We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
DEPR: deprecate SparseList


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `pandas/sparse/list.py`
Content:
```
1 import numpy as np
2 from pandas.core.base import PandasObject
3 from pandas.formats.printing import pprint_thing
4 
5 from pandas.types.common import is_scalar
6 from pandas.sparse.array import SparseArray
7 import pandas._sparse as splib
8 
9 
10 class SparseList(PandasObject):
11 
12     """
13     Data structure for accumulating data to be converted into a
14     SparseArray. Has similar API to the standard Python list
15 
16     Parameters
17     ----------
18     data : scalar or array-like
19     fill_value : scalar, default NaN
20     """
21 
22     def __init__(self, data=None, fill_value=np.nan):
23         self.fill_value = fill_value
24         self._chunks = []
25 
26         if data is not None:
27             self.append(data)
28 
29     def __unicode__(self):
30         contents = '\n'.join(repr(c) for c in self._chunks)
31         return '%s\n%s' % (object.__repr__(self), pprint_thing(contents))
32 
33     def __len__(self):
34         return sum(len(c) for c in self._chunks)
35 
36     def __getitem__(self, i):
37         if i < 0:
38             if i + len(self) < 0:  # pragma: no cover
39                 raise ValueError('%d out of range' % i)
40             i += len(self)
41 
42         passed = 0
43         j = 0
44         while i >= passed + len(self._chunks[j]):
45             passed += len(self._chunks[j])
46             j += 1
47         return self._chunks[j][i - passed]
48 
49     def __setitem__(self, i, value):
50         raise NotImplementedError
51 
52     @property
53     def nchunks(self):
54         return len(self._chunks)
55 
56     @property
57     def is_consolidated(self):
58         return self.nchunks == 1
59 
60     def consolidate(self, inplace=True):
61         """
62         Internally consolidate chunks of data
63 
64         Parameters
65         ----------
66         inplace : boolean, default True
67             Modify the calling object instead of constructing a new one
68 
69         Returns
70         -------
71         splist : SparseList
72             If inplace=False, new object, otherwise reference to existing
73             object
74         """
75         if not inplace:
76             result = self.copy()
77         else:
78             result = self
79 
80         if result.is_consolidated:
81             return result
82 
83         result._consolidate_inplace()
84         return result
85 
86     def _consolidate_inplace(self):
87         new_values = np.concatenate([c.sp_values for c in self._chunks])
88         new_index = _concat_sparse_indexes([c.sp_index for c in self._chunks])
89         new_arr = SparseArray(new_values, sparse_index=new_index,
90                               fill_value=self.fill_value)
91         self._chunks = [new_arr]
92 
93     def copy(self):
94         """
95         Return copy of the list
96 
97         Returns
98         -------
99         new_list : SparseList
100         """
101         new_splist = SparseList(fill_value=self.fill_value)
102         new_splist._chunks = list(self._chunks)
103         return new_splist
104 
105     def to_array(self):
106         """
107         Return SparseArray from data stored in the SparseList
108 
109         Returns
110         -------
111         sparr : SparseArray
112         """
113         self.consolidate(inplace=True)
114         return self._chunks[0]
115 
116     def append(self, value):
117         """
118         Append element or array-like chunk of data to the SparseList
119 
120         Parameters
121         ----------
122         value: scalar or array-like
123         """
124         if is_scalar(value):
125             value = [value]
126 
127         sparr = SparseArray(value, fill_value=self.fill_value)
128         self._chunks.append(sparr)
129         self._consolidated = False
130 
131 
132 def _concat_sparse_indexes(indexes):
133     all_indices = []
134     total_length = 0
135 
136     for index in indexes:
137         # increment by offset
138         inds = index.to_int_index().indices + total_length
139 
140         all_indices.append(inds)
141         total_length += index.length
142 
143     return splib.IntIndex(total_length, np.concatenate(all_indices))
144
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/pandas/sparse/list.py b/pandas/sparse/list.py
--- a/pandas/sparse/list.py
+++ b/pandas/sparse/list.py
@@ -1,3 +1,4 @@
+import warnings
 import numpy as np
 from pandas.core.base import PandasObject
