我有一个相当复杂的数据框，如下所示：
df = pd.DataFrame({'0': {('Total Number of End Points', '0.01um', '0hr'): 12,
('Total Number of End Points', '0.1um', '0hr'): 8,
('Total Number of End Points', 'Control', '0hr'): 4,
('Total Number of End Points', '0.01um', '24hr'): 18,
('Total Number of End Points', '0.1um', '24hr'): 12,
('Total Number of End Points', 'Control', '24hr'): 6,
('Total Vessel Length', '0.01um', '0hr'): 12,
('Total Vessel Length', '0.1um', '0hr'): 8,
('Total Vessel Length', 'Control', '0hr'): 4,
('Total Vessel Length', '0.01um', '24hr'): 18,
('Total Vessel Length', '0.1um', '24hr'): 12,
('Total Vessel Length', 'Control', '24hr'): 6},
'1': {('Total Number of End Points', '0.01um', '0hr'): 12,
('Total Number of End Points', '0.1um', '0hr'): 8,
('Total Number of End Points', 'Control', '0hr'): 4,
('Total Number of End Points', '0.01um', '24hr'): 18,
('Total Number of End Points', '0.1um', '24hr'): 12,
('Total Number of End Points', 'Control', '24hr'): 6,
('Total Vessel Length', '0.01um', '0hr'): 12,
('Total Vessel Length', '0.1um', '0hr'): 8,
('Total Vessel Length', 'Control', '0hr'): 4,
('Total Vessel Length', '0.01um', '24hr'): 18,
('Total Vessel Length', '0.1um', '24hr'): 12,
('Total Vessel Length', 'Control', '24hr'): 6},
'2': {('Total Number of End Points', '0.01um', '0hr'): 12,
('Total Number of End Points', '0.1um', '0hr'): 8,
('Total Number of End Points', 'Control', '0hr'): 4,
('Total Number of End Points', '0.01um', '24hr'): 18,
('Total Number of End Points', '0.1um', '24hr'): 12,
('Total Number of End Points', 'Control', '24hr'): 6,
('Total Vessel Length', '0.01um', '0hr'): 12,
('Total Vessel Length', '0.1um', '0hr'): 8,
('Total Vessel Length', 'Control', '0hr'): 4,
('Total Vessel Length', '0.01um', '24hr'): 18,
('Total Vessel Length', '0.1um', '24hr'): 12,
('Total Vessel Length', 'Control', '24hr'): 6}})
print(df)
0 1 2
Total Number of End Points 0.01um 0hr 12 12 12
24hr 18 18 18
0.1um 0hr 8 8 8
24hr 12 12 12
Control 0hr 4 4 4
24hr 6 6 6
Total Vessel Length 0.01um 0hr 12 12 12
24hr 18 18 18
0.1um 0hr 8 8 8
24hr 12 12 12
Control 0hr 4 4 4
24hr 6 6 6
我试图将每个值除以相应控制级别中列的平均值。我试过以下但是没有用。
df2 = df.divide(df.xs('Control', level=1).mean(axis=1), axis='index')
我对python和pandas很陌生，所以我倾向于用MS Excel术语来思考这个问题。
如果是在Excel中，A1的公式（＆＃39;终点总数＆＃39;，＆＃39; 0.01um＆＃39;，＆＃39; 0hr＆＃39;，0）看起来像是：
=A1 / AVERAGE($A$5:$C$5)
B1（＆＃39;终点总数＆＃39;，＆＃39; 0.01um＆＃39;，＆＃39; 0hr＆＃39;，1）将是：
=B1 / AVERAGE($A$5:$C$5)
和A2（＆＃39;终点总数＆＃39;，＆＃39; 0.01um＆＃39;，＆＃39; 24小时＆＃39;，0）将是
=A1 / AVERAGE($A$6:$C$6)
此示例的预期结果是：
0 1 2
Total Number of End Points 0.01um 0hr 3 3 3
24hr 3 3 3
0.1um 0hr 2 2 2
24hr 2 2 2
