CLN: post-post numpy bump (pandas-dev#24365)

* CLN: post-post numpy bumpy * Lint

Author: h-vetinari

pandas/_libs/lib.pyx

@@ -153,10 +153,10 @@ def is_scalar(val: object) -> bool:
  """
 
  return (cnp.PyArray_IsAnyScalar(val)
- # As of numpy-1.9, PyArray_IsAnyScalar misses bytearrays on Py3.
- or isinstance(val, (bytes, Fraction, Number))
- # We differ from numpy (as of 1.10), which claims that None is
- # not scalar in np.isscalar().
+ # PyArray_IsAnyScalar is always False for bytearrays on Py3
+ or isinstance(val, (Fraction, Number))
+ # We differ from numpy, which claims that None is not scalar;
+ # see np.isscalar
  or val is None
  or PyDate_Check(val)
  or PyDelta_Check(val)

pandas/_libs/tslibs/timedeltas.pyx

@@ -161,9 +161,6 @@ cpdef convert_to_timedelta64(object ts, object unit):
  - None/NaT
 
  Return an ns based int64
-
- # kludgy here until we have a timedelta scalar
- # handle the numpy < 1.7 case
  """
  if checknull_with_nat(ts):
  return np.timedelta64(NPY_NAT)

pandas/tests/arrays/categorical/test_api.py

@@ -455,7 +455,6 @@ def test_codes_immutable(self):
  c.codes = np.array([0, 1, 2, 0, 1], dtype='int8')
 
  # changes in the codes array should raise
- # np 1.6.1 raises RuntimeError rather than ValueError
  codes = c.codes
 
  with pytest.raises(ValueError):

pandas/tests/indexes/datetimes/test_misc.py

@@ -188,7 +188,6 @@ def test_datetimeindex_accessors(self):
  assert sum(dti.is_year_end) == 1
 
  # Ensure is_start/end accessors throw ValueError for CustomBusinessDay,
- # CBD requires np >= 1.7
  bday_egypt = offsets.CustomBusinessDay(weekmask='Sun Mon Tue Wed Thu')
  dti = date_range(datetime(2013, 4, 30), periods=5, freq=bday_egypt)
  pytest.raises(ValueError, lambda: dti.is_month_start)

pandas/tests/indexes/timedeltas/test_arithmetic.py

@@ -1,7 +1,6 @@
 # -*- coding: utf-8 -*-
 
 from datetime import timedelta
-from distutils.version import LooseVersion
 
 import numpy as np
 import pytest
@@ -197,15 +196,13 @@ def test_ops_ndarray(self):
  other = pd.to_timedelta(['1 day']).values
  expected = pd.to_timedelta(['2 days']).values
  tm.assert_numpy_array_equal(td + other, expected)
- if LooseVersion(np.__version__) >= LooseVersion('1.8'):
- tm.assert_numpy_array_equal(other + td, expected)
+ tm.assert_numpy_array_equal(other + td, expected)
  pytest.raises(TypeError, lambda: td + np.array([1]))
  pytest.raises(TypeError, lambda: np.array([1]) + td)
 
  expected = pd.to_timedelta(['0 days']).values
  tm.assert_numpy_array_equal(td - other, expected)
- if LooseVersion(np.__version__) >= LooseVersion('1.8'):
- tm.assert_numpy_array_equal(-other + td, expected)
+ tm.assert_numpy_array_equal(-other + td, expected)
  pytest.raises(TypeError, lambda: td - np.array([1]))
  pytest.raises(TypeError, lambda: np.array([1]) - td)
 
@@ -217,21 +214,18 @@ def test_ops_ndarray(self):
 
  tm.assert_numpy_array_equal(td / other,
  np.array([1], dtype=np.float64))
- if LooseVersion(np.__version__) >= LooseVersion('1.8'):
- tm.assert_numpy_array_equal(other / td,
- np.array([1], dtype=np.float64))
+ tm.assert_numpy_array_equal(other / td,
+ np.array([1], dtype=np.float64))
 
  # timedelta, datetime
  other = pd.to_datetime(['2000-01-01']).values
  expected = pd.to_datetime(['2000-01-02']).values
  tm.assert_numpy_array_equal(td + other, expected)
- if LooseVersion(np.__version__) >= LooseVersion('1.8'):
- tm.assert_numpy_array_equal(other + td, expected)
+ tm.assert_numpy_array_equal(other + td, expected)
 
  expected = pd.to_datetime(['1999-12-31']).values
  tm.assert_numpy_array_equal(-td + other, expected)
- if LooseVersion(np.__version__) >= LooseVersion('1.8'):
- tm.assert_numpy_array_equal(other - td, expected)
+ tm.assert_numpy_array_equal(other - td, expected)
 
  def test_tdi_ops_attributes(self):
  rng = timedelta_range('2 days', periods=5, freq='2D', name='x')

pandas/tests/util/test_util.py

@@ -30,7 +30,6 @@ def test_rands_array_2d():
 
 
 def test_numpy_err_state_is_default():
- # The defaults since numpy 1.6.0
  expected = {"over": "warn", "divide": "warn",
  "invalid": "warn", "under": "ignore"}
  import numpy as np