DOC: Added MultiIndex Example for Series Min #23338

vadakattu · 2018-10-25T21:27:04Z

Corollary to #23298

tests added / passed
passes git diff upstream/master -u -- "*.py" | flake8 --diff

# Conflicts: # pandas/core/generic.py

pep8speaks · 2018-10-25T21:27:09Z

Hello @vadakattu! Thanks for updating the PR.

Cheers ! There are no PEP8 issues in this Pull Request. 🍻

Comment last updated on December 22, 2018 at 18:21 Hours UTC

codecov · 2018-10-25T22:27:52Z

Codecov Report

Merging #23338 into master will increase coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master   #23338      +/-   ##
==========================================
+ Coverage    92.3%    92.3%   +<.01%     
==========================================
  Files         162      162              
  Lines       51875    51879       +4     
==========================================
+ Hits        47883    47888       +5     
+ Misses       3992     3991       -1

Flag	Coverage Δ
#multiple	`90.71% <100%> (ø)`	⬆️
#single	`42.99% <100%> (-0.01%)`	⬇️

Impacted Files	Coverage Δ
pandas/core/generic.py	`96.62% <100%> (ø)`	⬆️
pandas/util/testing.py	`87.84% <0%> (+0.09%)`	⬆️

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jreback · 2018-10-26T01:31:52Z

cc @datapythonista @WillAyd

datapythonista

lgtm, but we should avoid repeating this for every stats method, I'd prefer a template than repeating these examples many times.

jreback · 2018-10-26T01:52:57Z

@vadakattu can you template this as @datapythonista suggests?

datapythonista

Unfortunately we need to keep compatibility for Python 2.7 and 3.5 for some time more, so we can't use f-strings yet.

vadakattu · 2018-10-28T19:19:02Z

Of course! ...that was very absent minded of me.

Do you have any suggestions for a better way to do the templating? Otherwise I'll go ahead and switch it to old-style formatting.

datapythonista · 2018-10-29T04:22:46Z

You can do the same, but without f-strings. If you need a reference, upper, lower... in strings.py also share docstring.

datapythonista · 2018-11-04T10:05:29Z

@vadakattu can you please merge master into your branch and push, and make sure the CI is green, so we can merge this. Thanks!

…n 2.7

datapythonista

Added a comment, apply to all cases.

pandas/core/generic.py

datapythonista · 2018-12-01T12:21:27Z

The idea was to change the variables to the new format too (e.g. %s -> {})

vadakattu · 2018-12-01T14:13:53Z

Done.

datapythonista

Thanks @vadakattu, Looks good, couple of small things to make this standard with the rest of the docs.

Also, if you can run ./scripts/validate_docstrings.py pandas.Series.min... and check that there is nothing wrong with the docstrings based on the script, that would be great.

pandas/core/generic.py

datapythonista · 2018-12-02T00:41:15Z

I think this is modifying some the same things as #22554. @vadakattu @Roald87, can you coordinate and avoid duplicate work

vadakattu · 2018-12-02T23:55:02Z

Have now restructured _num_doc, and incorporated the minimal changes necessary across the stat functions (as desired in #22554) such min, max & sum have fully compliant docstrings.

./scripts/validate_docstrings.py pandas.Series.min

################################################################################
######################## Docstring (pandas.Series.min)  ########################
################################################################################

Return the minimum of the values for the requested axis.

            If you want the *index* of the minimum, use ``idxmin``. This is
            the equivalent of the ``numpy.ndarray`` method ``argmin``.

Parameters
----------
axis : {index (0)}
    Axis for the function to be applied on.
skipna : bool, default True
    Exclude NA/null values when computing the result.
level : int or level name, default None
    If the axis is a MultiIndex (hierarchical), count along a
    particular level, collapsing into a scalar.
numeric_only : bool, default None
    Include only float, int, boolean columns. If None, will attempt to use
    everything, then use only numeric data. Not implemented for Series.
**kwargs
    Additional keyword arguments to be passed to the function.

Returns
-------
min : scalar or Series (if level specified)

Examples
--------

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.min()
0

Min using level names, as well as indices.

>>> s.min(level='blooded')
blooded
warm    2
cold    0
Name: legs, dtype: int64

>>> s.min(level=0)
blooded
warm    2
cold    0
Name: legs, dtype: int64

See Also
--------
Series.sum : Return the sum.
Series.min : Return the minimum.
Series.max : Return the maximum.
Series.idxmin : Return the index of the minimum.
Series.idxmax : Return the index of the maximum.
DataFrame.min : Return the sum over the requested axis.
DataFrame.min : Return the minimum over the requested axis.
DataFrame.max : Return the maximum over the requested axis.
DataFrame.idxmin : Return the index of the minimum over the requested axis.
DataFrame.idxmax : Return the index of the maximum over the requested axis.

