read_ excel()

The load function is read_ The specific parameters of Excel () are as follows

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)

Common parameter analysis:

io : string, path object ; Excel path

sheetname: string, int, mixed list of Strings/ints, or none, default 0 returns multiple tables, use sheetname = [0,1], if sheetname = none returns the whole table, note: int/String returns dataframe, while none and list return Dict Of dataframe

header: int, list of ints, default 0 specifies the column name row, default 0, that is, take the first row, the data is the data below the column name row, if the data does not contain the column name, set header = none

skirows: list like, rows to skip at the beginning

skip_ Footer: int, default 0, omit the int line data from the tail

index_ Col: int, list of ints, default none specifies that the column is an index column. You can also use U “strings”

Names: array like, default none, specifies the name of the column

Data source:

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) Function prototype

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

Output: a 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 parameter: use sheetname = [0,1] to return multiple tables. If sheetname = none, return the whole table. Note: int/String returns dataframe, while none and list return Dict Of dataframe

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

Output: 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 parameter: specify the row of column name, 0 by default, that is, the first row. The data is the data below the row of column name. If the data does not contain column name, set header = none. Note that there is a row of column name here

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

(4) Skirows parameter: omits the data with the specified number of rows

data = pd.read_excel(basestation,skiprows = [1])
print data
Output: 
      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 parameter: omits the data from the int line of the tail number

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

(6)index_ Col parameter: specify column as index column, or use U “strings”

data = pd.read_excel(basestation, index_col="NUM-3")
print data
output：
          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) Name parameter: Specifies the name of the column

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

The specific parameters are as follows

>>> 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()

The storage function is pd.dataframe.to_ Excel (), note that dataframe must be written into excel, that is, write dataframe to an excel sheet. The specific parameters are as follows:

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)

Explanation of common parameters:

excel_writer: string or ExcelWriter object File path or existing ExcelWriter Target Path

sheet_name: string, default ‘Sheet1’ Name of sheet which will contain DataFrame, Fill the pages of excel

na_rep: string, default ”,Missing data representation Missing value fill

float_format: string, default None Format string for floating point numbers

columns: sequence, optional，Columns to write Select the output column.

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

Codes:

    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 

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

(1) Parameter Excel_ Writer, output path

data.to_excel(basestation_end)
Output: 
    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, store the data in the sheet page of Excel

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

(3)na_ Rep, missing value filling

data.to_excel(basestation_end,na_rep="NULL")
Output: 
    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) Columns parameters: sequence, optional, columns to write select the output column

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

(5) Header parameter: Boolean or list of string, which is true by default. You can use list to name columns. If header = false, no header will be output

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

(6) Index: Boolean, default true, write row names (index) is true by default, and the index is displayed. When index = false, the row index (name) is not displayed. index_ Label: string or sequence, default none set the column name of the index column

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