CODEWITHSUNDEEP
pythongroup-byapplymaskcase-folding
junaid
junaid

Reputation: 1

Not able to solve one condition in python code

I want to find in SOFTWARE column which is the new software with respect to VIN column.

for example 'c5D2-14N450-CBQ' to 'c5D2-14N450-CBR'(for both software C column value should be less than or equal to 10) so, 'c5D2-14N450-CBR' is my new software

condition:- Update should be done when the value of column C should be less than or equal to 10

Below is my data frame

import pandas as pd

data = {'VIN': ['aaaa','aaaa','aaaa','aaaa','bbb','bbb','bbb','bbb','CCCC','CCCC','CCCC','CCCC'],
        'SOFTWARE': ['P8G2-14B570-PRC','c5D2-14N450-CBR','P8G2-14B570-PRA','c5D2-14N450-CBQ',
              'K9A2-13V570-BAI','K9A2-13V570-BAH','K9A2-13V570-BAH','K9A2-13V570-BAH',
                     'J4E2-12K532-K7N','J4E2-12K532-K7O','J4E2-12K532-K7O','J4E2-12K532-K7N'],
        'C': [1,3,15,9,9,12,17,88,3,5,9,10]
        }

df = pd.DataFrame(data)

I tried below method but not getting what I expected:

df['RESULT'] = df.apply(lambda x: x['SOFTWARE'] if x['C'] >= 10 else (x['SOFTWARE']), axis=1)
df

I also tried by masking:


mask = df.groupby('VIN')['C'].diff().le(10)

df['Result'] = np.where(mask|mask.groupby(df['VIN']),1,0)

Below is my expected output:

data = {'VIN': ['aaaa','aaaa','aaaa','aaaa','bbb','bbb','bbb','bbb','CCCC','CCCC','CCCC','CCCC'],
        'SOFTWARE': ['P8G2-14B570-PRC','c5D2-14N450-CBR','P8G2-14B570-PRA','c5D2-14N450-CBQ',
              'K9A2-13V570-BAI','K9A2-13V570-BAH','K9A2-13V570-BAH','K9A2-13V570-BAH',
                     'J4E2-12K532-K7N','J4E2-12K532-K7O','J4E2-12K532-K7O','J4E2-12K532-K7N'],
        'C': [1,3,15,9,9,12,17,88,3,5,9,10],
        'RESULT': ['old software','new software','old software','old software','old software','old software',
                   'old software','old software','old software','new software','new software','old software',]
        }

df = pd.DataFrame(data)

print (df)

Upvotes: 0

Views: 51

Answers (1)

Serge de Gosson de Varennes
Serge de Gosson de Varennes

Reputation: 11474

You should do the following:

import pandas as pd

data = {'VIN': ['aaaa','aaaa','aaaa','aaaa','bbb','bbb','bbb','bbb','CCCC','CCCC','CCCC','CCCC'],
        'SOFTWARE': ['P8G2-14B570-PRC','c5D2-14N450-CBR','P8G2-14B570-PRA','c5D2-14N450-CBQ',
              'K9A2-13V570-BAI','K9A2-13V570-BAH','K9A2-13V570-BAH','K9A2-13V570-BAH',
                     'J4E2-12K532-K7N','J4E2-12K532-K7O','J4E2-12K532-K7O','J4E2-12K532-K7N'],
        'C': [1,3,15,9,9,12,17,88,3,5,9,10]
        }

df = pd.DataFrame(data)

g = df.groupby('VIN')
mask = (g['C'].diff() <= 10) & (g['C'].diff().shift(-1) >= 0)
df['RESULT'] = np.where(mask, 'new software', 'old software')

which returns your desired output:

     VIN         SOFTWARE   C        RESULT
0   aaaa  P8G2-14B570-PRC   1  old software
1   aaaa  c5D2-14N450-CBR   3  new software
2   aaaa  P8G2-14B570-PRA  15  old software
3   aaaa  c5D2-14N450-CBQ   9  old software
4    bbb  K9A2-13V570-BAI   9  old software
5    bbb  K9A2-13V570-BAH  12  old software
6    bbb  K9A2-13V570-BAH  17  old software
7    bbb  K9A2-13V570-BAH  88  old software
8   CCCC  J4E2-12K532-K7N   3  old software
9   CCCC  J4E2-12K532-K7O   5  new software
10  CCCC  J4E2-12K532-K7O   9  new software
11  CCCC  J4E2-12K532-K7N  10  old software

Upvotes: 0

Related Questions