Control 0hr 1 1 1
24hr 1 1 1
Total Vessel Length 0.01um 0hr 3 3 3
24hr 3 3 3
0.1um 0hr 2 2 2
24hr 2 2 2
Control 0hr 1 1 1
24hr 1 1 1
注意：实际数据中有许多索引和列。
答案 0 :(得分：1)
将Control值放在自己的列中会很有帮助。您可以使用unstack：
df.index.names = ['field', 'type', 'time']
df2 = df.unstack(['type']).swaplevel(0, 1, axis=1)
# type 0.01um 0.1um Control 0.01um 0.1um Control \
# 0 0 0 1 1 1
# field time
# Total Number of End Points 0hr 12 8 4 12 8 4
# 24hr 18 12 6 18 12 6
# Total Vessel Length 0hr 12 8 4 12 8 4
# 24hr 18 12 6 18 12 6
# type 0.01um 0.1um Control
# 2 2 2
# field time
# Total Number of End Points 0hr 12 8 4
# 24hr 18 12 6
# Total Vessel Length 0hr 12 8 4
# 24hr 18 12 6
现在找到每个Control的平均值：
ave = df2['Control'].mean(axis=1)
# field time
# Total Number of End Points 0hr 4
# 24hr 6
# Total Vessel Length 0hr 4
# 24hr 6
# dtype: float64
如您所料，您可以使用df2.divide来计算所需的结果。请务必使用axis=0告诉Pandas根据行索引匹配值（df2和ave）。
result = df2.divide(ave, axis=0)
# type 0.01um 0.1um Control 0.01um 0.1um Control \
# 0 0 0 1 1 1
# field time
# Total Number of End Points 0hr 3 2 1 3 2 1
# 24hr 3 2 1 3 2 1
# Total Vessel Length 0hr 3 2 1 3 2 1
# 24hr 3 2 1 3 2 1
# type 0.01um 0.1um Control
# 2 2 2
# field time
# Total Number of End Points 0hr 3 2 1
# 24hr 3 2 1
# Total Vessel Length 0hr 3 2 1
# 24hr 3 2 1
您所追求的基本上都是值。但是，如果您想重新排列DataFrame，使其与您发布的完全一致，那么：
result = result.stack(['type'])
result = result.reorder_levels(['field','type','time'], axis=0)
result = result.reindex(df.index)
产量
0 1 2
field type time
Total Number of End Points 0.01um 0hr 3 3 3
24hr 3 3 3
0.1um 0hr 2 2 2
24hr 2 2 2
Control 0hr 1 1 1
24hr 1 1 1
Total Vessel Length 0.01um 0hr 3 3 3
24hr 3 3 3
0.1um 0hr 2 2 2
24hr 2 2 2
Control 0hr 1 1 1
24hr 1 1 1
全部放在一起：
df.index.names = ['field', 'type', 'time']
df2 = df.unstack(['type']).swaplevel(0, 1, axis=1)
ave = df2['Control'].mean(axis=1)
result = df2.divide(ave, axis=0)
result = result.stack(['type'])
result = result.reorder_levels(['field','type','time'], axis=0)
result = result.reindex(df.index)
答案 1 :(得分：0)
这里的问题是pandas被组织起来可以轻松地计算列，并且该问题需要从其他行中扣除一行的平均值。熊猫不是那样设计的。
但是，您可以使用转置.T轻松切换行和列，然后它可能更容易处理，实际上控制均值是一个班轮。
>>> df.T[(u'Total Vessel Length', u'Control', u'0hr')].mean()
4.0
此4.0来自原始数据中的两个4.0值：
>>> df.T[(u'Total Vessel Length', u'Control', u'0hr')]
a 4
b 4
此时看起来循环会解决问题。
未测试：
for primary in (u'Total Vessel Length',u'Total Number of End Points'):
for um in (u'0.01um',u'0.1um'):
for hours in (u'0hr',u'24hr'):
df.T[(primary,um,hours)]=df.T[(primary,um,hours)]/df.T[(primary, u'Control', hours)].mean()
请注意，这并非划分非控制列，但很容易包含“控制”列。进入um循环。
更新这不起作用，不知道它是不是在修改数据框架。现在，我不确定为什么。
但是你可以通过在dict上调用pd.DataFrame来构造一个新的数据框 理解。
这似乎有效......
