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