We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[BUG] sktime.datatypes._panel._examples raises pandas.core.common.SettingWithCopyError
**Describe the bug**
Attempting to install [tsai](https://pypi.org/project/tsai/) as an upstream package also installs this package, but the install raises an error that traces to line 67 in "/opt/conda/lib/python3.8/site-packages/sktime/datatypes/_panel/_examples.py"
```
    X.iloc[0][0] = pd.Series([1, 2, 3])
```

**To Reproduce**
Importing any code that executes the code starting at line 67 of /sktime/datatypes/_panel/_examples.py can raise a Pandas error, depending on Pandas version that may be installed

**Expected behavior**
No error should be raised on install or import of sktime as a dependency. 

**Additional context**
<!--
Add any other context about the problem here.
-->

**Versions**
System:
    python: 3.9.1 (default, Sep 16 2021, 11:42:30)  [Clang 12.0.5 (clang-1205.0.22.11)]
executable: /.../.pyenv/versions/3.9.1/bin/python
   machine: macOS-11.6-x86_64-i386-64bit

Python dependencies:
          pip: 21.2.4
   setuptools: 49.2.1
      sklearn: 1.0
       sktime: 0.8.0
  statsmodels: 0.12.2
        numpy: 1.20.3
        scipy: 1.7.1
       Cython: None
       pandas: 1.3.3
   matplotlib: 3.4.3
       joblib: 1.0.1
        numba: 0.53.1
     pmdarima: None
      tsfresh: 0.18.0

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `sktime/datatypes/_panel/_examples.py`
Content:
```
1 # -*- coding: utf-8 -*-
2 """Example generation for testing.
3 
4 Exports dict of examples, useful for testing as fixtures.
5 
6 example_dict: dict indexed by triple
7   1st element = mtype - str
8   2nd element = considered as this scitype - str
9   3rd element = int - index of example
10 elements are data objects, considered examples for the mtype
11     all examples with same index are considered "same" on scitype content
12     if None, indicates that representation is not possible
13 
14 example_lossy: dict of bool indexed by pairs of str
15   1st element = mtype - str
16   2nd element = considered as this scitype - str
17   3rd element = int - index of example
18 elements are bool, indicate whether representation has information removed
19     all examples with same index are considered "same" on scitype content
20 
21 overall, conversions from non-lossy representations to any other ones
22     should yield the element exactly, identidally (given same index)
23 """
24 
25 import pandas as pd
26 import numpy as np
27 
28 example_dict = dict()
29 example_dict_lossy = dict()
30 
31 ###
32 
33 
34 X = np.array(
35     [[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 55, 6]], [[1, 2, 3], [42, 5, 6]]],
36     dtype=np.int64,
37 )
38 
39 example_dict[("numpy3D", "Panel", 0)] = X
40 example_dict_lossy[("numpy3D", "Panel", 0)] = False
41 
42 cols = [f"var_{i}" for i in range(2)]
43 Xlist = [
44     pd.DataFrame([[1, 4], [2, 5], [3, 6]], columns=cols),
45     pd.DataFrame([[1, 4], [2, 55], [3, 6]], columns=cols),
46     pd.DataFrame([[1, 42], [2, 5], [3, 6]], columns=cols),
47 ]
48 
49 example_dict[("df-list", "Panel", 0)] = Xlist
50 example_dict_lossy[("df-list", "Panel", 0)] = False
51 
52 cols = ["instances", "timepoints"] + [f"var_{i}" for i in range(2)]
53 
54 Xlist = [
55     pd.DataFrame([[0, 0, 1, 4], [0, 1, 2, 5], [0, 2, 3, 6]], columns=cols),
56     pd.DataFrame([[1, 0, 1, 4], [1, 1, 2, 55], [1, 2, 3, 6]], columns=cols),
57     pd.DataFrame([[2, 0, 1, 42], [2, 1, 2, 5], [2, 2, 3, 6]], columns=cols),
58 ]
59 X = pd.concat(Xlist)
60 X = X.set_index(["instances", "timepoints"])
61 
62 example_dict[("pd-multiindex", "Panel", 0)] = X
63 example_dict_lossy[("pd-multiindex", "Panel", 0)] = False
64 
65 cols = [f"var_{i}" for i in range(2)]
66 X = pd.DataFrame(columns=cols, index=[0, 1, 2])
67 X.iloc[0][0] = pd.Series([1, 2, 3])
68 X.iloc[0][1] = pd.Series([4, 5, 6])
69 X.iloc[1][0] = pd.Series([1, 2, 3])
70 X.iloc[1][1] = pd.Series([4, 55, 6])
71 X.iloc[2][0] = pd.Series([1, 2, 3])
72 X.iloc[2][1] = pd.Series([42, 5, 6])
73 
74 example_dict[("nested_univ", "Panel", 0)] = X
75 example_dict_lossy[("nested_univ", "Panel", 0)] = False
76
```
--- 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/sktime/datatypes/_panel/_examples.py b/sktime/datatypes/_panel/_examples.py
--- a/sktime/datatypes/_panel/_examples.py
+++ b/sktime/datatypes/_panel/_examples.py
@@ -64,12 +64,13 @@
 
