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DOC: Additions/updates to documentation #17150

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58 changes: 29 additions & 29 deletions README.md
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Expand Up @@ -53,15 +53,15 @@
<tr>
<td>Conda</td>
<td>
<a href="http://pandas.pydata.org">
<a href="https://pandas.pydata.org">
<img src="http://pubbadges.s3-website-us-east-1.amazonaws.com/pkgs-downloads-pandas.png" alt="conda default downloads" />
</a>
</td>
</tr>
<tr>
<td>Conda-forge</td>
<td>
<a href="http://pandas.pydata.org">
<a href="https://pandas.pydata.org">
<img src="https://anaconda.org/conda-forge/pandas/badges/downloads.svg" alt="conda-forge downloads" />
</a>
</td>
Expand Down Expand Up @@ -123,31 +123,31 @@ Here are just a few of the things that pandas does well:
moving window linear regressions, date shifting and lagging, etc.


[missing-data]: http://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data
[insertion-deletion]: http://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion
[alignment]: http://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures
[groupby]: http://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine
[conversion]: http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe
[slicing]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges
[fancy-indexing]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix
[subsetting]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing
[merging]: http://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging
[joining]: http://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index
[reshape]: http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables
[pivot-table]: http://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations
[mi]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex
[flat-files]: http://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files
[excel]: http://pandas.pydata.org/pandas-docs/stable/io.html#excel-files
[db]: http://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries
[hdfstore]: http://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables
[timeseries]: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-series-date-functionality
[missing-data]: https://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data
[insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion
[alignment]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures
[groupby]: https://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine
[conversion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe
[slicing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges
[fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix
[subsetting]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing
[merging]: https://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging
[joining]: https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index
[reshape]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables
[pivot-table]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations
[mi]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex
[flat-files]: https://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files
[excel]: https://pandas.pydata.org/pandas-docs/stable/io.html#excel-files
[db]: https://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries
[hdfstore]: https://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables
[timeseries]: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-series-date-functionality

## Where to get it
The source code is currently hosted on GitHub at:
http://github.com/pandas-dev/pandas
https://github.com/pandas-dev/pandas

Binary installers for the latest released version are available at the [Python
package index](http://pypi.python.org/pypi/pandas/) and on conda.
package index](https://pypi.python.org/pypi/pandas) and on conda.

```sh
# conda
Expand All @@ -161,11 +161,11 @@ pip install pandas

## Dependencies
- [NumPy](http://www.numpy.org): 1.7.0 or higher
- [python-dateutil](http://labix.org/python-dateutil): 1.5 or higher
- [pytz](http://pytz.sourceforge.net)
- [python-dateutil](https://labix.org/python-dateutil): 1.5 or higher
- [pytz](https://pythonhosted.org/pytz)
- Needed for time zone support with ``pandas.date_range``

See the [full installation instructions](http://pandas.pydata.org/pandas-docs/stable/install.html#dependencies)
See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies)
for recommended and optional dependencies.

## Installation from sources
Expand Down Expand Up @@ -197,13 +197,13 @@ mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs
pip install -e .
```

See the full instructions for [installing from source](http://pandas.pydata.org/pandas-docs/stable/install.html#installing-from-source).
See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/install.html#installing-from-source).

## License
BSD
[BSD 3](LICENSE)

## Documentation
The official documentation is hosted on PyData.org: http://pandas.pydata.org/pandas-docs/stable/
The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable

The Sphinx documentation should provide a good starting point for learning how
to use the library. Expect the docs to continue to expand as time goes on.
Expand All @@ -223,7 +223,7 @@ Most development discussion is taking place on github in this repo. Further, the
## Contributing to pandas
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.

A detailed overview on how to contribute can be found in the **[contributing guide.](http://pandas.pydata.org/pandas-docs/stable/contributing.html)**
A detailed overview on how to contribute can be found in the **[contributing guide.](https://pandas.pydata.org/pandas-docs/stable/contributing.html)**

If you are simply looking to start working with the pandas codebase, navigate to the [GitHub “issues” tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [Difficulty Novice](https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Novice%22) where you could start out.

Expand Down
16 changes: 8 additions & 8 deletions doc/source/gotchas.rst
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Expand Up @@ -144,7 +144,7 @@ To evaluate single-element pandas objects in a boolean context, use the method `
Bitwise boolean
~~~~~~~~~~~~~~~

Bitwise boolean operators like ``==`` and ``!=`` will return a boolean ``Series``,
Bitwise boolean operators like ``==`` and ``!=`` return a boolean ``Series``,
which is almost always what you want anyways.

.. code-block:: python
Expand Down Expand Up @@ -194,7 +194,7 @@ For lack of ``NA`` (missing) support from the ground up in NumPy and Python in
general, we were given the difficult choice between either

- A *masked array* solution: an array of data and an array of boolean values
indicating whether a value
indicating whether a value is there or is missing
- Using a special sentinel value, bit pattern, or set of sentinel values to
denote ``NA`` across the dtypes

Expand Down Expand Up @@ -247,16 +247,16 @@ dtype in order to store the NAs. These are summarized by this table:
``integer``, cast to ``float64``
``boolean``, cast to ``object``

While this may seem like a heavy trade-off, I have found very few
cases where this is an issue in practice. Some explanation for the motivation
here in the next section.
While this may seem like a heavy trade-off, I have found very few cases where
this is an issue in practice i.e. storing values greater than 2**53. Some
explanation for the motivation is in the next section.

Why not make NumPy like R?
~~~~~~~~~~~~~~~~~~~~~~~~~~

Many people have suggested that NumPy should simply emulate the ``NA`` support
present in the more domain-specific statistical programming language `R
<http://r-project.org>`__. Part of the reason is the NumPy type hierarchy:
<https://r-project.org>`__. Part of the reason is the NumPy type hierarchy:

.. csv-table::
:header: "Typeclass","Dtypes"
Expand Down Expand Up @@ -305,7 +305,7 @@ the ``DataFrame.copy`` method. If you are doing a lot of copying of DataFrame
objects shared among threads, we recommend holding locks inside the threads
where the data copying occurs.

See `this link <http://stackoverflow.com/questions/13592618/python-pandas-dataframe-thread-safe>`__
See `this link <https://stackoverflow.com/questions/13592618/python-pandas-dataframe-thread-safe>`__
for more information.


Expand All @@ -332,5 +332,5 @@ using something similar to the following:
s = pd.Series(newx)

See `the NumPy documentation on byte order
<http://docs.scipy.org/doc/numpy/user/basics.byteswapping.html>`__ for more
<https://docs.scipy.org/doc/numpy/user/basics.byteswapping.html>`__ for more
details.