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authorPierre de Buyl <pdebuyl@pdebuyl.be>2016-09-06 14:42:08 +0200
committerPierre de Buyl <pdebuyl@pdebuyl.be>2016-09-06 14:42:08 +0200
commit2a55233b81a6ea18a57d1dd4f7bc5fff9f2fb681 (patch)
tree1a4a81faf9e59d0cabf9cbace9dc967bad60caa1 /numpy/lib/nanfunctions.py
parent773e3cad9a71cb9a7849d8e251fb8a99ab35d06b (diff)
downloadnumpy-2a55233b81a6ea18a57d1dd4f7bc5fff9f2fb681.tar.gz
DOC: change Numpy to NumPy in dosctrings and comments
The strings in error messages were left untouched
Diffstat (limited to 'numpy/lib/nanfunctions.py')
-rw-r--r--numpy/lib/nanfunctions.py6
1 files changed, 3 insertions, 3 deletions
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index 7f7aea9bc..ab88c6a37 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -204,7 +204,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue):
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.
@@ -311,7 +311,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue):
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.
@@ -453,7 +453,7 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
Return the sum of array elements over a given axis treating Not a
Numbers (NaNs) as zero.
- In Numpy versions <= 1.8 Nan is returned for slices that are all-NaN or
+ In NumPy versions <= 1.8 Nan is returned for slices that are all-NaN or
empty. In later versions zero is returned.
Parameters