 cols = [f"var_{i}" for i in range(2)]
 X = pd.DataFrame(columns=cols, index=[0, 1, 2])
-X.iloc[0][0] = pd.Series([1, 2, 3])
-X.iloc[0][1] = pd.Series([4, 5, 6])
-X.iloc[1][0] = pd.Series([1, 2, 3])
-X.iloc[1][1] = pd.Series([4, 55, 6])
-X.iloc[2][0] = pd.Series([1, 2, 3])
-X.iloc[2][1] = pd.Series([42, 5, 6])
+X["var_0"] = pd.Series(
+    [pd.Series([1, 2, 3]), pd.Series([1, 2, 3]), pd.Series([1, 2, 3])]
+)
+
+X["var_1"] = pd.Series(
+    [pd.Series([4, 5, 6]), pd.Series([4, 55, 6]), pd.Series([42, 5, 6])]
+)
 
 example_dict[("nested_univ", "Panel", 0)] = X
 example_dict_lossy[("nested_univ", "Panel", 0)] = False

{"golden_diff": "diff --git a/sktime/datatypes/_panel/_examples.py b/sktime/datatypes/_panel/_examples.py\n--- a/sktime/datatypes/_panel/_examples.py\n+++ b/sktime/datatypes/_panel/_examples.py\n@@ -64,12 +64,13 @@\n \n cols = [f\"var_{i}\" for i in range(2)]\n X = pd.DataFrame(columns=cols, index=[0, 1, 2])\n-X.iloc[0][0] = pd.Series([1, 2, 3])\n-X.iloc[0][1] = pd.Series([4, 5, 6])\n-X.iloc[1][0] = pd.Series([1, 2, 3])\n-X.iloc[1][1] = pd.Series([4, 55, 6])\n-X.iloc[2][0] = pd.Series([1, 2, 3])\n-X.iloc[2][1] = pd.Series([42, 5, 6])\n+X[\"var_0\"] = pd.Series(\n+    [pd.Series([1, 2, 3]), pd.Series([1, 2, 3]), pd.Series([1, 2, 3])]\n+)\n+\n+X[\"var_1\"] = pd.Series(\n+    [pd.Series([4, 5, 6]), pd.Series([4, 55, 6]), pd.Series([42, 5, 6])]\n+)\n \n example_dict[(\"nested_univ\", \"Panel\", 0)] = X\n example_dict_lossy[(\"nested_univ\", \"Panel\", 0)] = False\n", "issue": "[BUG] sktime.datatypes._panel._examples raises pandas.core.common.SettingWithCopyError\n**Describe the bug**\r\nAttempting to install [tsai](https://pypi.org/project/tsai/) as an upstream package also installs this package, but the install raises an error that traces to line 67 in \"/opt/conda/lib/python3.8/site-packages/sktime/datatypes/_panel/_examples.py\"\r\n```\r\n    X.iloc[0][0] = pd.Series([1, 2, 3])\r\n```\r\n\r\n**To Reproduce**\r\nImporting any code that executes the code starting at line 67 of /sktime/datatypes/_panel/_examples.py can raise a Pandas error, depending on Pandas version that may be installed\r\n\r\n**Expected behavior**\r\nNo error should be raised on install or import of sktime as a dependency. \r\n\r\n**Additional context**\r\n<!--\r\nAdd any other context about the problem here.\r\n-->\r\n\r\n**Versions**\r\nSystem:\r\n    python: 3.9.1 (default, Sep 16 2021, 11:42:30)  [Clang 12.0.5 (clang-1205.0.22.11)]\r\nexecutable: /.../.pyenv/versions/3.9.1/bin/python\r\n   machine: macOS-11.6-x86_64-i386-64bit\r\n\r\nPython dependencies:\r\n          pip: 21.2.4\r\n   setuptools: 49.2.1\r\n      sklearn: 1.0\r\n       sktime: 0.8.0\r\n  statsmodels: 0.12.2\r\n        numpy: 1.20.3\r\n        scipy: 1.7.1\r\n       Cython: None\r\n       pandas: 1.3.3\r\n   matplotlib: 3.4.3\r\n       joblib: 1.0.1\r\n        numba: 0.53.1\r\n     pmdarima: None\r\n      tsfresh: 0.18.0\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\n\"\"\"Example generation for testing.