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BUG: Add SparseArray.all #17570

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merged 6 commits into from
Sep 28, 2017
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@Licht-T Licht-T commented Sep 18, 2017

  • closes #xxxx
  • tests added / passed
  • passes git diff upstream/master -u -- "*.py" | flake8 --diff
  • whatsnew entry

This is the part of #17386.
Block.where uses ndarray.all, but there is no such method in SparseArray.
This makes that all evaluates SparseArray([False, False, True]) as True.
https://github.com/pandas-dev/pandas/blob/master/pandas/core/internals.py#L1400

values = self.sp_values

if len(values) != len(self):
values = values.tolist()
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is there a reason you are converting to a list? and not a dense array?

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@Licht-T Licht-T Sep 18, 2017

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@jreback For the performance reason. This is sparse and self.to_dense() is not efficient.

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I am asking why you are converting .tolist(), then appending, that is non-performant.

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Simply I wanted to use np.all. I got an idea not to use tolist(). I'll change the solution.

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pls add a whatsnew note. do we have a specific issue for this? (other that the PR)?

@@ -614,6 +614,24 @@ def fillna(self, value, downcast=None):
return self._simple_new(new_values, self.sp_index,
fill_value=fill_value)

def all(self, axis=0, *args, **kwargs):
"""
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might as well add any as well.

@jreback jreback added Bug Sparse Sparse Data Type labels Sep 18, 2017
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Licht-T commented Sep 18, 2017

@jreback Thanks for your review.

  • Changed the fill_value check method and now we are not using tolist().
  • Removed dtype parameter validation check.
    There is no dtype parameter on ndarray.all.
  • Also added SparseArray.any.
  • There seems to be no specific issue for this.

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minor comment. ping on green

@@ -544,6 +544,7 @@ Sparse

- Bug in ``SparseSeries`` raises ``AttributeError`` when a dictionary is passed in as data (:issue:`16905`)
- Bug in :func:`SparseDataFrame.fillna` not filling all NaNs when frame was instantiated from SciPy sparse matrix (:issue:`16112`)
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say that these are now implemented to handle SparseArray


Returns
-------
all : bool
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add a See Also to point to np.all

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codecov bot commented Sep 21, 2017

Codecov Report

Merging #17570 into master will decrease coverage by 0.01%.
The diff coverage is 100%.

Impacted file tree graph

@@            Coverage Diff             @@
##           master   #17570      +/-   ##
==========================================
- Coverage   91.22%    91.2%   -0.02%     
==========================================
  Files         163      163              
  Lines       49625    49642      +17     
==========================================
+ Hits        45270    45277       +7     
- Misses       4355     4365      +10
Flag Coverage Δ
#multiple 88.99% <100%> (ø) ⬆️
#single 40.19% <41.17%> (-0.07%) ⬇️
Impacted Files Coverage Δ
pandas/compat/numpy/function.py 93.33% <100%> (+0.2%) ⬆️
pandas/core/sparse/array.py 91.31% <100%> (+0.01%) ⬆️
pandas/io/gbq.py 25% <0%> (-58.34%) ⬇️
pandas/core/frame.py 97.77% <0%> (-0.1%) ⬇️

Continue to review full report at Codecov.

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Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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codecov bot commented Sep 21, 2017

Codecov Report

Merging #17570 into master will decrease coverage by 0.04%.
The diff coverage is 100%.

Impacted file tree graph

@@            Coverage Diff             @@
##           master   #17570      +/-   ##
==========================================
- Coverage   91.24%    91.2%   -0.05%     
==========================================
  Files         163      163              
  Lines       49766    49642     -124     
==========================================
- Hits        45411    45276     -135     
- Misses       4355     4366      +11
Flag Coverage Δ
#multiple 88.99% <100%> (-0.04%) ⬇️
#single 40.19% <41.17%> (-0.21%) ⬇️
Impacted Files Coverage Δ
pandas/compat/numpy/function.py 93.33% <100%> (+0.2%) ⬆️
pandas/core/sparse/array.py 91.31% <100%> (-0.28%) ⬇️
pandas/io/gbq.py 25% <0%> (-58.34%) ⬇️
pandas/util/_decorators.py 66% <0%> (-12%) ⬇️
pandas/core/dtypes/missing.py 87.19% <0%> (-3.26%) ⬇️
pandas/core/base.py 96.01% <0%> (-0.56%) ⬇️
pandas/core/indexes/range.py 92.59% <0%> (-0.25%) ⬇️
pandas/tseries/offsets.py 97% <0%> (-0.15%) ⬇️
pandas/core/categorical.py 95.57% <0%> (-0.14%) ⬇️
... and 32 more

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Licht-T commented Sep 21, 2017

@jreback Thank you. These are now fixed.

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Licht-T commented Sep 22, 2017

@jreback I found the bug in this solution. When fill_value = np.array([1,1,1]), np.all(fill_value) returns True. In numpy, np.array([np.array([1,1,1]), 0, np.array([1,1,1])], dtype=np.object).all() raise ValueError, but this is not. I'll fix.

UPDATE: I found SparseArray does not allow ndarray as fill_value, so this will not happen. I am sorry for the mistake.

@jreback jreback added this to the 0.21.0 milestone Sep 28, 2017
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jreback commented Sep 28, 2017

lgtm. waiting for CI to go green again before merging (unrelated conda-ish issues)

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jreback commented Sep 28, 2017

can you rebase

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Licht-T commented Sep 28, 2017

@jreback Rebased.

@jreback jreback merged commit bbf0dda into pandas-dev:master Sep 28, 2017
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jreback commented Sep 28, 2017

thanks @Licht-T

alanbato pushed a commit to alanbato/pandas that referenced this pull request Nov 10, 2017
No-Stream pushed a commit to No-Stream/pandas that referenced this pull request Nov 28, 2017
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