0

如果熊猫数据框 df 包含：

    A    B    C    D
a   1    2    3    4
b   2    NaN  NaN  5
c   NaN  7    NaN  2
d   NaN  2    4    3

如何将第一行添加到所有其余行，仅在它们包含数字的地方，以获取结果数据框：

    A    B    C    D
b   3    NaN  NaN  9
c   NaN  9    NaN  6
d   NaN  4    7    7

我打算这样做，然后制作一个行名字典，并将第一个表的每一行的列乘积除以第二个表中的同一行，将值保存在字典中。我有执行此操作的工作代码（如下），但我担心它还不够“PANDAS”，而且对于我想要执行的简单任务来说它过于复杂。我有最佳解决方案，还是我遗漏了一些明显的东西？

如果 Pandas 代码仍然需要遍历行，那么这是不值得的，但我觉得应该有一种方法可以就地执行此操作。

代码：

import numpy as np
import pandas as pd

dindex = [1,2,3] #indices of drugs to select (set this)

def get_drugs(): #generates random "drug characteristics" as pandas df
    cduct = ['dose','g1','g2','g3','g4','g5']
    drg = ['d1','d2','d3','d4']
    return pd.DataFrame(abs(np.random.randn(6,4)),index=cduct,columns=drg)

def sel_drugs(dframe, selct): #removes unwanted drugs from df.
    #Pass df and dindex to this function
    return dframe.iloc[:,selct].values, dframe[1:].index.tolist()
    #returns a tuple of [values, names]

def cal_conduct(val, cnames): #calculates conductance scaling.
    #Pass values and names to this function
    cduct = {} #initialize dict
    for ix, gname in enumerate(cnames):
        _top = val[ix+1]; _bot = val[0]+val[ix+1]
        cduct[gname] = (np.product(_top[np.isfinite(_top)])/
                        np.product(_bot[np.isfinite(_bot)]))
    return cduct #return a dictionary of scaling factors

def main():
    selection =  sel_drugs(get_drugs(),dindex)
    print cal_conduct(selection[0], selection[1])

main()
4

2 回答 2

3

Pandas 自动对齐/广播，所以这很简单 

In [8]: df
Out[8]: 
    A   B   C  D
a   1   2   3  4
b   2 NaN NaN  5
c NaN   7 NaN  2
d NaN   2   4  3

In [11]: df.iloc[1:] + df.iloc[0]
Out[11]: 
    A   B   C  D
b   3 NaN NaN  9
c NaN   9 NaN  6
d NaN   4   7  7

如果我阅读正确，第二部分就是这个

In [12]: df2 = df.iloc[1:] + df.iloc[0]

In [13]: df.prod()
Out[13]: 
A      2
B     28
C     12
D    120
dtype: float64

In [14]: df2/df.prod()
Out[14]: 
     A         B         C         D
b  1.5       NaN       NaN  0.075000
c  NaN  0.321429       NaN  0.050000
d  NaN  0.142857  0.583333  0.058333
于 2013-10-12T21:24:14.870 回答
0

这是一些基于@Jeff 回答的代码。它慢了大约 40%，至少在小测试数据的情况下，但它更简单。

import numpy as np
import pandas as pd

dindex = [1,2,3] #indices of drugs to select (set this)

def get_drugs(): #generates random "drug characteristics" as pandas df
    cduct = ['dose','g1','g2','g3','g4','g5']
    drg = ['d1','d2','d3','d4']
    return pd.DataFrame(abs(np.random.randn(6,4)),index=cduct,columns=drg)

def cal_conduct(frame,selct): #calculates conductance scaling.
    #Pass df with selections made
    s = frame.iloc[:,selct]
    cduct = s.iloc[1:].prod(1)/(s.iloc[0]+s.iloc[1:]).prod(1)
    return cduct.to_dict() #return a dictionary of scaling factors

def main():
    scaling = cal_conduct(get_drugs(), dindex)
    print scaling

main()
于 2013-10-13T16:38:33.960 回答