PERF: Optimize read_excel nrows

asv_bench/benchmarks/io/excel.py

@@ -86,4 +86,15 @@ def time_read_excel(self, engine):
         read_excel(fname, engine=engine)
 
 
+class ReadExcelNRows(ReadExcel):
+    def time_read_excel(self, engine):
+        if engine == "xlrd":
+            fname = self.fname_excel_xls
+        elif engine == "odf":
+            fname = self.fname_odf
+        else:
+            fname = self.fname_excel
+        read_excel(fname, engine=engine, nrows=10)
+
+
 from ..pandas_vb_common import setup  # noqa: F401 isort:skip

doc/source/whatsnew/v1.5.0.rst

@@ -451,6 +451,7 @@ Performance improvements
 - Performance improvement when setting values in a pyarrow backed string array (:issue:`46400`)
 - Performance improvement in :func:`factorize` (:issue:`46109`)
 - Performance improvement in :class:`DataFrame` and :class:`Series` constructors for extension dtype scalars (:issue:`45854`)
+- Performance improvement in :func:`read_excel` when ``nrows`` argument provided (:issue:`32727`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_150.bug_fixes:

pandas/io/common.py

@@ -26,6 +26,7 @@
     Generic,
     Literal,
     Mapping,
+    Sequence,
     TypeVar,
     cast,
     overload,
@@ -54,7 +55,12 @@
 from pandas.util._decorators import doc
 from pandas.util._exceptions import find_stack_level
 
-from pandas.core.dtypes.common import is_file_like
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_file_like,
+    is_integer,
+    is_list_like,
+)
 
 from pandas.core.shared_docs import _shared_docs
 
@@ -175,12 +181,32 @@ def _expand_user(filepath_or_buffer: str | BaseBufferT) -> str | BaseBufferT:
 
 
 def validate_header_arg(header: object) -> None:
-    if isinstance(header, bool):
+    if header is None:
+        return
+    if is_integer(header):
+        header = cast(int, header)
+        if header < 0:
+            # GH 27779
+            raise ValueError(
+                "Passing negative integer to header is invalid. "
+                "For no header, use header=None instead"
+            )
+        return
+    if is_list_like(header):
+        header = cast(Sequence, header)
+        if not all(map(is_integer, header)):
+            raise ValueError("header must be integer or list of integers")
+        if any(i < 0 for i in header):
+            raise ValueError("cannot specify multi-index header with negative integers")
+        return
+    if is_bool(header):
         raise TypeError(
             "Passing a bool to header is invalid. Use header=None for no header or "
             "header=int or list-like of ints to specify "
             "the row(s) making up the column names"
         )
+    # GH 16338
+    raise ValueError("header must be integer or list of integers")
 
 
 @overload

pandas/io/excel/_base.py

@@ -2,6 +2,7 @@
 
 import abc
 import datetime
+from functools import partial
 from io import BytesIO
 import os
 from textwrap import fill
@@ -70,6 +71,7 @@
     pop_header_name,
 )
 from pandas.io.parsers import TextParser
+from pandas.io.parsers.readers import validate_integer
 
 _read_excel_doc = (
     """
@@ -563,7 +565,7 @@ def get_sheet_by_index(self, index: int):
         pass
 
     @abc.abstractmethod
-    def get_sheet_data(self, sheet, convert_float: bool):
+    def get_sheet_data(self, sheet, convert_float: bool, rows: int | None = None):
         pass
 
     def raise_if_bad_sheet_by_index(self, index: int) -> None:
@@ -577,6 +579,99 @@ def raise_if_bad_sheet_by_name(self, name: str) -> None:
         if name not in self.sheet_names:
             raise ValueError(f"Worksheet named '{name}' not found")
 
