window function for moving average

Question

I am trying to replicate SQL's window function in pandas.

SELECT avg(totalprice) OVER (
    PARTITION BY custkey
    ORDER BY orderdate
    RANGE BETWEEN interval '1' month PRECEDING AND CURRENT ROW)
FROM orders

I have this dataframe:

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))

And after calculating moving average restricted to last 1 month, it will look like this...

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))

As shown in this image:

https://trino.io/assets/blog/window-features/running-average-range.svg

I tried to write a function like this...

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)

But that returned a key_error.

Answer 1

You can use the rolling function on the last 30 days

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)
                   )

output:

  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