Python Pandas: calculate rolling mean (moving average) over variable number of rows

Question

Say I have the following dataframe

import pandas as pd
df = pd.DataFrame({ 'distance':[2.0, 3.0, 1.0, 4.0],
                    'velocity':[10.0, 20.0, 5.0, 40.0] })

gives the dataframe

   distance  velocity
0         2.0        10.0
1         3.0        20.0
2         1.0        5.0
3         4.0        40.0

How can I calculate the average of the velocity column over the rolling sum of the distance column? With the example above, create a rolling sum over the last N rows in order to get a minimum cumulative distance of 5, and then calculate the average velocity over those rows.

My target output would then be like this:

   distance  velocity    rv
0         2.0        10.0    NaN
1         3.0        20.0    15.0
2         1.0         5.0    11.7
3         4.0        40.0    22.5

where

15.0 = (10+20)/2        (2 because 3 + 2     >= 5)
11.7 = (10 + 20 + 5)/3  (3 because 1 + 3 + 2 >= 5) 
22.5 = (5 + 40)/2       (2 because 4 + 1     >= 5)

---

Update: in Pandas-speak, my code should find the index of the reverse cumulative distance sum back from my current record (such that it is 5 or greater), and then use that index to calculate the start of the moving average.

Answer 1

Not a particularly pandasy solution, but it sounds like you want to do something like

df['rv'] = np.nan
for i in range(len(df)):
    j = i
    s = 0
    while j >= 0 and s < 5:
        s += df['distance'].loc[j]
        j -= 1
    if s >= 5:
        df['rv'].loc[i] = df['velocity'][j+1:i+1].mean()

Update: Since this answer, the OP stated that they want a "valid Pandas solution (e.g. without loops)". If we take this to mean that they want something more performant than the above, then, perhaps ironically given the comment, the first optimization that comes to mind is to avoid the data frame unless needed:

l = len(df)
a = np.empty(l)
d = df['distance'].values
v = df['velocity'].values
for i in range(l):
    j = i
    s = 0
    while j >= 0 and s < 5:
        s += d[j]
        j -= 1
    if s >= 5:
        a[i] = v[j+1:i+1].mean()
df['rv'] = a

Moreover, as suggested by @JohnE, numba quickly comes in handy for further optimization. While it won't do much on the first solution above, the second solution can be decorated with a @numba.jit out-of-the-box with immediate benefits. Benchmarking all three solutions on

pd.DataFrame({'velocity': 50*np.random.random(10000), 'distance': 5*np.random.rand(10000)})

I get the following results:

          Method                 Benchmark
-----------------------------------------------
Original data frame based     4.65 s ± 325 ms
Pure numpy array based       80.8 ms ± 9.95 ms
Jitted numpy array based      766 µs ± 52 µs

Even the innocent-looking mean is enough to throw off numba; if we get rid of that and go instead with

@numba.jit
def numba_example():
    l = len(df)
    a = np.empty(l)
    d = df['distance'].values
    v = df['velocity'].values
    for i in range(l):
        j = i
        s = 0
        while j >= 0 and s < 5:
            s += d[j]
            j -= 1
        if s >= 5:
            for k in range(j+1, i+1):
                a[i] += v[k]
            a[i] /= (i-j)
    df['rv'] = a

then the benchmark reduces to 158 µs ± 8.41 µs.

Now, if you happen to know more about the structure of df['distance'], the while loop can probably be optimized further. (For example, if the values happen to always be much lower than 5, it will be faster to cut the cumulative sum from its tail, rather than recalculating everything.)