Repository: sktime/sktime
Pull request: [DOC] Documented ax argument and the figure in plot_series
#### Reference Issues/PRs

#### What does this implement/fix? Explain your changes.
This resolves #5323 . I have added the `ax` argument and the figure to the docstring of the plot_series.

#### Does your contribution introduce a new dependency? If yes, which one?
No

#### What should a reviewer concentrate their feedback on?
    If the docstring meets the requirements.

#### Did you add any tests for the change?
No

#### Any other comments?
Since I am new to open source and this being my first pull request any feedback would be helpful.

#### PR checklist
- [X] The PR title starts with either [ENH], [MNT], [DOC], or [BUG]. [BUG] - bugfix, [MNT] - CI, test framework, [ENH] - adding or improving code, [DOC] - writing or improving documentation or docstrings.


Problem statement:
[DOC] `plot_series` docstring should document the `ax` argument and the figure that is plotted
The `plot_series` docstring does not but should document:

* the `ax` argument
* the figure that is plotted

A good example for this does not exist in `sktime` as far as I can find, but one can look at `plot_crossplot_std` in `skpro` for some inspiration (or docstring copy-paste).

Hints from issue discussion:
Hello, I started working on this issue . Since I am new to open source any suggestions would be helpful.

Commit message: moved description to top and made reference to the actual parameters

Patch to review:
diff --git a/examples/01a_forecasting_sklearn.ipynb b/examples/01a_forecasting_sklearn.ipynb
index 1ac338607d5..da2a0263e73 100644
--- a/examples/01a_forecasting_sklearn.ipynb
+++ b/examples/01a_forecasting_sklearn.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:06.641893Z",
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,9 +90,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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BAAAAAAAARAVhJAAAAAAAAICoIIwEAAAAAAAAEBWEkQAAAAAAAACigjASAAAAAAAAQFQQRgIAAAAAAACICsJIAAAAAAAAAFFBGAkAAAAAAAAgKv4/lA6ksBlqPXsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "y_train, y_test = train_test_split(y)\n",
     "plot_series(y_train.sort_index(), y_test.sort_index(), labels=[\"y_train\", \"y_test\"]);"
@@ -111,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:06.861616Z",
@@ -120,7 +131,18 @@
      "shell.execute_reply": "2021-04-10T16:07:07.017191Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "y_train, y_test = temporal_train_test_split(y)\n",
     "plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"]);"
@@ -144,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:07.023411Z",
@@ -198,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:07.028644Z",
@@ -225,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:07.033532Z",
@@ -234,14 +256,29 @@
      "shell.execute_reply": "2021-04-10T16:07:07.035836Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
+       "       [ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10],\n",
+       "       [ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
+       "       [ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12],\n",
+       "       [ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "feature_window[:5, :]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:07.038874Z",
@@ -250,14 +287,25 @@
      "shell.execute_reply": "2021-04-10T16:07:07.040933Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 11, 12, 13, 14])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "target_window[:5]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:07.044178Z",
@@ -266,7 +314,15 @@
      "shell.execute_reply": "2021-04-10T16:07:07.046641Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(98, 10) (98,)\n"
+     ]
+    }
+   ],
    "source": [
     "# now we can split the actual values of the time series\n",
     "x_train, y_train, x_test, y_test = split_into_train_test(y.values, 10, len(fh))\n",
@@ -275,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:07.083439Z",
@@ -284,7 +340,21 @@
      "shell.execute_reply": "2021-04-10T16:07:07.167558Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor()"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.ensemble import RandomForestRegressor\n",
     "\n",
@@ -321,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:07.171836Z",
@@ -330,7 +400,15 @@
      "shell.execute_reply": "2021-04-10T16:07:07.173506Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(36, 10) (36,)\n"
+     ]
+    }
+   ],
    "source": [
     "print(x_test.shape, y_test.shape)\n",
     "\n",
@@ -340,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:07.180067Z",
@@ -356,9 +434,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.10672171646927153"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n",
     "\n",
@@ -380,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-04-10T16:07:07.228733Z",
@@ -413,9 +502,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.15244782705707155"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n",
     "\n",
@@ -451,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -462,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -473,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -513,7 +613,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.11.4"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/sktime/utils/plotting.py b/sktime/utils/plotting.py
index f6ca75e4ba8..240caa7e985 100644
--- a/sktime/utils/plotting.py
+++ b/sktime/utils/plotting.py
@@ -30,6 +30,22 @@ def plot_series(
 ):
     """Plot one or more time series.
 
+    Description
+    -----------
+    This function allows you to plot one or more
+    time series on a single figure via `series`.
+    Used for making comparisons between different series.
+
+    The resulting figure includes the time series data plotted on a graph with
+    x-axis as time by default and can be changed via `x_label` and
+    y-axis as value of time series can be renamed via `y_label` and
+    labels explaining the meaning of each series via `labels`,
+    markers for data points via `markers`.
+    You can also specify custom colors via `colors` for each series and
+    add a title to the figure via `title`.
+    If prediction intervals are available add them using `pred_interval`,
+    they can be overlaid on the plot to visualize uncertainty.
+
     Parameters
     ----------
     series : pd.Series or iterable of pd.Series
@@ -46,11 +62,18 @@ def plot_series(
     pred_interval: pd.DataFrame, default = None
         Output of `forecaster.predict_interval()`. Contains columns for lower
         and upper boundaries of confidence interval.
+    ax : matplotlib axes, optional
+        Axes to plot on, if None, a new figure is created and returned
 
     Returns
     -------
     fig : plt.Figure
+        It manages the final visual appearance and layout.
+        Create a new figure, or activate an existing figure.
     ax : plt.Axis
+        Axes containing the plot
+        If ax was None, a new figure is created and returned
+        If ax was not None, the same ax is returned with plot added
 
     Examples
     --------
@@ -58,6 +81,7 @@ def plot_series(
     >>> from sktime.datasets import load_airline
     >>> y = load_airline()
     >>> fig, ax = plot_series(y)  # doctest: +SKIP
+
     """
     _check_soft_dependencies("matplotlib", "seaborn")
     import matplotlib.pyplot as plt


Code review comments:
diff_hunk: @@ -30,6 +30,22 @@ def plot_series(
 ):
     """Plot one or more time series.
 
+    Description
path: sktime/utils/plotting.py
text: @user1:
Thanks!

can you kindly remove the "description" header, this is not an official numpydoc header.