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| author | Kevin Moore <k.james.moore@gmail.com> | 2020-05-21 16:34:15 -0400 |
|---|---|---|
| committer | Kevin Moore <k.james.moore@gmail.com> | 2020-05-21 16:40:26 -0400 |
| commit | 6dfce7f498a9ee7f4cb396105270793ddc828e80 (patch) | |
| tree | b1bc1754ef41429f0c5be43233c2270f0935da49 /doc/source/user | |
| parent | 41e254f96e8d5bd558d2edbf8b198eb4143e8b74 (diff) | |
| download | numpy-6dfce7f498a9ee7f4cb396105270793ddc828e80.tar.gz | |
DOC: Fix spelling typo - homogenous to homogeneous. (#16324)
Diffstat (limited to 'doc/source/user')
| -rw-r--r-- | doc/source/user/absolute_beginners.rst | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/doc/source/user/absolute_beginners.rst b/doc/source/user/absolute_beginners.rst index ad2cd2d80..bd44b70da 100644 --- a/doc/source/user/absolute_beginners.rst +++ b/doc/source/user/absolute_beginners.rst @@ -100,8 +100,8 @@ What’s the difference between a Python list and a NumPy array? NumPy gives you an enormous range of fast and efficient ways of creating arrays and manipulating numerical data inside them. While a Python list can contain different data types within a single list, all of the elements in a NumPy array -should be homogenous. The mathematical operations that are meant to be performed -on arrays would be extremely inefficient if the arrays weren't homogenous. +should be homogeneous. The mathematical operations that are meant to be performed +on arrays would be extremely inefficient if the arrays weren't homogeneous. **Why use NumPy?** |