Pandas之read_excel()和to_excel()函数解析

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

来源于https://blog.csdn.net/tongxinzhazha/article/details/78796952