。
你好,伙计们!
我有一个dfA
(表A),其中包含某些产品的可用天数(days_survived
)。我需要计算每天总共可用的产品数量(表B)。我的意思是,我需要对dfA
中的行进行计数,以发现头5天(df2
)每天的生存率。
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 |
+-------+----------------+
此结果意味着在第一天总共提供了6种产品,然后在第二天和第三天只有5种产品,然后在第四天只有4种产品,最后在第五天只有3种产品。
代码:
# 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)
有人可以帮助我吗? :)
答案 0 :(得分:2)
将列表理解与展平和过滤一起使用,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
使用Counter
的另一种解决方案:
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())})
答案 1 :(得分:0)
这是在使用“收藏夹”,它创建了一个项目存在的所有天数的列表,然后从列表中计算每天发生的次数
import pandas as pd
import numpy as np
from collections import Counter
df = pd.DataFrame(data={'id': [1,2,3,4,5,6], 'days_survived': [1,3,10,40,4,9]})
# We will create a new column having values as a list of all the days for which item was present
df['Days'] = df.apply(lambda a : list(np.arange(1,a.days_survived+1)),axis=1)
# Applyin Counter to the flattened list of all elements in 'Days' column
cnt= Counter([item for items in list(df['Days']) for item in items])
#Converting cnt Counter object to Dataframe
df_new = pd.DataFrame(data= {'Days':list(cnt.keys()),'count':list(cnt.values())})
希望这会有所帮助。