import abc
import datetime
from distutils.version import LooseVersion
import inspect
from io import BufferedIOBase, BytesIO, RawIOBase
import os
from textwrap import fill
from typing import IO, Any, Dict, Mapping, Optional, Union, cast
import warnings
import zipfile
from pandas._config import config
from pandas._libs.parsers import STR_NA_VALUES
from pandas._typing import Buffer, FilePathOrBuffer, StorageOptions
from pandas.compat._optional import get_version, import_optional_dependency
from pandas.errors import EmptyDataError
from pandas.util._decorators import Appender, deprecate_nonkeyword_arguments, doc
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.io.common import IOHandles, get_handle, stringify_path, validate_header_arg
from pandas.io.excel._util import (
fill_mi_header,
get_default_writer,
get_writer,
maybe_convert_usecols,
pop_header_name,
)
from pandas.io.parsers import TextParser
_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. Lists of strings/integers are used to request
multiple sheets. Specify None to get all sheets.
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 sheets.
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.
usecols : int, 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.
* If list of string, then indicates list of column names to be parsed.
.. versionadded:: 0.24.0
* 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.
.. versionadded:: 0.24.0
squeeze : bool, default False
If the parsed data only contains one column then return a Series.
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 `openpyxl `_ is installed,
then ``openpyxl`` will be used.
- Otherwise if ``xlrd >= 2.0`` is installed, a ``ValueError`` will be raised.
- Otherwise ``xlrd`` will be used and a ``FutureWarning`` will be raised. This
case will raise a ``ValueError`` in a future version of pandas.
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 unparseable 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.
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.
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).
convert_float : bool, default True
Convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric
data will be read in as floats: Excel stores all numbers as floats
internally.
mangle_dupe_cols : bool, default True
Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
'X'...'X'. Passing in False will cause data to be overwritten if there
are duplicate names in the columns.
storage_options : dict, optional
Extra options that make sense for a particular storage connection, e.g.
host, port, username, password, etc., if using a URL that will
be parsed by ``fsspec``, e.g., starting "s3://", "gcs://". An error
will be raised if providing this argument with a local path or
a file-like buffer. See the fsspec and backend storage implementation
docs for the set of allowed keys and values.
.. versionadded:: 1.2.0
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
"""
)
@deprecate_nonkeyword_arguments(allowed_args=2, version="2.0")
@Appender(_read_excel_doc)
def read_excel(
io,
sheet_name=0,
header=0,
names=None,
index_col=None,
usecols=None,
squeeze=False,
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skiprows=None,
nrows=None,
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
parse_dates=False,
date_parser=None,
thousands=None,
comment=None,
skipfooter=0,
convert_float=True,
mangle_dupe_cols=True,
storage_options: StorageOptions = None,
):
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,
squeeze=squeeze,
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,
thousands=thousands,
comment=comment,
skipfooter=skipfooter,
convert_float=convert_float,
mangle_dupe_cols=mangle_dupe_cols,
)
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):
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)
self.book = self.load_workbook(self.handles.handle)
elif isinstance(self.handles.handle, bytes):
self.book = self.load_workbook(BytesIO(self.handles.handle))
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):
self.handles.close()
@property
@abc.abstractmethod
def sheet_names(self):
pass
@abc.abstractmethod
def get_sheet_by_name(self, name):
pass
@abc.abstractmethod
def get_sheet_by_index(self, index):
pass
@abc.abstractmethod
def get_sheet_data(self, sheet, convert_float):
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 parse(
self,
sheet_name=0,
header=0,
names=None,
index_col=None,
usecols=None,
squeeze=False,
dtype=None,
true_values=None,
false_values=None,
skiprows=None,
nrows=None,
na_values=None,
verbose=False,
parse_dates=False,
date_parser=None,
thousands=None,
comment=None,
skipfooter=0,
convert_float=True,
mangle_dupe_cols=True,
**kwds,
):
validate_header_arg(header)
