组内自动递增 [英] auto increment inside group
本文介绍了组内自动递增的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我有一个 dataframe
:
df = pd.DataFrame.from_dict({
'product': ('a', 'a', 'a', 'a', 'c', 'b', 'b', 'b'),
'sales': ('-', '-', 'hot_price', 'hot_price', '-', 'min_price', 'min_price', 'min_price'),
'price': (100, 100, 50, 50, 90, 70, 70, 70),
'dt': ('2020-01-01 00:00:00', '2020-01-01 00:05:00', '2020-01-01 00:07:00', '2020-01-01 00:10:00', '2020-01-01 00:13:00', '2020-01-01 00:15:00', '2020-01-01 00:19:00', '2020-01-01 00:21:00')
})
product sales price dt
0 a - 100 2020-01-01 00:00:00
1 a - 100 2020-01-01 00:05:00
2 a hot_price 50 2020-01-01 00:07:00
3 a hot_price 50 2020-01-01 00:10:00
4 c - 90 2020-01-01 00:13:00
5 b min_price 70 2020-01-01 00:15:00
6 b min_price 70 2020-01-01 00:19:00
7 b min_price 70 2020-01-01 00:21:00
我需要下一个输出:
product sales price dt unique_group
0 a - 100 2020-01-01 00:00:00 0
1 a - 100 2020-01-01 00:05:00 0
2 a hot_price 50 2020-01-01 00:07:00 1
3 a hot_price 50 2020-01-01 00:10:00 1
4 c - 90 2020-01-01 00:13:00 2
5 b min_price 70 2020-01-01 00:15:00 3
6 b min_price 70 2020-01-01 00:19:00 3
7 b min_price 70 2020-01-01 00:21:00 3
我该怎么做:
unique_group = 0
df['unique_group'] = unique_group
for i in range(1, len(df)):
current, prev = df.loc[i], df.loc[i - 1]
if not all([
current['product'] == prev['product'],
current['sales'] == prev['sales'],
current['price'] == prev['price'],
]):
unique_group += 1
df.loc[i, 'unique_group'] = unique_group
是否可以不进行迭代?我尝试使用 cumsum()
, shift()
, ngroup()
, drop_duplicates()
,但未成功.
Is it possible to do it without iteration? I tried using cumsum()
, shift()
, ngroup()
, drop_duplicates()
but unsuccessfully.
推荐答案
IIUC, GroupBy.ngroup
:
df['unique_group'] = df.groupby(['product', 'sales', 'price'],sort=False).ngroup()
print(df)
product sales price dt unique_group
0 a - 100 2020-01-01 00:00:00 0
1 a - 100 2020-01-01 00:05:00 0
2 a hot_price 50 2020-01-01 00:07:00 1
3 a hot_price 50 2020-01-01 00:10:00 1
4 c - 90 2020-01-01 00:13:00 2
5 b min_price 70 2020-01-01 00:15:00 3
6 b min_price 70 2020-01-01 00:19:00 3
7 b min_price 70 2020-01-01 00:21:00 3
这两种方法都可以工作,即使数据帧没有排序
this works either way, even if the data frame is not ordered
另一种方法
这适用于有序数据框
cols = ['product','sales','price']
df['unique_group'] = df[cols].ne(df[cols].shift()).any(axis=1).cumsum().sub(1)
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