from __future__ import annotations
import abc
import datetime
from functools import partial
from io import BytesIO
import os
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Iterable,
List,
Literal,
Mapping,
Sequence,
Union,
cast,
overload,
)
import zipfile
from pandas._config import config
from pandas._libs import lib
from pandas._libs.parsers import STR_NA_VALUES
from pandas._typing import (
DtypeArg,
DtypeBackend,
FilePath,
IntStrT,
ReadBuffer,
StorageOptions,
WriteExcelBuffer,
)
from pandas.compat._optional import (
get_version,
import_optional_dependency,
)
from pandas.errors import EmptyDataError
from pandas.util._decorators import (
Appender,
doc,
)
from pandas.util._validators import check_dtype_backend
from pandas.core.dtypes.common import (
is_bool,
is_float,
is_integer,
is_list_like,
)
from pandas.core.frame import DataFrame
from pandas.core.shared_docs import _shared_docs
from pandas.util.version import Version
from pandas.io.common import (
IOHandles,
get_handle,
stringify_path,
validate_header_arg,
)
from pandas.io.excel._util import (
fill_mi_header,
get_default_engine,
get_writer,
maybe_convert_usecols,
pop_header_name,
)
from pandas.io.parsers import TextParser
from pandas.io.parsers.readers import validate_integer
_read_excel_doc = (
"""
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``.
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, 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.
If converters are specified, they will be applied INSTEAD
of dtype conversion.
engine : str, default None
If io is not a buffer or path, this must be set to identify io.
Supported engines: "xlrd", "openpyxl", "odf", "pyxlsb".
Engine compatibility :
- "xlrd" supports old-style Excel files (.xls).
- "openpyxl" supports newer Excel file formats.
- "odf" supports OpenDocument file formats (.odf, .ods, .odt).
- "pyxlsb" supports Binary Excel files.
.. versionchanged:: 1.2.0
The engine `xlrd `_
now only supports old-style ``.xls`` files.
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.
.. versionadded:: 1.3.0
- Otherwise ``openpyxl`` will be used.
.. versionchanged:: 1.3.0
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: '"""
+ fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent=" ")
+ """'.
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}
.. versionadded:: 1.2.0
dtype_backend : {{"numpy_nullable", "pyarrow"}}, defaults to NumPy backed DataFrames
Which dtype_backend to use, e.g. whether a DataFrame should have NumPy
arrays, nullable dtypes are used for all dtypes that have a nullable
implementation when "numpy_nullable" is set, pyarrow is used for all
dtypes if "pyarrow" is set.
The dtype_backends are still experimential.
.. versionadded:: 2.0
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.
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
"""
)
@overload
def read_excel(
io,
# sheet name is str or int -> DataFrame
sheet_name: str | int = ...,
*,
header: int | Sequence[int] | None = ...,
names: list[str] | None = ...,
index_col: int | Sequence[int] | None = ...,
usecols: int
| str
| Sequence[int]
| Sequence[str]
| Callable[[str], bool]
| None = ...,
dtype: DtypeArg | None = ...,
engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb"] | None = ...,
converters: dict[str, Callable] | dict[int, Callable] | None = ...,
true_values: Iterable[Hashable] | None = ...,
false_values: Iterable[Hashable] | None = ...,
skiprows: Sequence[int] | int | Callable[[int], object] | None = ...,
nrows: int | None = ...,
na_values=...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool = ...,
parse_dates: list | dict | bool = ...,
date_parser: Callable | lib.NoDefault = ...,
date_format: dict[Hashable, str] | str | None = ...,
thousands: str | None = ...,
decimal: str = ...,
comment: str | None = ...,
skipfooter: int = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
) -> DataFrame:
...
@overload
def read_excel(
io,
# sheet name is list or None -> dict[IntStrT, DataFrame]
sheet_name: list[IntStrT] | None,
*,
header: int | Sequence[int] | None = ...,
names: list[str] | None = ...,
index_col: int | Sequence[int] | None = ...,
usecols: int
| str
| Sequence[int]
| Sequence[str]
| Callable[[str], bool]
| None = ...,
dtype: DtypeArg | None = ...,
engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb"] | None = ...,
converters: dict[str, Callable] | dict[int, Callable] | None = ...,
true_values: Iterable[Hashable] | None = ...,
false_values: Iterable[Hashable] | None = ...,
skiprows: Sequence[int] | int | Callable[[int], object] | None = ...,
nrows: int | None = ...,
na_values=...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool = ...,
parse_dates: list | dict | bool = ...,
date_parser: Callable | lib.NoDefault = ...,
date_format: dict[Hashable, str] | str | None = ...,
thousands: str | None = ...,
decimal: str = ...,
comment: str | None = ...,
skipfooter: int = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
) -> dict[IntStrT, DataFrame]:
...
