CODEWITHSUNDEEP
pythonpandasdataframe
Roshankumar
Roshankumar

Reputation: 365

applying conditions basis the value in column to create new tag

Existing Dataframe :

Id     created_by
A        A
A        123
B        X
B        B

Expected Dataframe :

Id     created_by    status
A        A           category_1
A        123         category_2
B        X           category_3
B        B           category_1

I am looking to create a status tag basis the condition :

if Id == created_by  >> category_1

if id != created_by  >> category_2

if id != created_by & created_by == 'X'  >> category_3

i am using below code :

conditions = [
              df['Id'] == df['created_by'], 
              df['Id'] != df['created_by'],
              (df['Id'] != df['created_by']) & (df['created_by'] == 'X')

             ]

# Creating Labels
result = ['category_1','category_2','category_3']

# Creating status column
df['status'] = np.select(conditions, result , default='REST')

somehow i am not getting correct number for third condition. what am i missing

Upvotes: 0

Views: 14

Answers (1)

jezrael
jezrael

Reputation: 862671

Problem is in second condition, there is necessary add filtering non X values in created_by:

conditions = [
              df['Id'] == df['created_by'], 
              (df['Id'] != df['created_by']) & (df['created_by'] != 'X'),
              (df['Id'] != df['created_by']) & (df['created_by'] == 'X')

             ]

# Creating Labels
result = ['category_1','category_2','category_3']

# Creating status column
df['status'] = np.select(conditions, result , default='REST')
print (df)
  Id created_by      status
0  A          A  category_1
1  A        123  category_2
2  B          X  category_3
3  B          B  category_1

For improve solution (call conditions only once) you can create helper masks and chain like:

m1  = df['Id'] == df['created_by']
m2  = df['created_by'] == 'X'

conditions = [m1, ~m1 & ~m2, ~m1 & m2]

# Creating Labels
result = ['category_1','category_2','category_3']

# Creating status column
df['status'] = np.select(conditions, result , default='REST')

Upvotes: 1

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