docs: pandas boolean content tabs (#1394)

docs/backcompat.md

@@ -134,7 +134,7 @@ before making any change.
     ```
 
     To check if a dtype is a datetime (regardless of `time_unit` or `time_zone`)
-    we recommend using `==` instead, as that works consistenty
+    we recommend using `==` instead, as that works consistently
     across namespaces:
 
     ```python exec="1" source="above" session="backcompat"

docs/pandas_like_concepts/boolean.md

@@ -6,56 +6,81 @@ For example, if you do `nw.col('a')*2`, then:
 - Values which were non-null get multiplied by 2.
 - Null values stay null.
 
-```python exec="1" source="above" session="boolean" result="python"
+```python exec="1" source="above" session="boolean"
 import narwhals as nw
-
-import pandas as pd
-import polars as pl
-import pyarrow as pa
+from narwhals.typing import FrameT
 
 data = {"a": [1.4, None, 4.2]}
-print("pandas output")
-print(nw.from_native(pd.DataFrame(data)).with_columns(b=nw.col("a") * 2).to_native())
-print("\nPolars output")
-print(nw.from_native(pl.DataFrame(data)).with_columns(b=nw.col("a") * 2).to_native())
-print("\nPyArrow output")
-print(nw.from_native(pa.table(data)).with_columns(b=nw.col("a") * 2).to_native())
+
+
+def multiplication(df: FrameT) -> FrameT:
+    return nw.from_native(df).with_columns((nw.col("a") * 2).alias("a*2")).to_native()
 ```
 
+=== "pandas"
+    ```python exec="true" source="material-block" result="python" session="boolean"
+    import pandas as pd
+
+    df = pd.DataFrame(data)
+    print(multiplication(df))
+    ```
+
+=== "Polars (eager)"
+    ```python exec="true" source="material-block" result="python" session="boolean"
+    import polars as pl
+
+    df = pl.DataFrame(data)
+    print(multiplication(df))
+    ```
+
+=== "PyArrow"
+    ```python exec="true" source="material-block" result="python" session="boolean"
+    import pyarrow as pa
+
+    table = pa.table(data)
+    print(multiplication(table))
+    ```
+
 What do we do, however, when the result column is boolean? For
 example, `nw.col('a') > 0`?
 Unfortunately, this is backend-dependent:
 
 - for all backends except pandas, null values are preserved
 - for pandas, this depends on the dtype backend:
-    - for PyArrow dtypes and pandas nullable dtypes, null
-      values are preserved
-    - for the classic NumPy dtypes, null values are typically
-      filled in with `False`.
+    - for PyArrow dtypes and pandas nullable dtypes, null values are preserved
+    - for the classic NumPy dtypes, null values are typically filled in with `False`.
 
 pandas is generally moving towards nullable dtypes, and they
 [may become the default in the future](https://github.com/pandas-dev/pandas/pull/58988),
 so we hope that the classical NumPy dtypes not supporting null values will just
 be a temporary legacy pandas issue which will eventually go
 away anyway.
 
-```python exec="1" source="above" session="boolean" result="python"
-print("pandas output")
-print(nw.from_native(pd.DataFrame(data)).with_columns(b=nw.col("a") > 2).to_native())
-print("\npandas (nullable dtypes) output")
-print(
-    nw.from_native(pd.DataFrame(data, dtype="Float64"))
-    .with_columns(b=nw.col("a") > 2)
-    .to_native()
-)
-print("\npandas (pyarrow dtypes) output")
-print(
-    nw.from_native(pd.DataFrame(data, dtype="Float64[pyarrow]"))
-    .with_columns(b=nw.col("a") > 2)
-    .to_native()
-)
-print("\nPolars output")
-print(nw.from_native(pl.DataFrame(data)).with_columns(b=nw.col("a") > 2).to_native())
-print("\nPyArrow output")
-print(nw.from_native(pa.table(data)).with_columns(b=nw.col("a") > 2).to_native())
-```
+```python exec="1" source="above" session="boolean"
+def comparison(df: FrameT) -> FrameT:
+    return nw.from_native(df).with_columns((nw.col("a") > 2).alias("a>2")).to_native()
+```
+
+=== "pandas"
+    ```python exec="true" source="material-block" result="python" session="boolean"
+    import pandas as pd
+
+    df = pd.DataFrame(data)
+    print(comparison(df))
+    ```
+
+=== "Polars (eager)"
+    ```python exec="true" source="material-block" result="python" session="boolean"
+    import polars as pl
+
+    df = pl.DataFrame(data)
+    print(comparison(df))
+    ```
+
+=== "PyArrow"
+    ```python exec="true" source="material-block" result="python" session="boolean"
+    import pyarrow as pa
+
+    table = pa.table(data)
+    print(comparison(table))
+    ```