Resampling on non-time related buckets - python-3.x

Question

Say I have a df looking like this:
price quantity
0 100 20
1 102 31
2 105 25
3 99 40
4 104 10
5 103 20
6 101 55
There are no time intervals here. I need to calculate a Volume Weighted Average Price for every 50 items in quantity. Every row (index) in the output would represent 50 units (as opposed to say 5-min intervals), the output column would be the volume weighted price.
Any neat way to do this using pandas, or numpy for that matter?? I tried using a loop splitting every row into one item prices and them group them like this:
def grouper(n, iterable):
it = iter(iterable)
while True:
chunk = tuple(itertools.islice(it, n))
if not chunk:
return
yield chunk
But it takes for ever and I run out of memory.. The df is a few million rows.
EDIT:
The output I want to see based on the above is:
vwap
0 101.20
1 102.12
2 103.36
3 101.00
Each 50 items gets a new average price.

Answer 1

I struck out on my first at-bat facing this problem. Here's my next plate appearance. Hopefully I can put the ball in play and score a run.
First, let's address some of the comments related to the expected outcome of this effort. The OP posted what he thought the results should be using the small sample data he provided. However, #user7138814 and I both came up with the same outcome that differed from the OP's. Let me explain how I believe the weighted average of exactly 50 units should be calculated using the OP's example. I'll use this worksheet as an illustration.
The first 2 columns (A and B) are the original values given by the OP. Given those values, the goal is to calculate a weighted average for each block of exactly 50 units. Unfortunately, the quantities are not evenly divisible by 50. Columns C and D represent how to create even blocks of 50 units by subdividing the original quantities as needed. The yellow shaded areas show how the original quantity was subdivided and each of the green bounded cells sum to exactly 50 units. Once 50 units are determined the weighted average can be calculated in column E. As you can see, the values in E match what #user7138814 posted in his comment, so I think we agree on the methodology.
After much trial and error the final solution is a function that operates on the numpy arrays of the underlying price and quantity series. The function is further optimized using Numba decorator to jit-compile the Python code into machine-level code. On my laptop, it processed a 3 million row arrays in a second.
Here's the function.
#numba.jit
def vwap50_jit(price_col, quantity_col):
n_rows = len(price_col)
assert len(price_col) == len(quantity_col)
qty_cumdif = 50 # cum difference of quantity to track when 50 units are reached
pq = 0.0 # cumsum of price * quantity
vwap50 = [] # list of weighted averages
for i in range(n_rows):
price, qty = price_col[i], quantity_col[i]
# if current qty will cause more than 50 units
# divide the units
if qty_cumdif < qty:
pq += qty_cumdif * price
# at this point, 50 units accumulated. calculate average.
vwap50.append(pq / 50)
qty -= qty_cumdif
# continue dividing
while qty >= 50:
qty -= 50
vwap50.append(price)
# remaining qty and pq become starting
# values for next group of 50
qty_cumdif = 50 - qty
pq = qty * price
# process price, qty pair as-is
else:
qty_cumdif -= qty
pq += qty * price
return np.array(vwap50)
Results of processing the OP's sample data.
Out[6]:
price quantity
0 100 20
1 102 31
2 105 25
3 99 40
4 104 10
5 103 20
6 101 55
vwap50_jit(df.price.values, df.quantity.values)
Out[7]: array([101.2 , 102.06, 101.76, 101. ])
Notice that I use the .values method to pass the numpy arrays of the pandas series. That's one of the requirements of using numba. Numba is numpy-aware and doesn't work on pandas objects.
It performs pretty well on 3 million row arrays, creating an output array of 2.25 million weighted averages.
df = pd.DataFrame({'price': np.random.randint(95, 150, 3000000),
'quantity': np.random.randint(1, 75, 3000000)})
%timeit vwap50_jit(df.price.values, df.quantity.values)
154 ms ± 4.15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
vwap = vwap50_jit(df.price.values, df.quantity.values)
vwap.shape
Out[11]: (2250037,)