熊猫数据框计算

我有一个相当复杂的数据帧，看起来像这样：熊猫数据框计算

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

我试图通过相应的控制水平平均列来划分每个值。我尝试了以下，但它没有奏效。

df2 = df.divide(df.xs('Control', level=1).mean(axis=1), axis='index')

我对Python和熊猫很新，所以我倾向于用MS Excel术语思考这个问题。

如果它是在Excel中为A1的式（ '0.01um'， '0HR' '的终点总数'，0）将看起来是：

=A1/AVERAGE($A$5:$C$5)

B1（“总的终点， '0.01um'， '0HR号码'，1）将是：

=B1/AVERAGE($A$5:$C$5)

和A2（ '终点'， '0.01um'， '24小时'，0的总数）将是

=A1/AVERAGE($A$6:$C$6)

这个例子的期望的结果将是：

            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：有很多指标和列的真实数据。

来源

2015-04-17 agf1997

你能提供所需输出的一个例子？ – Andrew

当我把你的数据放到DataFrame中时，它与你在print（df）中得到的不同。 df = ...和print（df）是两个不同的DataFrame。您的打印（df）与上面的代码无关。您的输入栏为['a'，'b']，但您的印刷栏为[0,1,2]。你能否全部保持一致？谢谢。 –

@MarkGraph哎呀..你是对的..我会修复它。 – agf1997

这里的问题是，熊猫的组织方式可以轻松计算列数，并且该问题需要从其他行中扣除一行中的平均值。熊猫的设计并非如此。

但是，您可以轻松地切换行和列与转置.T，然后它可能更易于处理，事实上，控制手段是一个班轮。

>>> df.T[(u'Total Vessel Length', u'Control', u'0hr')].mean() 
4.0

这4.0来源于两个4.0值在原始数据：

>>> df.T[(u'Total Vessel Length', u'Control', u'0hr')] 
a 4 
b 4

在这一点上，它看起来像for循环将会把这个问题的关心。

未经测试：

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()

注意，这不分割非控制列，但它很容易包括“控制”到UM循环。

UPDATE这不起作用，不知何故它不修改数据帧。现在，我不知道为什么。

但是你可以通过调用pd.DataFrame构造一个新的数据帧，这个dd 理解。

这似乎是工作...

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

输出

[email protected]:~/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]

来源

2015-04-18 00:49:33 Paul

我得到了和in一样的结果。有什么地方需要'inplace = True'吗？ – agf1997

这里也一样。似乎很熟悉。我会环顾四周。 – Paul

也许有关。还在寻找。 http://stackoverflow.com/questions/17995328/changing-values-in-pandas-dataframe-doenst-work – Paul

它有助于在自己的列中的值Control。你可以做，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

现在找到的每个控制的平均值：

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，你可以使用df2.divide来计算期望的结果。请务必使用axis=0来告诉Pandas根据行索引匹配值（在df2和ave之间）。

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

基本上存在着你所追求的价值观。但是，如果要重新排列数据框看起来完全一样，你贴出来，然后：

result = result.stack(['type']) 
result = result.reorder_levels(['field','type','time'], axis=0) 
result = result.reindex(df.index)

产生

          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)

来源

2015-04-18 01:27:12 unutbu

有趣。我没有注意到索引可能是元组，并有所有这些关联的方法。 – Paul

熊猫数据框计算

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