本文整理汇总了Python中pandas.core.sparse.api.SparseDataFrame.applymap方法的典型用法代码示例。如果您正苦于以下问题：Python SparseDataFrame.applymap方法的具体用法？Python SparseDataFrame.applymap怎么用？Python SparseDataFrame.applymap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.applymap方法的1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: 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 applymap [as 别名]
#.........这里部分代码省略.........
# 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.],