Question

我需要从多个站点提取数据。

首先读取文件

dfs = pd.read_excel('Consumption Report.xlsx', sheet_name='Elec Monthly Cons', header=[0,1], index_col=[0,1])

我的Jupyter图片

到目前为止，我已经尝试过：

dfs.iloc[0]

输出：

Site        Profile 
2014-01-01  JAN 2014    10344.0
2014-02-01  FEB 2014        NaN
2014-03-01  MAR 2014        NaN
2014-04-01  APR 2014    16745.0
2014-05-01  MAY 2014        NaN
2014-06-01  JUN 2014        NaN
2014-07-01  JUL 2014     9284.0
2014-08-01  AUG 2014        NaN
2014-09-01  SEP 2014     9235.7
2014-10-01  OCT 2014        NaN
2014-11-01  NOV 2014     9966.0
2014-12-01  DEC 2014        NaN
2015-01-01  JAN 2015        NaN
2015-02-01  FEB 2015    14616.0
2015-03-01  MAR 2015        NaN
2015-04-01  APR 2015        NaN
2015-05-01  MAY 2015    15404.0

如何从最后一列中提取值？

这是索引

MultiIndex(levels=[[2014-01-01 00:00:00, 2014-02-01 00:00:00, 2014-03-01 00:00:00, 2014-04-01 00:00:00, 2014-05-01 00:00:00, 2014-06-01 00:00:00, 2014-07-01 00:00:00, 2014-08-01 00:00:00, 2014-09-01 00:00:00, 2014-10-01 00:00:00, 2014-11-01 00:00:00, 2014-12-01 00:00:00, 2015-01-01 00:00:00, 2015-02-01 00:00:00, 2015-03-01 00:00:00, 2015-04-01 00:00:00, 2015-05-01 00:00:00, 2015-06-01 00:00:00, 2015-07-01 00:00:00, 2015-08-01 00:00:00, 2015-09-01 00:00:00, 2015-10-01 00:00:00, 2015-11-01 00:00:00, 2015-12-01 00:00:00, 2016-01-01 00:00:00, 2016-02-01 00:00:00, 2016-03-01 00:00:00, 2016-04-01 00:00:00, 2016-05-01 00:00:00, 2016-06-01 00:00:00, 2016-07-01 00:00:00, 2016-08-01 00:00:00, 2016-09-01 00:00:00, 2016-10-01 00:00:00, 2016-11-01 00:00:00, 2016-12-01 00:00:00, 2017-01-01 00:00:00, 2017-02-01 00:00:00, 2017-03-01 00:00:00, 2017-04-01 00:00:00, 2017-05-01 00:00:00, 2017-06-01 00:00:00, 2017-07-01 00:00:00, 2017-08-01 00:00:00, 2017-09-01 00:00:00, 2017-10-01 00:00:00, 2017-11-01 00:00:00, 2017-12-01 00:00:00], ['APR 2014', 'APR 2015', 'APR 2016', 'APR 2017', 'AUG 2014', 'AUG 2015', 'AUG 2016', 'AUG 2017', 'DEC 2014', 'DEC 2015', 'DEC 2016', 'DEC 2017', 'FEB 2014', 'FEB 2015', 'FEB 2016', 'FEB 2017', 'JAN 2014', 'JAN 2015', 'JAN 2016', 'JAN 2017', 'JUL 2014', 'JUL 2015', 'JUL 2016', 'JUL 2017', 'JUN 2014', 'JUN 2015', 'JUN 2016', 'JUN 2017', 'MAR 2014', 'MAR 2015', 'MAR 2016', 'MAR 2017', 'MAY 2014', 'MAY 2015', 'MAY 2016', 'MAY 2017', 'NOV 2014', 'NOV 2015', 'NOV 2016', 'NOV 2017', 'OCT 2014', 'OCT 2015', 'OCT 2016', 'OCT 2017', 'SEP 2014', 'SEP 2015', 'SEP 2016', 'SEP 2017']],
           labels=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47], [16, 12, 28, 0, 32, 24, 20, 4, 44, 40, 36, 8, 17, 13, 29, 1, 33, 25, 21, 5, 45, 41, 37, 9, 18, 14, 30, 2, 34, 26, 22, 6, 46, 42, 38, 10, 19, 15, 31, 3, 35, 27, 23, 7, 47, 43, 39, 11]],
           names=['Site', 'Profile'])

