Add pip install magic for the clustering module

notebooks/clustering_ex_01.ipynb

@@ -45,6 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install seaborn\n",
     "import seaborn as sns\n",
     "\n",
     "_ = sns.pairplot(data)"

notebooks/clustering_ex_02.ipynb

@@ -83,6 +83,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install seaborn\n",
     "# Write your code here."
    ]
   },

notebooks/clustering_hdbscan.ipynb

@@ -269,8 +269,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install pyodide-http\n",
+    "import pyodide_http\n",
     "from sklearn.datasets import fetch_california_housing\n",
     "\n",
+    "pyodide_http.patch_all()\n",
+    "\n",
     "data, target = fetch_california_housing(return_X_y=True, as_frame=True)\n",
     "target *= 100  # rescale the target in k$"
    ]
@@ -289,6 +293,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install plotly nbformat\n",
     "import plotly.express as px\n",
     "\n",
     "\n",

notebooks/clustering_kmeans.ipynb

@@ -78,6 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install seaborn\n",
     "import seaborn as sns\n",
     "\n",
     "_ = sns.pairplot(penguins, hue=\"Sex\", height=4)"

notebooks/clustering_sol_01.ipynb

@@ -45,6 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install seaborn\n",
     "import seaborn as sns\n",
     "\n",
     "_ = sns.pairplot(data)"

notebooks/clustering_sol_02.ipynb

@@ -103,6 +103,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install seaborn\n",
     "# solution\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",

notebooks/clustering_supervised_metrics.ipynb

@@ -108,6 +108,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install skrub\n",
     "from skrub import StringEncoder\n",
     "from sklearn.cluster import KMeans\n",
     "from sklearn.pipeline import make_pipeline\n",

notebooks/clustering_transformer.ipynb

@@ -24,8 +24,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install pyodide-http\n",
+    "import pyodide_http\n",
     "from sklearn.datasets import fetch_california_housing\n",
     "\n",
+    "pyodide_http.patch_all()\n",
+    "\n",
     "data, target = fetch_california_housing(return_X_y=True, as_frame=True)\n",
     "target *= 100  # rescale the target in k$"
    ]
@@ -87,6 +91,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install plotly nbformat\n",
     "import plotly.express as px\n",
     "\n",
     "fig = px.scatter_map(\n",

python_scripts/clustering_ex_01.py

@@ -37,6 +37,7 @@
 # We can explore the data using a seaborn `pairplot`.
 
 # %%
+# %pip install seaborn
 import seaborn as sns
 
 _ = sns.pairplot(data)

python_scripts/clustering_ex_02.py

@@ -65,6 +65,7 @@
 # for the k-means cluster and the "true" labels).
 
 # %%
+# %pip install seaborn
 # Write your code here.
 
 # %% [markdown]

python_scripts/clustering_hdbscan.py

@@ -195,8 +195,12 @@ def make_wavy_blob(n_samples, shift=0.0, noise=0.2, freq=3):
 # of the California Housing Dataset.
 
 # %%
+# %pip install pyodide-http
+import pyodide_http
 from sklearn.datasets import fetch_california_housing
 
+pyodide_http.patch_all()
+
 data, target = fetch_california_housing(return_X_y=True, as_frame=True)
 target *= 100  # rescale the target in k$
 
@@ -205,6 +209,7 @@ def make_wavy_blob(n_samples, shift=0.0, noise=0.2, freq=3):
 # California.
 
 # %%
+# %pip install plotly nbformat
 import plotly.express as px
 
 

python_scripts/clustering_kmeans.py

@@ -54,6 +54,7 @@
 # `pairplot`:
 
 # %%
+# %pip install seaborn
 import seaborn as sns
 
 _ = sns.pairplot(penguins, hue="Sex", height=4)

python_scripts/clustering_sol_01.py

@@ -31,6 +31,7 @@
 # We can explore the data using a seaborn `pairplot`.
 
 # %%
+# %pip install seaborn
 import seaborn as sns
 
 _ = sns.pairplot(data)

python_scripts/clustering_sol_02.py

@@ -79,6 +79,7 @@
 # for the k-means cluster and the "true" labels).
 
 # %%
+# %pip install seaborn
 # solution
 import matplotlib.pyplot as plt
 import seaborn as sns

python_scripts/clustering_supervised_metrics.py

@@ -74,6 +74,7 @@
 # This encoder is well suited to cluster text using `KMeans`.
 
 # %%
+# %pip install skrub
 from skrub import StringEncoder
 from sklearn.cluster import KMeans
 from sklearn.pipeline import make_pipeline

python_scripts/clustering_transformer.py

@@ -20,8 +20,12 @@
 # performance.
 
 # %%
+# %pip install pyodide-http
+import pyodide_http
 from sklearn.datasets import fetch_california_housing
 
+pyodide_http.patch_all()
+
 data, target = fetch_california_housing(return_X_y=True, as_frame=True)
 target *= 100  # rescale the target in k$
 
@@ -63,6 +67,7 @@
 # coast:
 
 # %%
+# %pip install plotly nbformat
 import plotly.express as px
 
 fig = px.scatter_map(