如何操作MultiIndex熊猫系列？

Question

I need to extract data from multiple sites.

Firstly read file

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

My Jupyter image

What I have tried so far:

dfs.iloc[0]

Output:

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

How to extract values from the last column?

This is the index

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'])

If I go for what Evan suggested

df.index.get_level_values(level=-1)

Output

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')

Zero level

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)

How to get values from non-index column?

File uploaded

https://ufile.io/m5nbc

Answer 1

Given a dataframe:

"""
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

Output:

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

The values of the last column( ColumnB ) can be accessed via df.loc[:, "ColumnB"].values , or df.loc[:, "ColumnB"] . See: 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

The first argument to df.loc[rows, columns] or df.iloc[rows, columns] refers to the rows or columns to slice, respectively.

To get the values from the index:

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

Both return:

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

Is that what you had in mind?