我有一個DataFrame，其中包含dates、categories和一列，該列顯示該類別是否發生了一次性事件。我想創建一個新列，其中包含事件發生之前的時間，或者某個指標沒有事件，例如負時間。數據集非常大，我想有一個更好的解決方案，而不是使用 Pandas 更好的人會知道的循環暴力破解它！所以，簡而言之，如果我這樣創建我的數據集：import pandas as pd#create example datasetdata = {'categories':['a','b','c']*4,'dates':[i for i in range(4) for j in range(3)],'event':[0]*3*4}#add a couple of eventsdata['event'][4] = 1data['event'][9] = 1df = pd.DataFrame(data)我怎樣才能最好地得到這樣的輸出？ categories dates event time_until0 a 0 0 31 b 0 0 12 c 0 0 -13 a 1 0 24 b 1 1 05 c 1 0 -16 a 2 0 17 b 2 0 -18 c 2 0 -19 a 3 1 010 b 3 0 -111 c 3 0 -1謝謝你的幫助！
3 回答
慕森王
TA貢獻1777條經驗 獲得超3個贊
使用groupby
def f(s):
s = s.reset_index(drop=True)
one = s[s.eq(1)]
if one.empty: return -1
return -s.index + one.index[0]
df.groupby('categories').event.transform(f)
categories dates event time_until
0 a 0 0 3
1 b 0 0 1
2 c 0 0 -1
3 a 1 0 2
4 b 1 1 0
5 c 1 0 -1
6 a 2 0 1
7 b 2 0 -1
8 c 2 0 -1
9 a 3 1 0
10 b 3 0 -2
11 c 3 0 -1
請注意，即使在事件發生之后，它也會找到距離。因此，對于以下事件，您將獲得以下輸出
event = [0, 0, 0, 1, 0, 0]
until = [3, 2, 1, 0, -1, -2]
如果您需要使所有負值保持不變-1，那么只需在最后進行調整
df.time_until.where(df.time_until >= -1, -1)
眼眸繁星
TA貢獻1873條經驗 獲得超9個贊
替代解決方案：
df.sort_values(by=['categories', 'dates'], ascending=[True, False], inplace=True)
df['tmp'] = df.groupby('categories')['event'].transform('cumsum')
df['time_until'] = df.groupby('categories')['tmp'].transform('cumsum') - 1
df.drop(columns='tmp', inplace=True)
df.sort_values(by=['dates', 'categories'], ascending=[True, True], inplace=True)
輸出：
categories dates event time_until
0 a 0 0 3
1 b 0 0 1
2 c 0 0 -1
3 a 1 0 2
4 b 1 1 0
5 c 1 0 -1
6 a 2 0 1
7 b 2 0 -1
8 c 2 0 -1
9 a 3 1 0
10 b 3 0 -1
11 c 3 0 -1
三國紛爭
TA貢獻1804條經驗 獲得超7個贊
嘗試這樣的事情：
import pandas as pd
import numpy as np
data = {'categories':['a','b','c']*4,
'dates':[i for i in range(4) for j in range(3)],
'event':[0, 1, 0]*4}
df = pd.DataFrame(data)
print(df)
# One way
df.loc[df.event == 0, 'Newevents'] = 'Cancelled'
df.loc[df.event != 0, 'Newevents'] = 'Scheduled'
# Another way
conditions = [
(df['categories'] == "a"),
(df['categories'] == "b"),
(df['categories'] == "c")]
choices = ['None', 'Completed', 'Scheduled']
df['NewCategories'] = np.select(conditions, choices, default='black')
print(df)
輸出：
categories dates event
0 a 0 0
1 b 0 1
2 c 0 0
3 a 1 0
4 b 1 1
5 c 1 0
6 a 2 0
7 b 2 1
8 c 2 0
9 a 3 0
10 b 3 1
11 c 3 0
categories dates event Newevents NewCategories
0 a 0 0 Cancelled None
1 b 0 1 Scheduled Completed
2 c 0 0 Cancelled Scheduled
3 a 1 0 Cancelled None
4 b 1 1 Scheduled Completed
5 c 1 0 Cancelled Scheduled
6 a 2 0 Cancelled None
7 b 2 1 Scheduled Completed
8 c 2 0 Cancelled Scheduled
9 a 3 0 Cancelled None
10 b 3 1 Scheduled Completed
11 c 3 0 Cancelled
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