Fix Python code in playground HTML files for pandas 3.0 compatibility

- json.html: Add import io and wrap pd.read_json() string args with
  io.StringIO() in blocks 1-5 and 7
- stack_unstack.html: Remove deprecated dropna parameter from
  df.stack() calls; chain .dropna() after stack where needed
- csv.html: Fix skiprows=2 to skiprows=range(1, 3) to preserve header
  row in block 5

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Co-authored-by: mrjf <180956+mrjf@users.noreply.github.com>

Author: Copilot

playground/csv.html

@@ -402,7 +402,7 @@ <h2>5 · Limiting rows</h2>
 first3 = pd.read_csv(io.StringIO(csv), nrows=3)
 print("nrows=3:", list(first3["val"]))
 
-skip2 = pd.read_csv(io.StringIO(csv), skiprows=2)
+skip2 = pd.read_csv(io.StringIO(csv), skiprows=range(1, 3))
 print("skiprows=2:", list(skip2["val"]))</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
       <div class="playground-hint">Ctrl+Enter to run · Tab to indent</div>

playground/json.html

@@ -216,10 +216,11 @@ <h2>1 · Parse records JSON (default)</h2>
 console.log("names:", [...df.col("name").values]);
 console.log("ages:", [...df.col("age").values]);</textarea>
       <textarea class="playground-python" style="display:none">import pandas as pd
+import io
 
 json_text = '[{"name": "alice", "age": 30, "score": 88.5}, {"name": "bob", "age": 25, "score": 92.0}, {"name": "carol", "age": 35, "score": 75.3}]'
 
-df = pd.read_json(json_text)
+df = pd.read_json(io.StringIO(json_text))
 print("shape:", df.shape)
 print("columns:", list(df.columns))
 print("names:", list(df["name"]))
@@ -259,14 +260,15 @@ <h2>2 · Split orient</h2>
 console.log("y:", [...df.col("y").values]);</textarea>
       <textarea class="playground-python" style="display:none">import pandas as pd
 import json
+import io
 
 data = json.dumps({
     "columns": ["x", "y"],
     "index": [10, 20, 30],
     "data": [[1, 4], [2, 5], [3, 6]],
 })
 
-df = pd.read_json(data, orient="split")
+df = pd.read_json(io.StringIO(data), orient="split")
 print("orient auto-detected: split")
 print("shape:", df.shape)
 print("index:", list(df.index))
@@ -305,14 +307,15 @@ <h2>3 · Index orient</h2>
 console.log("b:", [...df.col("b").values]);</textarea>
       <textarea class="playground-python" style="display:none">import pandas as pd
 import json
+import io
 
 data = json.dumps({
     "r0": {"a": 1, "b": "foo"},
     "r1": {"a": 2, "b": "bar"},
     "r2": {"a": 3, "b": "baz"},
 })
 
-df = pd.read_json(data, orient="index")
+df = pd.read_json(io.StringIO(data), orient="index")
 print("shape:", df.shape)
 print("index:", list(df.index))
 print("a:", list(df["a"]))
@@ -350,13 +353,14 @@ <h2>4 · Columns orient</h2>
 console.log("y:", [...df.col("y").values]);</textarea>
       <textarea class="playground-python" style="display:none">import pandas as pd
 import json
+import io
 
 data = json.dumps({
     "x": {"0": 10, "1": 20, "2": 30},
     "y": {"0": 1.1, "1": 2.2, "2": 3.3},
 })
 
-df = pd.read_json(data, orient="columns")
+df = pd.read_json(io.StringIO(data), orient="columns")
 print("shape:", df.shape)
 print("index:", list(df.index))
 print("x:", list(df["x"]))
@@ -389,10 +393,11 @@ <h2>5 · Values orient</h2>
 console.log("columns:", [...df.columns.values]);
 console.log("col 0:", [...df.col("0").values]);</textarea>
       <textarea class="playground-python" style="display:none">import pandas as pd
+import io
 
 json_text = "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
 
-df = pd.read_json(json_text, orient="values")
+df = pd.read_json(io.StringIO(json_text), orient="values")
 print("shape:", df.shape)
 print("columns:", list(df.columns))
 print("col 0:", list(df[0]))</textarea>
@@ -474,14 +479,15 @@ <h2>7 · Round-trip</h2>
 console.log("label restored:", [...restored.col("label").values]);
 console.log("shapes match:", JSON.stringify(original.shape) === JSON.stringify(restored.shape));</textarea>
       <textarea class="playground-python" style="display:none">import pandas as pd
+import io
 
 original = pd.DataFrame({
     "x": [1, 2, 3, 4, 5],
     "label": ["a", "b", "c", "d", "e"],
 })
 
 json_text = original.to_json(orient="split")
-restored = pd.read_json(json_text, orient="split")
+restored = pd.read_json(io.StringIO(json_text), orient="split")
 
 print("JSON:", json_text)
 print("x restored:", list(restored["x"]))

playground/stack_unstack.html

@@ -215,7 +215,7 @@ <h2>1 · Basic stack</h2>
     index=["x", "y", "z"],
 )
 
-s = df.stack(dropna=False)
+s = df.stack()
 print("index:", list(s.index))
 print("values:", list(s.values))</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
@@ -261,13 +261,13 @@ <h2>2 · stack drops null by default</h2>
     index=["a", "b", "c"],
 )
 
-# default: dropna=True
-s = df.stack(dropna=True)
+# default: stack preserves NaN, chain dropna() to remove
+s = df.stack().dropna()
 print("dropna=True  index:", list(s.index))
 print("dropna=True  values:", list(s.values))
 
-# keep nulls
-s_full = df.stack(dropna=False)
+# keep nulls (default in pandas 3.0)
+s_full = df.stack()
 print("dropna=False index:", list(s_full.index))
 print("dropna=False values:", list(s_full.values))</textarea>
       <div class="playground-output">Click ▶ Run to execute</div>
@@ -308,7 +308,7 @@ <h2>3 · unstack: recover the original DataFrame</h2>
     index=["x", "y"],
 )
 
-s = df.stack(dropna=False)
+s = df.stack()
 recovered = s.unstack()
 
 print("index:", list(recovered.index))
@@ -355,8 +355,8 @@ <h2>4 · unstack fills missing cells</h2>
     index=["jan", "feb", "mar"],
 )
 
-# stack drops nulls by default
-s = df.stack(dropna=True)
+# stack then drop nulls
+s = df.stack().dropna()
 
 # unstack with fill_value=0
 recovered = s.unstack(fill_value=0)
@@ -400,7 +400,7 @@ <h2>5 · Custom separator</h2>
 )
 
 # pandas uses MultiIndex natively, no separator needed
-s = df.stack(dropna=False)
+s = df.stack()
 print("stacked index:", list(s.index))
 
 recovered = s.unstack()
@@ -455,8 +455,8 @@ <h2>6 · Reshape workflow: stack → filter → unstack</h2>
     index=["Q1", "Q2", "Q3", "Q4"],
 )
 
-# Stack (drops nulls), filter to values > 80, then unstack
-s = df.stack(dropna=True)
+# Stack then drop nulls, filter to values > 80, then unstack
+s = df.stack().dropna()
 filtered = s[s > 80]
 result = filtered.unstack(fill_value=0)