diff --git a/doc/api.rst b/doc/api.rst index 662ef567710..9a00630f88e 100644 --- a/doc/api.rst +++ b/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. diff --git a/doc/computation.rst b/doc/computation.rst index 3eec891494b..f1d1450a6dc 100644 --- a/doc/computation.rst +++ b/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 diff --git a/doc/examples/monthly-means.rst b/doc/examples/monthly-means.rst index cb41d5c59ad..7cc47eb2847 100644 --- a/doc/examples/monthly-means.rst +++ b/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 diff --git a/doc/interpolation.rst b/doc/interpolation.rst index 71e88079676..7c750506cf3 100644 --- a/doc/interpolation.rst +++ b/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`, diff --git a/doc/pandas.rst b/doc/pandas.rst index e0bad61f805..7a983872416 100644 --- a/doc/pandas.rst +++ b/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. diff --git a/doc/plotting.rst b/doc/plotting.rst index f8ba82febb0..bac6e6e51fd 100644 --- a/doc/plotting.rst +++ b/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 diff --git a/doc/roadmap.rst b/doc/roadmap.rst index 34d203c3f48..e336b35b2bc 100644 --- a/doc/roadmap.rst +++ b/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 diff --git a/doc/time-series.rst b/doc/time-series.rst index 7f5389d3ae1..8bd490d784b 100644 --- a/doc/time-series.rst +++ b/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: