Python 重置列的 MultiIndex 级别
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Reset a column's MultiIndex levels
提问by dmvianna
Is there a shorter way of dropping a column MultiIndex level (in my case, basic_amt) except transposing it twice?
basic_amt除了转置两次之外，是否有更短的方法来删除列 MultiIndex 级别（在我的情况下，）？
In [704]: test
Out[704]:
basic_amt
Faculty NSW QLD VIC All
All 1 1 2 4
Full Time 0 1 0 1
Part Time 1 0 2 3
In [705]: test.reset_index(level=0, drop=True)
Out[705]:
basic_amt
Faculty NSW QLD VIC All
0 1 1 2 4
1 0 1 0 1
2 1 0 2 3
In [711]: test.transpose().reset_index(level=0, drop=True).transpose()
Out[711]:
Faculty NSW QLD VIC All
All 1 1 2 4
Full Time 0 1 0 1
Part Time 1 0 2 3
采纳答案by jezrael
Another solution is use use MultiIndex.droplevelwith rename_axis(new in pandas0.18.0):
另一种解决方案是使用 use MultiIndex.droplevelwith rename_axis(new in pandas0.18.0)：
import pandas as pd
cols = pd.MultiIndex.from_arrays([['basic_amt']*4,
['NSW','QLD','VIC','All']],
names = [None, 'Faculty'])
idx = pd.Index(['All', 'Full Time', 'Part Time'])
df = pd.DataFrame([(1,1,2,4),
(0,1,0,1),
(1,0,2,3)], index = idx, columns=cols)
print (df)
basic_amt
Faculty NSW QLD VIC All
All 1 1 2 4
Full Time 0 1 0 1
Part Time 1 0 2 3
df.columns = df.columns.droplevel(0)
#pandas 0.18.0 and higher
df = df.rename_axis(None, axis=1)
#pandas bellow 0.18.0
#df.columns.name = None
print (df)
NSW QLD VIC All
All 1 1 2 4
Full Time 0 1 0 1
Part Time 1 0 2 3
print (df.columns)
Index(['NSW', 'QLD', 'VIC', 'All'], dtype='object')
If need both column names use listcomprehension:
如果需要两个列名使用list理解：
df.columns = ['_'.join(col) for col in df.columns]
print (df)
basic_amt_NSW basic_amt_QLD basic_amt_VIC basic_amt_All
All 1 1 2 4
Full Time 0 1 0 1
Part Time 1 0 2 3
print (df.columns)
Index(['basic_amt_NSW', 'basic_amt_QLD', 'basic_amt_VIC', 'basic_amt_All'], dtype='object')
回答by unutbu
How about simply reassigning df.columns:
如何简单地重新分配df.columns：
levels = df.columns.levels
labels = df.columns.labels
df.columns = levels[1][labels[1]]
For example:
例如：
import pandas as pd
columns = pd.MultiIndex.from_arrays([['basic_amt']*4,
['NSW','QLD','VIC','All']])
index = pd.Index(['All', 'Full Time', 'Part Time'], name = 'Faculty')
df = pd.DataFrame([(1,1,2,4),
(0,01,0,1),
(1,0,2,3)])
df.columns = columns
df.index = index
Before:
前：
print(df)
basic_amt
NSW QLD VIC All
Faculty
All 1 1 2 4
Full Time 0 1 0 1
Part Time 1 0 2 3
After:
后：
levels = df.columns.levels
labels = df.columns.labels
df.columns = levels[1][labels[1]]
print(df)
NSW QLD VIC All
Faculty
All 1 1 2 4
Full Time 0 1 0 1
Part Time 1 0 2 3
回答by firelynx
Zip levels together
将级别压缩在一起
Here is an alternative solution which zips the levels together and joins them with underscore.
这是一个替代解决方案，它将级别压缩在一起并用下划线将它们连接起来。
Derived from the above answer, and this was what I wanted to do when I found this answer. Thought I would share even if it does not answer the exact above question.
源自上面的答案，这就是我找到这个答案时想做的事情。我想即使它没有回答上述问题，我也会分享。
["_".join(pair) for pair in df.columns]
gives
给
['basic_amt_NSW', 'basic_amt_QLD', 'basic_amt_VIC', 'basic_amt_All']
Just set this as a the columns
只需将其设置为列
df.columns = ["_".join(pair) for pair in df.columns]
basic_amt_NSW basic_amt_QLD basic_amt_VIC basic_amt_All
Faculty
All 1 1 2 4
Full Time 0 1 0 1
Part Time 1 0 2 3
