diff --git a/README.md b/README.md index dc74828ba9863..ac043f5586498 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,7 @@ Conda - + conda default downloads @@ -61,7 +61,7 @@ Conda-forge - + conda-forge downloads @@ -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 @@ -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 @@ -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. @@ -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. diff --git a/doc/source/gotchas.rst b/doc/source/gotchas.rst index a3a90f514f142..a3062b4086673 100644 --- a/doc/source/gotchas.rst +++ b/doc/source/gotchas.rst @@ -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 @@ -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 @@ -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 -`__. Part of the reason is the NumPy type hierarchy: +`__. Part of the reason is the NumPy type hierarchy: .. csv-table:: :header: "Typeclass","Dtypes" @@ -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 `__ +See `this link `__ for more information. @@ -332,5 +332,5 @@ using something similar to the following: s = pd.Series(newx) See `the NumPy documentation on byte order -`__ for more +`__ for more details.