我正在尝试在 pandas 中复制 SQL 的窗口函数。
SELECT avg(totalprice) OVER (
PARTITION BY custkey
ORDER BY orderdate
RANGE BETWEEN interval '1' month PRECEDING AND CURRENT ROW)
FROM orders
我有这个数据框:
from io import StringIO
import pandas as pd
myst="""cust_1,2020-10-10,100
cust_2,2020-10-10,15
cust_1,2020-10-15,200
cust_1,2020-10-16,240
cust_2,2020-12-20,25
cust_1,2020-12-25,140
cust_2,2021-01-01,5
"""
u_cols=['customer_id', 'date', 'price']
myf = StringIO(myst)
import pandas as pd
df = pd.read_csv(StringIO(myst), sep=',', names = u_cols)
df=df.sort_values(list(df.columns))
并且在计算限制为最近 1 个月的移动平均线之后，它将看起来像这样......
from io import StringIO
import pandas as pd
myst="""cust_1,2020-10-10,100,100
cust_2,2020-10-10,15,15
cust_1,2020-10-15,200,150
cust_1,2020-10-16,240,180
cust_2,2020-12-20,25,25
cust_1,2020-12-25,140,140
cust_2,2021-01-01,5,15
"""
u_cols=['customer_id', 'date', 'price', 'my_average']
myf = StringIO(myst)
import pandas as pd
my_df = pd.read_csv(StringIO(myst), sep=',', names = u_cols)
my_df=my_df.sort_values(list(my_df.columns))
如图所示:
https://trino.io/assets/blog/window-features/running-average-range.svg
我试着写了一个这样的函数...
import numpy as np
def mylogic(myro):
mylist = list()
mydate = myro['date'][0]
for i in range(len(myro)):
if myro['date'][i] > mydate:
mylist.append(myro['price'][i])
mydate = myro['date'][i]
return np.mean(mylist)
但是返回了一个 key_error。
最佳答案
您可以使用 rolling最近30天的功能
df['date'] = pd.to_datetime(df['date'])
df['my_average'] = (df.groupby('customer_id')
.apply(lambda d: d.rolling('30D', on='date')['price'].mean())
.reset_index(level=0, drop=True)
.astype(int)
)
输出:
customer_id date price my_average
0 cust_1 2020-10-10 100 100
2 cust_1 2020-10-15 200 150
3 cust_1 2020-10-16 240 180
5 cust_1 2020-12-25 140 140
1 cust_2 2020-10-10 15 15
4 cust_2 2020-12-20 25 25
6 cust_2 2021-01-01 5 15
关于pandas - 移动平均的窗口函数，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/68268531/