## Issue

I have a time series "Ser" and I want to compute volatilities (standard deviations) with a rolling window. My current code correctly does it in this form:

```
w = 10
for timestep in range(length):
subSer = Ser[timestep:timestep + w]
mean_i = np.mean(subSer)
vol_i = (np.sum((subSer - mean_i)**2) / len(subSer))**0.5
volList.append(w_i)
```

This seems to me very inefficient. Does Pandas have built-in functionality for doing something like this?

## Solution

It looks like you are looking for `Series.rolling`

. You can apply the `std`

calculations to the resulting object:

```
roller = Ser.rolling(w)
volList = roller.std(ddof=0)
```

If you don’t plan on using the rolling window object again, you can write a one-liner:

```
volList = Ser.rolling(w).std(ddof=0)
```

Keep in mind that `ddof=0`

is necessary in this case because the normalization of the standard deviation is by `len(Ser)-ddof`

, and that `ddof`

defaults to `1`

in pandas.

Answered By – Mad Physicist

Answer Checked By – Mildred Charles (BugsFixing Admin)