How to manipulate MultiIndex pandas series?
0
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
python pandas
asked Nov 21 '18 at 19:50
MikiBelavistaMikiBelavista
53511118
add a comment |
0
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
python pandas
asked Nov 21 '18 at 19:50
MikiBelavistaMikiBelavista
53511118
add a comment |
0
0
0
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
python pandas
asked Nov 21 '18 at 19:50
MikiBelavistaMikiBelavista
53511118
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
python pandas
python pandas
asked Nov 21 '18 at 19:50
MikiBelavistaMikiBelavista
53511118
asked Nov 21 '18 at 19:50
MikiBelavistaMikiBelavista
53511118
edited Nov 22 '18 at 6:00
MikiBelavista
asked Nov 21 '18 at 19:50
MikiBelavistaMikiBelavista
53511118
asked Nov 21 '18 at 19:50
MikiBelavistaMikiBelavista
53511118
asked Nov 21 '18 at 19:50
MikiBelavistaMikiBelavista
53511118
53511118
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add a comment |
1 Answer
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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?
answered Nov 21 '18 at 21:48
EvanEvan
1,141516
I made an edit,last column is non-indexed.
– MikiBelavista
Nov 22 '18 at 5:54
I think I addressed this in my answer... ?
– Evan
Nov 26 '18 at 17:11
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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?
answered Nov 21 '18 at 21:48
EvanEvan
1,141516
I made an edit,last column is non-indexed.
– MikiBelavista
Nov 22 '18 at 5:54
I think I addressed this in my answer... ?
– Evan
Nov 26 '18 at 17:11
add a comment |
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?
answered Nov 21 '18 at 21:48
EvanEvan
1,141516
I made an edit,last column is non-indexed.
– MikiBelavista
Nov 22 '18 at 5:54
I think I addressed this in my answer... ?
– Evan
Nov 26 '18 at 17:11
add a comment |
1
1
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?
answered Nov 21 '18 at 21:48
EvanEvan
1,141516
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?
answered Nov 21 '18 at 21:48
EvanEvan
1,141516
answered Nov 21 '18 at 21:48
EvanEvan
1,141516
answered Nov 21 '18 at 21:48
EvanEvan
1,141516
answered Nov 21 '18 at 21:48
EvanEvan
1,141516
1,141516
I made an edit,last column is non-indexed.
– MikiBelavista
Nov 22 '18 at 5:54
I think I addressed this in my answer... ?
– Evan
Nov 26 '18 at 17:11
add a comment |
I made an edit,last column is non-indexed.
– MikiBelavista
Nov 22 '18 at 5:54
I think I addressed this in my answer... ?
– Evan
Nov 26 '18 at 17:11
I made an edit,last column is non-indexed.
– MikiBelavista
Nov 22 '18 at 5:54
I made an edit,last column is non-indexed.
– MikiBelavista
Nov 22 '18 at 5:54
I think I addressed this in my answer... ?
– Evan
Nov 26 '18 at 17:11
I think I addressed this in my answer... ?
– Evan
Nov 26 '18 at 17:11
add a comment |
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