用另一个查找填充 NaN table

fill NaN with another lookup table

有没有办法通过匹配名称、十字线和单元格 rev 来用 test=default 的值填充 NaN

使用 "test" 列中的几个变量:

有没有办法更新其他行的值?因为数据类型 "do" 的优先级高于 int 并删除 "do" 数据行?

数据:
测试数据类型名称值标线 cell_rev
默认 int s 0x45 CR1
默认 int s 0xCB CR3
默认 do s 0.68 CR1

我想得到:

测试数据类型名称值标线cell_rev
默认 int s 0.68 CR1
默认 int s 0xCB CR3

您可以使用 set_index with unstack for reshaping, then ffill for add missing values and last reshape to original by stack:

df = df.set_index(['name','value_old','reticle','test','cell_rev'])
       .unstack()
       .ffill()
       .stack()
       .reset_index()

print (df)
  name value_old reticle     test cell_rev value_new
0    s      0x8E     A28  default      CR1      0x8C
1    s      0x8E     A28  default      CR3      0x8E
2    s      0x8E     A28     etlc      CR1      0x8C
3    s      0x8E     A28     etlc      CR3      0x8E

通过评论编辑:

使用merge by subset df1 created by boolean indexing and then fill NaN values by combine_first or fillna:

df1 = df.ix[df.test == 'default']
print (df1)     
      test name value_old reticle cell_rev value_new
0  default    s      0x8E     A28      CR1      0x8E
1  default    s      0x8E     A28      CR3      0x8C

df2 = pd.merge(df, df1, how='left', on=['name','reticle','cell_rev'], suffixes=('','1'))
print (df2)
      test name value_old reticle cell_rev value_new    test1 value_old1  \
0  default    s      0x8E     A28      CR1      0x8E  default       0x8E   
1  default    s      0x8E     A28      CR3      0x8C  default       0x8E   
2     etlc    s      0x8E     A28      CR1      0x44  default       0x8E   
3     etlc    s      0x8E     A28      CR3      0x44  default       0x8E   
4      mlc    s      0x1E     A28      CR1       NaN  default       0x8E   
5      mlc    s      0x1E     A28      CR3       NaN  default       0x8E   
6      slc    s      0x2E     A28      CR1       NaN  default       0x8E   
7      slc    s      0x2E     A28      CR3       NaN  default       0x8E   

  value_new1  
0       0x8E  
1       0x8C  
2       0x8E  
3       0x8C  
4       0x8E  
5       0x8C  
6       0x8E  
7       0x8C 
df['value_new'] = df2['value_new'].combine_first(df2['value_new1'])
#df['value_new'] = df2['value_new'].fillna(df2['value_new1'])
print (df)
      test name value_old reticle cell_rev value_new
0  default    s      0x8E     A28      CR1      0x8E
1  default    s      0x8E     A28      CR3      0x8C
2     etlc    s      0x8E     A28      CR1      0x44
3     etlc    s      0x8E     A28      CR3      0x44
4      mlc    s      0x1E     A28      CR1      0x8E
5      mlc    s      0x1E     A28      CR3      0x8C
6      slc    s      0x2E     A28      CR1      0x8E
7      slc    s      0x2E     A28      CR3      0x8C
for i in range(len(df)):
    if df.loc[i, 'value_new'] != df.loc[i, 'value_new']:
        df.loc[i, 'value_new'] = df.loc[(df.test == 'default') &
                                        (df.name == df.loc[i, 'name']) &
                                        (df.reticle == df.loc[i, 'reticle']) &
                                        (df.cell_rev == df.loc[i, 'cell_rev']),
                                        'value_new']

我认为有更有效的解决方案,但这应该可行。