Python Pandas: Using groupby for to track POI's-CodePudding

Im looking to mark key points in cycles based on certain conditions.

Here is the result I am looking for:

import pandas as pd
import numpy as np

Data3 = {'Cycle': ['1', '1', '1', '1', '1', '1', '2','2', '2', '2', '2', '2'],
        'Value': [20, 24, 25, 18,15,12,1,2,19,18,12,1],
        'Diff2':[1,10,18,-12,-50,-14,14,150,130,-140,12,14],
        'POI':['-','-','A','-','B','-','-','-','A','B','-','-']} 
    
df = pd.DataFrame(Data3)

Based off another answer I understand I need to use groupby and have come up with something like this:

Data3['POI'] = np.select([Data3['Value'].eq(POI.transform('max')),
                          Data3['Diff2'].eq(POI.transform('min'))]
                         ['A','B'])

I need to mark A and B for each cycle, as shown in the Data frame above.

To get A: A is simply the max in the "Value" column

To get B: B is the minimum number in the Diff2 column

Im not sure how to use the groupby for multiple columns, so any help would be great.

Data3 = {'Cycle': ['1', '1', '1', '1', '1', '1', '2','2', '2', '2', '2', '2'],
        'Value': [20, 24, 25, 18,15,12,1,2,19,18,12,1],
        'Diff2':[1,10,18,-12,-50,-14,14,150,130,-140,12,14],
        } 

df = pd.DataFrame(Data3)


df['POI'] = Data3.groupby('Cycle').apply(
    lambda g: np.select([g['Value'] == g['Value'].max(),
                         g['Diff2'] == g['Diff2'].min()], ['A', 'B'], default='-')
).explode().set_axis(df.index, axis=0)

CodePudding user response：

here is one way to do it. i created POI2, to have a comparison with the expected output

cond= [(df['Value'].eq(df.groupby('Cycle')['Value'].transform(max)) ),
       (df['Diff2'].eq(df.groupby('Cycle')['Diff2'].transform(min)) )]
choice=['A','B']
df['POI2'] = np.select(cond, choice, '-')
df

    Cycle    Value  Diff2   POI     POI2
0       1       20    1       -     -
1       1       24    10      -     -
2       1       25    18      A     A
3       1       18   -12      -     -
4       1       15   -50      B     B
5       1       12   -14      -     -
6       2       1     14      -     -
7       2       2    150      -     -
8       2       19   130      A     A
9       2       18  -140      B     B
10      2       12    12      -     -
11      2       1     14      -     -

Screenshot

CodePudding user response：

df['POI'] = df.groupby('Cycle').apply(
    lambda g: np.select([g['Value'] == g['Value'].max(),
                         g['Diff2'] == g['Diff2'].min()], ['A', 'B'], default='-')
    ).explode().set_axis(df.index, axis=0)