from collections.abc import (
Callable,
Hashable,
Iterable,
Mapping,
Sequence,
)
from types import TracebackType
from typing import (
Any,
Literal,
overload,
)
from odf.opendocument import OpenDocument
from openpyxl.workbook.workbook import Workbook
from pandas.core.frame import DataFrame
import pyxlsb.workbook
from typing_extensions import Self
from xlrd.book import Book
from pandas._libs.lib import _NoDefaultDoNotUse
from pandas._typing import (
Dtype,
DtypeBackend,
ExcelReadEngine,
ExcelWriteEngine,
ExcelWriterIfSheetExists,
FilePath,
IntStrT,
ListLikeHashable,
ReadBuffer,
StorageOptions,
UsecolsArgType,
WriteExcelBuffer,
)
@overload
def read_excel(
io: (
FilePath
| ReadBuffer[bytes]
| ExcelFile
| Workbook
| Book
| OpenDocument
| pyxlsb.workbook.Workbook
),
sheet_name: list[IntStrT],
*,
header: int | Sequence[int] | None = ...,
names: ListLikeHashable | None = ...,
index_col: int | Sequence[int] | str | None = ...,
usecols: str | UsecolsArgType = ...,
dtype: str | Dtype | Mapping[str, str | Dtype] | None = ...,
engine: ExcelReadEngine | None = ...,
converters: Mapping[int | str, Callable[[object], object]] | None = ...,
true_values: Iterable[Hashable] | None = ...,
false_values: Iterable[Hashable] | None = ...,
skiprows: int | Sequence[int] | Callable[[object], bool] | None = ...,
nrows: int | None = ...,
na_values: Sequence[str] | dict[str | int, Sequence[str]] | None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool = ...,
parse_dates: (
bool
| Sequence[int]
| Sequence[Sequence[str] | Sequence[int]]
| dict[str, Sequence[int] | list[str]]
) = ...,
date_format: dict[Hashable, str] | str | None = ...,
thousands: str | None = ...,
decimal: str = ...,
comment: str | None = ...,
skipfooter: int = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | _NoDefaultDoNotUse = ...,
engine_kwargs: dict[str, Any] | None = ...,
) -> dict[IntStrT, DataFrame]:
"""
Read an Excel file into a ``pandas`` ``DataFrame``.
Supports `xls`, `xlsx`, `xlsm`, `xlsb`, `odf`, `ods` and `odt` file extensions
read from a local filesystem or URL. Supports an option to read
a single sheet or a list of sheets.
Parameters
----------
io : str, bytes, ExcelFile, xlrd.Book, path object, or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be: ``file://localhost/path/to/table.xlsx``.
If you want to pass in a path object, pandas accepts any ``os.PathLike``.
By file-like object, we refer to objects with a ``read()`` method,
such as a file handle (e.g. via builtin ``open`` function)
or ``StringIO``.
.. deprecated:: 2.1.0
Passing byte strings is deprecated. To read from a
byte string, wrap it in a ``BytesIO`` object.
sheet_name : str, int, list, or None, default 0
Strings are used for sheet names. Integers are used in zero-indexed
sheet positions (chart sheets do not count as a sheet position).
Lists of strings/integers are used to request multiple sheets.
Specify ``None`` to get all worksheets.
Available cases:
* Defaults to ``0``: 1st sheet as a `DataFrame`
* ``1``: 2nd sheet as a `DataFrame`
* ``"Sheet1"``: Load sheet with name "Sheet1"
* ``[0, 1, "Sheet5"]``: Load first, second and sheet named "Sheet5"
as a dict of `DataFrame`
* ``None``: All worksheets.
header : int, list of int, default 0
Row (0-indexed) to use for the column labels of the parsed
DataFrame. If a list of integers is passed those row positions will
be combined into a ``MultiIndex``. Use None if there is no header.
names : array-like, default None
List of column names to use. If file contains no header row,
then you should explicitly pass header=None.
index_col : int, str, list of int, default None
Column (0-indexed) to use as the row labels of the DataFrame.
Pass None if there is no such column. If a list is passed,
those columns will be combined into a ``MultiIndex``. If a
subset of data is selected with ``usecols``, index_col
is based on the subset.
