本文整理汇总了Python中pandas.core.sparse.api.SparseDataFrame.as_matrix方法的典型用法代码示例。如果您正苦于以下问题：Python SparseDataFrame.as_matrix方法的具体用法？Python SparseDataFrame.as_matrix怎么用？Python SparseDataFrame.as_matrix使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pandas.core.sparse.api.SparseDataFrame的用法示例。
在下文中一共展示了SparseDataFrame.as_matrix方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_as_matrix
# 需要导入模块: from pandas.core.sparse.api import SparseDataFrame [as 别名]
# 或者: from pandas.core.sparse.api.SparseDataFrame import as_matrix [as 别名]
def test_as_matrix(self):
empty = self.empty.as_matrix()
self.assertEqual(empty.shape, (0, 0))
no_cols = SparseDataFrame(index=np.arange(10))
mat = no_cols.as_matrix()
self.assertEqual(mat.shape, (10, 0))
no_index = SparseDataFrame(columns=np.arange(10))
mat = no_index.as_matrix()
self.assertEqual(mat.shape, (0, 10))示例2: test_as_matrix
# 需要导入模块: from pandas.core.sparse.api import SparseDataFrame [as 别名]
# 或者: from pandas.core.sparse.api.SparseDataFrame import as_matrix [as 别名]
def test_as_matrix(self):
empty = self.empty.as_matrix()
assert empty.shape == (0, 0)
no_cols = SparseDataFrame(index=np.arange(10))
mat = no_cols.as_matrix()
assert mat.shape == (10, 0)
no_index = SparseDataFrame(columns=np.arange(10))
mat = no_index.as_matrix()
assert mat.shape == (0, 10)示例3: TestSparseDataFrame
# 需要导入模块: from pandas.core.sparse.api import SparseDataFrame [as 别名]
# 或者: from pandas.core.sparse.api.SparseDataFrame import as_matrix [as 别名]
class TestSparseDataFrame(tm.TestCase, SharedWithSparse):
klass = SparseDataFrame
def setUp(self):
self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
'C': np.arange(10, dtype=np.float64),
'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
self.dates = bdate_range('1/1/2011', periods=10)
self.orig = pd.DataFrame(self.data, index=self.dates)
self.iorig = pd.DataFrame(self.data, index=self.dates)
self.frame = SparseDataFrame(self.data, index=self.dates)
self.iframe = SparseDataFrame(self.data, index=self.dates,
default_kind='integer')
values = self.frame.values.copy()
values[np.isnan(values)] = 0
self.zorig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'],
index=self.dates)
self.zframe = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
default_fill_value=0, index=self.dates)
values = self.frame.values.copy()
values[np.isnan(values)] = 2
self.fill_orig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'],
index=self.dates)
self.fill_frame = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
default_fill_value=2,
index=self.dates)
self.empty = SparseDataFrame()
def test_fill_value_when_combine_const(self):
# GH12723
dat = np.array([0, 1, np.nan, 3, 4, 5], dtype='float')
df = SparseDataFrame({'foo': dat}, index=range(6))
exp = df.fillna(0).add(2)
res = df.add(2, fill_value=0)
tm.assert_sp_frame_equal(res, exp)
def test_as_matrix(self):
empty = self.empty.as_matrix()
self.assertEqual(empty.shape, (0, 0))
no_cols = SparseDataFrame(index=np.arange(10))
mat = no_cols.as_matrix()
self.assertEqual(mat.shape, (10, 0))
no_index = SparseDataFrame(columns=np.arange(10))
mat = no_index.as_matrix()
self.assertEqual(mat.shape, (0, 10))
def test_copy(self):
cp = self.frame.copy()
assert isinstance(cp, SparseDataFrame)
tm.assert_sp_frame_equal(cp, self.frame)
# as of v0.15.0
# this is now identical (but not is_a )
self.assertTrue(cp.index.identical(self.frame.index))
def test_constructor(self):
for col, series in compat.iteritems(self.frame):
assert isinstance(series, SparseSeries)
assert isinstance(self.iframe['A'].sp_index, IntIndex)
# constructed zframe from matrix above
self.assertEqual(self.zframe['A'].fill_value, 0)
tm.assert_numpy_array_equal(pd.SparseArray([1., 2., 3., 4., 5., 6.]),
self.zframe['A'].values)
tm.assert_numpy_array_equal(np.array([0., 0., 0., 0., 1., 2.,
3., 4., 5., 6.]),
self.zframe['A'].to_dense().values)
# construct no data
sdf = SparseDataFrame(columns=np.arange(10), index=np.arange(10))
for col, series in compat.iteritems(sdf):
assert isinstance(series, SparseSeries)
# construct from nested dict
data = {}
for c, s in compat.iteritems(self.frame):
data[c] = s.to_dict()
sdf = SparseDataFrame(data)
tm.assert_sp_frame_equal(sdf, self.frame)
# TODO: test data is copied from inputs
# init dict with different index
idx = self.frame.index[:5]
cons = SparseDataFrame(
self.frame, index=idx, columns=self.frame.columns,
#.........这里部分代码省略.........