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

Docstring for "pandas.Series.min" correct. :)

./scripts/validate_docstrings.py pandas.Series.max

################################################################################
######################## Docstring (pandas.Series.max)  ########################
################################################################################

Return the maximum of the values for the requested axis.

            If you want the *index* of the maximum, use ``idxmax``. This is
            the equivalent of the ``numpy.ndarray`` method ``argmax``.

Parameters
----------
axis : {index (0)}
    Axis for the function to be applied on.
skipna : bool, default True
    Exclude NA/null values when computing the result.
level : int or level name, default None
    If the axis is a MultiIndex (hierarchical), count along a
    particular level, collapsing into a scalar.
numeric_only : bool, default None
    Include only float, int, boolean columns. If None, will attempt to use
    everything, then use only numeric data. Not implemented for Series.
**kwargs
    Additional keyword arguments to be passed to the function.

Returns
-------
max : scalar or Series (if level specified)

Examples
--------

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.max()
8

Max using level names, as well as indices.

>>> s.max(level='blooded')
blooded
warm    4
cold    8
Name: legs, dtype: int64

>>> s.max(level=0)
blooded
warm    4
cold    8
Name: legs, dtype: int64

See Also
--------
Series.sum : Return the sum.
Series.min : Return the minimum.
Series.max : Return the maximum.
Series.idxmin : Return the index of the minimum.
Series.idxmax : Return the index of the maximum.
DataFrame.min : Return the sum over the requested axis.
DataFrame.min : Return the minimum over the requested axis.
DataFrame.max : Return the maximum over the requested axis.
DataFrame.idxmin : Return the index of the minimum over the requested axis.
DataFrame.idxmax : Return the index of the maximum over the requested axis.

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

Docstring for "pandas.Series.max" correct. :)

./scripts/validate_docstrings.py pandas.Series.sum

################################################################################
######################## Docstring (pandas.Series.sum)  ########################
################################################################################

Return the sum of the values for the requested axis.

            This is equivalent to the method ``numpy.sum``.

Parameters
----------
axis : {index (0)}
    Axis for the function to be applied on.
skipna : bool, default True
    Exclude NA/null values when computing the result.
level : int or level name, default None
    If the axis is a MultiIndex (hierarchical), count along a
    particular level, collapsing into a scalar.
numeric_only : bool, default None
    Include only float, int, boolean columns. If None, will attempt to use
    everything, then use only numeric data. Not implemented for Series.
min_count : int, default 0
    The required number of valid values to perform the operation. If fewer than
    ``min_count`` non-NA values are present the result will be NA.

    .. versionadded :: 0.22.0

       Added with the default being 0. This means the sum of an all-NA
       or empty Series is 0, and the product of an all-NA or empty
       Series is 1.
**kwargs
    Additional keyword arguments to be passed to the function.

Returns
-------
sum : scalar or Series (if level specified)

Examples
--------

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.sum()
14

Sum using level names, as well as indices.

>>> s.sum(level='blooded')
blooded
warm    6
cold    8
Name: legs, dtype: int64

>>> s.sum(level=0)
blooded
warm    6
cold    8
Name: legs, dtype: int64

By default, the sum of an empty or all-NA Series is ``0``.

>>> pd.Series([]).sum()  # min_count=0 is the default
0.0

This can be controlled with the ``min_count`` parameter. For example, if
you'd like the sum of an empty series to be NaN, pass ``min_count=1``.

>>> pd.Series([]).sum(min_count=1)
nan

Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
empty series identically.

>>> pd.Series([np.nan]).sum()
0.0

>>> pd.Series([np.nan]).sum(min_count=1)
nan

See Also
--------
Series.sum : Return the sum.
Series.min : Return the minimum.
Series.max : Return the maximum.
Series.idxmin : Return the index of the minimum.
Series.idxmax : Return the index of the maximum.
DataFrame.min : Return the sum over the requested axis.
DataFrame.min : Return the minimum over the requested axis.
DataFrame.max : Return the maximum over the requested axis.
DataFrame.idxmin : Return the index of the minimum over the requested axis.
DataFrame.idxmax : Return the index of the maximum over the requested axis.

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

Docstring for "pandas.Series.sum" correct. :)

datapythonista · 2018-12-02T23:56:31Z

@vadakattu can you fix the conflicts? we merged the PR that I mentioned it was touching same code as this one

# Conflicts: # pandas/core/generic.py

vadakattu · 2018-12-03T20:11:05Z

I believed I dealt with it last night -- the single circle ci failure seems unrelated.
Pulling master again to re-test.