Missing values will be forward filled to allow roundtripping with
``to_excel`` for ``merged_cells=True``. To avoid forward filling the
missing values use ``set_index`` after reading the data instead of
``index_col``.
usecols : str, list-like, or callable, default None
* If None, then parse all columns.
* If str, then indicates comma separated list of Excel column letters
and column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive of
both sides.
* If list of int, then indicates list of column numbers to be parsed
(0-indexed).
* If list of string, then indicates list of column names to be parsed.
* If callable, then evaluate each column name against it and parse the
column if the callable returns ``True``.
Returns a subset of the columns according to behavior above.
dtype : Type name or dict of column -> type, default None
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
Use ``object`` to preserve data as stored in Excel and not interpret dtype,
which will necessarily result in ``object`` dtype.
If converters are specified, they will be applied INSTEAD
of dtype conversion.
If you use ``None``, it will infer the dtype of each column based on the data.
engine : {'openpyxl', 'calamine', 'odf', 'pyxlsb', 'xlrd'}, default None
If io is not a buffer or path, this must be set to identify io.
Engine compatibility :
- ``openpyxl`` supports newer Excel file formats.
- ``calamine`` supports Excel (.xls, .xlsx, .xlsm, .xlsb)
and OpenDocument (.ods) file formats.
- ``odf`` supports OpenDocument file formats (.odf, .ods, .odt).
- ``pyxlsb`` supports Binary Excel files.
- ``xlrd`` supports old-style Excel files (.xls).
When ``engine=None``, the following logic will be used to determine the engine:
- If ``path_or_buffer`` is an OpenDocument format (.odf, .ods, .odt),
then `odf `_ will be used.
- Otherwise if ``path_or_buffer`` is an xls format, ``xlrd`` will be used.
- Otherwise if ``path_or_buffer`` is in xlsb format, ``pyxlsb`` will be used.
- Otherwise ``openpyxl`` will be used.
converters : dict, default None
Dict of functions for converting values in certain columns. Keys can
either be integers or column labels, values are functions that take one
input argument, the Excel cell content, and return the transformed
content.
true_values : list, default None
Values to consider as True.
false_values : list, default None
Values to consider as False.
skiprows : list-like, int, or callable, optional
Line numbers to skip (0-indexed) or number of lines to skip (int) at the
start of the file. If callable, the callable function will be evaluated
against the row indices, returning True if the row should be skipped and
False otherwise. An example of a valid callable argument would be ``lambda
x: x in [0, 2]``.
nrows : int, default None
Number of rows to parse.
na_values : scalar, str, list-like, or dict, default None
Additional strings to recognize as NA/NaN. If dict passed, specific
per-column NA values. By default the following values are interpreted
as NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
'1.#IND', '1.#QNAN', '', 'N/A', 'NA', 'NULL', 'NaN', 'None',
'n/a', 'nan', 'null'.
keep_default_na : bool, default True
Whether or not to include the default NaN values when parsing the data.
Depending on whether ``na_values`` is passed in, the behavior is as follows:
* If ``keep_default_na`` is True, and ``na_values`` are specified,
``na_values`` is appended to the default NaN values used for parsing.
* If ``keep_default_na`` is True, and ``na_values`` are not specified, only
the default NaN values are used for parsing.
* If ``keep_default_na`` is False, and ``na_values`` are specified, only
the NaN values specified ``na_values`` are used for parsing.
* If ``keep_default_na`` is False, and ``na_values`` are not specified, no
strings will be parsed as NaN.
Note that if `na_filter` is passed in as False, the ``keep_default_na`` and
``na_values`` parameters will be ignored.
na_filter : bool, default True
Detect missing value markers (empty strings and the value of na_values). In
data without any NAs, passing ``na_filter=False`` can improve the
performance of reading a large file.
verbose : bool, default False
Indicate number of NA values placed in non-numeric columns.
parse_dates : bool, list-like, or dict, default False
The behavior is as follows:
* ``bool``. If True -> try parsing the index.
* ``list`` of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
each as a separate date column.
* ``list`` of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as
a single date column.
