Update Python versions

.circleci/config.yml

@@ -7,7 +7,7 @@ commands:
   test_core:
     parameters:
       py:
-        default: "36"
+        default: "310"
         type: string
     steps:
       - checkout
@@ -32,7 +32,7 @@ commands:
   test_optional:
     parameters:
       py:
-        default: "36"
+        default: "310"
         type: string
     steps:
       - checkout
@@ -95,7 +95,7 @@ commands:
   test_orca:
     parameters:
       py:
-        default: "36"
+        default: "310"
         type: string
     steps:
       - checkout
@@ -153,20 +153,6 @@ jobs:
             black --check . --exclude venv
 
   # Core
-  python_36_core:
-    docker:
-      - image: cimg/python:3.6-browsers
-    steps:
-      - test_core:
-          py: "36"
-
-  python_37_core:
-    docker:
-      - image: cimg/python:3.7-browsers
-    steps:
-      - test_core:
-          py: "37"
-
   python_38_core:
     docker:
       - image: cimg/python:3.8-browsers
@@ -181,20 +167,28 @@ jobs:
       - test_core:
           py: "39"
 
-  # Optional
-  python_36_optional:
+  python_310_core:
     docker:
-      - image: cimg/python:3.6-browsers
+      - image: cimg/python:3.10-browsers
     steps:
-      - test_optional:
-          py: "36"
+      - test_core:
+          py: "310"
 
-  python_37_optional:
+  python_311_core:
     docker:
-      - image: cimg/python:3.7-browsers
+      - image: cimg/python:3.11-browsers
     steps:
-      - test_optional:
-          py: "37"
+      - test_core:
+          py: "311"
+
+  python_312_core:
+    docker:
+      - image: cimg/python:3.12-browsers
+    steps:
+      - test_core:
+          py: "312"
+
+  # Optional
 
   python_38_optional:
     docker:
@@ -210,6 +204,27 @@ jobs:
       - test_optional:
           py: "39"
 
+  python_310_optional:
+    docker:
+      - image: cimg/python:3.10-browsers
+    steps:
+      - test_optional:
+          py: "310"
+
+  python_311_optional:
+    docker:
+      - image: cimg/python:3.11-browsers
+    steps:
+      - test_optional:
+          py: "311"
+
+  python_312_optional:
+    docker:
+      - image: cimg/python:3.12-browsers
+    steps:
+      - test_optional:
+          py: "312"
+
   # Pandas
 
   python_39_pandas_2_optional:
@@ -228,7 +243,7 @@ jobs:
           py: "38"
 
   # Percy
-  python_37_percy:
+  python_39_percy:
     docker:
       - image: cimg/python:3.9-browsers
         environment:
@@ -277,9 +292,9 @@ jobs:
             rm test/percy/*.html
 
   # Chart studio
-  python_37_chart_studio:
+  python_38_chart_studio:
     docker:
-      - image: cimg/python:3.7
+      - image: cimg/python:3.8
     resource_class: large
 
     steps:
@@ -291,7 +306,7 @@ jobs:
             python -m venv venv
             . venv/bin/activate
             pip install --upgrade pip wheel
-            pip install -r ./test_requirements/requirements_37.txt
+            pip install -r ./test_requirements/requirements_38.txt
       - run:
           name: Tests
           command: |
@@ -302,7 +317,7 @@ jobs:
 
   plotlyjs_dev_build:
     docker:
-      - image: cimg/python:3.7-node
+      - image: cimg/python:3.8-node
         environment:
           LANG: en_US.UTF-8
     resource_class: large
@@ -316,7 +331,7 @@ jobs:
             python -m venv venv
             . venv/bin/activate
             pip install --upgrade pip wheel
-            pip install -r ./test_requirements/requirements_37_core.txt black inflect
+            pip install -r ./test_requirements/requirements_38_core.txt black inflect
             pip install jupyterlab~=3.0
       - run:
           name: Update jupyterlab-plotly version
@@ -433,7 +448,7 @@ jobs:
     docker:
       # specify the version you desire here
       # use `-browsers` prefix for selenium tests, for example, `3.6.1-browsers`
-      - image: cimg/python:3.7-browsers
+      - image: cimg/python:3.9-browsers
 
     steps:
       - add_ssh_keys:
@@ -570,15 +585,17 @@ workflows:
 
   build:
     jobs:
-      - python_36_core
-      - python_37_core
       - python_38_core
       - python_39_core
-      - python_36_optional
-      - python_37_optional
+      - python_310_core
+      - python_311_core
+      - python_312_core
       - python_38_optional
       - python_39_optional
+      - python_310_optional
+      - python_311_optional
+      - python_312_optional
       - python_39_pandas_2_optional
       - python_38_orca
-      - python_37_percy
+      - python_39_percy
       - build-doc

