本文整理汇总了Python中pandas.sparse.array.SparseArray.astype方法的典型用法代码示例。如果您正苦于以下问题：Python SparseArray.astype方法的具体用法？Python SparseArray.astype怎么用？Python SparseArray.astype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.sparse.array.SparseArray的用法示例。
在下文中一共展示了SparseArray.astype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: _concat_sparse
# 需要导入模块: from pandas.sparse.array import SparseArray [as 别名]
# 或者: from pandas.sparse.array.SparseArray import astype [as 别名]
def _concat_sparse(to_concat, axis=0, typs=None):
"""
provide concatenation of an sparse/dense array of arrays each of which is a
single dtype
Parameters
----------
to_concat : array of arrays
axis : axis to provide concatenation
typs : set of to_concat dtypes
Returns
-------
a single array, preserving the combined dtypes
"""
from pandas.sparse.array import SparseArray, _make_index
def convert_sparse(x, axis):
# coerce to native type
if isinstance(x, SparseArray):
x = x.get_values()
x = x.ravel()
if axis > 0:
x = np.atleast_2d(x)
return x
if typs is None:
typs = com.get_dtype_kinds(to_concat)
if len(typs) == 1:
# concat input as it is if all inputs are sparse
# and have the same fill_value
fill_values = set(c.fill_value for c in to_concat)
if len(fill_values) == 1:
sp_values = [c.sp_values for c in to_concat]
indexes = [c.sp_index.to_int_index() for c in to_concat]
indices = []
loc = 0
for idx in indexes:
indices.append(idx.indices + loc)
loc += idx.length
sp_values = np.concatenate(sp_values)
indices = np.concatenate(indices)
sp_index = _make_index(loc, indices, kind=to_concat[0].sp_index)
return SparseArray(sp_values, sparse_index=sp_index,
fill_value=to_concat[0].fill_value)
# input may be sparse / dense mixed and may have different fill_value
# input must contain sparse at least 1
sparses = [c for c in to_concat if com.is_sparse(c)]
fill_values = [c.fill_value for c in sparses]
sp_indexes = [c.sp_index for c in sparses]
# densify and regular concat
to_concat = [convert_sparse(x, axis) for x in to_concat]
result = np.concatenate(to_concat, axis=axis)
if not len(typs - set(['sparse', 'f', 'i'])):
# sparsify if inputs are sparse and dense numerics
# first sparse input's fill_value and SparseIndex is used
result = SparseArray(result.ravel(), fill_value=fill_values[0],
kind=sp_indexes[0])
else:
# coerce to object if needed
result = result.astype('object')
return result