用零填充行其他列有一些值，否则其他列没有值在python pandas中用NaN填充答案

【问题标题】：fill the rows with zero other column has some value or else other column does not has value fill it with NaN in python pandas用零填充行其他列有一些值，否则其他列没有值在python pandas中用NaN填充
【发布时间】：2020-05-14 06:53:58
【问题描述】：

我有如下给出的数据框

import pandas as pd
import numpy as np

k={'ID':[1,2,3,4,5,6],'m1':[20,'',30,40,50,60],
   'm2':['',40,40,90,'',''],
   'm3':['','','','','',''],
   'm4':['','','','','',''],
   'm5':['','','','',1,''],
   'm6':[10,'','',90,'','']}

df=pd.DataFrame(data=k)

我们已经检查了同一行的其他行中是否有任何现有值，然后我们需要用零填充它，或者其他是否包含我们必须填充它的任何值，NAN

我的结果显示如下

ID  m1  m2  m3  m4  m5  m6
1   20  0.0 0.0 0.0 0.0 10.0 # first row and last row has value some vale so  we have fill it Zero others rows  
2   0   40.0    NaN NaN NaN NaN  # there are no value after the second row we have will it with NAN
3   30  40.0    NaN NaN NaN NaN  # there are no value after the second row we have will it with NAN
4   40  90.0    0.0 0.0 0.0 90.0 # first row,second row  and last row has some value so we have fill it Zero to other rows  
5   50  0.0 0.0 0.0 1.0 NaN   # first row,and fifth row has some value so we have fill it Zero to other rows and last row with Nan  
6   60  NaN NaN NaN NaN NaN # there are no value after the first row we have will it with NAN

【问题讨论】：

标签： python arrays pandas numpy dataframe

【解决方案1】：

让我们试试bfill 和mask

s=df.iloc[:,1:]
df.iloc[:,1:]=s.mask(s.mask(s=='').bfill(1).notna()&(s==''),0)
df
   ID  m1  m2 m3 m4 m5  m6
0   1  20   0  0  0  0  10
1   2   0  40             
2   3  30  40             
3   4  40  90  0  0  0  90
4   5  50   0  0  0  1    
5   6  60

【讨论】：