Skip to content

DOC: Fix EX01 in DataFrame.drop_duplicates #33283

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 9 commits into from
Apr 10, 2020
Merged

DOC: Fix EX01 in DataFrame.drop_duplicates #33283

Changes from 2 commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
988542c
DOC: Fix EX01 in DataFrame.drop_duplicates
farhanreynaldo Apr 4, 2020
1618a0c
DOC: Fix EX01 in DataFrame.drop_duplicates
farhanreynaldo Apr 4, 2020
f238c81
Remove empty lines
farhanreynaldo Apr 5, 2020
e34db4f
Merge branch 'master' of https://github.com/pandas-dev/pandas into dr…
farhanreynaldo Apr 5, 2020
71b8e05
Remove index name
farhanreynaldo Apr 8, 2020
caffe84
Fix indentation
farhanreynaldo Apr 8, 2020
6ff5453
Add indentation
farhanreynaldo Apr 9, 2020
cbabca4
move brand to fits inline
farhanreynaldo Apr 9, 2020
27a2045
Add period on sentences
farhanreynaldo Apr 9, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
43 changes: 43 additions & 0 deletions pandas/core/frame.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4673,6 +4673,49 @@ def drop_duplicates(
See Also
--------
DataFrame.value_counts: Count unique combinations of columns.

Examples
--------

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Remove the empty line.

Consider dataset containing ramen rating.

>>> brand = ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie']
>>> df = pd.DataFrame({'brand': brand,
... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
... 'rating': [4, 4, 3.5, 15, 5]},
... index=['TH', 'TH', 'ID', 'ID', 'ID'])
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I'm not sure if the index adds much value to the example. I'd remove it if it doesn't, so things are simpler and faster to read.

Also, did you run the validation script scripts/validate_docstrings.py pandas.DataFrame.drop_duplicates? I'm wondering if the indentation above is PEP-8 correct. The script should tell.

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Hm, I agree, the index doesn't have additional values, I might as well remove it.
I also have run the validate scripts, and it returns:

(pandas-dev) E:\pandas>python scripts/validate_docstrings.py pandas.DataFrame.drop_duplicates

################################################################################
################# Docstring (pandas.DataFrame.drop_duplicates) #################
################################################################################

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes
are ignored.

Parameters
----------
subset : column label or sequence of labels, optional
    Only consider certain columns for identifying duplicates, by
    default use all of the columns.
keep : {'first', 'last', False}, default 'first'
    Determines which duplicates (if any) to keep.
    - ``first`` : Drop duplicates except for the first occurrence.
    - ``last`` : Drop duplicates except for the last occurrence.
    - False : Drop all duplicates.
inplace : bool, default False
    Whether to drop duplicates in place or to return a copy.
ignore_index : bool, default False
    If True, the resulting axis will be labeled 0, 1, …, n - 1.

    .. versionadded:: 1.0.0

Returns
-------
DataFrame
    DataFrame with duplicates removed or None if ``inplace=True``.

See Also
--------
DataFrame.value_counts: Count unique combinations of columns.

Examples
--------
Consider dataset containing ramen rating.

>>> brand = ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie']
>>> df = pd.DataFrame({'brand': brand,
... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
... 'rating': [4, 4, 3.5, 15, 5]})
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, it removes duplicate rows based on all columns

>>> df.drop_duplicates()
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

To remove duplicates on specific column(s), use ``subset``

>>> df.drop_duplicates(subset=['brand'])
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

To remove duplicates and keep last occurences, use ``keep``

>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
    brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0

################################################################################
################################## Validation ##################################
################################################################################

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

hehe, I think the important part is the one that you didn't past, after the Validation header. :) That's where it says if any error has been found, or if everything is ok.

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Yeah, there's no error showed after validation header ._.

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

IIRC there should be a message saying there are no errors if that's the case. May be there is something broken.

But in any case, the CI is green, and this is a nice improvement. If there is any validation problem we can take care at it in the future.

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

But I am wondering, which part of the documentation I could improve regarding the PEP-8 indentation? I could change it and run the validation scripts once again.

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

It's the indentation, see:

df = pd.DataFrame({'brand': brand,
'style': ['cup', 'cup', 'cup', 'pack', 'pack'],

df = pd.DataFrame({'brand': brand,
                   'style': ['cup', 'cup', 'cup', 'pack', 'pack'],

df = pd.DataFrame({
    'brand': brand,
    'style': ['cup', 'cup', 'cup', 'pack', 'pack'],

The first one is the one in your code, and doesn't seem correct to me. The other two seem correct.


Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Remove the empty line, so it won't split into sections

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I missed that, I'm gonna fix this issue.

>>> df
brand style rating
TH Yum Yum cup 4.0
TH Yum Yum cup 4.0
ID Indomie cup 3.5
ID Indomie pack 15.0
ID Indomie pack 5.0

By default, it removes duplicate rows based on all columns

>>> df.drop_duplicates()
brand style rating
TH Yum Yum cup 4.0
ID Indomie cup 3.5
ID Indomie pack 15.0
ID Indomie pack 5.0

To remove duplicates on specific column(s), use ``subset``

>>> df.drop_duplicates(subset=['brand'])
brand style rating
TH Yum Yum cup 4.0
ID Indomie cup 3.5

To remove duplicates and keep last occurences, use ``keep``

>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
brand style rating
TH Yum Yum cup 4.0
ID Indomie cup 3.5
ID Indomie pack 5.0
"""
if self.empty:
return self.copy()
Expand Down