import pandas as pd
df = pd.DataFrame({'0': {('Total Number of End Points', '0.01um', '0hr'): 12,
('Total Number of End Points', '0.1um', '0hr'): 8,
('Total Number of End Points', 'Control', '0hr'): 4,
('Total Number of End Points', '0.01um', '24hr'): 18,
('Total Number of End Points', '0.1um', '24hr'): 12,
('Total Number of End Points', 'Control', '24hr'): 6,
('Total Vessel Length', '0.01um', '0hr'): 12,
('Total Vessel Length', '0.1um', '0hr'): 8,
('Total Vessel Length', 'Control', '0hr'): 4,
('Total Vessel Length', '0.01um', '24hr'): 18,
('Total Vessel Length', '0.1um', '24hr'): 12,
('Total Vessel Length', 'Control', '24hr'): 6},
'1': {('Total Number of End Points', '0.01um', '0hr'): 12,
('Total Number of End Points', '0.1um', '0hr'): 8,
('Total Number of End Points', 'Control', '0hr'): 4,
('Total Number of End Points', '0.01um', '24hr'): 18,
('Total Number of End Points', '0.1um', '24hr'): 12,
('Total Number of End Points', 'Control', '24hr'): 6,
('Total Vessel Length', '0.01um', '0hr'): 12,
('Total Vessel Length', '0.1um', '0hr'): 8,
('Total Vessel Length', 'Control', '0hr'): 4,
('Total Vessel Length', '0.01um', '24hr'): 18,
('Total Vessel Length', '0.1um', '24hr'): 12,
('Total Vessel Length', 'Control', '24hr'): 6},
'2': {('Total Number of End Points', '0.01um', '0hr'): 12,
('Total Number of End Points', '0.1um', '0hr'): 8,
('Total Number of End Points', 'Control', '0hr'): 4,
('Total Number of End Points', '0.01um', '24hr'): 18,
('Total Number of End Points', '0.1um', '24hr'): 12,
('Total Number of End Points', 'Control', '24hr'): 6,
('Total Vessel Length', '0.01um', '0hr'): 12,
('Total Vessel Length', '0.1um', '0hr'): 8,
('Total Vessel Length', 'Control', '0hr'): 4,
('Total Vessel Length', '0.01um', '24hr'): 18,
('Total Vessel Length', '0.1um', '24hr'): 12,
('Total Vessel Length', 'Control', '24hr'): 6}})
print df
df2 = pd.DataFrame({(primary,um,hours):df.T[(primary,um,hours)]/df.T[(primary,u'Control',hours)].mean() for primary in (u'Total Vessel Length',u'Total Number of End Points') for um in (u'0.01um',u'0.1um') for hours in (u'0hr',u'24hr')})
print df2.T
<强>输出
paul@home:~/SO$ python ./r.py
0 1 2
(Total Number of End Points, 0.01um, 0hr) 12 12 12
(Total Number of End Points, 0.01um, 24hr) 18 18 18
(Total Number of End Points, 0.1um, 0hr) 8 8 8
(Total Number of End Points, 0.1um, 24hr) 12 12 12
(Total Number of End Points, Control, 0hr) 4 4 4
(Total Number of End Points, Control, 24hr) 6 6 6
(Total Vessel Length, 0.01um, 0hr) 12 12 12
(Total Vessel Length, 0.01um, 24hr) 18 18 18
(Total Vessel Length, 0.1um, 0hr) 8 8 8
(Total Vessel Length, 0.1um, 24hr) 12 12 12
(Total Vessel Length, Control, 0hr) 4 4 4
(Total Vessel Length, Control, 24hr) 6 6 6
[12 rows x 3 columns]
0 1 2
(Total Number of End Points, 0.01um, 0hr) 3 3 3
(Total Number of End Points, 0.01um, 24hr) 3 3 3
(Total Number of End Points, 0.1um, 0hr) 2 2 2
(Total Number of End Points, 0.1um, 24hr) 2 2 2
(Total Vessel Length, 0.01um, 0hr) 3 3 3
(Total Vessel Length, 0.01um, 24hr) 3 3 3
(Total Vessel Length, 0.1um, 0hr) 2 2 2
(Total Vessel Length, 0.1um, 24hr) 2 2 2
[8 rows x 3 columns]