 from pandas.formats.printing import pprint_thing
@@ -20,6 +21,11 @@
     """
 
     def __init__(self, data=None, fill_value=np.nan):
+
+        # see gh-13784
+        warnings.warn("SparseList is deprecated and will be removed "
+                      "in a future version", FutureWarning, stacklevel=2)
+
         self.fill_value = fill_value
         self._chunks = []

{"golden_diff": "diff --git a/pandas/sparse/list.py b/pandas/sparse/list.py\n--- a/pandas/sparse/list.py\n+++ b/pandas/sparse/list.py\n@@ -1,3 +1,4 @@\n+import warnings\n import numpy as np\n from pandas.core.base import PandasObject\n from pandas.formats.printing import pprint_thing\n@@ -20,6 +21,11 @@\n     \"\"\"\n \n     def __init__(self, data=None, fill_value=np.nan):\n+\n+        # see gh-13784\n+        warnings.warn(\"SparseList is deprecated and will be removed \"\n+                      \"in a future version\", FutureWarning, stacklevel=2)\n+\n         self.fill_value = fill_value\n         self._chunks = []\n", "issue": "DEPR: deprecate SparseList\n\n", "before_files": [{"content": "import numpy as np\nfrom pandas.core.base import PandasObject\nfrom pandas.formats.printing import pprint_thing\n\nfrom pandas.types.common import is_scalar\nfrom pandas.sparse.array import SparseArray\nimport pandas._sparse as splib\n\n\nclass SparseList(PandasObject):\n\n    \"\"\"\n    Data structure for accumulating data to be converted into a\n    SparseArray. Has similar API to the standard Python list\n\n    Parameters\n    ----------\n    data : scalar or array-like\n    fill_value : scalar, default NaN\n    \"\"\"\n\n    def __init__(self, data=None, fill_value=np.nan):\n        self.fill_value = fill_value\n        self._chunks = []\n\n        if data is not None:\n            self.append(data)\n\n    def __unicode__(self):\n        contents = '\\n'.join(repr(c) for c in self._chunks)\n        return '%s\\n%s' % (object.__repr__(self), pprint_thing(contents))\n\n    def __len__(self):\n        return sum(len(c) for c in self._chunks)\n\n    def __getitem__(self, i):\n        if i < 0:\n            if i + len(self) < 0:  # pragma: no cover\n                raise ValueError('%d out of range' % i)\n            i += len(self)\n\n        passed = 0\n        j = 0\n        while i >= passed + len(self._chunks[j]):\n            passed += len(self._chunks[j])\n            j += 1\n        return self._chunks[j][i - passed]\n\n    def __setitem__(self, i, value):\n        raise NotImplementedError\n\n    @property\n    def nchunks(self):\n        return len(self._chunks)\n\n    @property\n    def is_consolidated(self):\n        return self.nchunks == 1\n\n    def consolidate(self, inplace=True):\n        \"\"\"\n        Internally consolidate chunks of data\n\n        Parameters\n        ----------\n        inplace : boolean, default True\n            Modify the calling object instead of constructing a new one\n\n        Returns\n        -------\n        splist : SparseList\n            If inplace=False, new object, otherwise reference to existing\n            object\n        \"\"\"\n        if not inplace:\n            result = self.copy()\n        else:\n            result = self\n\n        if result.is_consolidated:\n            return result\n\n        result._consolidate_inplace()\n        return result\n\n    def _consolidate_inplace(self):\n        new_values = np.concatenate([c.sp_values for c in self._chunks])\n        new_index = _concat_sparse_indexes([c.sp_index for c in self._chunks])\n        new_arr = SparseArray(new_values, sparse_index=new_index,\n                              fill_value=self.fill_value)\n        self._chunks = [new_arr]\n\n    def copy(self):\n        \"\"\"\n        Return copy of the list\n\n        Returns\n        -------\n        new_list : SparseList\n        \"\"\"\n        new_splist = SparseList(fill_value=self.fill_value)\n        new_splist._chunks = list(self._chunks)\n        return new_splist\n\n    def to_array(self):\n        \"\"\"\n        Return SparseArray from data stored in the SparseList\n\n        Returns\n        -------\n        sparr : SparseArray\n        \"\"\"\n        self.consolidate(inplace=True)\n        return self._chunks[0]\n\n    def append(self, value):\n        \"\"\"\n        Append element or array-like chunk of data to the SparseList\n\n        Parameters\n        ----------\n        value: scalar or array-like\n        \"\"\"\n        if is_scalar(value):\n            value = [value]\n\n        sparr = SparseArray(value, fill_value=self.fill_value)\n        