class_docstr: Data structure for labeled, sparse floating point data
  
  Parameters
  ----------
  data : {array-like, Series, SparseSeries, dict}
      .. versionchanged :: 0.23.0
         If data is a dict, argument order is maintained for Python 3.6
         and later.
  
  kind : {'block', 'integer'}
  fill_value : float
      Code for missing value. Defaults depends on dtype.
      0 for int dtype, False for bool dtype, and NaN for other dtypes
  sparse_index : {BlockIndex, IntIndex}, optional
      Only if you have one. Mainly used internally
  
  Notes
  -----
  SparseSeries objects are immutable via the typical Python means. If you
  must change values, convert to dense, make your changes, then convert back
  to sparse
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code: def __init__(
      self,
      data=None,
      index=None,
      sparse_index=None,
      kind="block",
      fill_value=None,
      name=None,
      dtype=None,
      copy=False,
      fastpath=False,
  ):
      # TODO: Most of this should be refactored and shared with Series
      # 1. BlockManager -> array
      # 2. Series.index, Series.name, index, name reconciliation
      # 3. Implicit reindexing
      # 4. Implicit broadcasting
      # 5. Dict construction
      if data is None:
          data = []
      elif isinstance(data, SingleBlockManager):
          index = data.index
          data = data.blocks[0].values
      elif isinstance(data, (ABCSeries, ABCSparseSeries)):
          index = data.index if index is None else index
          dtype = data.dtype if dtype is None else dtype
          name = data.name if name is None else name
  
          if index is not None:
              data = data.reindex(index)
  
      elif isinstance(data, compat.Mapping):
          data, index = Series()._init_dict(data, index=index)
  
      elif is_scalar(data) and index is not None:
          data = np.full(len(index), fill_value=data)
  
      super(SparseSeries, self).__init__(
          SparseArray(
              data,
              sparse_index=sparse_index,
              kind=kind,
              dtype=dtype,
              fill_value=fill_value,
              copy=copy,
          ),
          index=index,
          name=name,
          copy=False,
          fastpath=fastpath,
      )
  
imports: import warnings
  import numpy
  import pandas._libs.index
  import pandas._libs.sparse
  from pandas._libs.sparse import BlockIndex, IntIndex
  import pandas.compat
  from pandas.compat.numpy import function
  from pandas.util._decorators import Appender, Substitution
  from pandas.core.dtypes.common import is_integer, is_scalar
  from pandas.core.dtypes.generic import ABCSeries, ABCSparseSeries
  from pandas.core.dtypes.missing import isna, notna
  from pandas.core import generic
  from pandas.core.arrays import SparseArray
  from pandas.core.arrays.sparse import SparseAccessor
  from pandas.core.index import Index
  from pandas.core.internals import SingleBlockManager
  import pandas.core.ops
  from pandas.core.series import Series
  from pandas.core.sparse.scipy_sparse import _coo_to_sparse_series, _sparse_series_to_coo
label_desc: Use image node_id 1 to create a new SparseSeries object from inherited base classes: Series with example: obj = SparseSeries(data, index, sparse_index, kind, fill_value, name, dtype, copy, fastpath)