@doc(storage_options=_shared_docs["storage_options"])
@Appender(_read_excel_doc)
def read_excel(
io,
sheet_name: str | int | list[IntStrT] | None = 0,
*,
header: int | Sequence[int] | None = 0,
names: list[str] | None = None,
index_col: int | Sequence[int] | None = None,
usecols: int
| str
| Sequence[int]
| Sequence[str]
| Callable[[str], bool]
| None = None,
dtype: DtypeArg | None = None,
engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb"] | None = None,
converters: dict[str, Callable] | dict[int, Callable] | None = None,
true_values: Iterable[Hashable] | None = None,
false_values: Iterable[Hashable] | None = None,
skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
nrows: int | None = None,
na_values=None,
keep_default_na: bool = True,
na_filter: bool = True,
verbose: bool = False,
parse_dates: list | dict | bool = False,
date_parser: Callable | lib.NoDefault = lib.no_default,
date_format: dict[Hashable, str] | str | None = None,
thousands: str | None = None,
decimal: str = ".",
comment: str | None = None,
skipfooter: int = 0,
storage_options: StorageOptions = None,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
) -> DataFrame | dict[IntStrT, DataFrame]:
check_dtype_backend(dtype_backend)
should_close = False
if not isinstance(io, ExcelFile):
should_close = True
io = ExcelFile(io, storage_options=storage_options, engine=engine)
elif engine and engine != io.engine:
raise ValueError(
"Engine should not be specified when passing "
"an ExcelFile - ExcelFile already has the engine set"
)
try:
data = io.parse(
sheet_name=sheet_name,
header=header,
names=names,
index_col=index_col,
usecols=usecols,
dtype=dtype,
converters=converters,
true_values=true_values,
false_values=false_values,
skiprows=skiprows,
nrows=nrows,
na_values=na_values,
keep_default_na=keep_default_na,
na_filter=na_filter,
verbose=verbose,
parse_dates=parse_dates,
date_parser=date_parser,
date_format=date_format,
thousands=thousands,
decimal=decimal,
comment=comment,
skipfooter=skipfooter,
dtype_backend=dtype_backend,
)
finally:
# make sure to close opened file handles
if should_close:
io.close()
return data
class BaseExcelReader(metaclass=abc.ABCMeta):
def __init__(
self, filepath_or_buffer, storage_options: StorageOptions = None
) -> None:
# First argument can also be bytes, so create a buffer
if isinstance(filepath_or_buffer, bytes):
filepath_or_buffer = BytesIO(filepath_or_buffer)
self.handles = IOHandles(
handle=filepath_or_buffer, compression={"method": None}
)
if not isinstance(filepath_or_buffer, (ExcelFile, self._workbook_class)):
self.handles = get_handle(
filepath_or_buffer, "rb", storage_options=storage_options, is_text=False
)
if isinstance(self.handles.handle, self._workbook_class):
self.book = self.handles.handle
elif hasattr(self.handles.handle, "read"):
# N.B. xlrd.Book has a read attribute too
self.handles.handle.seek(0)
try:
self.book = self.load_workbook(self.handles.handle)
except Exception:
self.close()
raise
else:
raise ValueError(
"Must explicitly set engine if not passing in buffer or path for io."
