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：
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
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
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持呐喊教程。
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