Rolling difference in Pandas

This should work:

import numpy as np

x = np.array([1, 3, 6, 1, -5, 6, 4, 1, 6])

def running_diff(arr, N):
    return np.array([arr[i] - arr[i-N] for i in range(N, len(arr))])

running_diff(x, 4)  # array([-6,  3, -2,  0, 11])

For a given pd.Series, you will have to define what you want for the first few items. The below example just returns the initial series values.

s_roll_diff = np.hstack((s.values[:4], running_diff(s.values, 4)))

This works because you can assign a np.array directly to a pd.DataFrame, e.g. for a column s, df.s_roll_diff = np.hstack((df.s.values[:4], running_diff(df.s.values, 4)))

What about:

import pandas

x = pandas.DataFrame({
    'x_1': [0, 1, 2, 3, 0, 1, 2, 500, ],},
    index=[0, 1, 2, 3, 4, 5, 6, 7])

x['x_1'].rolling(window=2).apply(lambda x: x.iloc[1] - x.iloc[0])

in general you can replace the lambda function with your own function. Note that in this case the first item will be NaN.

Update

Defining the following:

n_steps = 2
def my_fun(x):
    return x.iloc[-1] - x.iloc[0]

x['x_1'].rolling(window=n_steps).apply(my_fun)

you can compute the differences between values at n_steps.

You can do the same thing as in https://stackoverflow.com/a/48345749/1011724 if you work directly on the underlying numpy array:

import numpy as np
diff_kernel = np.array([1,-1])
np.convolve(rs,diff_kernel ,'same')

where rs is your pandas series