Updated the documentation with the missing read options

docs/api/index.md

@@ -13,6 +13,69 @@ It's the best place to look if you need information on a specific function.
 The Python API reference is built using Sphinx.
 It's available in [our docs](https://docs.pola.rs/py-polars/html/reference/index.html).
 
+
+## polars.read_excel
+
+Read Excel spreadsheet data into a DataFrame.
+
+## Parameters
+
+- `source`: Path to a file or a file-like object.
+- `sheet_id`: Sheet number(s) to convert (set 0 to load all sheets as DataFrames) and return a `{sheetname:frame,}` dict.
+- `sheet_name`: Sheet name(s) to convert; cannot be used in conjunction with `sheet_id`.
+- `engine`: Library used to parse the spreadsheet file.
+- `engine_options`: Additional options passed to the underlying engine's primary parsing constructor.
+- `read_options`: Extra options passed to the function that reads the sheet data.
+- `schema_overrides`: Support type specification or override of one or more columns.
+- `raise_if_empty`: When there is no data in the sheet, `NoDataError` is raised. If set to False, an empty DataFrame is returned instead.
+
+## Returns
+
+- `DataFrame`: If reading a single sheet.
+- `dict`: If reading multiple sheets, a `{sheetname: DataFrame, …}` dict is returned.
+
+## Notes
+
+- The default engine is `"xlsx2csv"`.
+- You can pass additional options to `read_options` to influence the parsing pipeline.
+- The `openpyxl` engine is slower but supports additional automatic type inference.
+- The `pyxlsb` engine is used for Excel Binary Workbooks.
+- The `calamine` engine can be used for reading all major types of Excel Workbook and is faster than other options.
+
+## Examples
+
+```python
+import polars as pl
+
+# Read the “data” worksheet from an Excel file into a DataFrame.
+pl.read_excel(
+    source="test.xlsx",
+    sheet_name="data",
+)
+
+# Read table data from sheet 3 in an Excel workbook as a DataFrame while skipping empty lines.
+pl.read_excel(
+    source="test.xlsx",
+    sheet_id=3,
+    engine_options={"skip_empty_lines": True},
+    read_options={"has_header": False, "new_columns": ["a", "b", "c"]},
+)
+
+# Use schema_overrides to specify column types.
+pl.read_excel(
+    source="test.xlsx",
+    read_options={"infer_schema_length": 1000},
+    schema_overrides={"dt": pl.Date},
+)
+
+# Use the openpyxl engine for better type detection.
+pl.read_excel(
+    source="test.xlsx",
+    engine="openpyxl",
+    schema_overrides={"dt": pl.Datetime, "value": pl.Int32},
+)
+```
+
 ## Rust
 
 The Rust API reference is built using Cargo.