read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, index_col=None,names=None, parse_cols=None, parse_dates=False,date_parser=None,na_values=None,thousands=None, convert_float=True, has_index_names=None, converters=None,dtype=None, true_values=None, false_values=None, engine=None, squeeze=False, **kwds)

sheet1:
ID NUM-1  NUM-2  NUM-3
36901  142 168 661
36902  78 521 602
36903  144 600 521
36904  95 457 468
36905  69 596 695

sheet2：
ID NUM-1  NUM-2  NUM-3
36906  190 527 691
36907  101 403 470

basestation ="F://pythonBook_PyPDAM/data/test.xls"
data = pd.read_excel(basestation)
print data

   ID NUM-1 NUM-2 NUM-3
0 36901  142  168  661
1 36902   78  521  602
2 36903  144  600  521
3 36904   95  457  468
4 36905   69  596  695

data_1 = pd.read_excel(basestation,sheetname=[0,1])
print data_1
print type(data_1)

OrderedDict([(0,    ID NUM-1 NUM-2 NUM-3
0 36901  142  168  661
1 36902   78  521  602
2 36903  144  600  521
3 36904   95  457  468
4 36905   69  596  695), 
(1,    ID NUM-1 NUM-2 NUM-3
0 36906  190  527  691
1 36907  101  403  470)])

data = pd.read_excel(basestation,header=None)
print data
輸出：
    0   1   2   3
0   ID NUM-1 NUM-2 NUM-3
1 36901  142  168  661
2 36902   78  521  602
3 36903  144  600  521
4 36904   95  457  468
5 36905   69  596  695

data = pd.read_excel(basestation,header=[3])
print data
輸出：
  36903 144  600  521 
0 36904   95  457  468
1 36905   69  596  695

data = pd.read_excel(basestation,skiprows = [1])
print data
輸出:
   ID NUM-1 NUM-2 NUM-3
0 36902   78  521  602
1 36903  144  600  521
2 36904   95  457  468
3 36905   69  596  695

data = pd.read_excel(basestation, skip_footer=3)
print data
輸出：
   ID NUM-1 NUM-2 NUM-3
0 36901  142  168  661
1 36902   78  521  602

data = pd.read_excel(basestation, index_col="NUM-3")
print data
輸出：
     ID NUM-1 NUM-2
NUM-3           
661  36901  142  168
602  36902   78  521
521  36903  144  600
468  36904   95  457
695  36905   69  596

data = pd.read_excel(basestation,names=["a","b","c","e"])
print data
    a  b  c  e
0 36901 142 168 661
1 36902  78 521 602
2 36903 144 600 521
3 36904  95 457 468
4 36905  69 596 695

>>> print help(pandas.read_excel)
Help on function read_excel in module pandas.io.excel:

read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, index_col=None, names=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, converters=None, dtype=None, true_values=None, false_values=None, engine=None, squeeze=False, **kwds)
  Read an Excel table into a pandas DataFrame

  Parameters
  ----------
  io : string, path object (pathlib.Path or py._path.local.LocalPath),
    file-like object, pandas ExcelFile, or xlrd workbook.
    The string could be a URL. Valid URL schemes include http, ftp, s3,
    and file. For file URLs, a host is expected. For instance, a local
    file could be file://localhost/path/to/workbook.xlsx
  sheetname : string, int, mixed list of strings/ints, 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.

    str|int -> DataFrame is returned.
    list|None -> Dict of DataFrames is returned, with keys representing
    sheets.

    Available Cases

    * Defaults to 0 -> 1st sheet as a DataFrame
    * 1 -> 2nd sheet as a DataFrame
    * "Sheet1" -> 1st sheet as a DataFrame
    * [0,1,"Sheet5"] -> 1st, 2nd & 5th sheet as a dictionary of DataFrames
    * None -> All sheets as a dictionary of DataFrames

  header : int, list of ints, 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``
  skiprows : list-like
    Rows to skip at the beginning (0-indexed)
  skip_footer : int, default 0
    Rows at the end to skip (0-indexed)
  index_col : int, list of ints, 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 ``parse_cols``, index_col
    is based on the subset.
  names : array-like, default None
    List of column names to use. If file contains no header row,
    then you should explicitly pass header=None
  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.
  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

  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

  parse_cols : int or list, default None
    * If None then parse all columns,
    * If int then indicates last column to be parsed
    * If list of ints then indicates list of column numbers to be parsed
    * If string 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.
  squeeze : boolean, default False
    If the parsed data only contains one column then return a Series
  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', 'nan'.
  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.
  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 : boolean, default False
    Indicate number of NA values placed in non-numeric columns
  engine: string, default None
    If io is not a buffer or path, this must be set to identify io.
    Acceptable values are None or xlrd
  convert_float : boolean, 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
  has_index_names : boolean, default None
    DEPRECATED: for version 0.17+ index names will be automatically
    inferred based on index_col. To read Excel output from 0.16.2 and
    prior that had saved index names, use True.

  Returns

to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None,columns=None, header=True, index=True, index_label=None,startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None,
inf_rep='inf', verbose=True, freeze_panes=None)

  ID NUM-1  NUM-2  NUM-3
0  36901  142 168 661
1  36902  78 521 602
2  36903  144 600 521
3  36904  95 457 468
4  36905  69 596 695
5  36906  165 453 

加載數(shù)據(jù)：
basestation ="F://python/data/test.xls"
basestation_end ="F://python/data/test_end.xls"
data = pd.read_excel(basestation)

data.to_excel(basestation_end)
輸出：
  ID NUM-1  NUM-2  NUM-3
0  36901  142 168 661
1  36902  78 521 602
2  36903  144 600 521
3  36904  95 457 468
4  36905  69 596 695
5  36906  165 453 

data.to_excel(basestation_end,sheet_name="sheet2")

data.to_excel(basestation_end,na_rep="NULL")
輸出：
  ID NUM-1  NUM-2  NUM-3
0  36901  142 168 661
1  36902  78 521 602
2  36903  144 600 521
3  36904  95 457 468
4  36905  69 596 695
5  36906  165 453 NULL

data.to_excel(basestation_end,columns=["ID"])
輸出
  ID
0  36901
1  36902
2  36903
3  36904
4  36905
5  36906

data.to_excel(basestation_end,header=["a","b","c","d"])
輸出：
  a  b  c  d
0  36901  142 168 661
1  36902  78 521 602
2  36903  144 600 521
3  36904  95 457 468
4  36905  69 596 695
5  36906  165 453 


data.to_excel(basestation_end,header=False,columns=["ID"])
header = False 則不輸出題頭
輸出：
0  36901
1  36902
2  36903
3  36904
4  36905
5  36906

data.to_excel(basestation_end,index=False)
輸出：
ID NUM-1  NUM-2  NUM-3
36901  142 168 661
36902  78 521 602
36903  144 600 521
36904  95 457 468
36905  69 596 695
36906  165 453 

data.to_excel(basestation_end,index_label=["f"])
輸出：
f  ID NUM-1  NUM-2  NUM-3
0  36901  142 168 661
1  36902  78 521 602
2  36903  144 600 521
3  36904  95 457 468
4  36905  69 596 695
5  36906  165 453