)
@property
@abc.abstractmethod
def _workbook_class(self):
pass
@abc.abstractmethod
def load_workbook(self, filepath_or_buffer):
pass
def close(self) -> None:
if hasattr(self, "book"):
if hasattr(self.book, "close"):
# pyxlsb: opens a TemporaryFile
# openpyxl: https://stackoverflow.com/questions/31416842/
# openpyxl-does-not-close-excel-workbook-in-read-only-mode
self.book.close()
elif hasattr(self.book, "release_resources"):
# xlrd
# https://github.com/python-excel/xlrd/blob/2.0.1/xlrd/book.py#L548
self.book.release_resources()
self.handles.close()
@property
@abc.abstractmethod
def sheet_names(self) -> list[str]:
pass
@abc.abstractmethod
def get_sheet_by_name(self, name: str):
pass
@abc.abstractmethod
def get_sheet_by_index(self, index: int):
pass
@abc.abstractmethod
def get_sheet_data(self, sheet, rows: int | None = None):
pass
def raise_if_bad_sheet_by_index(self, index: int) -> None:
n_sheets = len(self.sheet_names)
if index >= n_sheets:
raise ValueError(
f"Worksheet index {index} is invalid, {n_sheets} worksheets found"
)
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,
header: int | Sequence[int] | None = 0,
names=None,
index_col: int | Sequence[int] | None = None,
usecols=None,
dtype: DtypeArg | None = None,
true_values: Iterable[Hashable] | None = None,
false_values: Iterable[Hashable] | None = None,
skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
nrows: int | None = None,
na_values=None,
verbose: bool = False,
parse_dates: list | dict | bool = False,
date_parser: Callable | lib.NoDefault = lib.no_default,
date_format: dict[Hashable, str] | str | None = None,
thousands: str | None = None,
decimal: str = ".",
comment: str | None = None,
skipfooter: int = 0,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
**kwds,
):
validate_header_arg(header)
validate_integer("nrows", nrows)
ret_dict = False
# Keep sheetname to maintain backwards compatibility.
sheets: list[int] | list[str]
if isinstance(sheet_name, list):
sheets = sheet_name
ret_dict = True
elif sheet_name is None:
sheets = self.sheet_names
ret_dict = True
elif isinstance(sheet_name, str):
sheets = [sheet_name]
else:
sheets = [sheet_name]
# handle same-type duplicates.
sheets = cast(Union[List[int], List[str]], list(dict.fromkeys(sheets).keys()))
output = {}
last_sheetname = None
for asheetname in sheets:
last_sheetname = asheetname
if verbose:
print(f"Reading sheet {asheetname}")
if isinstance(asheetname, str):
sheet = self.get_sheet_by_name(asheetname)
else: # assume an integer if not a string
sheet = self.get_sheet_by_index(asheetname)
file_rows_needed = self._calc_rows(header, index_col, skiprows, nrows)
data = self.get_sheet_data(sheet, file_rows_needed)
if hasattr(sheet, "close"):
# pyxlsb opens two TemporaryFiles
sheet.close()
usecols = maybe_convert_usecols(usecols)
if not data:
output[asheetname] = DataFrame()
continue
is_list_header = False
is_len_one_list_header = False
if is_list_like(header):
assert isinstance(header, Sequence)
is_list_header = True
if len(header) == 1:
is_len_one_list_header = True
if is_len_one_list_header:
header = cast(Sequence[int], header)[0]
# forward fill and pull out names for MultiIndex column
header_names = None
if header is not None and is_list_like(header):
assert isinstance(header, Sequence)
header_names = []
control_row = [True] * len(data[0])
for row in header:
if is_integer(skiprows):
assert isinstance(skiprows, int)
row += skiprows
if row > len(data) - 1:
raise ValueError(
f"header index {row} exceeds maximum index "
f"{len(data) - 1} of data.",
)
data[row], control_row = fill_mi_header(data[row], control_row)
if index_col is not None:
header_name, _ = pop_header_name(data[row], index_col)
header_names.append(header_name)
# If there is a MultiIndex header and an index then there is also
# a row containing just the index name(s)
has_index_names = False
if is_list_header and not is_len_one_list_header and index_col is not None:
index_col_list: Sequence[int]
if isinstance(index_col, int):
index_col_list = [index_col]
else:
assert isinstance(index_col, Sequence)
index_col_list = index_col
# We have to handle mi without names. If any of the entries in the data
# columns are not empty, this is a regular row
assert isinstance(header, Sequence)
if len(header) < len(data):
potential_index_names = data[len(header)]
potential_data = [
x
for i, x in enumerate(potential_index_names)
if not control_row[i] and i not in index_col_list
]
has_index_names = all(x == "" or x is None for x in potential_data)
if is_list_like(index_col):
