How to calculate new column based on values from multiple columns

I’m trying to calculate the standard deviation of values from other columns, but also based on another column value, like this:

Consider this sample dataframe:

The sales_months column indicates how many months of sales the product has had. So, if a product has only 2 months of sales (because it’s a new product, as is the case with the second row) we only use the data for the last 2 months to get the standard deviation. If it has a 4 in the sales_months column, it indicates it has been sold for the whole 4 month period, so we can use the data of all 4 columns to get the standar deviation.
So, I would like to get something like this:

How can I do this in for all rows of the dataframe. I tried building a function and applying it the dataframe, with my limited pandas knowledge, but I just caused the kernel to die:

def get_std_dev(row):
    std_dev = 0.0
    months = row.SALES_MONTHS
    if months < 4:
        m_tmp = int(months)
        std_dev = df_co_info.iloc[:, 1:m_tmp].std(axis=1)
    else:
        std_dev = df_co_info.iloc[:, 1:4].std(axis=1)
    return std_dev

df_co_info['stdDev'] = df_co_info.apply(get_std_dev, axis = 1)

>Solution :

Using numpy and a mask:

df2 = df.drop(columns=['product', 'sales_months'])
a = df2.to_numpy()
mask = np.arange(a.shape[1]) >= a.shape[1]-df['sales_months'].to_numpy()[:,None]

df['std_dev'] = df2.where(mask).std(axis=1)

output:

  product  january  february  march  april  sales_months    std_dev
0   prod1        3         6      7      1             4   2.753785
1   prod2        0         0      5     14             2   6.363961
2   prod3        2         4     67      1             4  32.357379

masked df2:

   january  february  march  april
0      3.0       6.0      7      1
1      NaN       NaN      5     14
2      2.0       4.0     67      1