不包括数据框中某些列的最后一行

Question

所以我试图在索引 0 交换到另一个索引之前找到它的平均值。 数据框的示例：

column_a	value_b	sum_c	count_d_	avg_e
0	10	10	1
0	20	30	2
0	30	60	3	20
1	10	10	1
1	20	30	2
1	30	60	3	20
0	10	10	1
0	20	30	2	15
1	10	10	1
1	20	30	2
1	30	60	3	20
0	10	10	1
0	20

但是，只有 sum 和 count 的最后一行不可用，因此无法为其计算 avg

part of the code...
#sum and avg for each section

for i, row in df.iloc[0:-1].iterrows():
  if df['column_a'][i] == 0:
    sum = sum + df['value_b'][i]
    df['sum_c'][i] = sum
    count = count + 1
    df['count_d'][i] = count
  else:
    sum = 0 
    count = 0
    df['sum_c'][i] = sum
    df['count_d'][i] = count

totcount = 0
for m, row in df.iloc[0:-1].iterrows():
  if df.loc[m, 'column_a'] == 0 :
    if (df.loc[m+1, 'sum_c'] == 0) :
      totcount = df.loc[m, 'count_d']
      avg_e = (df.loc[m, 'sum_c']) / totcount
      df.loc[m, 'avg_e'] = avg_e

已尝试仅使用 df.iloc[0:].iterrows 但它会产生错误。

Answer 1

您可以使用groupby.cummax、groupby.cumcount、groupby.transform('mean') 重写完整代码，并使用where 进行屏蔽。

# compute a mask with True for the last value per successive group
m = df['column_a'].ne(df['column_a'].shift(-1))[::-1]
# make a grouper
group = m.cumsum()

# for each group
g = df.groupby(group)['value_b']
# compute the cumsum
df['sum_c'] = g.cumsum()
# compute the cumcount
df['count_d_'] = g.cumcount().add(1)
# compute the mean and assign to the last row per group
df['avg_e'] = g.transform('mean').where(m)

输出：

    column_a  value_b  sum_c  count_d_  avg_e
0          0       10     10         1    NaN
1          0       20     30         2    NaN
2          0       30     60         3   20.0
3          1       10     10         1    NaN
4          1       20     30         2    NaN
5          1       30     60         3   20.0
6          0       10     10         1    NaN
7          0       20     30         2   15.0
8          1       10     10         1    NaN
9          1       20     30         2    NaN
10         1       30     60         3   20.0
11         0       10     10         1    NaN
12         0       20     30         2   15.0