# Forward fill values for MultiIndex index.
if header is None:
offset = 0
elif isinstance(header, int):
offset = 1 + header
else:
offset = 1 + max(header)
# GH34673: if MultiIndex names present and not defined in the header,
# offset needs to be incremented so that forward filling starts
# from the first MI value instead of the name
if has_index_names:
offset += 1
# Check if we have an empty dataset
# before trying to collect data.
if offset < len(data):
assert isinstance(index_col, Sequence)
for col in index_col:
last = data[offset][col]
for row in range(offset + 1, len(data)):
if data[row][col] == "" or data[row][col] is None:
data[row][col] = last
else:
last = data[row][col]
# GH 12292 : error when read one empty column from excel file
try:
parser = TextParser(
data,
names=names,
header=header,
index_col=index_col,
has_index_names=has_index_names,
dtype=dtype,
true_values=true_values,
false_values=false_values,
skiprows=skiprows,
nrows=nrows,
na_values=na_values,
skip_blank_lines=False, # GH 39808
parse_dates=parse_dates,
date_parser=date_parser,
date_format=date_format,
thousands=thousands,
decimal=decimal,
comment=comment,
skipfooter=skipfooter,
usecols=usecols,
dtype_backend=dtype_backend,
**kwds,
)
output[asheetname] = parser.read(nrows=nrows)
if header_names:
output[asheetname].columns = output[asheetname].columns.set_names(
header_names
)
except EmptyDataError:
# No Data, return an empty DataFrame
output[asheetname] = DataFrame()
except Exception as err:
err.args = (f"{err.args[0]} (sheet: {asheetname})", *err.args[1:])
raise err
if last_sheetname is None:
raise ValueError("Sheet name is an empty list")
if ret_dict:
return output
else:
return output[last_sheetname]
@doc(storage_options=_shared_docs["storage_options"])
class ExcelWriter(metaclass=abc.ABCMeta):
"""
Class for writing DataFrame objects into excel sheets.
Default is to use:
* `xlsxwriter `__ for xlsx files if xlsxwriter
is installed otherwise `openpyxl `__
* `odswriter `__ for ods files
See ``DataFrame.to_excel`` for typical usage.
The writer should be used as a context manager. Otherwise, call `close()` to save
and close any opened file handles.
Parameters
----------
path : str or typing.BinaryIO
Path to xls or xlsx or ods file.
engine : str (optional)
Engine to use for writing. If None, defaults to
``io.excel..writer``. NOTE: can only be passed as a keyword
argument.
date_format : str, default None
Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
datetime_format : str, default None
Format string for datetime objects written into Excel files.
(e.g. 'YYYY-MM-DD HH:MM:SS').
mode : {{'w', 'a'}}, default 'w'
File mode to use (write or append). Append does not work with fsspec URLs.
{storage_options}
.. versionadded:: 1.2.0
if_sheet_exists : {{'error', 'new', 'replace', 'overlay'}}, default 'error'
How to behave when trying to write to a sheet that already
exists (append mode only).
* error: raise a ValueError.
* new: Create a new sheet, with a name determined by the engine.
* replace: Delete the contents of the sheet before writing to it.
* overlay: Write contents to the existing sheet without removing the old
contents.
.. versionadded:: 1.3.0
.. versionchanged:: 1.4.0
Added ``overlay`` option
engine_kwargs : dict, optional
Keyword arguments to be passed into the engine. These will be passed to
the following functions of the respective engines:
* xlsxwriter: ``xlsxwriter.Workbook(file, **engine_kwargs)``
* openpyxl (write mode): ``openpyxl.Workbook(**engine_kwargs)``
* openpyxl (append mode): ``openpyxl.load_workbook(file, **engine_kwargs)``
* odswriter: ``odf.opendocument.OpenDocumentSpreadsheet(**engine_kwargs)``
.. versionadded:: 1.3.0
Notes
-----
For compatibility with CSV writers, ExcelWriter serializes lists
and dicts to strings before writing.