\n\nExports dict of examples, useful for testing as fixtures.\n\nexample_dict: dict indexed by triple\n  1st element = mtype - str\n  2nd element = considered as this scitype - str\n  3rd element = int - index of example\nelements are data objects, considered examples for the mtype\n    all examples with same index are considered \"same\" on scitype content\n    if None, indicates that representation is not possible\n\nexample_lossy: dict of bool indexed by pairs of str\n  1st element = mtype - str\n  2nd element = considered as this scitype - str\n  3rd element = int - index of example\nelements are bool, indicate whether representation has information removed\n    all examples with same index are considered \"same\" on scitype content\n\noverall, conversions from non-lossy representations to any other ones\n    should yield the element exactly, identidally (given same index)\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\n\nexample_dict = dict()\nexample_dict_lossy = dict()\n\n###\n\n\nX = np.array(\n    [[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 55, 6]], [[1, 2, 3], [42, 5, 6]]],\n    dtype=np.int64,\n)\n\nexample_dict[(\"numpy3D\", \"Panel\", 0)] = X\nexample_dict_lossy[(\"numpy3D\", \"Panel\", 0)] = False\n\ncols = [f\"var_{i}\" for i in range(2)]\nXlist = [\n    pd.DataFrame([[1, 4], [2, 5], [3, 6]], columns=cols),\n    pd.DataFrame([[1, 4], [2, 55], [3, 6]], columns=cols),\n    pd.DataFrame([[1, 42], [2, 5], [3, 6]], columns=cols),\n]\n\nexample_dict[(\"df-list\", \"Panel\", 0)] = Xlist\nexample_dict_lossy[(\"df-list\", \"Panel\", 0)] = False\n\ncols = [\"instances\", \"timepoints\"] + [f\"var_{i}\" for i in range(2)]\n\nXlist = [\n    pd.DataFrame([[0, 0, 1, 4], [0, 1, 2, 5], [0, 2, 3, 6]], columns=cols),\n    pd.DataFrame([[1, 0, 1, 4], [1, 1, 2, 55], [1, 2, 3, 6]], columns=cols),\n    pd.DataFrame([[2, 0, 1, 42], [2, 1, 2, 5], [2, 2, 3, 6]], columns=cols),\n]\nX = pd.concat(Xlist)\nX = X.set_index([\"instances\", \"timepoints\"])\n\nexample_dict[(\"pd-multiindex\", \"Panel\", 0)] = X\nexample_dict_lossy[(\"pd-multiindex\", \"Panel\", 0)] = False\n\ncols = [f\"var_{i}\" for i in range(2)]\nX = pd.DataFrame(columns=cols, index=[0, 1, 2])\nX.iloc[0][0] = pd.Series([1, 2, 3])\nX.iloc[0][1] = pd.Series([4, 5, 6])\nX.iloc[1][0] = pd.Series([1, 2, 3])\nX.iloc[1][1] = pd.Series([4, 55, 6])\nX.iloc[2][0] = pd.Series([1, 2, 3])\nX.iloc[2][1] = pd.Series([42, 5, 6])\n\nexample_dict[(\"nested_univ\", \"Panel\", 0)] = X\nexample_dict_lossy[(\"nested_univ\", \"Panel\", 0)] = False\n", "path": "sktime/datatypes/_panel/_examples.py"}], "after_files": [{"content": "# -*- coding: utf-8 -*-\n\"\"\"Example generation for testing.