Python 根据循环中另一列的值将列的值更改为nan

我有很多列的后缀是“mean”或“sum”。有时带有“mean”后缀的是NaN。当这种情况发生时，我想把后缀为sum的也变成NaN。我有很多变量，所以我需要（？）使用循环。我已经创建了一个假的数据框架，并在SO中添加了我在类似帖子的基础上尝试过的3件事情。不幸的是，一切都不起作用

original_data_set = (pd.DataFrame
(
{
    'customerId':[1,2]
    ,'usage_1_sum':[100, 200]
    ,'usage_1_mean':[np.nan,100]
    ,'usage_2_sum':[420,330]
    ,'usage_2_mean':[45,np.nan]
}
)
             )

print('original dataset')
original_data_set

desired_data_set = (pd.DataFrame
(
{
    'customerId':[1,2]
    ,'usage_1_sum':[np.nan, 200]
    ,'usage_1_mean':[np.nan,100]
    ,'usage_2_sum':[420,np.nan]
    ,'usage_2_mean':[45,np.nan]
}
)
             )

print('desired dataset')
desired_data_set



holder_set = original_data_set.copy()

for number in range(1,3):
    holder_set['usage_{}_sum'.format(number)] = (
        
        holder_set['usage_{}_sum'.format(number)]
        .where(holder_set['usage_{}_mean'.format(number)] == np.nan, np.nan
              )
                                                )

print('using an np.where statement changed all sum variables into NaN with no discretion')
holder_set


holder_set = original_data_set.copy()

for number in range(1,3):
    conditions = [holder_set['usage_{}_mean'.format(number)]==np.nan]
    outcome = [np.nan]
    holder_set['usage_{}_sum'.format(number)] = np.select(conditions, outcome, default=holder_set['usage_{}_sum'.format(number)])
    
    
print('using an np.select did not have any effect on the dataframe')
holder_set


holder_set = original_data_set.copy()

for number in range(1,3):
    holder_set.loc[holder_set['usage_{}_mean'.format(number)]==np.nan, 'usage_{}_sum'.format(number)] = 12

print('using a loc did not have any effect on the dataframe')
holder_set

---

假设

原始

数据帧为

df

：

df = pd.DataFrame({'customerId': [1, 2], 'usage_1_sum': [100, 200], 'usage_1_mean': [
                  np.nan, 100], 'usage_2_sum': [420, 330], 'usage_2_mean': [45, np.nan]})

使用，过滤以

\u mean

结尾的列，然后对于以

\u mean

结尾的列中的每一列，将

\u sum

列中的相应值更改为

NaN

，其中平均列中的值为

NaN

：

for col in df.columns[df.columns.str.endswith('_mean')]:
    df.loc[df[col].isna(), col.rstrip('_mean') + '_sum'] = np.nan

结果:

# print(df)
   customerId  usage_1_sum  usage_1_mean  usage_2_sum  usage_2_mean
0           1          NaN           NaN        420.0          45.0
1           2        200.0         100.0          NaN           NaN

---

也许可以尝试查看

DataFrame.where（）

功能。您应该能够直接索引到问题区域，而无需自己编写循环。