fix a few typos in rst files (pydata#2607)

doc/api.rst

@@ -377,7 +377,7 @@ Universal functions
 .. warning::
 
    With recent versions of numpy, dask and xarray, NumPy ufuncs are now
-   supported directly on all xarray and dask objects. This obliviates the need
+   supported directly on all xarray and dask objects. This obviates the need
    for the ``xarray.ufuncs`` module, which should not be used for new code
    unless compatibility with versions of NumPy prior to v1.13 is required.
 

doc/computation.rst

@@ -267,7 +267,7 @@ This means, for example, that you always subtract an array from its transpose:
 
     c - c.T
 
-You can explicitly broadcast xaray data structures by using the
+You can explicitly broadcast xarray data structures by using the
 :py:func:`~xarray.broadcast` function:
 
 .. ipython:: python

doc/examples/monthly-means.rst

@@ -130,7 +130,7 @@ for each monthly data record - calculate weights using
 ``groupby('time.season')``
 
 Finally, we just need to multiply our weights by the ``Dataset`` and sum
-allong the time dimension.
+along the time dimension.
 
 .. code:: python
 

doc/interpolation.rst

@@ -57,7 +57,7 @@ To interpolate data with a :py:func:`numpy.datetime64` coordinate you can pass a
     da_dt64.interp(time='2000-01-02')
 
 The interpolated data can be merged into the original :py:class:`~xarray.DataArray`
-by specifing the time periods required.
+by specifying the time periods required.
 
 .. ipython:: python
 
@@ -232,7 +232,7 @@ then make the interpolation
     dropped
     dropped.interp(x=[0.5, 1.5, 2.5], method='cubic')
 
-If NaNs are distributed rondomly in your multidimensional array,
+If NaNs are distributed randomly in your multidimensional array,
 dropping all the columns containing more than one NaNs by
 :py:meth:`~xarray.DataArray.dropna` may lose a significant amount of information.
 In such a case, you can fill NaN by :py:meth:`~xarray.DataArray.interpolate_na`,

doc/pandas.rst

@@ -152,7 +152,7 @@ However, you will need to set dimension names explicitly, either with the
 Transitioning from pandas.Panel to xarray
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-:py:class:`~pandas.Panel`, pandas's data structure for 3D arrays, has always
+:py:class:`~pandas.Panel`, pandas' data structure for 3D arrays, has always
 been a second class data structure compared to the Series and DataFrame. To
 allow pandas developers to focus more on its core functionality built around
 the DataFrame, pandas plans to eventually deprecate Panel.

doc/plotting.rst

@@ -461,7 +461,7 @@ about three or four dimensional arrays? That's where facets become helpful.
 Consider the temperature data set. There are 4 observations per day for two
 years which makes for 2920 values along the time dimension.
 One way to visualize this data is to make a
-seperate plot for each time period.
+separate plot for each time period.
 
 The faceted dimension should not have too many values;
 faceting on the time dimension will produce 2920 plots. That's

doc/roadmap.rst

@@ -186,7 +186,7 @@ Engaging more users
 
 Like many open-source projects, the documentation of xarray has grown
 together with the library's features. While we think that the xarray
-documentation is comprehensive already, we aknowledge that the adoption
+documentation is comprehensive already, we acknowledge that the adoption
 of xarray might be slowed down because of the substantial time
 investment required to learn its working principles. In particular,
 non-computer scientists or users less familiar with the pydata ecosystem
@@ -198,7 +198,7 @@ In order to lower this adoption barrier, we propose to:
 -  Develop entry-level tutorials for users with different backgrounds. For
    example, we would like to develop tutorials for users with or without
    previous knowledge of pandas, numpy, netCDF, etc. These tutorials may be
-   built as part of xarray's documentation or included in a seperate repository
+   built as part of xarray's documentation or included in a separate repository
    to enable interactive use (e.g. mybinder.org).
 -  Document typical user workflows in a dedicated website, following the example
    of `dask-stories

doc/time-series.rst

@@ -82,7 +82,7 @@ Datetime indexing
 
 xarray borrows powerful indexing machinery from pandas (see :ref:`indexing`).
 
-This allows for several useful and suscinct forms of indexing, particularly for
+This allows for several useful and succinct forms of indexing, particularly for
 `datetime64` data. For example, we support indexing with strings for single
 items and with the `slice` object: