Question

在此回答了该问题的早期版本：

How to vectorize comparison in pandas dataframe?

现在，我用Machine添加了一个新条件：

+---------+-----+-------+---------+
| Machine | nr  |  Time |   Event |
+---------+-----+-------+---------+
| a       |  70 |     8 |    1    |
| a       |  70 |     0 |    1    |
| b       |  70 |     0 |    1    |
| c       |  74 |    52 |    1    |
| c       |  74 |    12 |    2    |
| c       |  74 |     0 |    2    |
+---------+-----+-------+---------+

我想将事件分配到最后一列。每个Machine的第一项默认为1。也就是说，如果它是新的Machine，则Event从1重新开始。

If Time[i] < 7 and nr[i] != nr[i-1], then Event[i]=Event[i-1]+1. 

If Time[i] < 7 and nr[i] = nr[i-1], then Event[i]=Event[i-1]

If Time[i] > 7 then Event[i]=Event[i-1]+1.

如何有效地向量化？我想避免循环。我尝试使用

扩展现有解决方案

m = df.Machine.ne(df.Machine.shift()) 
o = np.select([t & n, t & ~n, m], [1, 0, 1], 1)

但是我知道，这不会将新的Event的{{1}}重置为1，只会增加它。关于如何整合这一点的任何指示？

Answer 1

根据先前的解决方案进行开发。在您的样本上看起来是正确的：

t = df.Time.lt(7)
n = df.nr.ne(df.nr.shift())
m = df.Machine.ne(df.Machine.shift()) 

df['Event'] = np.select([m | t & n, t & ~n], [1, 0], 1)
df['Event'] = df.groupby('Machine').Event.cumsum()

Out[279]:
  Machine  nr  Time  Event
0       a  70     8      1
1       a  70     0      1
2       b  70     0      1
3       c  74    52      1
4       c  74    12      2
5       c  74     0      2

Answer 2

以下应该产生您想要的输出：

# Given you have a dataframe as df
# Create a series for grouping and looking for consecutive runs
mach_nr = df["Machine"] + df["nr"].astype("str")
mach_nr_runs = mach_nr.eq(mach_nr.shift())
# Groupby consecutive runs of each 'Machine'/'nr' combination by its
# that combination value, and take the cumulative sum of the equality
# of shifted combinations
df["Event"] = (
    mach_nr_runs.groupby(mach_nr)
                .cumsum()
                .astype("int")
                .add(1)
)
# Correct the rows where there were consecutive runs, and where 'Time' < 7
lt_7_runs = (df["Time"] < 7) & mach_nr_runs
df["Event"] -= (
    lt_7_runs.groupby(mach_nr)
              .cumsum()
              .astype("int")
)

df现在如下所示：

  Machine  nr  Time  Event
0       a  70     8      1
1       a  70     0      1
2       b  70     0      1
3       c  74    52      1
4       c  74    12      2
5       c  74     0      2

Answer 3

根据您先前的问题（及其出色的答案），您可以执行groupby('machine')并应用该函数，就好像只有一个数据框一样。

def get_event(x):
    t = x.Time.lt(7)
    n = x.nr.ne(x.nr.shift())

    o = np.select([t & n, t & ~n], [1, 0], 1)
    o[0] = 1                               # You say first value is 1
    return pd.Series(o.cumsum(), index=x.index)


df['Event'] = df.groupby('Machine', group_keys=False).apply(get_event)

熊猫的向量化比较

3 个答案: