rank('first') | ||
|
There was a problem hiding this comment. This might be clearer as is, but could be written more concisely along the lines of |
||
| >>> df | ||
| group value average_rank min_rank max_rank dense_rank first_rank | ||
| 0 a 0.20 2.0 1.0 3.0 1.0 1.0 | ||
| 1 a 0.40 5.0 5.0 5.0 3.0 5.0 | ||
| 2 a 0.20 2.0 1.0 3.0 1.0 2.0 | ||
| 3 b 0.01 1.5 1.0 2.0 1.0 1.0 | ||
| 4 a 0.30 4.0 4.0 4.0 2.0 4.0 | ||
| 5 b 0.11 3.0 3.0 3.0 2.0 3.0 | ||
| 6 b 0.21 4.0 4.0 4.0 3.0 4.0 | ||
| 7 b 0.40 5.0 5.0 5.0 4.0 5.0 | ||
| 8 b 0.01 1.5 1.0 2.0 1.0 2.0 | ||
| 9 a 0.20 2.0 1.0 3.0 1.0 3.0 | ||
| """ | ||
| if na_option not in {"keep", "top", "bottom"}: | ||
| msg = "na_option must be one of 'keep', 'top', or 'bottom'" | ||
|
|
||
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
I know that other groupby docs include Series/DataFrame .groupby in the See Also, but IMO they're not helpful (especially since they don't link to anything).
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Have opened #42406 for this