我有dfA(表A)包含某些产品可用的天数(days_survived)。我需要计算每天可用的产品数量(表B)。我的意思是,我需要计算行数,dfA以发现前5天每天的存活率(df2)。
表A:
+-------+--------------+
| id | days_survived|
+-------+--------------+
| 1 | 1 |
| 2 | 3 |
| 3 | 10 |
| 4 | 40 |
| 5 | 4 |
| 6 | 9 |
+-------+--------------+
表B(分析前5天的预期结果):
+-------+----------------+
| day | #count_survived|
+-------+----------------+
| 1 | 6 |
| 2 | 5 |
| 3 | 5 |
| 4 | 4 |
| 5 | 3 |
+-------+----------------+
这个结果意味着在第一天总共有6个产品可用,第二天和第三天只有5个,第四天只有4个,最后在第五天只有3个。
码:
# create df
import pandas as pd
d = {'id': [1,2,3,4,5,6], 'days_survived': [1,3,10,40,4,9]}
dfA = pd.DataFrame(data=d)
有人可以帮帮我吗?:)








找到解决办法了:使用列表推导与展平和过滤,然后计数:
comp = [y for x in dfA['days_survived'] for y in range(1, x + 1) if y < 6]
s = pd.Series(comp).value_counts().rename_axis('day').reset_index(name='#count_survived')
print (s)
day #count_survived
0 1 6
1 3 5
2 2 5
3 4 4
4 5 3
另一个解决方案Counter:
from collections import Counter
comp = [y for x in dfA['days_survived'] for y in range(1, x + 1) if y < 6]
d = Counter(comp)
df = pd.DataFrame({'day':list(d.keys()), '#count_survived':list(d.values())})