aggregate data for last seven day for each date

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I have a dataset: 

 app id geo  date        count
    90  NO  2018-09-04  27
    66  HK  2018-09-03  2
    66  HK  2018-09-02  4   
    80  QA  2018-04-22  5  
    85  MA  2018-04-20  1   
    80  BR  2018-04-19  68

I am trying to generate a field which would aggregate data for each date for last seven days. My dataset should look like that:

 app id geo  date        count   count_last_7_days
        90  NO  2018-09-04  27    33
        66  HK  2018-09-03  2     6
        66  HK  2018-09-02  4     4
        80  QA  2018-04-22  5     74
        85  MA  2018-04-20  1     69
        80  BR  2018-04-19  68    68

I am trying this code:

 df['date'] = pd.to_datetime(df['date']) - pd.to_timedelta(7, unit='d')
    df = df.groupby(['geo','app_id', pd.Grouper(key='date', freq='W')]) . 
   ['count'].sum().reset_index().sort_values('date')

But even thought I use Grouper with weekly frequency (freq='W' ), It considers start of the week on Sunday and I don't have 7 days lag for non-Sunday entries.

Please, suggest how I can calculate that field.

pandas grouping date python
1 Answers

A dirty one-liner would be

import numpy as np
df['count_last_7_days'] = [np.sum(df['count'][np.logical_and(df['date'][i] - df['date'] < pd.to_timedelta(7,unit='d'),df['date'][i] - df['date'] >= pd.to_timedelta(0,unit='d'))]) for i in range(df.shape[0])]

Note that I converted the time column to datetime using pd.to_datetime() first.

What this does is: for each day it finds all other rows within the desired one-week timespan, flags them with a boolean value and sums them after

user_102935480
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