self._chunks.append(sparr)\n        self._consolidated = False\n\n\ndef _concat_sparse_indexes(indexes):\n    all_indices = []\n    total_length = 0\n\n    for index in indexes:\n        # increment by offset\n        inds = index.to_int_index().indices + total_length\n\n        all_indices.append(inds)\n        total_length += index.length\n\n    return splib.IntIndex(total_length, np.concatenate(all_indices))\n", "path": "pandas/sparse/list.py"}], "after_files": [{"content": "import warnings\nimport numpy as np\nfrom pandas.core.base import PandasObject\nfrom pandas.formats.printing import pprint_thing\n\nfrom pandas.types.common import is_scalar\nfrom pandas.sparse.array import SparseArray\nimport pandas._sparse as splib\n\n\nclass SparseList(PandasObject):\n\n    \"\"\"\n    Data structure for accumulating data to be converted into a\n    SparseArray. Has similar API to the standard Python list\n\n    Parameters\n    ----------\n    data : scalar or array-like\n    fill_value : scalar, default NaN\n    \"\"\"\n\n    def __init__(self, data=None, fill_value=np.nan):\n\n        # see gh-13784\n        warnings.warn(\"SparseList is deprecated and will be removed \"\n                      \"in a future version\", FutureWarning, stacklevel=2)\n\n        self.fill_value = fill_value\n        self._chunks = []\n\n        if data is not None:\n            self.append(data)\n\n    def __unicode__(self):\n        contents = '\\n'.join(repr(c) for c in self._chunks)\n        return '%s\\n%s' % (object.__repr__(self), pprint_thing(contents))\n\n    def __len__(self):\n        return sum(len(c) for c in self._chunks)\n\n    def __getitem__(self, i):\n        if i < 0:\n            if i + len(self) < 0:  # pragma: no cover\n                raise ValueError('%d out of range' % i)\n            i += len(self)\n\n        passed = 0\n        j = 0\n        while i >= passed + len(self._chunks[j]):\n            passed += len(self._chunks[j])\n            j += 1\n        return self._chunks[j][i - passed]\n\n    def __setitem__(self, i, value):\n        raise NotImplementedError\n\n    @property\n    def nchunks(self):\n        return len(self._chunks)\n\n    @property\n    def is_consolidated(self):\n        return self.nchunks == 1\n\n    def consolidate(self, inplace=True):\n        \"\"\"\n        Internally consolidate chunks of data\n\n        Parameters\n        ----------\n        inplace : boolean, default True\n            Modify the calling object instead of constructing a new one\n\n        Returns\n        -------\n        splist : SparseList\n            If inplace=False, new object, otherwise reference to existing\n            object\n        \"\"\"\n        if not inplace:\n            result = self.copy()\n        else:\n            result = self\n\n        if result.is_consolidated:\n            return result\n\n        result._consolidate_inplace()\n        return result\n\n    def _consolidate_inplace(self):\n        new_values = np.concatenate([c.sp_values for c in self._chunks])\n        new_index = _concat_sparse_indexes([c.sp_index for c in self._chunks])\n        new_arr = SparseArray(new_values, sparse_index=new_index,\n                              fill_value=self.fill_value)\n        self._chunks = [new_arr]\n\n    def copy(self):\n        \"\"\"\n        Return copy of the list\n\n        Returns\n        -------\n        new_list : SparseList\n        \"\"\"\n        new_splist = SparseList(fill_value=self.fill_value)\n        new_splist._chunks = list(self._chunks)\n        return new_splist\n\n    def to_array(self):\n        \"\"\"\n        Return SparseArray from data stored in the SparseList\n\n        Returns\n        -------\n        sparr : SparseArray\n        \"\"\"\n        self.consolidate(inplace=True)\n        return self._chunks[0]\n\n    def append(self, value):\n        \"\"\"\n        Append element or array-like chunk of data to the SparseList\n\n        Parameters\n        ----------\n        value: scalar or array-like\n        \"\"\"\n        if is_scalar(value):\n            value = [value]\n\n        sparr = SparseArray(value, fill_value=self.fill_value)\n        self._chunks.append(sparr)\n        self._consolidated = False\n\n\ndef _concat_sparse_indexes(indexes):\n    all_indices = []\n    total_length = 0\n\n    for index in indexes:\n        # increment by offset\n        inds = index.to_int_index().indices + total_length\n\n        all_indices.append(inds)\n        total_length += index.length\n\n    return splib.IntIndex(total_length, np.concatenate(all_indices))\n", "path": "pandas/sparse/list.py"}]}