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()