"""
Module parse to/from Excel
"""

# ---------------------------------------------------------------------
# ExcelFile class
import abc
from datetime import date, datetime, time, timedelta
from distutils.version import LooseVersion
from io import UnsupportedOperation
import os
from textwrap import fill
import warnings

import numpy as np

import pandas._libs.json as json
import pandas.compat as compat
from pandas.compat import (
    OrderedDict, add_metaclass, lrange, map, range, string_types, u, zip)
from pandas.errors import EmptyDataError
from pandas.util._decorators import Appender, deprecate_kwarg

from pandas.core.dtypes.common import (
    is_bool, is_float, is_integer, is_list_like)

from pandas.core import config
from pandas.core.frame import DataFrame

from pandas.io.common import (
    _NA_VALUES, _is_url, _stringify_path, _urlopen, _validate_header_arg,
    get_filepath_or_buffer)
from pandas.io.formats.printing import pprint_thing
from pandas.io.parsers import TextParser

__all__ = ["read_excel", "ExcelWriter", "ExcelFile"]

_writer_extensions = ["xlsx", "xls", "xlsm"]
_writers = {}

_read_excel_doc = """
Read an Excel file into a pandas DataFrame.

Support both `xls` and `xlsx` file extensions from a local filesystem or URL.
Support an option to read a single sheet or a list of sheets.

Parameters
----------
io : str, file descriptor, pathlib.Path, ExcelFile or xlrd.Book
    The string could be a URL. Valid URL schemes include http, ftp, s3,
    gcs, and file. For file URLs, a host is expected. For instance, a local
    file could be /path/to/workbook.xlsx.
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.
parse_cols : int or list, default None
    Alias of `usecols`.

    .. deprecated:: 0.21.0
       Use `usecols` instead.

usecols : int, str, list-like, or callable default None
    Return a subset of the columns.
    * If None, then parse all columns.
    * If int, then indicates last column to be parsed.

    .. deprecated:: 0.24.0
       Pass in a list of int instead from 0 to `usecols` inclusive.

    * 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``.

    .. 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.

    .. versionadded:: 0.20.0

engine : str, default None
    If io is not a buffer or path, this must be set to identify io.
    Acceptable values are None or xlrd.
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.

    .. versionadded:: 0.19.0

false_values : list, default None
    Values to consider as False.

    .. versionadded:: 0.19.0

skiprows : list-like
    Rows to skip at the beginning (0-indexed).
nrows : int, default None
    Number of rows to parse.

    .. versionadded:: 0.23.0

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(_NA_VALUES)), 70, subsequent_indent="    ") + """'.
keep_default_na : bool, default True
    If na_values are specified and keep_default_na is False the default NaN
    values are overridden, otherwise they're appended to.
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. For non-standard
    datetime parsing, use ``pd.to_datetime`` after ``pd.read_csv``

    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.
skip_footer : int, default 0
    Alias of `skipfooter`.

    .. deprecated:: 0.23.0
       Use `skipfooter` instead.
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.
**kwds : optional
        Optional keyword arguments can be passed to ``TextFileReader``.

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
--------
to_excel : Write DataFrame to an Excel file.
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
"""


def register_writer(klass):
    """Adds engine to the excel writer registry. You must use this method to
    integrate with ``to_excel``. Also adds config options for any new
    ``supported_extensions`` defined on the writer."""
    if not compat.callable(klass):
        raise ValueError("Can only register callables as engines")
    engine_name = klass.engine
    _writers[engine_name] = klass
    for ext in klass.supported_extensions:
        if ext.startswith('.'):
            ext = ext[1:]
        if ext not in _writer_extensions:
            config.register_option("io.excel.{ext}.writer".format(ext=ext),
                                   engine_name, validator=str)
            _writer_extensions.append(ext)


def _get_default_writer(ext):
    _default_writers = {'xlsx': 'openpyxl', 'xlsm': 'openpyxl', 'xls': 'xlwt'}
    try:
        import xlsxwriter  # noqa
        _default_writers['xlsx'] = 'xlsxwriter'
    except ImportError:
        pass
    return _default_writers[ext]


def get_writer(engine_name):
    try:
        return _writers[engine_name]
    except KeyError:
        raise ValueError("No Excel writer '{engine}'"
                         .format(engine=engine_name))


@Appender(_read_excel_doc)
@deprecate_kwarg("parse_cols", "usecols")
@deprecate_kwarg("skip_footer", "skipfooter")
def read_excel(io,
               sheet_name=0,
               header=0,
               names=None,
               index_col=None,
               parse_cols=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,
               verbose=False,
               parse_dates=False,
               date_parser=None,
               thousands=None,
               comment=None,
               skip_footer=0,
               skipfooter=0,
               convert_float=True,
               mangle_dupe_cols=True,
               **kwds):

    # Can't use _deprecate_kwarg since sheetname=None has a special meaning
    if is_integer(sheet_name) and sheet_name == 0 and 'sheetname' in kwds:
        warnings.warn("The `sheetname` keyword is deprecated, use "
                      "`sheet_name` instead", FutureWarning, stacklevel=2)
        sheet_name = kwds.pop("sheetname")

    if 'sheet' in kwds: