DOC: update the Series.between docstring

[mrw34]

Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

• PR title is "DOC: update the docstring"
• The validation script passes: scripts/validate_docstrings.py <your-function-or-method>
• The PEP8 style check passes: git diff upstream/master -u -- "*.py" | flake8 --diff
• The html version looks good: python doc/make.py --single <your-function-or-method>
• It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

################################################################################
###################### Docstring (pandas.Series.between)  ######################
################################################################################

Return boolean Series equivalent to left <= series <= right.

This function returns a boolean vector containing `True` wherever the
corresponding Series element is between the boundary values `left` and
`right`. NA values are treated as `False`.

Parameters
----------
left : scalar
    Left boundary.
right : scalar
    Right boundary.
inclusive : bool, default True
    Include boundaries.

Returns
-------
is_between : Series

Examples
--------

>>> s = pd.Series([2, 0, 4, 8, np.nan])

Boundary values are included by default:

>>> s.between(1, 4)
0     True
1    False
2     True
3    False
4    False
dtype: bool

With `inclusive` set to `False` boundary values are excluded:

>>> s.between(1, 4, inclusive=False)
0     True
1    False
2    False
3    False
4    False
dtype: bool

`left` and `right` can be any scalar value:

>>> s = pd.Series(['Alice', 'Bob', 'Carol', 'Eve'])
>>> s.between('Anna', 'Daniel')
0    False
1     True
2     True
3    False
dtype: bool

See Also
--------
DataFrame.query : Query the columns of a frame with a boolean
    expression.

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

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

[pep8speaks]

Hello @mrw34! Thanks for updating the PR.

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

Comment last updated on March 24, 2018 at 07:57 Hours UTC

[codecov]

Codecov Report

Merging #20443 into master will increase coverage by 0.02%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #20443      +/-   ##
==========================================
+ Coverage   91.82%   91.85%   +0.02%     
==========================================
  Files         152      152              
  Lines       49248    49231      -17     
==========================================
- Hits        45222    45220       -2     
+ Misses       4026     4011      -15

Flag	Coverage Δ
#multiple	90.23% <ø> (+0.02%)	⬆️
#single	41.83% <ø> (-0.06%)	⬇️

Impacted Files	Coverage Δ
pandas/core/series.py	93.84% <ø> (ø)	⬆️
pandas/core/arrays/base.py	83.33% <0%> (-0.82%)	⬇️
pandas/core/algorithms.py	94.29% <0%> (-0.07%)	⬇️
pandas/core/base.py	96.78% <0%> (-0.01%)	⬇️
pandas/core/indexes/base.py	96.68% <0%> (-0.01%)	⬇️
pandas/core/frame.py	97.18% <0%> (-0.01%)	⬇️
pandas/core/generic.py	95.85% <0%> (ø)	⬆️
pandas/core/indexes/period.py	92.61% <0%> (ø)	⬆️
pandas/core/reshape/reshape.py	100% <0%> (ø)	⬆️
... and 6 more

---

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[datapythonista]

Looks great, added couple of comments.

[datapythonista]

Personally I don't find DataFrame.query so related. But if we list it, I'd explain it a bit better than it can be used to perform comparisons, so users understand why it's related.

The ones that I do find related are Series.gt and Series.lt.

The See Also section should be placed before the examples, not after.

[datapythonista]

I think we don't need this blank line.

[datapythonista]

You can leave just Series, or Series of bool to be more explicit. The returned name is_between is not needed.

[mrw34]

@datapythonista Thanks Marc! Hopefully looking better now...

[datapythonista]

lgtm

[TomAugspurger]

Thanks @mrw34