* ``dict``, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call
result 'foo'
If a column or index contains an unparsable date, the entire column or
index will be returned unaltered as an object data type. If you don`t want to
parse some cells as date just change their type in Excel to "Text".
For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_excel``.
Note: A fast-path exists for iso8601-formatted dates.
date_parser : function, optional
Function to use for converting a sequence of string columns to an array of
datetime instances. The default uses ``dateutil.parser.parser`` to do the
conversion. Pandas will try to call `date_parser` in three different ways,
advancing to the next if an exception occurs: 1) Pass one or more arrays
(as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
string values from the columns defined by `parse_dates` into a single array
and pass that; and 3) call `date_parser` once for each row using one or
more strings (corresponding to the columns defined by `parse_dates`) as
arguments.
.. deprecated:: 2.0.0
Use ``date_format`` instead, or read in as ``object`` and then apply
:func:`to_datetime` as-needed.
date_format : str or dict of column -> format, default ``None``
If used in conjunction with ``parse_dates``, will parse dates according to this
format. For anything more complex,
please read in as ``object`` and then apply :func:`to_datetime` as-needed.
.. versionadded:: 2.0.0
thousands : str, default None
Thousands separator for parsing string columns to numeric. Note that
this parameter is only necessary for columns stored as TEXT in Excel,
any numeric columns will automatically be parsed, regardless of display
format.
decimal : str, default '.'
Character to recognize as decimal point for parsing string columns to numeric.
Note that this parameter is only necessary for columns stored as TEXT in Excel,
any numeric columns will automatically be parsed, regardless of display
format.(e.g. use ',' for European data).
.. versionadded:: 1.4.0
comment : str, default None
Comments out remainder of line. Pass a character or characters to this
argument to indicate comments in the input file. Any data between the
comment string and the end of the current line is ignored.
skipfooter : int, default 0
Rows at the end to skip (0-indexed).
storage_options : dict, optional
Extra options that make sense for a particular storage connection, e.g.
host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
are forwarded to ``urllib.request.Request`` as header options. For other
URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
details, and for more examples on storage options refer `here
`_.
dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
Back-end data type applied to the resultant :class:`DataFrame`
(still experimental). Behaviour is as follows:
* ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
(default).
* ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
DataFrame.
.. versionadded:: 2.0
engine_kwargs : dict, optional
Arbitrary keyword arguments passed to excel engine.
Returns
-------
DataFrame or dict of DataFrames
DataFrame from the passed in Excel file. See notes in sheet_name
argument for more information on when a dict of DataFrames is returned.
See Also
--------
DataFrame.to_excel : Write DataFrame to an Excel file.
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
read_csv : Read a comma-separated values (csv) file into DataFrame.
read_fwf : Read a table of fixed-width formatted lines into DataFrame.
Notes
-----
For specific information on the methods used for each Excel engine, refer to the pandas
:ref:`user guide `
Examples
--------
The file can be read using the file name as string or an open file object:
>>> pd.read_excel('tmp.xlsx', index_col=0) # doctest: +SKIP
Name Value
0 string1 1
1 string2 2
2 #Comment 3
>>> pd.read_excel(open('tmp.xlsx', 'rb'),
... sheet_name='Sheet3') # doctest: +SKIP
Unnamed: 0 Name Value
0 0 string1 1
1 1 string2 2
2 2 #Comment 3
Index and header can be specified via the `index_col` and `header` arguments
>>> pd.read_excel('tmp.xlsx', index_col=None, header=None) # doctest: +SKIP
0 1 2
0 NaN Name Value
1 0.0 string1 1
2 1.0 string2 2
3 2.0 #Comment 3
Column types are inferred but can be explicitly specified
>>> pd.read_excel('tmp.xlsx', index_col=0,
... dtype={'Name': str, 'Value': float}) # doctest: +SKIP
Name Value
0 string1 1.0
1 string2 2.0
2 #Comment 3.0
True, False, and NA values, and thousands separators have defaults,
but can be explicitly specified, too. Supply the values you would like
as strings or lists of strings!
>>> pd.read_excel('tmp.xlsx', index_col=0,
... na_values=['string1', 'string2']) # doctest: +SKIP
Name Value
0 NaN 1
1 NaN 2
2 #Comment 3
Comment lines in the excel input file can be skipped using the
``comment`` kwarg.