Examples
--------
Default usage:
>>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"]) # doctest: +SKIP
>>> with pd.ExcelWriter("path_to_file.xlsx") as writer:
... df.to_excel(writer) # doctest: +SKIP
To write to separate sheets in a single file:
>>> df1 = pd.DataFrame([["AAA", "BBB"]], columns=["Spam", "Egg"]) # doctest: +SKIP
>>> df2 = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"]) # doctest: +SKIP
>>> with pd.ExcelWriter("path_to_file.xlsx") as writer:
... df1.to_excel(writer, sheet_name="Sheet1") # doctest: +SKIP
... df2.to_excel(writer, sheet_name="Sheet2") # doctest: +SKIP
You can set the date format or datetime format:
>>> from datetime import date, datetime # doctest: +SKIP
>>> df = pd.DataFrame(
... [
... [date(2014, 1, 31), date(1999, 9, 24)],
... [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)],
... ],
... index=["Date", "Datetime"],
... columns=["X", "Y"],
... ) # doctest: +SKIP
>>> with pd.ExcelWriter(
... "path_to_file.xlsx",
... date_format="YYYY-MM-DD",
... datetime_format="YYYY-MM-DD HH:MM:SS"
... ) as writer:
... df.to_excel(writer) # doctest: +SKIP
You can also append to an existing Excel file:
>>> with pd.ExcelWriter("path_to_file.xlsx", mode="a", engine="openpyxl") as writer:
... df.to_excel(writer, sheet_name="Sheet3") # doctest: +SKIP
Here, the `if_sheet_exists` parameter can be set to replace a sheet if it
already exists:
>>> with ExcelWriter(
... "path_to_file.xlsx",
... mode="a",
... engine="openpyxl",
... if_sheet_exists="replace",
... ) as writer:
... df.to_excel(writer, sheet_name="Sheet1") # doctest: +SKIP
You can also write multiple DataFrames to a single sheet. Note that the
``if_sheet_exists`` parameter needs to be set to ``overlay``:
>>> with ExcelWriter("path_to_file.xlsx",
... mode="a",
... engine="openpyxl",
... if_sheet_exists="overlay",
... ) as writer:
... df1.to_excel(writer, sheet_name="Sheet1")
... df2.to_excel(writer, sheet_name="Sheet1", startcol=3) # doctest: +SKIP
You can store Excel file in RAM:
>>> import io
>>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])
>>> buffer = io.BytesIO()
>>> with pd.ExcelWriter(buffer) as writer:
... df.to_excel(writer)
You can pack Excel file into zip archive:
>>> import zipfile # doctest: +SKIP
>>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"]) # doctest: +SKIP
>>> with zipfile.ZipFile("path_to_file.zip", "w") as zf:
... with zf.open("filename.xlsx", "w") as buffer:
... with pd.ExcelWriter(buffer) as writer:
... df.to_excel(writer) # doctest: +SKIP
You can specify additional arguments to the underlying engine:
>>> with pd.ExcelWriter(
... "path_to_file.xlsx",
... engine="xlsxwriter",
... engine_kwargs={{"options": {{"nan_inf_to_errors": True}}}}
... ) as writer:
... df.to_excel(writer) # doctest: +SKIP
In append mode, ``engine_kwargs`` are passed through to
openpyxl's ``load_workbook``:
>>> with pd.ExcelWriter(
... "path_to_file.xlsx",
... engine="openpyxl",
... mode="a",
... engine_kwargs={{"keep_vba": True}}
... ) as writer:
... df.to_excel(writer, sheet_name="Sheet2") # doctest: +SKIP
"""
# Defining an ExcelWriter implementation (see abstract methods for more...)
# - Mandatory
# - ``write_cells(self, cells, sheet_name=None, startrow=0, startcol=0)``
# --> called to write additional DataFrames to disk
# - ``_supported_extensions`` (tuple of supported extensions), used to
# check that engine supports the given extension.
# - ``_engine`` - string that gives the engine name. Necessary to
# instantiate class directly and bypass ``ExcelWriterMeta`` engine
# lookup.
# - ``save(self)`` --> called to save file to disk
# - Mostly mandatory (i.e. should at least exist)
# - book, cur_sheet, path
# - Optional:
# - ``__init__(self, path, engine=None, **kwargs)`` --> always called
# with path as first argument.
# You also need to register the class with ``register_writer()``.
# Technically, ExcelWriter implementations don't need to subclass
# ExcelWriter.
_engine: str
_supported_extensions: tuple[str, ...]
