Python在循环中更新相同的命名列并进行其他计算

在dataframe中，我希望在相同的命名列上进行迭代，当它们的和超过“val_n”值时进行迭代。我想要4样东西： 1） 超出时间（从“val_n”值超过的迭代次数） 2） sum_col（相同命名列的总和） 3） 在EXCEND when时，我想将相应的col值替换为（col-（sum\u col-val\n） 4） 当超过_点后，我想将剩余的cols值替换为0

数据帧看起来像：

id  col1    col2    col3    col4    col5    col6    col7    col8    col9    col10   col11   col12   col13   col14   val_n
1   350 350 350 350 350 350 350 350 350 350 0   0   0   0   3105.61
2   50  50  55  105 50  0   50  100 50  50  50  50  1025    1066.86 3185.6
3   0   0   0   0   0   3495.1  0   0   0   0   0   0   0   3495.1  3477.76

所需数据帧：

id  col1    col2    col3    col4    col5    col6    col7    col8    col9    col10   col11   col12   col13   col14   val_n   exceed_when sum_col
1   350 350 350 350 350 350 350 350 305.61  0   0   0   0   0   3105.61 9   3500
2   50  50  55  105 50  0   50  100 50  50  50  50  1025    1066.86 3185.6      2751.86
3   0   0   0   0   0   3477.76 0   0   0   0   0   0   0   0   3477.76 6   6990.2

这就是我尝试过的：

def trans(row):
    row['sum_col'] = 0
    row['exceed_ind'] = 0

    for i in range(1, 15):
        row['sum_col'] += row['col' + str(i)]

        if ((row['exceed_ind'] == 0) &
            (row['sum_col'] >= row['val_n'])):

            row['exceed_ind'] = 1
            row['exceed_when'] = i

        else:
            continue

        if row['exceed_when'] == i:
            row['col' + str(i)] = (
                row['col' + str(i)] - (
                    row['sum_col'] - row['val_n']))

        elif row['exceed_when'] < i:
            row['col' + str(i)] = 0

        else:
            row['col' + str(i)] = row['col' + str(i)]
    return row

df1 = df.apply(trans, axis=1)

---

谢谢

据我所知，

.apply

函数将只传递

行的一个副本

，所有更新仅发生在副本上，而不是原始的

数据帧

本身。在这种情况下，您必须遍历行并使用索引更新它们

df['sum_col'] = 0
df['exceed_ind'] = 0
df['exceed_when'] = 0

for idx, row in df.iterrows():
    sum_col = 0
    exceed_ind = 0
    exceed_when = 0

    for i in range(1, 15):
        sum_col += row['col' + str(i)]

        if ((exceed_ind == 0) &
            (sum_col >= row['val_n'])):

            exceed_ind = 1
            exceed_when = i
            df.loc[idx, 'exceed_ind'] = exceed_ind
            df.loc[idx, 'exceed_when'] = exceed_when
            df.loc[idx, 'col' + str(i)] = (row['col' + str(i)] - (sum_col - row['val_n']))

        elif (exceed_ind==1) & (exceed_when < i):
            df.loc[idx, 'col' + str(i)] = 0

        df.loc[idx, 'sum_col'] = sum_col

print(df)

---

谢谢大卫比拉！谢谢你的回答，它是正确的。但是你的回答提醒了我我在代码中遗漏了什么，即初始化df['except_when']=0，如果条件为0，则初始化一个。Anku很高兴我能提供帮助！干杯：）

df['sum_col'] = 0
df['exceed_ind'] = 0
df['exceed_when'] = 0

for idx, row in df.iterrows():
    sum_col = 0
    exceed_ind = 0
    exceed_when = 0

    for i in range(1, 15):
        sum_col += row['col' + str(i)]

        if ((exceed_ind == 0) &
            (sum_col >= row['val_n'])):

            exceed_ind = 1
            exceed_when = i
            df.loc[idx, 'exceed_ind'] = exceed_ind
            df.loc[idx, 'exceed_when'] = exceed_when
            df.loc[idx, 'col' + str(i)] = (row['col' + str(i)] - (sum_col - row['val_n']))

        elif (exceed_ind==1) & (exceed_when < i):
            df.loc[idx, 'col' + str(i)] = 0

        df.loc[idx, 'sum_col'] = sum_col

print(df)

    col1  col2  col3  col4  col5     col6  col7  col8    col9  col10  col11  \
id                                                                            
1    350   350   350   350   350   350.00   350   350  305.61      0      0   
2     50    50    55   105    50     0.00    50   100   50.00     50     50   
3      0     0     0     0     0  3477.76     0     0    0.00      0      0   

    col12  col13    col14    val_n  sum_col  exceed_ind  exceed_when  
id                                                                    
1       0      0     0.00  3105.61  3500.00           1            9  
2      50   1025  1066.86  3185.60  2751.86           0            0  
3       0      0     0.00  3477.76  6990.20           1            6