ret_dict = False
# Keep sheetname to maintain backwards compatibility.
if isinstance(sheet_name, list):
sheets = sheet_name
ret_dict = True
elif sheet_name is None:
sheets = self.sheet_names
ret_dict = True
else:
sheets = [sheet_name]
# handle same-type duplicates.
sheets = list(dict.fromkeys(sheets).keys())
output = {}
for asheetname in sheets:
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)
data = self.get_sheet_data(sheet, convert_float)
usecols = maybe_convert_usecols(usecols)
if not data:
output[asheetname] = DataFrame()
continue
if is_list_like(header) and len(header) == 1:
header = header[0]
# forward fill and pull out names for MultiIndex column
header_names = None
if header is not None and is_list_like(header):
header_names = []
control_row = [True] * len(data[0])
for row in header:
if is_integer(skiprows):
row += skiprows
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 is_list_like(index_col):
# Forward fill values for MultiIndex index.
if header is None:
offset = 0
elif not is_list_like(header):
offset = 1 + header
else:
offset = 1 + max(header)
# Check if we have an empty dataset
# before trying to collect data.
if offset < len(data):
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]
has_index_names = is_list_like(header) and len(header) > 1
# 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,
squeeze=squeeze,
dtype=dtype,
true_values=true_values,
false_values=false_values,
skiprows=skiprows,
nrows=nrows,
na_values=na_values,
parse_dates=parse_dates,
date_parser=date_parser,
thousands=thousands,
comment=comment,
skipfooter=skipfooter,
usecols=usecols,
mangle_dupe_cols=mangle_dupe_cols,
**kwds,
)
output[asheetname] = parser.read(nrows=nrows)
if not squeeze or isinstance(output[asheetname], DataFrame):
if header_names:
output[asheetname].columns = output[
asheetname
].columns.set_names(header_names)
except EmptyDataError:
# No Data, return an empty DataFrame
output[asheetname] = DataFrame()
if ret_dict:
return output
else:
return output[asheetname]
class ExcelWriter(metaclass=abc.ABCMeta):
"""
Class for writing DataFrame objects into excel sheets.
Default is to use xlwt for xls, openpyxl for xlsx, odf for ods.
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.
.. deprecated:: 1.2.0
As the `xlwt `__ package is no longer
maintained, the ``xlwt`` engine will be removed in a future
version of pandas.
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.
.. versionadded:: 0.24.0
storage_options : dict, optional
Extra options that make sense for a particular storage connection, e.g.
host, port, username, password, etc., if using a URL that will
be parsed by ``fsspec``, e.g., starting "s3://", "gcs://".
.. versionadded:: 1.2.0
Attributes
----------
None
Methods
-------
None
Notes
-----
None of the methods and properties are considered public.
For compatibility with CSV writers, ExcelWriter serializes lists
and dicts to strings before writing.
Examples
--------
Default usage:
>>> with ExcelWriter('path_to_file.xlsx') as writer:
... df.to_excel(writer)
To write to separate sheets in a single file:
>>> with ExcelWriter('path_to_file.xlsx') as writer:
... df1.to_excel(writer, sheet_name='Sheet1')
... df2.to_excel(writer, sheet_name='Sheet2')
You can set the date format or datetime format:
>>> with ExcelWriter('path_to_file.xlsx',
... date_format='YYYY-MM-DD',
... datetime_format='YYYY-MM-DD HH:MM:SS') as writer:
... df.to_excel(writer)
You can also append to an existing Excel file:
>>> with ExcelWriter('path_to_file.xlsx', mode='a') as writer:
... df.to_excel(writer, sheet_name='Sheet3')
You can store Excel file in RAM:
>>> import io
>>> buffer = io.BytesIO()
>>> with pd.ExcelWriter(buffer) as writer:
... df.to_excel(writer)
You can pack Excel file into zip archive:
>>> import zipfile
>>> 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)
"""
