本文整理匯總了Python中pandas.core.sparse.api.SparseDataFrame.applymap方法的典型用法代碼示例。如果您正苦於以下問題：Python SparseDataFrame.applymap方法的具體用法？Python SparseDataFrame.applymap怎麽用？Python SparseDataFrame.applymap使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pandas.core.sparse.api.SparseDataFrame的用法示例。
在下文中一共展示了SparseDataFrame.applymap方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: TestSparseDataFrame
# 需要導入模塊: from pandas.core.sparse.api import SparseDataFrame [as 別名]
# 或者: from pandas.core.sparse.api.SparseDataFrame import applymap [as 別名]
#.........這裏部分代碼省略.........
# agg / broadcast
broadcasted = self.frame.apply(np.sum, broadcast=True)
assert isinstance(broadcasted, SparseDataFrame)
exp = self.frame.to_dense().apply(np.sum, broadcast=True)
tm.assert_frame_equal(broadcasted.to_dense(), exp)
assert self.empty.apply(np.sqrt) is self.empty
from pandas.core import nanops
applied = self.frame.apply(np.sum)
tm.assert_series_equal(applied,
self.frame.to_dense().apply(nanops.nansum))
def test_apply_nonuq(self):
orig = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=['a', 'a', 'c'])
sparse = orig.to_sparse()
res = sparse.apply(lambda s: s[0], axis=1)
exp = orig.apply(lambda s: s[0], axis=1)
# dtype must be kept
assert res.dtype == np.int64
# ToDo: apply must return subclassed dtype
assert isinstance(res, pd.Series)
tm.assert_series_equal(res.to_dense(), exp)
# df.T breaks
sparse = orig.T.to_sparse()
res = sparse.apply(lambda s: s[0], axis=0) # noqa
exp = orig.T.apply(lambda s: s[0], axis=0)
# TODO: no non-unique columns supported in sparse yet
# tm.assert_series_equal(res.to_dense(), exp)
def test_applymap(self):
# just test that it works
result = self.frame.applymap(lambda x: x * 2)
assert isinstance(result, SparseDataFrame)
def test_astype(self):
sparse = pd.SparseDataFrame({'A': SparseArray([1, 2, 3, 4],
dtype=np.int64),
'B': SparseArray([4, 5, 6, 7],
dtype=np.int64)})
assert sparse['A'].dtype == np.int64
assert sparse['B'].dtype == np.int64
res = sparse.astype(np.float64)
exp = pd.SparseDataFrame({'A': SparseArray([1., 2., 3., 4.],
fill_value=0.),
'B': SparseArray([4., 5., 6., 7.],
fill_value=0.)},
default_fill_value=np.nan)
tm.assert_sp_frame_equal(res, exp)
assert res['A'].dtype == np.float64
assert res['B'].dtype == np.float64
sparse = pd.SparseDataFrame({'A': SparseArray([0, 2, 0, 4],
dtype=np.int64),
'B': SparseArray([0, 5, 0, 7],
dtype=np.int64)},
default_fill_value=0)
assert sparse['A'].dtype == np.int64
assert sparse['B'].dtype == np.int64
res = sparse.astype(np.float64)
exp = pd.SparseDataFrame({'A': SparseArray([0., 2., 0., 4.],