DOC: Fix EX01 in DataFrame.drop_duplicates #33283
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pandas/core/frame.py
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| Examples | ||
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pandas/core/frame.py
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| ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'], | ||
| ... 'rating': [4, 4, 3.5, 15, 5]}, | ||
| ... index=['TH', 'TH', 'ID', 'ID', 'ID']) | ||
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Remove the empty line, so it won't split into sections
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I missed that, I'm gonna fix this issue.
pandas/core/frame.py
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| >>> df = pd.DataFrame({'brand': brand, | ||
| ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'], | ||
| ... 'rating': [4, 4, 3.5, 15, 5]}, | ||
| ... index=['TH', 'TH', 'ID', 'ID', 'ID']) |
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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.
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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 ##################################
################################################################################
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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.
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Yeah, there's no error showed after validation header ._.
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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.
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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.
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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.
Co-Authored-By: Marc Garcia <[email protected]>
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thanks @farhanreynaldo |
black pandasgit diff upstream/master -u -- "*.py" | flake8 --diffRelated to #27977.