问题
I want to find the way change name of specific column in a multilevel dataframe.
With this data:
data = {
('A', '1', 'I'): [1, 2, 3, 4, 5],
('B', '2', 'II'): [1, 2, 3, 4, 5],
('C', '3', 'I'): [1, 2, 3, 4, 5],
('D', '4', 'II'): [1, 2, 3, 4, 5],
('E', '5', 'III'): [1, 2, 3, 4, 5],
}
dataDF = pd.DataFrame(data)
This code not working:
dataDF.rename(columns = {('A', '1', 'I'):('Z', '100', 'Z')}, inplace=True)
Result:
A B C D E
1 2 3 4 5
I II I II III
0 1 1 1 1 1
1 2 2 2 2 2
2 3 3 3 3 3
3 4 4 4 4 4
4 5 5 5 5 5
And also not:
dataDF.columns.values[0] = ('Z', '100', 'Z')
Result:
A B C D E
1 2 3 4 5
I II I II III
0 1 1 1 1 1
1 2 2 2 2 2
2 3 3 3 3 3
3 4 4 4 4 4
4 5 5 5 5 5
But with combination of above codes working!!!
dataDF.columns.values[0] = ('Z', '100', 'Z')
dataDF.rename(columns = {('A', '1', 'I'):('Z', '100', 'Z')}, inplace=True)
dataDF
Result:
Z B C D E
100 2 3 4 5
Z II I II III
0 1 1 1 1 1
1 2 2 2 2 2
2 3 3 3 3 3
3 4 4 4 4 4
4 5 5 5 5 5
Is this bug of Pandas?
回答1:
This is my theory
pandas does not want pd.Indexs to be mutable. We can see this if we try to change the first element of the index ourselves
dataDF.columns[0] = ('Z', '100', 'Z')
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-32-2c0b76762235> in <module>() ----> 1 dataDF.columns[0] = ('Z', '100', 'Z') //anaconda/envs/3.5/lib/python3.5/site-packages/pandas/indexes/base.py in __setitem__(self, key, value) 1372 1373 def __setitem__(self, key, value): -> 1374 raise TypeError("Index does not support mutable operations") 1375 1376 def __getitem__(self, key): TypeError: Index does not support mutable operations
But pandas can't control what you do the values attribute.
dataDF.columns.values[0] = ('Z', '100', 'Z')
we see that dataDF.columns looks the same, but dataDF.columns.values clearly reflects the change. Unfortunately, df.columns.values isn't what shows up on the display of the dataframe.
On the other hand, this really does seem like it should work. The fact that it doesn't feels wrong to me.
dataDF.rename(columns={('A', '1', 'I'): ('Z', '100', 'Z')}, inplace=True)
I believe the reason this only works after having changed the values, is that rename is forcing the reconstruction of the columns by looking at the values. Since we change the values, it now works. This is exceptionally kludgy and I don't recommend building a process that relies on this.
my recommendation
- identify location of column name you want to change
- assign name of column to the array of values
- build new columns from scratch, explicity
from_col = ('A', '1', 'I')
to_col = ('Z', '100', 'Z')
colloc = dataDF.columns.get_loc(from_col)
cvals = dataDF.columns.values
cvals[colloc] = to_col
dataDF.columns = pd.MultiIndex.from_tuples(cvals.tolist())
dataDF
[![enter code here][1]][1]
回答2:
You can simply change it like DF.columns.levels=[[u'Z', u'B', u'C', u'D', u'E'],[u'5', u'2', u'3', u'4', u'5'],[u'IIIIII', u'II', u'III']]
回答3:
I came across this question as I was myself trying to find the solution for renaming the column names in a data frame with multiple levels. I tried the solution provided by @Dark Matter since it appeared to be very simple solution:
dataDF.columns.levels = [[u'Z', u'B', u'C', u'D', u'E'], [u'100', u'2', u'3', u'4', u'5'], [u'Z', u'II', u'III']]
But an error message was displayed:
C:\anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: setting `levels` directly is deprecated. Use set_levels instead
"""Entry point for launching an IPython kernel.
It appears that it worked but does not work anymore. So I used:
dataDF.columns.set_levels([['Z', 'B', 'C', 'D', 'E'],
['100', '2', '3', '4', '5'],
['Z', 'II', 'III']],
[0, 1, 2], inplace=True)
Result: dataDF
Z B C D E
100 2 3 4 5
Z II Z II III
0 1 1 1 1 1
1 2 2 2 2 2
2 3 3 3 3 3
3 4 4 4 4 4
4 5 5 5 5 5
来源：https://stackoverflow.com/questions/40459254/pandas-change-a-specific-column-name-in-dataframe-having-multilevel-columns