Add Python/pandas equivalent tab to all playground pages with CI validation

.github/workflows/ci.yml

@@ -59,3 +59,20 @@ jobs:
         with:
           name: dist
           path: dist/
+
+  validate-python-examples:
+    name: Validate Python Examples
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install Python dependencies
+        run: pip install pandas numpy
+
+      - name: Validate Python playground examples
+        run: python scripts/validate-python-examples.py playground/

.github/workflows/pages.yml

@@ -36,6 +36,17 @@ jobs:
       - name: Bundle TypeScript compiler for offline playground
         run: cp node_modules/typescript/lib/typescript.js ./playground/dist/typescript.js
 
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install Python dependencies
+        run: pip install pandas numpy
+
+      - name: Validate Python playground examples
+        run: python scripts/validate-python-examples.py playground/
+
       - name: Setup Pages
         uses: actions/configure-pages@v5
         with:

playground/cat_accessor.html

@@ -212,6 +212,22 @@ <h2>1 · Categories and codes</h2>
 
 console.log("nCategories:", size.cat.nCategories);
 console.log("ordered:", size.cat.ordered);</textarea>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+size = pd.Series(
+    ["M", "S", "L", "S", "M", "XL"],
+    name="size",
+    dtype="category",
+)
+
+# Sorted unique categories
+print("categories:", size.cat.categories.tolist())
+
+# Integer codes (position in categories array; -1 for null)
+print("codes:", size.cat.codes.tolist())
+
+print("nCategories:", len(size.cat.categories))
+print("ordered:", size.cat.ordered)</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
       <div class="playground-hint">Ctrl+Enter to run · Tab to indent</div>
     </div>
@@ -237,6 +253,13 @@ <h2>2 · Nulls are encoded as -1</h2>
 console.log("categories:", [...s.cat.categories.values]);
 console.log("codes:", s.cat.codes.toArray());
 console.log("nCategories:", s.cat.nCategories);</textarea>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+s = pd.Series(["a", None, "b", None, "a"], dtype="category")
+
+print("categories:", s.cat.categories.tolist())
+print("codes:", s.cat.codes.tolist())
+print("nCategories:", len(s.cat.categories))</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
       <div class="playground-hint">Ctrl+Enter to run · Tab to indent</div>
     </div>
@@ -267,6 +290,18 @@ <h2>3 · Add and remove categories</h2>
 const s3 = s.cat.removeCategories(["red"]);
 console.log("after remove values:", s3.toArray());
 console.log("after remove cats:", [...s3.cat.categories.values]);</textarea>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+s = pd.Series(["red", "blue", "red"], dtype="category")
+
+# Add a new category (not yet in data)
+s2 = s.cat.add_categories(["green"])
+print("after add:", s2.cat.categories.tolist())
+
+# Remove a category — matching values become null
+s3 = s.cat.remove_categories(["red"])
+print("after remove values:", s3.tolist())
+print("after remove cats:", s3.cat.categories.tolist())</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
       <div class="playground-hint">Ctrl+Enter to run · Tab to indent</div>
     </div>
@@ -294,6 +329,16 @@ <h2>4 · Remove unused categories</h2>
 const s3 = s2.cat.removeUnusedCategories();
 console.log("after:", s3.cat.nCategories);
 console.log("cats:", [...s3.cat.categories.values]);</textarea>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+s = pd.Series(["a", "b"], dtype="category")
+# Manually add extra categories
+s2 = s.cat.add_categories(["c", "d", "e"])
+print("before:", len(s2.cat.categories))
+
+s3 = s2.cat.remove_unused_categories()
+print("after:", len(s3.cat.categories))
+print("cats:", s3.cat.categories.tolist())</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
       <div class="playground-hint">Ctrl+Enter to run · Tab to indent</div>
     </div>
@@ -323,6 +368,18 @@ <h2>5 · Rename categories</h2>
 // Array replacement (same order as categories)
 const s3 = s.cat.renameCategories(["H", "L", "M"]);
 console.log("array rename:", s3.toArray());</textarea>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+s = pd.Series(["low", "mid", "high", "mid"], dtype="category")
+
+# Object mapping
+s2 = s.cat.rename_categories({"low": "L", "mid": "M", "high": "H"})
+print("renamed values:", s2.tolist())
+print("renamed cats:", s2.cat.categories.tolist())
+
+# Array replacement (same order as categories)
+s3 = s.cat.rename_categories(["H", "L", "M"])
+print("array rename:", s3.tolist())</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
       <div class="playground-hint">Ctrl+Enter to run · Tab to indent</div>
     </div>
@@ -354,6 +411,18 @@ <h2>6 · Set and reorder categories</h2>
 const s3 = s.cat.reorderCategories(["XS", "S", "M", "L", "XL"], true);
 console.log("ordered:", s3.cat.ordered);
 console.log("custom order:", [...s3.cat.categories.values]);</textarea>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+s = pd.Series(["XS", "S", "M", "L", "XL"], dtype="category")
+
+# set_categories: restrict to a subset
+s2 = s.cat.set_categories(["S", "M", "L"])
+print("set cats:", s2.tolist())
+
+# reorder_categories: custom ordering (e.g., by size not alphabetically)
+s3 = s.cat.reorder_categories(["XS", "S", "M", "L", "XL"], ordered=True)
+print("ordered:", s3.cat.ordered)
+print("custom order:", s3.cat.categories.tolist())</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
       <div class="playground-hint">Ctrl+Enter to run · Tab to indent</div>
     </div>
@@ -380,6 +449,14 @@ <h2>7 · Value counts per category</h2>
 const counts = gradesFull.cat.valueCounts();
 console.log("categories:", [...gradesFull.cat.categories.values]);
 console.log("counts:", counts.toArray());</textarea>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+grades = pd.Series(["A", "B", "A", "C", "B", "A"], dtype="category")
+grades_full = grades.cat.add_categories(["D", "F"])
+
+counts = grades_full.value_counts()
+print("categories:", grades_full.cat.categories.tolist())
+print("counts:", counts.tolist())</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
       <div class="playground-hint">Ctrl+Enter to run · Tab to indent</div>
     </div>
@@ -411,6 +488,20 @@ <h2>8 · Ordered categories</h2>
 