+    def _check_skiprows_func(
+        self,
+        skiprows: Callable,
+        rows_to_use: int,
+    ) -> int:
+        """
+        Determine how many file rows are required to obtain `nrows` data
+        rows when `skiprows` is a function.
+
+        Parameters
+        ----------
+        skiprows : function
+            The function passed to read_excel by the user.
+        rows_to_use : int
+            The number of rows that will be needed for the header and
+            the data.
+
+        Returns
+        -------
+        int
+        """
+        i = 0
+        rows_used_so_far = 0
+        while rows_used_so_far < rows_to_use:
+            if not skiprows(i):
+                rows_used_so_far += 1
+            i += 1
+        return i
+
+    def _calc_rows(
+        self,
+        header: int | Sequence[int] | None,
+        index_col: int | Sequence[int] | None,
+        skiprows: Sequence[int] | int | Callable[[int], object] | None,
+        nrows: int | None,
+    ) -> int | None:
+        """
+        If nrows specified, find the number of rows needed from the
+        file, otherwise return None.
+
+
+        Parameters
+        ----------
+        header : int, list of int, or None
+            See read_excel docstring.
+        index_col : int, list of int, or None
+            See read_excel docstring.
+        skiprows : list-like, int, callable, or None
+            See read_excel docstring.
+        nrows : int or None
+            See read_excel docstring.
+
+        Returns
+        -------
+        int or None
+        """
+        if nrows is None:
+            return None
+        if header is None:
+            header_rows = 1
+        elif is_integer(header):
+            header = cast(int, header)
+            header_rows = 1 + header
+        else:
+            header = cast(Sequence, header)
+            header_rows = 1 + header[-1]
+        # If there is a MultiIndex header and an index then there is also
+        # a row containing just the index name(s)
+        if is_list_like(header) and index_col is not None:
+            header = cast(Sequence, header)
+            if len(header) > 1:
+                header_rows += 1
+        if skiprows is None:
+            return header_rows + nrows
+        if is_integer(skiprows):
+            skiprows = cast(int, skiprows)
+            return header_rows + nrows + skiprows
+        if is_list_like(skiprows):
+
+            def f(skiprows: Sequence, x: int) -> bool:
+                return x in skiprows
+
+            skiprows = cast(Sequence, skiprows)
+            return self._check_skiprows_func(partial(f, skiprows), header_rows + nrows)
+        if callable(skiprows):
+            return self._check_skiprows_func(
+                skiprows,
+                header_rows + nrows,
+            )
+        # else unexpected skiprows type: read_excel will not optimize
+        # the number of rows read from file
+        return None
+
     def parse(
         self,
         sheet_name: str | int | list[int] | list[str] | None = 0,
@@ -613,6 +708,7 @@ def parse(
             )
 
         validate_header_arg(header)
+        validate_integer("nrows", nrows)
 
         ret_dict = False
 
@@ -643,7 +739,8 @@ def parse(
             else:  # assume an integer if not a string
                 sheet = self.get_sheet_by_index(asheetname)
 
-            data = self.get_sheet_data(sheet, convert_float)
+            file_rows_needed = self._calc_rows(header, index_col, skiprows, nrows)
+            data = self.get_sheet_data(sheet, convert_float, file_rows_needed)
             if hasattr(sheet, "close"):
                 # pyxlsb opens two TemporaryFiles
                 sheet.close()

pandas/io/excel/_odfreader.py

@@ -90,7 +90,7 @@ def get_sheet_by_name(self, name: str):
         raise ValueError(f"sheet {name} not found")
 
     def get_sheet_data(
-        self, sheet, convert_float: bool
+        self, sheet, convert_float: bool, file_rows_needed: int | None = None
     ) -> list[list[Scalar | NaTType]]:
         """
         Parse an ODF Table into a list of lists
@@ -148,6 +148,8 @@ def get_sheet_data(
                 empty_rows = 0
                 for _ in range(row_repeat):
                     table.append(table_row)
+            if file_rows_needed is not None and len(table) >= file_rows_needed:
+                break
 
         # Make our table square
         for row in table:

pandas/io/excel/_openpyxl.py

@@ -588,7 +588,9 @@ def _convert_cell(self, cell, convert_float: bool) -> Scalar:
 
         return cell.value
 
-    def get_sheet_data(self, sheet, convert_float: bool) -> list[list[Scalar]]:
+    def get_sheet_data(
+        self, sheet, convert_float: bool, file_rows_needed: int | None = None
+    ) -> list[list[Scalar]]:
 
         if self.book.read_only:
             sheet.reset_dimensions()
@@ -603,6 +605,8 @@ def get_sheet_data(self, sheet, convert_float: bool) -> list[list[Scalar]]:
             if converted_row:
                 last_row_with_data = row_number
             data.append(converted_row)
+            if file_rows_needed is not None and len(data) >= file_rows_needed:
+                break
 