binder/requirements.txt

@@ -2,8 +2,8 @@ jupytext
 plotly==5.18.0
 jupyter
 notebook
-pandas==1.0.3
-statsmodels==0.11.1
+pandas==1.1.3
+statsmodels==0.12.1
 scipy
 patsy==0.5.1
 numpy

doc/python/county-choropleth.md

@@ -47,9 +47,9 @@ Run the following commands to install the correct versions of the following modu
 
 ```python
 !pip install plotly-geo==1.0.0
-!pip install geopandas==0.3.0
-!pip install pyshp==1.2.10
-!pip install shapely==1.6.3
+!pip install geopandas==0.8.1
+!pip install pyshp==2.1.2
+!pip install shapely==1.7.1
 ```
 
 If you are using Windows, follow this post to properly install geopandas and dependencies: http://geoffboeing.com/2014/09/using-geopandas-windows/. If you are using Anaconda, do not use PIP to install the packages above. Instead use conda to install them:

doc/python/ml-pca.md

@@ -6,9 +6,9 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.3'
-      jupytext_version: 1.14.1
+      jupytext_version: 1.16.1
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.8.8
+    version: 3.10.11
   plotly:
     description: Visualize Principle Component Analysis (PCA) of your high-dimensional
       data in Python with Plotly.
@@ -105,17 +105,18 @@ fig.show()
 
 When you will have too many features to visualize, you might be interested in only visualizing the most relevant components. Those components often capture a majority of the [explained variance](https://en.wikipedia.org/wiki/Explained_variation), which is a good way to tell if those components are sufficient for modelling this dataset.
 
-In the example below, our dataset contains 10 features, but we only select the first 4 components, since they explain over 99% of the total variance.
+In the example below, our dataset contains 8 features, but we only select the first 2 components.
 
 ```python
 import pandas as pd
 import plotly.express as px
 from sklearn.decomposition import PCA
-from sklearn.datasets import load_boston
+from sklearn.datasets import fetch_california_housing
 
-boston = load_boston()
-df = pd.DataFrame(boston.data, columns=boston.feature_names)
-n_components = 4
+housing = fetch_california_housing(as_frame=True)
+df = housing.data
+
+n_components = 2
 
 pca = PCA(n_components=n_components)
 components = pca.fit_transform(df)
@@ -127,7 +128,7 @@ labels['color'] = 'Median Price'
 
 fig = px.scatter_matrix(
     components,
-    color=boston.target,
+    color=housing.target,
     dimensions=range(n_components),
     labels=labels,
     title=f'Total Explained Variance: {total_var:.2f}%',
@@ -136,6 +137,10 @@ fig.update_traces(diagonal_visible=False)
 fig.show()
 ```
 
+```python
+df
+```
+
 ## PCA analysis in Dash
 
 [Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`.

doc/python/ml-regression.md

@@ -6,9 +6,9 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.3'
-      jupytext_version: 1.14.1
+      jupytext_version: 1.16.1
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.8.8
+    version: 3.10.0
   plotly:
     description: Visualize regression in scikit-learn with Plotly.
     display_as: ai_ml
@@ -412,6 +412,7 @@ import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 from sklearn.linear_model import LassoCV
+from sklearn.preprocessing import StandardScaler
 
 N_FOLD = 6
 
@@ -421,9 +422,13 @@ X = df.drop(columns=['lifeExp', 'iso_num'])
 X = pd.get_dummies(X, columns=['country', 'continent', 'iso_alpha'])
 y = df['lifeExp']
 
+# Normalize the data
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
 # Train model to predict life expectancy
-model = LassoCV(cv=N_FOLD, normalize=True)
-model.fit(X, y)
+model = LassoCV(cv=N_FOLD)
+model.fit(X_scaled, y)
 mean_alphas = model.mse_path_.mean(axis=-1)
 
 fig = go.Figure([

doc/requirements.txt

@@ -4,9 +4,9 @@ ipywidgets==7.7.2
 jupyter-client<7
 jupyter
 notebook
-pandas==1.0.3
-statsmodels==0.11.1
-scipy==1.3.1
+pandas==1.2.0
+statsmodels==0.12.1
+scipy==1.5.4
 patsy==0.5.1
 numpy==1.19.5
 plotly-geo
@@ -16,7 +16,7 @@ pyshp==2.1.2
 shapely==1.7.1
 psutil
 requests
-networkx
+networkx==2.6.3
 squarify
 scikit-image==0.18.1
 scikit-learn
@@ -25,7 +25,7 @@ sphinx_bootstrap_theme
 recommonmark
 pathlib
 python-frontmatter
-datashader
+datashader==0.14.4
 pyarrow
 cufflinks==0.17.3
 kaleido