Checking for subset in a column?

Question

I'm trying to flag some price data as "stale" if the quoted price of the security hasn't changed over lets say 3 trading days. I'm currently trying it with:

firm["dev"] = np.std(firm["Price"],firm["Price"].shift(1),firm["Price"].shift(2))
firm["flag"] == np.where(firm["dev"] = 0, 1, 0)

But I'm getting nowhere with it. This is what my dataframe would look like.

Index	Price	Flag
1	10	0
2	11	0
3	12	0
4	12	0
5	12	1
6	11	0
7	13	0

Any help is appreciated!

Answer 1

If you are okay with other conditions, you can first check if series.diff equals 0 and take cumsum to check if you have a cumsum of 2 (n-1). Also check if the next row is equal to current, when both these conditions suffice, assign a flag of 1 else 0.

n=3
firm['Flag'] = (firm['Price'].diff().eq(0).cumsum().eq(n-1) & 
                firm['Price'].eq(firm['Price'].shift())).astype(int)

---

EDIT, to make it a generalized function with consecutive n, use this:

def fun(df,col,n):
    c = df[col].diff().eq(0)
    return (c|c.shift(-1)).cumsum().ge(n) & df[col].eq(df[col].shift())

---

firm['flag_2'] = fun(firm,'Price',2).astype(int)
firm['flag_3'] = fun(firm,'Price',3).astype(int)

print(firm)

       Price  Flag  flag_2  flag_3
Index                             
1         10     0       0       0
2         11     0       0       0
3         12     0       0       0
4         12     0       1       0
5         12     1       1       1
6         11     0       0       0
7         13     0       0       0