Fix documentation typos (#3396)

Author: crypto-jeronimo

doc/faq.rst

@@ -55,9 +55,9 @@ natively represented as an array with four dimensions: time, row, column and
 color.
 
 Pandas has historically supported N-dimensional panels, but deprecated them in
-version 0.20 in favor of Xarray data structures.  There are now built-in methods
-on both sides to convert between pandas and Xarray, allowing for more focussed
-development effort.  Xarray objects have a much richer model of dimensionality -
+version 0.20 in favor of Xarray data structures. There are now built-in methods
+on both sides to convert between pandas and Xarray, allowing for more focused
+development effort. Xarray objects have a much richer model of dimensionality -
 if you were using Panels:
 
 - You need to create a new factory type for each dimensionality.

doc/whats-new.rst

@@ -75,7 +75,7 @@ Enhancements
 
   - Added a ``GroupBy.dims`` property that mirrors the dimensions
     of each group.(:issue:`3344`)
-    
+
 - Speed up :meth:`Dataset.isel` up to 33% and :meth:`DataArray.isel` up to 25% for small
   arrays (:issue:`2799`, :pull:`3375`) by
   `Guido Imperiale <https://github.com/crusaderky>`_.
@@ -107,8 +107,8 @@ Documentation
   By `Deepak Cherian <https://github.com/dcherian/>`_.
 - Add examples for :py:meth:`Dataset.swap_dims` and :py:meth:`DataArray.swap_dims`.
   By `Justus Magin <https://github.com/keewis>`_.
-- Add examples for :py:meth:`align`, :py:meth:`merge`, :py:meth:`combine_by_coords`, 
-  :py:meth:`full_like`, :py:meth:`zeros_like`, :py:meth:`ones_like`, :py:meth:`Dataset.pipe`, 
+- Add examples for :py:meth:`align`, :py:meth:`merge`, :py:meth:`combine_by_coords`,
+  :py:meth:`full_like`, :py:meth:`zeros_like`, :py:meth:`ones_like`, :py:meth:`Dataset.pipe`,
   :py:meth:`Dataset.assign`, :py:meth:`Dataset.reindex`, :py:meth:`Dataset.fillna`.
   By `Anderson Banihirwe <https://github.com/andersy005>`_.
 - Fixed documentation to clean up an unwanted file created in ``ipython`` example
@@ -3740,7 +3740,7 @@ Enhancements
 
   .. ipython:: python
 
-     ds = xray.Dataset({'tmin': ([], 25, {'units': 'celcius'})})
+     ds = xray.Dataset({'tmin': ([], 25, {'units': 'celsius'})})
      ds.tmin.units
 
   Tab-completion for these variables should work in editors such as IPython.

doc/why-xarray.rst

@@ -67,7 +67,7 @@ The power of the dataset over a plain dictionary is that, in addition to
 pulling out arrays by name, it is possible to select or combine data along a
 dimension across all arrays simultaneously. Like a
 :py:class:`~pandas.DataFrame`, datasets facilitate array operations with
-heterogeneous data -- the difference is that the arrays in a dataset can have 
+heterogeneous data -- the difference is that the arrays in a dataset can have
 not only different data types, but also different numbers of dimensions.
 
 This data model is borrowed from the netCDF_ file format, which also provides
@@ -87,7 +87,7 @@ Xarray contributes domain-agnostic data-structures and tools for labeled
 multi-dimensional arrays to Python's SciPy_ ecosystem for numerical computing.
 In particular, xarray builds upon and integrates with NumPy_ and pandas_:
 
-- Our user-facing interfaces aim to be more explicit verisons of those found in
+- Our user-facing interfaces aim to be more explicit versions of those found in
   NumPy/pandas.
 - Compatibility with the broader ecosystem is a major goal: it should be easy
   to get your data in and out.