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authorMark Wiebe <mwiebe@enthought.com>2011-06-25 15:45:48 -0500
committerMark Wiebe <mwiebe@enthought.com>2011-06-27 10:32:42 -0500
commit8d74430d2d98bf760a0db6d1044565623c406a69 (patch)
tree644e7c7d8724bce7b740a5f225f5c4bb4f860830 /doc/neps
parent95bb76a6dc8b538fd844d3bcb9b94f12d47d78c7 (diff)
downloadnumpy-8d74430d2d98bf760a0db6d1044565623c406a69.tar.gz
NEP: c-masked-array: Typo feedback from WeatherGod
Diffstat (limited to 'doc/neps')
-rw-r--r--doc/neps/c-masked-array.rst4
1 files changed, 2 insertions, 2 deletions
diff --git a/doc/neps/c-masked-array.rst b/doc/neps/c-masked-array.rst
index 36c5445ad..7215b503f 100644
--- a/doc/neps/c-masked-array.rst
+++ b/doc/neps/c-masked-array.rst
@@ -16,7 +16,7 @@ The existing masked array functionality in NumPy is useful for many
people, however it has a number of issues that prevent it from being
the preferred solution in some important cases. By implementing mask
functionality into the core ndarray object, all the current issues
-with the system can be resolved in a high performance and flexible manner.
+with the system can be resolved in a high-performance and flexible manner.
The integration with ufuncs and other numpy core functions like sum is weak.
This could be dealt with either through a better function overloading
@@ -79,7 +79,7 @@ the data as if the NA values are not part of the data set.
Data That Is Being Temporarily Ignored
======================================
-Iterpreting the meaning of temporarily ignored data requires
+Interpreting the meaning of temporarily ignored data requires
choosing between one of the missing data interpretations above.
This is a common use case for masks, which are an elegant mechanism
to implement this.