         # Trim trailing empty rows
         data = data[: last_row_with_data + 1]

pandas/io/excel/_pyxlsb.py

@@ -79,7 +79,12 @@ def _convert_cell(self, cell, convert_float: bool) -> Scalar:
 
         return cell.v
 
-    def get_sheet_data(self, sheet, convert_float: bool) -> list[list[Scalar]]:
+    def get_sheet_data(
+        self,
+        sheet,
+        convert_float: bool,
+        file_rows_needed: int | None = None,
+    ) -> list[list[Scalar]]:
         data: list[list[Scalar]] = []
         prevous_row_number = -1
         # When sparse=True the rows can have different lengths and empty rows are
@@ -94,6 +99,8 @@ def get_sheet_data(self, sheet, convert_float: bool) -> list[list[Scalar]]:
                 data.extend([[]] * (row_number - prevous_row_number - 1))
                 data.append(converted_row)
                 prevous_row_number = row_number
+            if file_rows_needed is not None and len(data) >= file_rows_needed:
+                break
         if data:
             # extend rows to max_width
             max_width = max(len(data_row) for data_row in data)

pandas/io/excel/_xlrd.py

@@ -1,8 +1,13 @@
+from __future__ import annotations
+
 from datetime import time
 
 import numpy as np
 
-from pandas._typing import StorageOptions
+from pandas._typing import (
+    Scalar,
+    StorageOptions,
+)
 from pandas.compat._optional import import_optional_dependency
 from pandas.util._decorators import doc
 
@@ -56,7 +61,9 @@ def get_sheet_by_index(self, index):
         self.raise_if_bad_sheet_by_index(index)
         return self.book.sheet_by_index(index)
 
-    def get_sheet_data(self, sheet, convert_float):
+    def get_sheet_data(
+        self, sheet, convert_float: bool, file_rows_needed: int | None = None
+    ) -> list[list[Scalar]]:
         from xlrd import (
             XL_CELL_BOOLEAN,
             XL_CELL_DATE,
@@ -107,7 +114,10 @@ def _parse_cell(cell_contents, cell_typ):
 
         data = []
 
-        for i in range(sheet.nrows):
+        nrows = sheet.nrows
+        if file_rows_needed is not None:
+            nrows = min(nrows, file_rows_needed)
+        for i in range(nrows):
             row = [
                 _parse_cell(value, typ)
                 for value, typ in zip(sheet.row_values(i), sheet.row_types(i))

pandas/io/parsers/base_parser.py

@@ -120,13 +120,7 @@ def __init__(self, kwds) -> None:
 
         # validate header options for mi
         self.header = kwds.get("header")
-        if isinstance(self.header, (list, tuple, np.ndarray)):
-            if not all(map(is_integer, self.header)):
-                raise ValueError("header must be integer or list of integers")
-            if any(i < 0 for i in self.header):
-                raise ValueError(
-                    "cannot specify multi-index header with negative integers"
-                )
+        if is_list_like(self.header):
             if kwds.get("usecols"):
                 raise ValueError(
                     "cannot specify usecols when specifying a multi-index header"
@@ -138,31 +132,20 @@ def __init__(self, kwds) -> None:
 
             # validate index_col that only contains integers
             if self.index_col is not None:
-                is_sequence = isinstance(self.index_col, (list, tuple, np.ndarray))
                 if not (
-                    is_sequence
+                    is_list_like(self.index_col)
                     and all(map(is_integer, self.index_col))
                     or is_integer(self.index_col)
                 ):
                     raise ValueError(
                         "index_col must only contain row numbers "
                         "when specifying a multi-index header"
                     )
-        elif self.header is not None:
+        elif self.header is not None and self.prefix is not None:
             # GH 27394
-            if self.prefix is not None:
-                raise ValueError(
-                    "Argument prefix must be None if argument header is not None"
-                )
-            # GH 16338
-            elif not is_integer(self.header):
-                raise ValueError("header must be integer or list of integers")
-            # GH 27779
-            elif self.header < 0:
-                raise ValueError(
-                    "Passing negative integer to header is invalid. "
-                    "For no header, use header=None instead"
-                )
+            raise ValueError(
+                "Argument prefix must be None if argument header is not None"
+            )
 
         self._name_processed = False