diff options
Diffstat (limited to 'numpy/lib')
| -rw-r--r-- | numpy/lib/arraypad.py | 4 | ||||
| -rw-r--r-- | numpy/lib/recfunctions.py | 2 | ||||
| -rw-r--r-- | numpy/lib/tests/test_histograms.py | 2 |
3 files changed, 4 insertions, 4 deletions
diff --git a/numpy/lib/arraypad.py b/numpy/lib/arraypad.py index 247eed07c..7569e7651 100644 --- a/numpy/lib/arraypad.py +++ b/numpy/lib/arraypad.py @@ -232,7 +232,7 @@ def _get_linear_ramps(padded, axis, width_pair, end_value_pair): def _get_stats(padded, axis, width_pair, length_pair, stat_func): """ - Calculate statistic for the empty-padded array in given dimnsion. + Calculate statistic for the empty-padded array in given dimension. Parameters ---------- @@ -271,7 +271,7 @@ def _get_stats(padded, axis, width_pair, length_pair, stat_func): if (left_length == 0 or right_length == 0) \ and stat_func in {np.amax, np.amin}: - # amax and amin can't operate on an emtpy array, + # amax and amin can't operate on an empty array, # raise a more descriptive warning here instead of the default one raise ValueError("stat_length of 0 yields no value for padding") diff --git a/numpy/lib/recfunctions.py b/numpy/lib/recfunctions.py index 93aa67a3b..4e62169f4 100644 --- a/numpy/lib/recfunctions.py +++ b/numpy/lib/recfunctions.py @@ -436,7 +436,7 @@ def merge_arrays(seqarrays, fill_value=-1, flatten=False, if seqdtype.names is None: seqdtype = np.dtype([('', seqdtype)]) if not flatten or _zip_dtype((seqarrays,), flatten=True) == seqdtype: - # Minimal processing needed: just make sure everythng's a-ok + # Minimal processing needed: just make sure everything's a-ok seqarrays = seqarrays.ravel() # Find what type of array we must return if usemask: diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py index c21103891..fc16b7396 100644 --- a/numpy/lib/tests/test_histograms.py +++ b/numpy/lib/tests/test_histograms.py @@ -81,7 +81,7 @@ class TestHistogram: a, b = histogram(v, bins, density=False) assert_array_equal(a, [1, 2, 3, 4]) - # Variale bin widths are especially useful to deal with + # Variable bin widths are especially useful to deal with # infinities. v = np.arange(10) bins = [0, 1, 3, 6, np.inf] |