>>> pd.read_excel('tmp.xlsx', index_col=0, comment='#') # doctest: +SKIP
Name Value
0 string1 1.0
1 string2 2.0
2 None NaN
"""
pass
@overload
def read_excel(
io: (
FilePath
| ReadBuffer[bytes]
| ExcelFile
| Workbook
| Book
| OpenDocument
| pyxlsb.workbook.Workbook
),
sheet_name: None,
*,
header: int | Sequence[int] | None = ...,
names: ListLikeHashable | None = ...,
index_col: int | Sequence[int] | str | None = ...,
usecols: str | UsecolsArgType = ...,
dtype: str | Dtype | Mapping[str, str | Dtype] | None = ...,
engine: ExcelReadEngine | None = ...,
converters: Mapping[int | str, Callable[[object], object]] | None = ...,
true_values: Iterable[Hashable] | None = ...,
false_values: Iterable[Hashable] | None = ...,
skiprows: int | Sequence[int] | Callable[[object], bool] | None = ...,
nrows: int | None = ...,
na_values: Sequence[str] | dict[str | int, Sequence[str]] | None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool = ...,
parse_dates: (
bool
| Sequence[int]
| Sequence[Sequence[str] | Sequence[int]]
| dict[str, Sequence[int] | list[str]]
) = ...,
date_format: dict[Hashable, str] | str | None = ...,
thousands: str | None = ...,
decimal: str = ...,
comment: str | None = ...,
skipfooter: int = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | _NoDefaultDoNotUse = ...,
engine_kwargs: dict[str, Any] | None = ...,
) -> dict[str, DataFrame]: ...
@overload
# mypy says this won't be matched
def read_excel( # type: ignore[overload-cannot-match]
io: (
FilePath
| ReadBuffer[bytes]
| ExcelFile
| Workbook
| Book
| OpenDocument
| pyxlsb.workbook.Workbook
),
sheet_name: list[int | str],
*,
header: int | Sequence[int] | None = ...,
names: ListLikeHashable | None = ...,
index_col: int | Sequence[int] | str | None = ...,
usecols: str | UsecolsArgType = ...,
dtype: str | Dtype | Mapping[str, str | Dtype] | None = ...,
engine: ExcelReadEngine | None = ...,
converters: Mapping[int | str, Callable[[object], object]] | None = ...,
true_values: Iterable[Hashable] | None = ...,
false_values: Iterable[Hashable] | None = ...,
skiprows: int | Sequence[int] | Callable[[object], bool] | None = ...,
nrows: int | None = ...,
na_values: Sequence[str] | dict[str | int, Sequence[str]] | None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool = ...,
parse_dates: (
bool
| Sequence[int]
| Sequence[Sequence[str] | Sequence[int]]
| dict[str, Sequence[int] | list[str]]
) = ...,
date_format: dict[Hashable, str] | str | None = ...,
thousands: str | None = ...,
decimal: str = ...,
comment: str | None = ...,
skipfooter: int = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | _NoDefaultDoNotUse = ...,
engine_kwargs: dict[str, Any] | None = ...,
) -> dict[int | str, DataFrame]: ...
@overload
def read_excel(
io: (
FilePath
| ReadBuffer[bytes]
| ExcelFile
| Workbook
| Book
| OpenDocument
| pyxlsb.workbook.Workbook
),
sheet_name: int | str = ...,
*,
header: int | Sequence[int] | None = ...,
names: ListLikeHashable | None = ...,
index_col: int | Sequence[int] | str | None = ...,
usecols: str | UsecolsArgType = ...,
dtype: str | Dtype | Mapping[str, str | Dtype] | None = ...,
engine: ExcelReadEngine | None = ...,
converters: Mapping[int | str, Callable[[object], object]] | None = ...,
true_values: Iterable[Hashable] | None = ...,
false_values: Iterable[Hashable] | None = ...,
skiprows: int | Sequence[int] | Callable[[object], bool] | None = ...,
nrows: int | None = ...,
na_values: Sequence[str] | dict[str | int, Sequence[str]] | None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool = ...,
parse_dates: (
bool
| Sequence[int]
| Sequence[Sequence[str] | Sequence[int]]
| dict[str, Sequence[int] | list[str]]
) = ...,
date_format: dict[Hashable, str] | str | None = ...,
thousands: str | None = ...,
decimal: str = ...,
comment: str | None = ...,
skipfooter: int = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | _NoDefaultDoNotUse = ...,
engine_kwargs: dict[str, Any] | None = ...,
) -> DataFrame: ...
