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pandas read_excel()和to_excel()函数解析

前言

数据分析时候,需要将数据进行加载和存储,本文主要介绍和excel的交互。

read_excel()

加载函数为read_excel(),其具体参数如下。

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)

常用参数解析:

  • io : string, path object ; excel 路径。
  • sheetname : string, int, mixed list of strings/ints, or None, default 0 返回多表使用sheetname=[0,1],若sheetname=None是返回全表 注意:int/string 返回的是dataframe,而none和list返回的是dict of dataframe
  • header : int, list of ints, default 0 指定列名行,默认0,即取第一行,数据为列名行以下的数据 若数据不含列名,则设定 header = None
  • skiprows : list-like,Rows to skip at the beginning,省略指定行数的数据
  • skip_footer : int,default 0, 省略从尾部数的int行数据
  • index_col : int, list of ints, default None指定列为索引列,也可以使用u”strings”
  • names : array-like, default None, 指定列的名字。

数据源:

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

(1)函数原型

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

输出:是一个dataframe

   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

(2) sheetname参数:返回多表使用sheetname=[0,1],若sheetname=None是返回全表 注意:int/string 返回的是dataframe,而none和list返回的是dict of dataframe

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

输出:dict of dataframe

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)])

(3)header参数:指定列名行,默认0,即取第一行,数据为列名行以下的数据 若数据不含列名,则设定 header = None ,注意这里还有列名的一行。

data = pd.read_excel(basestation,header=None)
print data
输出:
    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
输出:
  36903 144  600  521 
0 36904   95  457  468
1 36905   69  596  695

(4) skiprows 参数:省略指定行数的数据

data = pd.read_excel(basestation,skiprows = [1])
print data
输出:
   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

(5)skip_footer参数:省略从尾部数的int行的数据

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

(6)index_col参数:指定列为索引列,也可以使用u”strings”

data = pd.read_excel(basestation, index_col="NUM-3")
print data
输出:
     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

(7)names参数: 指定列的名字。

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

具体参数如下:

>>> 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()

存储函数为pd.DataFrame.to_excel(),注意,必须是DataFrame写入excel, 即Write DataFrame to an excel sheet。其具体参数如下:

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)

常用参数解析

  • - excel_writer : string or ExcelWriter object File path or existing ExcelWriter目标路径
  • - sheet_name : string, default ‘Sheet1' Name of sheet which will contain DataFrame,填充excel的第几页
  • - na_rep : string, default ”,Missing data representation 缺失值填充
  • - float_format : string, default None Format string for floating point numbers
  • - columns : sequence, optional,Columns to write 选择输出的的列。
  • - header : boolean or list of string, default True Write out column names. If a list of string is given it is assumed to be aliases for the column names
  • - index : boolean, default True,Write row names (index)
  • - index_label : string or sequence, default None, Column label for index column(s) if desired. If None is given, andheader and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
  • - startrow :upper left cell row to dump data frame
  • - startcol :upper left cell column to dump data frame
  • - engine : string, default None ,write engine to use - you can also set this via the options,io.excel.xlsx.writer, io.excel.xls.writer, andio.excel.xlsm.writer.
  • - merge_cells : boolean, default True Write MultiIndex and Hierarchical Rows as merged cells.
  • - encoding: string, default None encoding of the resulting excel file. Only necessary for xlwt,other writers support unicode natively.
  • - inf_rep : string, default ‘inf' Representation for infinity (there is no native representation for infinity in Excel)
  • - freeze_panes : tuple of integer (length 2), default None Specifies the one-based bottommost row and rightmost column that is to be frozen

数据源:

  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 

加载数据:
basestation ="F://python/data/test.xls"
basestation_end ="F://python/data/test_end.xls"
data = pd.read_excel(basestation)

(1)参数excel_writer,输出路径。

data.to_excel(basestation_end)
输出:
  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 

(2)sheet_name,将数据存储在excel的那个sheet页面。

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

(3)na_rep,缺失值填充

data.to_excel(basestation_end,na_rep="NULL")
输出:
  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

(4) colums参数: sequence, optional,Columns to write 选择输出的的列。

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

(5)header 参数: boolean or list of string,默认为True,可以用list命名列的名字。header = False 则不输出题头。

data.to_excel(basestation_end,header=["a","b","c","d"])
输出:
  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 则不输出题头
输出:
0  36901
1  36902
2  36903
3  36904
4  36905
5  36906

(6)index : boolean, default True Write row names (index)

默认为True,显示index,当index=False 则不显示行索引(名字)。

index_label : string or sequence, default None

设置索引列的列名。

data.to_excel(basestation_end,index=False)
输出:
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"])
输出:
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 
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