 // bad=0, good=1, excellent=2
 console.log("codes:", ordered.cat.codes.toArray());</textarea>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+rating = pd.Series(["good", "bad", "excellent", "good"], dtype="category")
+
+# Establish a meaningful order
+ordered = rating.cat.reorder_categories(
+    ["bad", "good", "excellent"], ordered=True
+)
+
+print("ordered:", ordered.cat.ordered)
+print("categories:", ordered.cat.categories.tolist())
+
+# bad=0, good=1, excellent=2
+print("codes:", ordered.cat.codes.tolist())</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
       <div class="playground-hint">Ctrl+Enter to run · Tab to indent</div>
     </div>

playground/concat.html

@@ -217,6 +217,13 @@ <h2>1 · Stack Series vertically (axis=0)</h2>
 
 const result = concat([s1, s2]);
 console.log(result.toString());</pre>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+s1 = pd.Series([10, 20], index=["a", "b"])
+s2 = pd.Series([30, 40], index=["c", "d"])
+
+result = pd.concat([s1, s2])
+print(result.to_string())</textarea>
       <pre class="playground-output">Click ▶ Run to execute</pre>
       <div class="playground-hint">Ctrl+Enter to run</div>
     </div>
@@ -242,6 +249,14 @@ <h2>2 · Stack DataFrames vertically (axis=0)</h2>
 // join="outer" by default — fills missing columns with null
 const result = concat([df1, df2]);
 console.log(result.toString());</pre>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+df1 = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
+df2 = pd.DataFrame({"b": [5], "c": [6]})
+
+# join="outer" by default — fills missing columns with NaN
+result = pd.concat([df1, df2])
+print(result.to_string())</textarea>
       <pre class="playground-output">Click ▶ Run to execute</pre>
       <div class="playground-hint">Ctrl+Enter to run</div>
     </div>
@@ -276,6 +291,21 @@ <h2>3 · Column-wise concat (axis=1)</h2>
 