=================================== FAILURES ===================================
___________________________ test_tick_add_sub[Milli] ___________________________

cls = <class 'pandas.tseries.offsets.Milli'>

    @pytest.mark.parametrize('cls', tick_classes)
>   @example(n=2, m=3)
    @example(n=800, m=300)
    @example(n=1000, m=5)
    @given(n=st.integers(-999, 999), m=st.integers(-999, 999))
    def test_tick_add_sub(cls, n, m):

pandas/tests/tseries/offsets/test_ticks.py:42: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/conda/envs/pandas-dev/lib/python3.6/site-packages/hypothesis/core.py:552: in execute
    ) % (test.__name__, text_repr[0],))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <hypothesis.core.StateForActualGivenExecution object at 0x7fb7828ac5f8>
message = 'Hypothesis test_tick_add_sub(cls=Milli, n=0, m=100) produces unreliable results: Falsified on the first call but did not on a subsequent one'

    def __flaky(self, message):
        if len(self.falsifying_examples) <= 1:
>           raise Flaky(message)
E           hypothesis.errors.Flaky: Hypothesis test_tick_add_sub(cls=Milli, n=0, m=100) produces unreliable results: Falsified on the first call but did not on a subsequent one

vadakattu · 2018-12-03T22:32:48Z

Tests pass, and docstrings fully compliant per validate_docstrings.

Roald87 · 2018-12-09T13:55:20Z

@datapythonista seems as if @vadakattu already made the full docs, so there's no point in merging my PR I guess?

datapythonista · 2018-12-09T14:48:48Z

I didn't check in detail how much overlap exists in both PRs. But as pointed out in #23338 (comment), you should talk with @vadakattu and see if it makes sense to have all the changes in one and close the other, or what.

Roald87 · 2018-12-09T15:01:53Z

@vadakattu what do you think? I'm fine with taking yours, I don't mine adds much.

vadakattu · 2018-12-09T15:38:49Z

👍🏽 Sounds good.

datapythonista · 2018-12-16T02:17:18Z

@vadakattu can you fix the conflicts please. Sorry for the delay.

# Conflicts: # pandas/core/generic.py

vadakattu · 2018-12-22T18:57:33Z

Conflicts resolved.

datapythonista

lgtm, thanks @vadakattu

WillAyd · 2018-12-25T00:41:29Z

Thanks @vadakattu

Krishna added 10 commits October 23, 2018 15:57

DOC: Added multi-indexed example for series.max

1a7247a

DOC: truncated line for PEP compliance

29f7887

Merge branch 'master' of github.com:pandas-dev/pandas into series-max

3a13f43

DOC: Added Examples for Series Min

ae6aadf

DOC: Typo corrected.

c16f48b

DOC: Restructured indexing as requested.

260790d

DOC: Switched from MultiIndex.from_arrays to MultiIndex.from_product

ad97502

Merge branch 'master' into series-min

469b764

# Conflicts: # pandas/core/generic.py

Merge branch 'master' of github.com:pandas-dev/pandas into series-min

5084c31

# Conflicts: # pandas/core/generic.py

Formatting Indentation for PEP compliance

6ac044d

jreback added Docs MultiIndex labels Oct 26, 2018

jreback added this to the 0.24.0 milestone Oct 26, 2018

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Krishna added 2 commits October 26, 2018 11:02

Merge branch 'master' of github.com:pandas-dev/pandas into series-min

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DOC: Initial templating of stat_func multiIndex examples

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DOC: Update Series min and max docstring. GH22459 #22554

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Krishna added 2 commits November 4, 2018 14:45

DOC: Refactored templating of stat_func multiIndex examples for Pytho…

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…n 2.7

DOC: Removed spurious f

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Krishna added 2 commits November 4, 2018 16:22

Merge branch 'master' of github.com:pandas-dev/pandas into series-min

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Merge branch 'master' of github.com:pandas-dev/pandas into series-min

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Krishna added 2 commits December 1, 2018 10:54

DOC: Updated stat func base example

77c866e

DOC: Updated stat func sum, min and max examples for clarity.

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DOC: Updated string variables from old format -> new.

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Krishna added 2 commits December 2, 2018 22:21

DOC: Formatting update.

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DOC: Updated sum, min & max for complete doc string compliance.

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Krishna added 2 commits December 3, 2018 00:16

Merge branch 'master' of github.com:pandas-dev/pandas into series-min

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# Conflicts: # pandas/core/generic.py

Merge branch 'master' of github.com:pandas-dev/pandas into series-min

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# Conflicts: # pandas/core/generic.py

Merge branch 'master' of github.com:pandas-dev/pandas into series-min

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Krishna added 2 commits December 22, 2018 18:04

Merge branch 'master' into series-min

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# Conflicts: # pandas/core/generic.py

Merge branch 'master' of github.com:pandas-dev/pandas into series-min

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# Conflicts: # pandas/core/generic.py

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WillAyd merged commit 159772d into pandas-dev:master Dec 25, 2018

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DOC: Added MultiIndex Example for Series Min (pandas-dev#23338)

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DOC: Added MultiIndex Example for Series Min #23338

DOC: Added MultiIndex Example for Series Min #23338

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