pandas change all rows with Type X if 1 Type X Result = 1 - pandas

Question

Here is a simple pandas df:
>>> df
Type Var1 Result
0 A 1 NaN
1 A 2 NaN
2 A 3 NaN
3 B 4 NaN
4 B 5 NaN
5 B 6 NaN
6 C 1 NaN
7 C 2 NaN
8 C 3 NaN
9 D 4 NaN
10 D 5 NaN
11 D 6 NaN
The object of the exercise is: if column Var1 = 3, set Result = 1 for all that Type.
This finds the rows with 3 in Var1 and sets Result to 1,
df['Result'] = df['Var1'].apply(lambda x: 1 if x == 3 else 0)
but I can't figure out how to then catch all the same Type and make them 1. In this case it should be all the As and all the Cs. Doesn't have to be a one-liner.
Any tips please?

Answer 1

Create boolean mask and for True/False to 1/0 mapp convert values to integers:
df['Result'] = df['Type'].isin(df.loc[df['Var1'].eq(3), 'Type']).astype(int)
#alternative
df['Result'] = np.where(df['Type'].isin(df.loc[df['Var1'].eq(3), 'Type']), 1, 0)
print (df)
Type Var1 Result
0 A 1 1
1 A 2 1
2 A 3 1
3 B 4 0
4 B 5 0
5 B 6 0
6 C 1 1
7 C 2 1
8 C 3 1
9 D 4 0
10 D 5 0
11 D 6 0
Details:
Get all Type values if match condition:
print (df.loc[df['Var1'].eq(3), 'Type'])
2 A
8 C
Name: Type, dtype: object
Test original column Type by filtered types:
print (df['Type'].isin(df.loc[df['Var1'].eq(3), 'Type']))
0 True
1 True
2 True
3 False
4 False
5 False
6 True
7 True
8 True
9 False
10 False
11 False
Name: Type, dtype: bool
Or use GroupBy.transform with any for test if match at least one value, thi solution is slowier if larger df:
df['Result'] = df['Var1'].eq(3).groupby(df['Type']).transform('any').astype(int)