\n\nExports dict of examples, useful for testing as fixtures.\n\nexample_dict: dict indexed by triple\n  1st element = mtype - str\n  2nd element = considered as this scitype - str\n  3rd element = int - index of example\nelements are data objects, considered examples for the mtype\n    all examples with same index are considered \"same\" on scitype content\n    if None, indicates that representation is not possible\n\nexample_lossy: dict of bool indexed by pairs of str\n  1st element = mtype - str\n  2nd element = considered as this scitype - str\n  3rd element = int - index of example\nelements are bool, indicate whether representation has information removed\n    all examples with same index are considered \"same\" on scitype content\n\noverall, conversions from non-lossy representations to any other ones\n    should yield the element exactly, identidally (given same index)\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\n\nexample_dict = dict()\nexample_dict_lossy = dict()\n\n###\n\n\nX = np.array(\n    [[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 55, 6]], [[1, 2, 3], [42, 5, 6]]],\n    dtype=np.int64,\n)\n\nexample_dict[(\"numpy3D\", \"Panel\", 0)] = X\nexample_dict_lossy[(\"numpy3D\", \"Panel\", 0)] = False\n\ncols = [f\"var_{i}\" for i in range(2)]\nXlist = [\n    pd.DataFrame([[1, 4], [2, 5], [3, 6]], columns=cols),\n    pd.DataFrame([[1, 4], [2, 55], [3, 6]], columns=cols),\n    pd.DataFrame([[1, 42], [2, 5], [3, 6]], columns=cols),\n]\n\nexample_dict[(\"df-list\", \"Panel\", 0)] = Xlist\nexample_dict_lossy[(\"df-list\", \"Panel\", 0)] = False\n\ncols = [\"instances\", \"timepoints\"] + [f\"var_{i}\" for i in range(2)]\n\nXlist = [\n    pd.DataFrame([[0, 0, 1, 4], [0, 1, 2, 5], [0, 2, 3, 6]], columns=cols),\n    pd.DataFrame([[1, 0, 1, 4], [1, 1, 2, 55], [1, 2, 3, 6]], columns=cols),\n    pd.DataFrame([[2, 0, 1, 42], [2, 1, 2, 5], [2, 2, 3, 6]], columns=cols),\n]\nX = pd.concat(Xlist)\nX = X.set_index([\"instances\", \"timepoints\"])\n\nexample_dict[(\"pd-multiindex\", \"Panel\", 0)] = X\nexample_dict_lossy[(\"pd-multiindex\", \"Panel\", 0)] = False\n\ncols = [f\"var_{i}\" for i in range(2)]\nX = pd.DataFrame(columns=cols, index=[0, 1, 2])\nX[\"var_0\"] = pd.Series(\n    [pd.Series([1, 2, 3]), pd.Series([1, 2, 3]), pd.Series([1, 2, 3])]\n)\n\nX[\"var_1\"] = pd.Series(\n    [pd.Series([4, 5, 6]), pd.Series([4, 55, 6]), pd.Series([42, 5, 6])]\n)\n\nexample_dict[(\"nested_univ\", \"Panel\", 0)] = X\nexample_dict_lossy[(\"nested_univ\", \"Panel\", 0)] = False\n", "path": "sktime/datatypes/_panel/_examples.py"}]}