如果我接受Evan的建议

df.index.get_level_values(level=-1)

输出

Index(['JAN 2014', 'FEB 2014', 'MAR 2014', 'APR 2014', 'MAY 2014', 'JUN 2014',
       'JUL 2014', 'AUG 2014', 'SEP 2014', 'OCT 2014', 'NOV 2014', 'DEC 2014',
       'JAN 2015', 'FEB 2015', 'MAR 2015', 'APR 2015', 'MAY 2015', 'JUN 2015',
       'JUL 2015', 'AUG 2015', 'SEP 2015', 'OCT 2015', 'NOV 2015', 'DEC 2015',
       'JAN 2016', 'FEB 2016', 'MAR 2016', 'APR 2016', 'MAY 2016', 'JUN 2016',
       'JUL 2016', 'AUG 2016', 'SEP 2016', 'OCT 2016', 'NOV 2016', 'DEC 2016',
       'JAN 2017', 'FEB 2017', 'MAR 2017', 'APR 2017', 'MAY 2017', 'JUN 2017',
       'JUL 2017', 'AUG 2017', 'SEP 2017', 'OCT 2017', 'NOV 2017', 'DEC 2017'],
      dtype='object', name='Profile')

零级

df.index.get_level_values(level=0)

DatetimeIndex(['2014-01-01', '2014-02-01', '2014-03-01', '2014-04-01',
               '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
               '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01',
               '2015-01-01', '2015-02-01', '2015-03-01', '2015-04-01',
               '2015-05-01', '2015-06-01', '2015-07-01', '2015-08-01',
               '2015-09-01', '2015-10-01', '2015-11-01', '2015-12-01',
               '2016-01-01', '2016-02-01', '2016-03-01', '2016-04-01',
               '2016-05-01', '2016-06-01', '2016-07-01', '2016-08-01',
               '2016-09-01', '2016-10-01', '2016-11-01', '2016-12-01',
               '2017-01-01', '2017-02-01', '2017-03-01', '2017-04-01',
               '2017-05-01', '2017-06-01', '2017-07-01', '2017-08-01',
               '2017-09-01', '2017-10-01', '2017-11-01', '2017-12-01'],
              dtype='datetime64[ns]', name='Site', freq=None)

如何从非索引列获取值？

文件上传

https://ufile.io/m5nbc

Answer 1

给出一个数据框：

"""
IndexID IndexDateTime IndexAttribute ColumnA ColumnB
   1      2015-02-05        8           A       B
   1      2015-02-05        7           C       D
   1      2015-02-10        7           X       Y
"""

import pandas as pd
import numpy as np

df = pd.read_clipboard(parse_dates=["IndexDateTime"]).set_index(["IndexID", "IndexDateTime", "IndexAttribute"])
df

输出：

                                     ColumnA ColumnB
IndexID IndexDateTime IndexAttribute                
1       2015-02-05    8                    A       B
                      7                    C       D
        2015-02-10    7                    X       Y

可以通过ColumnB或df.loc[:, "ColumnB"].values访问最后一列（df.loc[:, "ColumnB"]）的值。参见：https://pandas.pydata.org/pandas-docs/stable/indexing.html

IndexID  IndexDateTime  IndexAttribute
1        2015-02-05     8                 B
                        7                 D
         2015-02-10     7                 Y
Name: ColumnB, dtype: object

df.loc[rows, columns]或df.iloc[rows, columns]的第一个参数分别指要切片的行或列。

要从索引中获取值：

df.index.get_level_values(level=-1)
df.index.get_level_values(level="IndexAttribute")

两次返回：

Int64Index([8, 7, 7], dtype='int64', name='IndexAttribute')

这是您的初衷吗？

如何操作MultiIndex熊猫系列？

1 个答案: