DOC: pandas.DateTimeIndex.to_frame

[ryananyangu]

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

• [ X] PR title is "DOC: pandas.DateTimeIndex.to_frame"
• [X ] The validation script passes: ./scripts/validate_docstrings.py pandas.DatetimeIndex.to_frame
• [ X] The PEP8 style check passes: git diff upstream/master -u -- "*.py" | flake8 --diff
• [X ] The html version looks good: python doc/make.py --single pandas.DatetimeIndex.to_frame
• [ X] It has been proofread on language by another sprint participant

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

################################################################################
################## Docstring (pandas.DatetimeIndex.to_frame)  ##################
################################################################################

Create a DataFrame with a column containing the Index.

.. versionadded:: 0.21.0

Parameters
----------
index : boolean, default True
    Set the index of the returned DataFrame as the original Index.

Returns
-------
DataFrame
    DataFrame containing the original Index data.

Examples
--------
>>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')
>>> idx.to_frame()
    animal
animal       
Ant       Ant
Bear     Bear
Cow       Cow

By default, the original Index is reused. To enforce a new Index:

>>> idx.to_frame(index=False)
    animal
0   Ant
1  Bear
2   Cow

Application to datetimes

>>> dt = ['04/27/2008 05:28 AM', '04/22/2008 07:19 AM',
...       '10/09/2008 01:44 PM', '12/25/2008 09:50 AM',
...       '08/19/2008 07:30 PM', '03/07/2008 11:04 PM',
...       '12/22/2008 12:08 PM', '07/01/2008 05:36 AM', 
...       '09/06/2008 04:53 AM', '01/23/2008 07:43 PM']
>>> con_dt = pd.to_datetime(dt)
>>> df = con_dt.to_frame(index=True)
>>> df[0]
2008-04-27 05:28:00   2008-04-27 05:28:00
2008-04-22 07:19:00   2008-04-22 07:19:00
2008-10-09 13:44:00   2008-10-09 13:44:00
2008-12-25 09:50:00   2008-12-25 09:50:00
2008-08-19 19:30:00   2008-08-19 19:30:00
2008-03-07 23:04:00   2008-03-07 23:04:00
2008-12-22 12:08:00   2008-12-22 12:08:00
2008-07-01 05:36:00   2008-07-01 05:36:00
2008-09-06 04:53:00   2008-09-06 04:53:00
2008-01-23 19:43:00   2008-01-23 19:43:00
Name: 0, dtype: datetime64[ns]

See Also
--------
pandas.Series.to_frame : Convert Series to DataFrame

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

Docstring for "pandas.DatetimeIndex.to_frame" correct. :)

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.

[pep8speaks]

Hello @ryananyangu! Thanks for updating the PR.

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

Comment last updated on March 16, 2018 at 21:20 Hours UTC

[datapythonista]

@ryananyangu can you edit the title of the PR to be "DOC: pandas.DateTimeIndex.to_frame" instead of "Jovixe"? That'll help us review it. Thanks!

[jorisvandenbossche]

I am personally not really sure about the added value of showing the same but with datetime values.

But the "See Also" is certainly a good addition

[datapythonista]

See Also should go before examples. Also, Index also have a .to_series() method that is worth listing here.

I think the preferred option is to avoid using the pandas. prefix in the `See Also section.

[datapythonista]

I agree with @jorisvandenbossche, while the example is really nice, it adds a lot of reading for the users for the value it provides. I'd keep the examples as they were.

[codecov]

Codecov Report

Merging #20139 into master will decrease coverage by 0.02%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #20139      +/-   ##
==========================================
- Coverage   91.72%    91.7%   -0.03%     
==========================================
  Files         150      150              
  Lines       49156    49152       -4     
==========================================
- Hits        45090    45074      -16     
- Misses       4066     4078      +12

Flag	Coverage Δ
#multiple	90.08% <ø> (-0.03%)	⬇️
#single	41.84% <ø> (-0.01%)	⬇️

Impacted Files	Coverage Δ
pandas/core/indexes/base.py	96.66% <ø> (-0.01%)	⬇️
pandas/plotting/_converter.py	65.07% <0%> (-1.74%)	⬇️
pandas/core/base.py	96.78% <0%> (-0.02%)	⬇️
pandas/core/series.py	93.85% <0%> (-0.01%)	⬇️
pandas/core/indexes/multi.py	95.06% <0%> (ø)	⬆️
pandas/core/generic.py	95.84% <0%> (ø)	⬆️
pandas/plotting/_core.py	82.23% <0%> (ø)	⬆️

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[codecov]

Codecov Report

❗ No coverage uploaded for pull request base (master@34649f2). Click here to learn what that means.
The diff coverage is n/a.

@@            Coverage Diff            @@
##             master   #20139   +/-   ##
=========================================
  Coverage          ?    91.7%           
=========================================
  Files             ?      150           
  Lines             ?    49152           
  Branches          ?        0           
=========================================
  Hits              ?    45074           
  Misses            ?     4078           
  Partials          ?        0

Flag	Coverage Δ
#multiple	90.08% <ø> (?)
#single	41.84% <ø> (?)

Impacted Files	Coverage Δ
pandas/core/indexes/base.py	96.66% <ø> (ø)

---

Continue to review full report at Codecov.

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Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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[TomAugspurger]

Thanks @ryananyangu !