 console.log("\n=== DataFrame axis=1 ===");
 console.log(concat([left, right], { axis: 1 }).toString());</pre>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+# Series → DataFrame (each Series becomes a column)
+age = pd.Series([25, 30, 35], name="age")
+score = pd.Series([88, 92, 79], name="score")
+
+print("=== Series axis=1 ===")
+print(pd.concat([age, score], axis=1).to_string())
+
+# DataFrame side-by-side
+left = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
+right = pd.DataFrame({"c": [5, 6], "d": [7, 8]})
+
+print("\n=== DataFrame axis=1 ===")
+print(pd.concat([left, right], axis=1).to_string())</textarea>
       <pre class="playground-output">Click ▶ Run to execute</pre>
       <div class="playground-hint">Ctrl+Enter to run</div>
     </div>
@@ -317,6 +347,27 @@ <h2>4 · Join modes — outer vs inner</h2>
 
 console.log("\n=== axis=1, join='inner' (only shared row 'b') ===");
 console.log(concat([s1, s2], { axis: 1, join: "inner" }).toString());</pre>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+# axis=0: outer vs inner columns
+df1 = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
+df2 = pd.DataFrame({"b": [5], "c": [6]})
+
+print("=== axis=0, join='outer' (default) ===")
+print(pd.concat([df1, df2]).to_string())
+
+print("\n=== axis=0, join='inner' (only shared col 'b') ===")
+print(pd.concat([df1, df2], join="inner").to_string())
+
+# axis=1: outer vs inner row indexes
+s1 = pd.Series([1, 2], index=["a", "b"], name="s1")
+s2 = pd.Series([3, 4], index=["b", "c"], name="s2")
+
+print("\n=== axis=1, join='outer' (union of row indexes) ===")
+print(pd.concat([s1, s2], axis=1).to_string())
+
+print("\n=== axis=1, join='inner' (only shared row 'b') ===")
+print(pd.concat([s1, s2], axis=1, join="inner").to_string())</textarea>
       <pre class="playground-output">Click ▶ Run to execute</pre>
       <div class="playground-hint">Ctrl+Enter to run</div>
     </div>
@@ -349,6 +400,21 @@ <h2>5 · ignoreIndex — reset to RangeIndex</h2>
 
 console.log("\n=== DataFrame ignoreIndex ===");
 console.log(concat([df1, df2], { ignoreIndex: true }).toString());</pre>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+# Series with string indexes → reset to 0, 1, 2
+a = pd.Series([1, 2], index=["x", "y"])
+b = pd.Series([3], index=["z"])
+
+print("=== Series ignore_index ===")
+print(pd.concat([a, b], ignore_index=True).to_string())
+
+# DataFrame ignore_index
+df1 = pd.DataFrame({"v": [10, 20]})
+df2 = pd.DataFrame({"v": [30, 40]})
+
+print("\n=== DataFrame ignore_index ===")
+print(pd.concat([df1, df2], ignore_index=True).to_string())</textarea>
       <pre class="playground-output">Click ▶ Run to execute</pre>
       <div class="playground-hint">Ctrl+Enter to run</div>
     </div>
@@ -390,6 +456,29 @@ <h2>🧪 Scratch Pad</h2>
 
 console.log("\n=== Side-by-side columns ===");
 console.log(concat([names, q1rev, q2rev], { axis: 1 }).toString());</pre>
+      <textarea class="playground-python" style="display:none">import pandas as pd
+
+# Try it! Combine DataFrames in creative ways.
+q1 = pd.DataFrame({
+    "product": ["Widget", "Gadget"],
+    "revenue": [1000, 1500],
+})
+
+q2 = pd.DataFrame({
+    "product": ["Widget", "Gadget"],
+    "revenue": [1200, 1800],
+})
+
+print("=== Q1 + Q2 stacked ===")
+print(pd.concat([q1, q2], ignore_index=True).to_string())
+
+# Side-by-side with axis=1
+names = pd.Series(["Widget", "Gadget"], name="product")
+q1_rev = pd.Series([1000, 1500], name="q1_rev")
+q2_rev = pd.Series([1200, 1800], name="q2_rev")
+
+print("\n=== Side-by-side columns ===")
+print(pd.concat([names, q1_rev, q2_rev], axis=1).to_string())</textarea>
       <pre class="playground-output">Click ▶ Run to execute</pre>
       <div class="playground-hint">Ctrl+Enter to run</div>
     </div>