def __new__(
cls: type[ExcelWriter],
path: FilePath | WriteExcelBuffer | ExcelWriter,
engine: str | None = None,
date_format: str | None = None,
datetime_format: str | None = None,
mode: str = "w",
storage_options: StorageOptions = None,
if_sheet_exists: Literal["error", "new", "replace", "overlay"] | None = None,
engine_kwargs: dict | None = None,
) -> ExcelWriter:
# only switch class if generic(ExcelWriter)
if cls is ExcelWriter:
if engine is None or (isinstance(engine, str) and engine == "auto"):
if isinstance(path, str):
ext = os.path.splitext(path)[-1][1:]
else:
ext = "xlsx"
try:
engine = config.get_option(f"io.excel.{ext}.writer", silent=True)
if engine == "auto":
engine = get_default_engine(ext, mode="writer")
except KeyError as err:
raise ValueError(f"No engine for filetype: '{ext}'") from err
# for mypy
assert engine is not None
cls = get_writer(engine)
return object.__new__(cls)
# declare external properties you can count on
_path = None
@property
def supported_extensions(self) -> tuple[str, ...]:
"""Extensions that writer engine supports."""
return self._supported_extensions
@property
def engine(self) -> str:
"""Name of engine."""
return self._engine
@property
@abc.abstractmethod
def sheets(self) -> dict[str, Any]:
"""Mapping of sheet names to sheet objects."""
@property
@abc.abstractmethod
def book(self):
"""
Book instance. Class type will depend on the engine used.
This attribute can be used to access engine-specific features.
"""
@abc.abstractmethod
def _write_cells(
self,
cells,
sheet_name: str | None = None,
startrow: int = 0,
startcol: int = 0,
freeze_panes: tuple[int, int] | None = None,
) -> None:
"""
Write given formatted cells into Excel an excel sheet
Parameters
----------
cells : generator
cell of formatted data to save to Excel sheet
sheet_name : str, default None
Name of Excel sheet, if None, then use self.cur_sheet
startrow : upper left cell row to dump data frame
startcol : upper left cell column to dump data frame
freeze_panes: int tuple of length 2
contains the bottom-most row and right-most column to freeze
"""
@abc.abstractmethod
def _save(self) -> None:
"""
Save workbook to disk.
"""
def __init__(
self,
path: FilePath | WriteExcelBuffer | ExcelWriter,
engine: str | None = None,
date_format: str | None = None,
datetime_format: str | None = None,
mode: str = "w",
storage_options: StorageOptions = None,
if_sheet_exists: str | None = None,
engine_kwargs: dict[str, Any] | None = None,
) -> None:
# validate that this engine can handle the extension
if isinstance(path, str):
ext = os.path.splitext(path)[-1]
self.check_extension(ext)
# use mode to open the file
if "b" not in mode:
mode += "b"
# use "a" for the user to append data to excel but internally use "r+" to let
# the excel backend first read the existing file and then write any data to it
mode = mode.replace("a", "r+")
if if_sheet_exists not in (None, "error", "new", "replace", "overlay"):
raise ValueError(
f"'{if_sheet_exists}' is not valid for if_sheet_exists. "
"Valid options are 'error', 'new', 'replace' and 'overlay'."
)
if if_sheet_exists and "r+" not in mode:
raise ValueError("if_sheet_exists is only valid in append mode (mode='a')")
if if_sheet_exists is None:
if_sheet_exists = "error"
self._if_sheet_exists = if_sheet_exists
# cast ExcelWriter to avoid adding 'if self._handles is not None'
self._handles = IOHandles(
cast(IO[bytes], path), compression={"compression": None}
)
if not isinstance(path, ExcelWriter):
self._handles = get_handle(
path, mode, storage_options=storage_options, is_text=False
)
self._cur_sheet = None
if date_format is None:
self._date_format = "YYYY-MM-DD"
else:
self._date_format = date_format
if datetime_format is None:
self._datetime_format = "YYYY-MM-DD HH:MM:SS"
else:
self._datetime_format = datetime_format
self._mode = mode
@property
def date_format(self) -> str:
"""
Format string for dates written into Excel files (e.g. ‘YYYY-MM-DD’).
"""
return self._date_format
@property
def datetime_format(self) -> str:
"""
Format string for dates written into Excel files (e.g. ‘YYYY-MM-DD’).
"""
return self._datetime_format
@property
def if_sheet_exists(self) -> str:
"""
How to behave when writing to a sheet that already exists in append mode.