class ExcelWriter:
def __init__(
self,
path: FilePath | WriteExcelBuffer | ExcelWriter,
engine: ExcelWriteEngine | Literal["auto"] | None = ...,
date_format: str | None = ...,
datetime_format: str | None = ...,
mode: Literal["w", "a"] = ...,
storage_options: StorageOptions = ...,
if_sheet_exists: ExcelWriterIfSheetExists | None = ...,
engine_kwargs: dict[str, Any] | None = ...,
) -> None: ...
@property
def supported_extensions(self) -> tuple[str, ...]: ...
@property
def engine(self) -> ExcelWriteEngine: ...
@property
def sheets(self) -> dict[str, Any]: ...
@property
def book(self) -> Workbook | OpenDocument: ...
@property
def date_format(self) -> str: ...
@property
def datetime_format(self) -> str: ...
@property
def if_sheet_exists(self) -> Literal["error", "new", "replace", "overlay"]: ...
def __fspath__(self) -> str: ...
def __enter__(self) -> Self: ...
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> None: ...
def close(self) -> None: ...
class ExcelFile:
engine = ...
io: FilePath | ReadBuffer[bytes] | bytes = ...
def __init__(
self,
path_or_buffer: FilePath | ReadBuffer[bytes] | bytes,
engine: ExcelReadEngine | None = ...,
storage_options: StorageOptions = ...,
engine_kwargs: dict[str, Any] | None = ...,
) -> None: ...
def __fspath__(self): ...
@overload
def parse(
self,
sheet_name: list[int | str] | None,
header: int | Sequence[int] | None = ...,
names: ListLikeHashable | None = ...,
index_col: int | Sequence[int] | None = ...,
usecols: str | UsecolsArgType = ...,
converters: dict[int | str, Callable[[object], object]] | None = ...,
true_values: Iterable[Hashable] | None = ...,
false_values: Iterable[Hashable] | None = ...,
skiprows: int | Sequence[int] | Callable[[object], bool] | None = ...,
nrows: int | None = ...,
na_values: Sequence[str] | dict[str | int, Sequence[str]] = ...,
parse_dates: (
bool
| Sequence[int]
| Sequence[Sequence[str] | Sequence[int]]
| dict[str, Sequence[int] | list[str]]
) = ...,
date_parser: Callable | None = ...,
thousands: str | None = ...,
comment: str | None = ...,
skipfooter: int = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
**kwds: Any,
) -> dict[int | str, DataFrame]: ...
@overload
def parse(
self,
sheet_name: int | str,
header: int | Sequence[int] | None = ...,
names: ListLikeHashable | None = ...,
index_col: int | Sequence[int] | None = ...,
usecols: str | UsecolsArgType = ...,
converters: dict[int | str, Callable[[object], object]] | None = ...,
true_values: Iterable[Hashable] | None = ...,
false_values: Iterable[Hashable] | None = ...,
skiprows: int | Sequence[int] | Callable[[object], bool] | None = ...,
nrows: int | None = ...,
na_values: Sequence[str] | dict[str | int, Sequence[str]] = ...,
parse_dates: (
bool
| Sequence[int]
| Sequence[Sequence[str] | Sequence[int]]
| dict[str, Sequence[int] | list[str]]
) = ...,
date_parser: Callable | None = ...,
thousands: str | None = ...,
comment: str | None = ...,
skipfooter: int = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
**kwds: Any,
) -> DataFrame: ...
@property
def book(self) -> Workbook | Book | OpenDocument | pyxlsb.workbook.Workbook: ...
@property
def sheet_names(self) -> list[int | str]: ...
def close(self) -> None: ...
def __enter__(self) -> Self: ...
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> None: ...
def __del__(self) -> None: ...