pandas read_excel()和to_excel()函数解析


Posted in Python onSeptember 19, 2019

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

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三水点靠木。

Python 相关文章推荐
对于Python的框架中一些会话程序的管理
Apr 20 Python
简单解析Django框架中的表单验证
Jul 17 Python
caffe binaryproto 与 npy相互转换的实例讲解
Jul 09 Python
python实现排序算法解析
Sep 08 Python
对Python中实现两个数的值交换的集中方法详解
Jan 11 Python
ML神器:sklearn的快速使用及入门
Jul 11 Python
Django REST框架创建一个简单的Api实例讲解
Nov 05 Python
最新2019Pycharm安装教程 亲测
Feb 28 Python
Python中如何添加自定义模块
Jun 09 Python
opencv 阈值分割的具体使用
Jul 08 Python
python 如何在测试中使用 Mock
Mar 01 Python
用Python写一个简易版弹球游戏
Apr 13 Python
python openvc 裁剪、剪切图片 提取图片的行和列
Sep 19 #Python
vscode 配置 python3开发环境的方法
Sep 19 #Python
python实现简易学生信息管理系统
Apr 05 #Python
Python字符串大小写转换拼接删除空白
Sep 19 #Python
python BlockingScheduler定时任务及其他方式的实现
Sep 19 #Python
python实现简单成绩录入系统
Sep 19 #Python
淘宝秒杀python脚本 扫码登录版
Sep 19 #Python
You might like
Laravel中基于Artisan View扩展包创建及删除应用视图文件的方法
2016/10/08 PHP
PHP使用反向Ajax技术实现在线客服系统详解
2019/07/01 PHP
PHP简单实现图片格式转换(jpg转png,gif转png等)
2019/10/30 PHP
PHP设计模式(八)装饰器模式Decorator实例详解【结构型】
2020/05/02 PHP
Javascript 修改String 对象 增加去除空格功能(示例代码)
2013/11/30 Javascript
jquery实现input输入框实时输入触发事件代码
2014/01/28 Javascript
Extjs表单常见验证小结
2014/03/07 Javascript
jQuery中replaceWith()方法用法实例
2014/12/25 Javascript
详解maxlength属性在textarea里奇怪的表现
2015/12/27 Javascript
jQuery插件扩展extend的简单实现原理
2016/06/24 Javascript
jQuery获取选中单选按钮radio的值
2016/12/27 Javascript
js实现一个简单的数字时钟效果
2017/03/29 Javascript
node简单实现一个更改头像功能的示例
2017/12/29 Javascript
详解vue文件中使用echarts.js的两种方式
2018/10/18 Javascript
利用hasOwnProperty给数组去重的面试题分享
2018/11/05 Javascript
JS为什么说async/await是generator的语法糖详解
2019/07/11 Javascript
python爬虫常用的模块分析
2014/08/29 Python
tensorflow使用神经网络实现mnist分类
2018/09/08 Python
对Python之gzip文件读写的方法详解
2019/02/08 Python
详解用python自制微信机器人,定时发送天气预报
2019/03/25 Python
python 实现查找文件并输出满足某一条件的数据项方法
2019/06/12 Python
Python找出列表中出现次数最多的元素三种方式
2020/02/24 Python
CSS3实现的渐变幻灯片效果
2020/12/07 HTML / CSS
HTML5 canvas基本绘图之绘制曲线
2016/06/27 HTML / CSS
Yankee Candle官网:美国最畅销蜡烛品牌之一
2020/01/05 全球购物
中兴通讯全球官方网站:ZTE
2020/12/26 全球购物
公民授权委托书范本
2014/09/17 职场文书
五四青年节活动总结
2015/02/10 职场文书
学习与创新自我评价
2015/03/09 职场文书
2015年大学生村官工作总结
2015/04/21 职场文书
考生诚信考试承诺书
2015/04/29 职场文书
小组口号霸气押韵
2015/12/24 职场文书
Python趣味实战之手把手教你实现举牌小人生成器
2021/06/07 Python
Redis如何使用乐观锁(CAS)保证数据一致性
2022/03/25 Redis
MYSQL常用函数介绍
2022/05/05 MySQL
mysql实现将字符串字段转为数字排序或比大小
2022/06/14 MySQL