Python 基于表中其他列的值对列执行操作

我有一个数据帧

df = pd.DataFrame([["A",1,98,88,"",567,453,545,656,323,756], ["B",1,99,"","",231,232,234,943,474,345], ["C",1,97,67,23,543,458,456,876,935,876], ["B",1,"",79,84,895,237,678,452,545,453], ["A",1,45,"",58,334,778,234,983,858,657], ["C",1,23,55,"",183,565,953,565,234,234]], columns=["id","date","col1","col2","col3","col1_num","col1_deno","col3_num","col3_deno","col2_num","col2_deno"])

我需要分别为列名称的_num和_deno设置Nan/空值。例如：如果“col1”的特定行为空，则将的“col1\u num”和的“col1\u deno”的值设为Nan/空。基于“col2”对“col2\u num”和基于“col2\u deno”对“col3\u num”和“col3\u deno”重复相同的过程

预期输出：

df_out = pd.DataFrame([["A",1,98,88,"",567,453,"","",323,756], ["B",1,99,"","",231,232,"","","",""], ["C",1,97,67,23,543,458,456,876,935,876], ["B",1,"",79,84,"","",678,452,545,453], ["A",1,45,"",58,334,778,234,983,"",""], ["C",1,23,55,"",183,565,"","",234,234]], columns=["id","date","col1","col2","col3","col1_num","col1_deno","col3_num","col3_deno","col2_num","col2_deno"])

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如何操作？

让我们尝试使用布尔掩蔽：

# select the columns
c = pd.Index(['col1', 'col2', 'col3'])

# create boolean mask
m = df[c].eq('').to_numpy()

# mask the values in `_num` and `_deno` like columns
df[c + '_num'] = df[c + '_num'].mask(m, '')
df[c + '_deno'] = df[c + '_deno'].mask(m, '')

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@shubham的答案很简单，切中要害，我相信也会更快；这只是一个选项，您可能无法（或希望）列出所有列

获取需要更改的列的列表：

cols = [col for col in df if col.startswith('col')]

['col1',
 'col2',
 'col3',
 'col1_num',
 'col1_deno',
 'col3_num',
 'col3_deno',
 'col2_num',
 'col2_deno']

创建字典，将col1与要更改的列配对，与col2相同，依此类推：

from collections import defaultdict
d = defaultdict(list)

for col in cols:
    if "_" in col:
        d[col.split("_")[0]].append(col)

d

defaultdict(list,
            {'col1': ['col1_num', 'col1_deno'],
             'col3': ['col3_num', 'col3_deno'],
             'col2': ['col2_num', 'col2_deno']})

迭代dict以分配新值：

for key, val in d.items():
    df.loc[df[key].eq(""), val] = ""




 id  date col1 col2 col3 col1_num col1_deno col3_num col3_deno col2_num col2_deno
0  A     1   98   88           567       453                         323       756
1  B     1   99                231       232                                      
2  C     1   97   67   23      543       458      456       876      935       876
3  B     1        79   84                         678       452      545       453
4  A     1   45        58      334       778      234       983                   
5  C     1   23   55           183       565                         234       234

---

使用

多索引的解决方案

：

#first convert not processing and testing columns to index
df1 = df.set_index(['id','date'])
cols = df1.columns
#split columns by _ for MultiIndex
df1.columns = df1.columns.str.split('_', expand=True)

#compare columns without _ (with NaN in second level) by empty string
m = df1.xs(np.nan, axis=1, level=1).eq('')
#create mask by all columns
mask = m.reindex(df1.columns, axis=1, level=0)
#set new values by mask, overwrite columns names
df1 = df1.mask(mask, '').set_axis(cols, axis=1).reset_index()
print (df1)
  id  date col1 col2 col3 col1_num col1_deno col3_num col3_deno col2_num  \
0  A     1   98   88           567       453                         323   
1  B     1   99                231       232                               
2  C     1   97   67   23      543       458      456       876      935   
3  B     1        79   84                         678       452      545   
4  A     1   45        58      334       778      234       983            
5  C     1   23   55           183       565                         234   

  col2_deno  
0       756  
1            
2       876  
3       453  
4            
5       234  

#first convert not processing and testing columns to index
df1 = df.set_index(['id','date'])
cols = df1.columns
#split columns by _ for MultiIndex
df1.columns = df1.columns.str.split('_', expand=True)

#compare columns without _ (with NaN in second level) by empty string
m = df1.xs(np.nan, axis=1, level=1).eq('')
#create mask by all columns
mask = m.reindex(df1.columns, axis=1, level=0)
#set new values by mask, overwrite columns names
df1 = df1.mask(mask, '').set_axis(cols, axis=1).reset_index()
print (df1)
  id  date col1 col2 col3 col1_num col1_deno col3_num col3_deno col2_num  \
0  A     1   98   88           567       453                         323   
1  B     1   99                231       232                               
2  C     1   97   67   23      543       458      456       876      935   
3  B     1        79   84                         678       452      545   
4  A     1   45        58      334       778      234       983            
5  C     1   23   55           183       565                         234   

  col2_deno  
0       756  
1            
2       876  
3       453  
4            
5       234