CODEWITHSUNDEEP
pythonpandasdataframe
Maria
Maria

Reputation: 1297

new python pandas dataframe column based on value of variable, using function

I have a variable, 'ImageName' which ranges from 0-1600. I want to create a new variable, 'LocationCode', based on the value of 'ImageName'.

If 'ImageName' is less than 70, I want 'LocationCode' to be 1. if 'ImageName' is between 71 and 90, I want 'LocationCode' to be 2. I have 13 different codes in all. I'm not sure how to write this in python pandas. Here's what I tried:

def spatLoc(ImageName):
    if ImageName <=70:
        LocationCode = 1
    elif ImageName >70 and ImageName <=90:
        LocationCode = 2
   return LocationCode

df['test'] = df.apply(spatLoc(df['ImageName'])

but it returned an error. I'm clearly not defining things the right way but I can't figure out how to.

Upvotes: 2

Views: 3098

Answers (2)

Ahmad Senousi
Ahmad Senousi

Reputation: 633

In Python, you use the dictionary lookup notation to find a field within a row. The field name is ImageName. In the spatLoc() function below, the parameter row is a dictionary containing the entire row, and you would find an individual column by using the field name as key to the dictionary.

def spatLoc(row):
    if row['ImageName'] <=70:
        LocationCode = 1
    elif row['ImageName']  >70 and row['ImageName']  <=90:
        LocationCode = 2
    return LocationCode

df['test'] = df.apply(spatLoc, axis=1)

Upvotes: 0

EdChum
EdChum

Reputation: 394071

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.

Upvotes: 3

Related Questions