Filtering specific float values based on a condition-CodePudding

I have a dataFrame column price having a price feature having 10000 values ranging from 0$ to 399.99$.

I'm trying to segregate value as per certain price band but getting incorrect values.

Price(values) given are:


array([  0.  ,   4.99,   3.99,   6.99,   1.49,   2.99,   7.99,   5.99,
         3.49,   1.99,   9.99,   7.49,   0.99,   9.  ,   5.49,  10.  ,
        24.99,  11.99,  79.99,  16.99,  14.99,   1.  ,  29.99,  12.99,
       109.99, 154.99,   3.08,   2.59,   4.8 ,   1.96,  19.4 ,   3.9 ,
         4.59,  15.46,   3.04,   4.29,   2.6 ,   3.28,   4.6 ,  28.99,
         2.95,   2.9 ,   1.97, 200.  ,  89.99,   2.56,  30.99,   3.61,
       394.99,   1.26,   1.2 ,   1.04], dtype=float32)

Tried below code, but getting wrong Output despite having value > 28,

(data['Price'].any() > 28:
        print('Max')
# is returning False


def Priceband():
    if data['Price'].any() < 7:
        print('Cheap')
    if data['Price'].any() >= 7 & data['Price'].any() < 14:
        print('Normal')
    if data['Price'].any() >= 14 & data['Price'].any() < 21:
        print('Slight Expensive')
    if data['Price'].any() >= 21 & data['Price'].any() < 28:
        print('Expensive')
    if data['Price'].any() > 28:
        print('Max')

Getting 'False' even for 'True' Conditions

CodePudding user response：

The use of any is not correct. Additionally, with the use of elif you don't do unneccessary if-checks:

if (data['Price'] < 7).any():
    print('Cheap')
elif (data['Price'] >= 7 and data['Price'] < 14).any():
    print('Normal')
elif (data['Price'] >= 14 and data['Price'] < 21).any():
    print('Slight Expensive')
elif (data['Price'] >= 21 and data['Price'] < 28).any():
    print('Expensive')
elif (data['Price'] > 28).any():
    print('Max')

However, consider using pandas.cut. In case one of the borders changes, you only have to adjust one hard-coded number instead of two. Plus readability increases.

bins = [0, 7, 14, 21, 28, 400]
labels = ['Cheap', 'Normal', 'Slight Expensive', 'Expensive', 'Max']
data['Price Band'] = pd.cut(data['Price'], bins=bins, labels=labels)

CodePudding user response：

any checks whether any value is True. Thus, you need to do the check before and then call any:

if (data['Price'] < 7).any():
        print('Cheap')

Any numeric value that is above 0 will return true for a boolean check:

pd.DataFrame({'Price': [0., 0.1, 0.5, 1.0, 2.0]}).astype('bool')
>>> Price
0   False
1   True
2   True
3   True
4   True