"""
return self._if_sheet_exists
def __fspath__(self) -> str:
return getattr(self._handles.handle, "name", "")
def _get_sheet_name(self, sheet_name: str | None) -> str:
if sheet_name is None:
sheet_name = self._cur_sheet
if sheet_name is None: # pragma: no cover
raise ValueError("Must pass explicit sheet_name or set _cur_sheet property")
return sheet_name
def _value_with_fmt(self, val) -> tuple[object, str | None]:
"""
Convert numpy types to Python types for the Excel writers.
Parameters
----------
val : object
Value to be written into cells
Returns
-------
Tuple with the first element being the converted value and the second
being an optional format
"""
fmt = None
if is_integer(val):
val = int(val)
elif is_float(val):
val = float(val)
elif is_bool(val):
val = bool(val)
elif isinstance(val, datetime.datetime):
fmt = self._datetime_format
elif isinstance(val, datetime.date):
fmt = self._date_format
elif isinstance(val, datetime.timedelta):
val = val.total_seconds() / 86400
fmt = "0"
else:
val = str(val)
return val, fmt
@classmethod
def check_extension(cls, ext: str) -> Literal[True]:
"""
checks that path's extension against the Writer's supported
extensions. If it isn't supported, raises UnsupportedFiletypeError.
"""
if ext.startswith("."):
ext = ext[1:]
if not any(ext in extension for extension in cls._supported_extensions):
raise ValueError(f"Invalid extension for engine '{cls.engine}': '{ext}'")
return True
# Allow use as a contextmanager
def __enter__(self) -> ExcelWriter:
return self
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> None:
self.close()
def close(self) -> None:
"""synonym for save, to make it more file-like"""
self._save()
self._handles.close()
XLS_SIGNATURES = (
b"\x09\x00\x04\x00\x07\x00\x10\x00", # BIFF2
b"\x09\x02\x06\x00\x00\x00\x10\x00", # BIFF3
b"\x09\x04\x06\x00\x00\x00\x10\x00", # BIFF4
b"\xD0\xCF\x11\xE0\xA1\xB1\x1A\xE1", # Compound File Binary
)
ZIP_SIGNATURE = b"PK\x03\x04"
PEEK_SIZE = max(map(len, XLS_SIGNATURES + (ZIP_SIGNATURE,)))
@doc(storage_options=_shared_docs["storage_options"])
def inspect_excel_format(
content_or_path: FilePath | ReadBuffer[bytes],
storage_options: StorageOptions = None,
) -> str | None:
"""
Inspect the path or content of an excel file and get its format.
Adopted from xlrd: https://github.com/python-excel/xlrd.
Parameters
----------
content_or_path : str or file-like object
Path to file or content of file to inspect. May be a URL.
{storage_options}
Returns
-------
str or None
Format of file if it can be determined.
Raises
------
ValueError
If resulting stream is empty.
BadZipFile
If resulting stream does not have an XLS signature and is not a valid zipfile.
"""
if isinstance(content_or_path, bytes):
content_or_path = BytesIO(content_or_path)
with get_handle(
content_or_path, "rb", storage_options=storage_options, is_text=False
) as handle:
stream = handle.handle
stream.seek(0)
buf = stream.read(PEEK_SIZE)
if buf is None:
raise ValueError("stream is empty")
assert isinstance(buf, bytes)
peek = buf
stream.seek(0)
if any(peek.startswith(sig) for sig in XLS_SIGNATURES):
return "xls"
elif not peek.startswith(ZIP_SIGNATURE):
return None
with zipfile.ZipFile(stream) as zf:
# Workaround for some third party files that use forward slashes and
# lower case names.
component_names = [
name.replace("\\", "/").lower() for name in zf.namelist()
]
if "xl/workbook.xml" in component_names:
return "xlsx"
if "xl/workbook.bin" in component_names:
return "xlsb"
if "content.xml" in component_names:
return "ods"
return "zip"
class ExcelFile:
"""
Class for parsing tabular Excel sheets into DataFrame objects.
See read_excel for more documentation.
Parameters
----------
path_or_buffer : str, bytes, path object (pathlib.Path or py._path.local.LocalPath),
A file-like object, xlrd workbook or openpyxl workbook.
If a string or path object, expected to be a path to a
.xls, .xlsx, .xlsb, .xlsm, .odf, .ods, or .odt file.
engine : str, default None
If io is not a buffer or path, this must be set to identify io.
Supported engines: ``xlrd``, ``openpyxl``, ``odf``, ``pyxlsb``
Engine compatibility :
- ``xlrd`` supports old-style Excel files (.xls).