# 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.
def __new__(cls, path, engine=None, **kwargs):
# 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_writer(ext)
except KeyError as err:
raise ValueError(f"No engine for filetype: '{ext}'") from err
if engine == "xlwt":
xls_config_engine = config.get_option(
"io.excel.xls.writer", silent=True
)
# Don't warn a 2nd time if user has changed the default engine for xls
if xls_config_engine != "xlwt":
warnings.warn(
"As the xlwt package is no longer maintained, the xlwt "
"engine will be removed in a future version of pandas. "
"This is the only engine in pandas that supports writing "
"in the xls format. Install openpyxl and write to an xlsx "
"file instead. You can set the option io.excel.xls.writer "
"to 'xlwt' to silence this warning. While this option is "
"deprecated and will also raise a warning, it can "
"be globally set and the warning suppressed.",
FutureWarning,
stacklevel=4,
)
cls = get_writer(engine)
return object.__new__(cls)
# declare external properties you can count on
curr_sheet = None
path = None
@property
@abc.abstractmethod
def supported_extensions(self):
"""Extensions that writer engine supports."""
pass
@property
@abc.abstractmethod
def engine(self):
"""Name of engine."""
pass
@abc.abstractmethod
def write_cells(
self, cells, sheet_name=None, startrow=0, startcol=0, freeze_panes=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
"""
pass
@abc.abstractmethod
def save(self):
"""
Save workbook to disk.
"""
pass
def __init__(
self,
path: Union[FilePathOrBuffer, "ExcelWriter"],
engine=None,
date_format=None,
datetime_format=None,
mode: str = "w",
storage_options: StorageOptions = None,
**engine_kwargs,
):
# 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+")
# cast ExcelWriter to avoid adding 'if self.handles is not None'
self.handles = IOHandles(cast(Buffer, path), compression={"copression": None})
if not isinstance(path, ExcelWriter):
self.handles = get_handle(
path, mode, storage_options=storage_options, is_text=False
)
self.sheets: Dict[str, Any] = {}
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
def __fspath__(self):
return getattr(self.handles.handle, "name", "")
def _get_sheet_name(self, sheet_name):
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):
"""
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() / float(86400)
fmt = "0"
else:
val = str(val)
return val, fmt
@classmethod
def check_extension(cls, ext: str):
"""
checks that path's extension against the Writer's supported
extensions. If it isn't supported, raises UnsupportedFiletypeError.
"""
if ext.startswith("."):
ext = ext[1:]
# error: "Callable[[ExcelWriter], Any]" has no attribute "__iter__"
# (not iterable) [attr-defined]
if not any(
ext in extension
for extension in cls.supported_extensions # type: ignore[attr-defined]
):
raise ValueError(f"Invalid extension for engine '{cls.engine}': '{ext}'")
else:
return True
# Allow use as a contextmanager
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def close(self):
"""synonym for save, to make it more file-like"""
content = self.save()
self.handles.close()
return content
XLS_SIGNATURE = b"\xD0\xCF\x11\xE0\xA1\xB1\x1A\xE1"
ZIP_SIGNATURE = b"PK\x03\x04"
PEEK_SIZE = max(len(XLS_SIGNATURE), len(ZIP_SIGNATURE))
@doc(storage_options=_shared_docs["storage_options"])
def inspect_excel_format(
path: Optional[str] = None,
content: Union[None, BufferedIOBase, RawIOBase, bytes] = None,
storage_options: StorageOptions = None,
) -> str:
"""
Inspect the path or content of an excel file and get its format.
At least one of path or content must be not None. If both are not None,
content will take precedence.
Adopted from xlrd: https://github.com/python-excel/xlrd.
Parameters
----------
path : str, optional
Path to file to inspect. May be a URL.
content : file-like object, optional
Content of file to inspect.
{storage_options}
Returns
-------
str
Format of file.
Raises
------
ValueError
If resulting stream is empty.
BadZipFile
If resulting stream does not have an XLS signature and is not a valid zipfile.
