Daily to Weekly Pandas conversion - pandas

Question

I am trying to convert my 15ys worth of daily data into weekly by taking the mean, diff and count of certain features. I tried using .resample but I was not sure if that is the most efficient way.
My sample data:
Date,Product,New Quantity,Price,Refund Flag
8/16/1994,abc,10,0.5,
8/17/1994,abc,11,0.9,1
8/18/1994,abc,15,0.6,
8/19/1994,abc,19,0.4,
8/22/1994,abc,22,0.2,1
8/23/1994,abc,19,0.1,
8/16/1994,xyz,16,0.5,1
8/17/1994,xyz,10,0.9,1
8/18/1994,xyz,12,0.6,1
8/19/1994,xyz,19,0.4,
8/22/1994,xyz,26,0.2,1
8/23/1994,xyz,30,0.1,
8/16/1994,pqr,0,0,
8/17/1994,pqr,0,0,
8/18/1994,pqr,1,1,
8/19/1994,pqr,2,0.6,
8/22/1994,pqr,9,0.1,
8/23/1994,pqr,12,0.2,
This is the output I am looking for:
Date,Product,Net_Quantity_diff,Price_avg,Refund
8/16/1994,abc,9,0.6,1
8/22/1994,abc,-3,0.15,0
8/16/1994,xyz,3,0.6,3
8/22/1994,xyz,4,0.15,1
8/16/1994,pqr,2,0.4,0
8/22/1994,pqr,3,0.15,0

Answer 1

I think the pandas resample method is indeed ideal for this. You can pass a dictionary to the agg method, defining which aggregation function to use for each column. For example:
import numpy as np
import pandas as pd
df = pd.read_csv('sales.txt') # your sample data
df['Date'] = pd.to_datetime(df['Date'])
df = df.set_index(df['Date'])
del df['Date']
df['Refund Flag'] = df['Refund Flag'].fillna(0).astype(bool)
def span(s):
return np.max(s) - np.min(s)
df_weekly = df.resample('w').agg({'New Quantity': span,
'Price': np.mean,
'Refund Flag': np.sum})
df_weekly
New Quantity Price Refund Flag
Date
1994-08-21 19 0.533333 4
1994-08-28 21 0.150000 2