You can just use 2 boolean masks:
df.loc[df['ImageName'] <= 70, 'Test'] = 1
df.loc[(df['ImageName'] > 70) & (df['ImageName'] <= 90), 'Test'] = 2
By using the masks you only set the value where the boolean condition is met, for the second mask you need to use the & operator to and the conditions and enclose the conditions in parentheses due to operator precedence
Actually I think it would be better to define your bin values and call cut, example:
In [20]:
df = pd.DataFrame({'ImageName': np.random.randint(0, 100, 20)})
df
Out[20]:
ImageName
0 48
1 78
2 5
3 4
4 9
5 81
6 49
7 11
8 57
9 17
10 92
11 30
12 74
13 62
14 83
15 21
16 97
17 11
18 34
19 78
In [22]:
df['group'] = pd.cut(df['ImageName'], range(0, 105, 10), right=False)
df
Out[22]:
ImageName group
0 48 [40, 50)
1 78 [70, 80)
2 5 [0, 10)
3 4 [0, 10)
4 9 [0, 10)
5 81 [80, 90)
6 49 [40, 50)
7 11 [10, 20)
8 57 [50, 60)
9 17 [10, 20)
10 92 [90, 100)
11 30 [30, 40)
12 74 [70, 80)
13 62 [60, 70)
14 83 [80, 90)
15 21 [20, 30)
16 97 [90, 100)
17 11 [10, 20)
18 34 [30, 40)
19 78 [70, 80)
Here the bin values were generated using range but you could pass your list of bin values yourself, once you have the bin values you can define a lookup dict:
In [32]:
d = dict(zip(df['group'].unique(), range(len(df['group'].unique()))))
d
Out[32]:
{'[0, 10)': 2,
'[10, 20)': 4,
'[20, 30)': 9,
'[30, 40)': 7,
'[40, 50)': 0,
'[50, 60)': 5,
'[60, 70)': 8,
'[70, 80)': 1,
'[80, 90)': 3,
'[90, 100)': 6}
You can now call map and add your new column:
In [33]:
df['test'] = df['group'].map(d)
df
Out[33]:
ImageName group test
0 48 [40, 50) 0
1 78 [70, 80) 1
2 5 [0, 10) 2
3 4 [0, 10) 2
4 9 [0, 10) 2
5 81 [80, 90) 3
6 49 [40, 50) 0
7 11 [10, 20) 4
8 57 [50, 60) 5
9 17 [10, 20) 4
10 92 [90, 100) 6
11 30 [30, 40) 7
12 74 [70, 80) 1
13 62 [60, 70) 8
14 83 [80, 90) 3
15 21 [20, 30) 9
16 97 [90, 100) 6
17 11 [10, 20) 4
18 34 [30, 40) 7
19 78 [70, 80) 1
The above can be modified to suit your needs but it's just to demonstrate an approach which should be fast and without the need to iterate over your df.