"""
content_or_path: Union[None, str, BufferedIOBase, RawIOBase, IO[bytes]]
if isinstance(content, bytes):
content_or_path = BytesIO(content)
else:
content_or_path = content or path
assert content_or_path is not None
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")
else:
assert isinstance(buf, bytes)
peek = buf
stream.seek(0)
if peek.startswith(XLS_SIGNATURE):
return "xls"
elif not peek.startswith(ZIP_SIGNATURE):
raise ValueError("File is not a recognized excel file")
# ZipFile typing is overly-strict
# https://github.com/python/typeshed/issues/4212
zf = zipfile.ZipFile(stream) # type: ignore[arg-type]
# 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, path object (pathlib.Path or py._path.local.LocalPath),
a file-like object, xlrd workbook or openpypl 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 `openpyxl `_ is installed,
then ``openpyxl`` will be used.
- Otherwise if ``xlrd >= 2.0`` is installed, a ``ValueError`` will be raised.
- Otherwise ``xlrd`` will be used and a ``FutureWarning`` will be raised.
This case will raise a ``ValueError`` in a future version of pandas.
.. 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=None, storage_options: StorageOptions = None
):
if engine is not None and engine not in self._engines:
raise ValueError(f"Unknown engine: {engine}")
# 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", raise_on_missing=False, on_version="ignore"
)
is None
):
xlrd_version = None
else:
import xlrd
xlrd_version = LooseVersion(get_version(xlrd))
ext = None
if engine is None:
# Only determine ext if it is needed
if xlrd_version is not None and isinstance(path_or_buffer, xlrd.Book):
ext = "xls"
else:
ext = inspect_excel_format(
content=path_or_buffer, storage_options=storage_options
)
if ext == "ods":
engine = "odf"
elif ext == "xls":
engine = "xlrd"
else:
# GH 35029 - Prefer openpyxl except for xls files
if (
import_optional_dependency(
"openpyxl", raise_on_missing=False, on_version="ignore"
)
is not None
):
engine = "openpyxl"
else:
engine = "xlrd"
if engine == "xlrd" and xlrd_version is not None:
if ext is None:
# Need ext to determine ext in order to raise/warn
if isinstance(path_or_buffer, xlrd.Book):
ext = "xls"
else:
ext = inspect_excel_format(
content=path_or_buffer, storage_options=storage_options
)
if ext != "xls" and xlrd_version >= "2":
raise ValueError(
f"Your version of xlrd is {xlrd_version}. In xlrd >= 2.0, "
f"only the xls format is supported. Install openpyxl instead."
)
elif ext != "xls":
caller = inspect.stack()[1]
if (
caller.filename.endswith(
os.path.join("pandas", "io", "excel", "_base.py")
)
and caller.function == "read_excel"
):
stacklevel = 4
else:
stacklevel = 2
warnings.warn(
f"Your version of xlrd is {xlrd_version}. In xlrd >= 2.0, "
f"only the xls format is supported. As a result, the "
f"openpyxl engine will be used if it is installed and the "
f"engine argument is not specified. Install "
f"openpyxl instead.",
FutureWarning,
stacklevel=stacklevel,
)
assert engine in self._engines, f"Engine {engine} not recognized"
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=0,
header=0,
names=None,
index_col=None,
usecols=None,
squeeze=False,
converters=None,
true_values=None,
false_values=None,
skiprows=None,
nrows=None,
na_values=None,
parse_dates=False,
date_parser=None,
thousands=None,
comment=None,
skipfooter=0,
convert_float=True,
mangle_dupe_cols=True,
**kwds,
):
"""
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,
squeeze=squeeze,
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,
thousands=thousands,
comment=comment,
skipfooter=skipfooter,
convert_float=convert_float,
mangle_dupe_cols=mangle_dupe_cols,
**kwds,
)
@property
def book(self):
return self._reader.book
@property
def sheet_names(self):
return self._reader.sheet_names
def close(self):
"""close io if necessary"""
self._reader.close()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def __del__(self):
# Ensure we don't leak file descriptors, but put in try/except in case
# attributes are already deleted
try:
self.close()
except AttributeError:
pass