Fill in the value in the column based on the condition in another column

I have the dataframe.

data={"ID":[1,1,1,1,1,1,1,1,1,2,2,2],
"Year":[2000,2001,2002,2003,2004,1997,1998,2003,2004,1997,1998,2005],
"Firm":["A","A","B","B","A","A","A","A","B","B","A","A"],
"Count":[0,1,0,0,0,0,0,0,0,0,0,0]}

df1=pd.DataFrame(data)

The expected output is this.

data={"ID":[1,1,1,1,1,1,1,1,1,2,2,2],
"Year":[2000,2001,2002,2003,2004,1997,1998,2003,2004,1997,1998,2005],
"Firm":["A","A","B","B","A","A","A","A","B","B","A","A"],
"Count":[0,1,0,0,0,0,0,0,0,0,0,0],
"Count_1":[0,1,1,1,1,0,0,1,1,0,0,0]}

df2=pd.DataFrame(data)

I can achieve the expected output by my code.

df_1=df1.sort_values(by=["ID","Year"],ascending=True)
df_1["Count_1"]=np.where(df_1["Count"]==1,1,np.NaN)
df_1["Count_1"]=df_1.groupby(["ID"],as_index=None)["Count_1"].ffill()
df_1.drop(columns=["Count"],inplace=True)
df_1.fillna(0)

However, I am looking for a shorter and cleaner code.

>Solution :

Try:

df1 = df1.sort_values(by=["ID", "Year"])
df1["Count_1"] = (df1.groupby("ID")["Count"].cumsum() > 0).astype(int)
print(df1)

Prints:

    ID  Year Firm  Count  Count_1
5    1  1997    A      0        0
6    1  1998    A      0        0
0    1  2000    A      0        0
1    1  2001    A      1        1
2    1  2002    B      0        1
3    1  2003    B      0        1
7    1  2003    A      0        1
4    1  2004    A      0        1
8    1  2004    B      0        1
9    2  1997    B      0        0
10   2  1998    A      0        0
11   2  2005    A      0        0