minor update

examples/00_getting_started.ipynb

@@ -161,11 +161,17 @@
    "id": "13",
    "metadata": {},
    "source": [
-    "# Back to the dashboard\n",
+    "## Back to the dashboard\n",
     "\n",
     "1. On the top left, create a new `View`.\n",
     "2. From the `Elements` section on the bottom left, you can add stored items to this view, either by double-cliking on them or by doing drag-and-drop."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {

examples/00_getting_started.py

@@ -95,7 +95,10 @@
 project.put("my_fitted_pipeline", my_pipeline)
 
 # %% [markdown]
-# # Back to the dashboard
+# ## Back to the dashboard
 #
 # 1. On the top left, create a new `View`.
 # 2. From the `Elements` section on the bottom left, you can add stored items to this view, either by double-cliking on them or by doing drag-and-drop.
+
+# %% [markdown]
+#

examples/01_basic_usage.ipynb

@@ -13,7 +13,7 @@
    "id": "1",
    "metadata": {},
    "source": [
-    "# Introduction\n",
+    "## Introduction\n",
     "\n",
     "This guide is to illustrate some of the main features that `skore` currently provides. `skore` an open-source package that aims at enable data scientist to:\n",
     "1. Store objects of different types from their Python code: python lists and dictionaries, `numpy` arrays, `pandas` dataframes, `scikit-learn` fitted pipelines, `matplotlib` / `plotly` / `altair` figures, and more.\n",
@@ -28,7 +28,7 @@
    "id": "2",
    "metadata": {},
    "source": [
-    "## Imports"
+    "Imports:"
    ]
   },
   {
@@ -60,7 +60,7 @@
    "id": "4",
    "metadata": {},
    "source": [
-    "# Initialize and use a Project\n",
+    "## Initialize and use a Project\n",
     "\n",
     "From your shell, initialize a `skore` project, here named `project.skore`, that will be in your current working directory:\n",
     "```bash\n",
@@ -113,7 +113,7 @@
    "id": "7",
    "metadata": {},
    "source": [
-    "## Storing an integer"
+    "### Storing an integer"
    ]
   },
   {
@@ -241,7 +241,7 @@
    "id": "21",
    "metadata": {},
    "source": [
-    "## Storing a string"
+    "### Storing a string"
    ]
   },
   {
@@ -368,7 +368,7 @@
    "id": "33",
    "metadata": {},
    "source": [
-    "## Storing many kinds of data"
+    "### Storing many kinds of data"
    ]
   },
   {
@@ -455,7 +455,7 @@
    "id": "42",
    "metadata": {},
    "source": [
-    "## Data visualization\n",
+    "### Data visualization\n",
     "\n",
     "Note that, in the dashboard, the interactivity of plots is supported, for example for `altair` and `plotly`."
    ]
@@ -593,7 +593,7 @@
    "id": "53",
    "metadata": {},
    "source": [
-    "## Scikit-learn models and pipelines\n",
+    "### Scikit-learn models and pipelines\n",
     "\n",
     "As `skore` is developed by :probabl., the spin-off of scikit-learn, `skore` treats scikit-learn models and pipelines as first-class citizens.\n",
     "\n",
@@ -669,7 +669,7 @@
    "metadata": {},
    "source": [
     "---\n",
-    "# Manipulating the skore UI\n",
+    "## Manipulating the skore UI\n",
     "\n",
     "The following is just some `skore` strings that we generate in order to provide more context on the obtained report."
    ]

examples/01_basic_usage.py

@@ -17,7 +17,7 @@
 # # Basic usage of `skore`
 
 # %% [markdown]
-# # Introduction
+# ## Introduction
 #
 # This guide is to illustrate some of the main features that `skore` currently provides. `skore` an open-source package that aims at enable data scientist to:
 # 1. Store objects of different types from their Python code: python lists and dictionaries, `numpy` arrays, `pandas` dataframes, `scikit-learn` fitted pipelines, `matplotlib` / `plotly` / `altair` figures, and more.
@@ -27,7 +27,7 @@
 # This notebook stores some items that have been used to generated a `skore` report available at [this link](https://sylvaincom.github.io/files/probabl/skore/01_basic_usage.html).
 
 # %% [markdown]
-# ## Imports
+# Imports:
 
 # %%
 import altair as alt
@@ -47,7 +47,7 @@
 from skore.item import MediaItem
 
 # %% [markdown]
-# # Initialize and use a Project
+# ## Initialize and use a Project
 #
 # From your shell, initialize a `skore` project, here named `project.skore`, that will be in your current working directory:
 # ```bash
@@ -83,7 +83,7 @@
 project = load("project.skore")
 
 # %% [markdown]
-# ## Storing an integer
+# ### Storing an integer
 
 # %% [markdown]
 # Now, let us store our first object, for example an integer:
@@ -127,7 +127,7 @@
 project.list_item_keys()
 
 # %% [markdown]
-# ## Storing a string
+# ### Storing a string
 
 # %% [markdown]
 # We just stored a integer, now let us store some text using strings!
@@ -182,7 +182,7 @@ def my_func(x):
 project.put("my_string_4", f"The value of `x` is {x} and the value of `y` is {y}.")
 
 # %% [markdown]
-# ## Storing many kinds of data
+# ### Storing many kinds of data
 
 # %% [markdown]
 # Python list:
@@ -216,7 +216,7 @@ def my_func(x):
 project.put("my_df", my_df)
 
 # %% [markdown]
-# ## Data visualization
+# ### Data visualization
 #
 # Note that, in the dashboard, the interactivity of plots is supported, for example for `altair` and `plotly`.
 
@@ -289,7 +289,7 @@ def my_func(x):
 project.put("my_pil_image", my_pil_image)
 
 # %% [markdown]
-# ## Scikit-learn models and pipelines
+# ### Scikit-learn models and pipelines
 #
 # As `skore` is developed by :probabl., the spin-off of scikit-learn, `skore` treats scikit-learn models and pipelines as first-class citizens.
 #
@@ -324,7 +324,7 @@ def my_func(x):
 
 # %% [markdown]
 # ---
-# # Manipulating the skore UI
+# ## Manipulating the skore UI
 #
 # The following is just some `skore` strings that we generate in order to provide more context on the obtained report.