Here are the examples of the python api numpy.nan taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
145 Examples
3
View Complete Implementation : test_datetime.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_series_set_value():
# #1561
dates = [datetime(2001, 1, 1), datetime(2001, 1, 2)]
index = DatetimeIndex(dates)
with tm.astert_produces_warning(FutureWarning,
check_stacklevel=False):
s = Series().set_value(dates[0], 1.)
with tm.astert_produces_warning(FutureWarning,
check_stacklevel=False):
s2 = s.set_value(dates[1], np.nan)
exp = Series([1., np.nan], index=index)
astert_series_equal(s2, exp)
3
View Complete Implementation : test_calibration.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_calibration_nan_imputer():
"""Test that calibration can accept nan"""
X, y = make_clastification(n_samples=10, n_features=2,
n_informative=2, n_redundant=0,
random_state=42)
X[0, 0] = np.nan
clf = Pipeline(
[('imputer', Imputer()),
('rf', RandomForestClastifier(n_estimators=1))])
clf_c = CalibratedClastifierCV(clf, cv=2, method='isotonic')
clf_c.fit(X, y)
clf_c.predict(X)
3
View Complete Implementation : test_combine_concat.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_concat_different_fill(self):
val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
val2 = np.array([3, np.nan, 4, 0, 0])
for kind in ['integer', 'block']:
sparse1 = pd.SparseSeries(val1, name='x', kind=kind)
sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0)
res = pd.concat([sparse1, sparse2])
exp = pd.concat([pd.Series(val1), pd.Series(val2)])
exp = pd.SparseSeries(exp, kind=kind)
tm.astert_sp_series_equal(res, exp)
res = pd.concat([sparse2, sparse1])
exp = pd.concat([pd.Series(val2), pd.Series(val1)])
exp = pd.SparseSeries(exp, kind=kind, fill_value=0)
tm.astert_sp_series_equal(res, exp)
3
View Complete Implementation : test_missing.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_interp_combo(self):
df = DataFrame({'A': [1., 2., np.nan, 4.],
'B': [1, 4, 9, np.nan],
'C': [1, 2, 3, 5],
'D': list('abcd')})
result = df['A'].interpolate()
expected = Series([1., 2., 3., 4.], name='A')
astert_series_equal(result, expected)
result = df['A'].interpolate(downcast='infer')
expected = Series([1, 2, 3, 4], name='A')
astert_series_equal(result, expected)
3
View Complete Implementation : test_utils.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_equal_nan_default(self):
# Make sure equal_nan default behavior remains unchanged. (All
# of these functions use astert_array_compare under the hood.)
# None of these should raise.
a = np.array([np.nan])
b = np.array([np.nan])
astert_array_equal(a, b)
astert_array_almost_equal(a, b)
astert_array_less(a, b)
astert_allclose(a, b)
3
View Complete Implementation : test_alter_index.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_reindex_like(test_data):
other = test_data.ts[::2]
astert_series_equal(test_data.ts.reindex(other.index),
test_data.ts.reindex_like(other))
# GH 7179
day1 = datetime(2013, 3, 5)
day2 = datetime(2013, 5, 5)
day3 = datetime(2014, 3, 5)
series1 = Series([5, None, None], [day1, day2, day3])
series2 = Series([None, None], [day1, day3])
result = series1.reindex_like(series2, method='pad')
expected = Series([5, np.nan], index=[day1, day3])
astert_series_equal(result, expected)
3
View Complete Implementation : test_nanfunctions.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_allnans(self):
# Check for FutureWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = np.nansum([np.nan]*3, axis=None)
astert_(res == 0, 'result is not 0')
astert_(len(w) == 0, 'warning raised')
# Check scalar
res = np.nansum(np.nan)
astert_(res == 0, 'result is not 0')
astert_(len(w) == 0, 'warning raised')
# Check there is no warning for not all-nan
np.nansum([0]*3, axis=None)
astert_(len(w) == 0, 'unwanted warning raised')
3
View Complete Implementation : test_minpack.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_NaN_handling(self):
# Test for correct handling of NaNs in input data: gh-3422
# create input with NaNs
xdata = np.array([1, np.nan, 3])
ydata = np.array([1, 2, 3])
astert_raises(ValueError, curve_fit,
lambda x, a, b: a*x + b, xdata, ydata)
astert_raises(ValueError, curve_fit,
lambda x, a, b: a*x + b, ydata, xdata)
astert_raises(ValueError, curve_fit, lambda x, a, b: a*x + b,
xdata, ydata, **{"check_finite": True})
3
View Complete Implementation : test_nat.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
@pytest.mark.parametrize('klast', [Timestamp, Timedelta, Period])
def test_equality(klast):
# nat
if klast is not Period:
klast('').value == iNaT
klast('nat').value == iNaT
klast('NAT').value == iNaT
klast(None).value == iNaT
klast(np.nan).value == iNaT
astert isna(klast('nat'))
3
View Complete Implementation : test_indexing.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_take_fill_value(self):
orig = pd.Series([1, np.nan, 0, 3, 0],
index=list('ABCDE'))
sparse = orig.to_sparse(fill_value=0)
tm.astert_sp_series_equal(sparse.take([0]),
orig.take([0]).to_sparse(fill_value=0))
exp = orig.take([0, 1, 3]).to_sparse(fill_value=0)
tm.astert_sp_series_equal(sparse.take([0, 1, 3]), exp)
exp = orig.take([-1, -2]).to_sparse(fill_value=0)
tm.astert_sp_series_equal(sparse.take([-1, -2]), exp)