- ``openpyxl`` supports newer Excel file formats.
- ``odf`` supports OpenDocument file formats (.odf, .ods, .odt).
- ``pyxlsb`` supports Binary Excel files.
.. versionchanged:: 1.2.0
The engine `xlrd `_
now only supports old-style ``.xls`` files.
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.
.. versionadded:: 1.3.0
- Otherwise if `openpyxl `_ is installed,
then ``openpyxl`` will be used.
- Otherwise if ``xlrd >= 2.0`` is installed, a ``ValueError`` will be raised.
.. warning::
Please do not report issues when using ``xlrd`` to read ``.xlsx`` files.
This is not supported, switch to using ``openpyxl`` instead.
"""
from pandas.io.excel._odfreader import ODFReader
from pandas.io.excel._openpyxl import OpenpyxlReader
from pandas.io.excel._pyxlsb import PyxlsbReader
from pandas.io.excel._xlrd import XlrdReader
_engines: Mapping[str, Any] = {
"xlrd": XlrdReader,
"openpyxl": OpenpyxlReader,
"odf": ODFReader,
"pyxlsb": PyxlsbReader,
}
def __init__(
self,
path_or_buffer,
engine: str | None = None,
storage_options: StorageOptions = None,
) -> None:
if engine is not None and engine not in self._engines:
raise ValueError(f"Unknown engine: {engine}")
# First argument can also be bytes, so create a buffer
if isinstance(path_or_buffer, bytes):
path_or_buffer = BytesIO(path_or_buffer)
# Could be a str, ExcelFile, Book, etc.
self.io = path_or_buffer
# Always a string
self._io = stringify_path(path_or_buffer)
# Determine xlrd version if installed
if import_optional_dependency("xlrd", errors="ignore") is None:
xlrd_version = None
else:
import xlrd
xlrd_version = Version(get_version(xlrd))
if engine is None:
# Only determine ext if it is needed
ext: str | None
if xlrd_version is not None and isinstance(path_or_buffer, xlrd.Book):
ext = "xls"
else:
ext = inspect_excel_format(
content_or_path=path_or_buffer, storage_options=storage_options
)
if ext is None:
raise ValueError(
"Excel file format cannot be determined, you must specify "
"an engine manually."
)
engine = config.get_option(f"io.excel.{ext}.reader", silent=True)
if engine == "auto":
engine = get_default_engine(ext, mode="reader")
assert engine is not None
self.engine = engine
self.storage_options = storage_options
self._reader = self._engines[engine](self._io, storage_options=storage_options)
def __fspath__(self):
return self._io
def parse(
self,
sheet_name: str | int | list[int] | list[str] | None = 0,
header: int | Sequence[int] | None = 0,
names=None,
index_col: int | Sequence[int] | None = None,
usecols=None,
converters=None,
true_values: Iterable[Hashable] | None = None,
false_values: Iterable[Hashable] | None = None,
skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
nrows: int | None = None,
na_values=None,
parse_dates: list | dict | bool = False,
date_parser: Callable | lib.NoDefault = lib.no_default,
date_format: str | dict[Hashable, str] | None = None,
thousands: str | None = None,
comment: str | None = None,
skipfooter: int = 0,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
**kwds,
) -> DataFrame | dict[str, DataFrame] | dict[int, DataFrame]:
"""
Parse specified sheet(s) into a DataFrame.
Equivalent to read_excel(ExcelFile, ...) See the read_excel
docstring for more info on accepted parameters.
Returns
-------
DataFrame or dict of DataFrames
DataFrame from the passed in Excel file.
"""
return self._reader.parse(
sheet_name=sheet_name,
header=header,
names=names,
index_col=index_col,
usecols=usecols,
converters=converters,
true_values=true_values,
false_values=false_values,
skiprows=skiprows,
nrows=nrows,
na_values=na_values,
parse_dates=parse_dates,
date_parser=date_parser,
date_format=date_format,
thousands=thousands,
comment=comment,
skipfooter=skipfooter,
dtype_backend=dtype_backend,
**kwds,
)
@property
def book(self):
return self._reader.book
@property
def sheet_names(self):
return self._reader.sheet_names
def close(self) -> None:
"""close io if necessary"""
self._reader.close()
def __enter__(self) -> ExcelFile:
return self
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> None:
self.close()