Pandas Python conditional

Question

d = [{'Number':'0001',  'Name':'A','Course':'Eng','Score':81 },{'Number':'0001',  'Name':'A','Course':'Geo','Score':75},
       {'Number':'0002',  'Name':'B','Course':'Eng','Score': 76} , {'Number':'0002',  'Name':'B','Course':'Geo','Score':90 },
    {'Number':'0003',  'Name':'C','Course':'Eng','Score':81 },{'Number':'0003',  'Name':'C','Course':'Geo','Score':100, },
     {'Number':'0003',  'Name':'C','Course':'Bio','Score':90 }]


data = pd.DataFrame(d, index=[1,2,3,4,5,6,7])
data

Course  Name    Number  Score
1   Eng A   0001    81
2   Geo A   0001    75
3   Eng B   0002    76
4   Geo B   0002    90
5   Eng C   0003    81
6   Geo C   0003    100
7   Bio C   0003    90

what I want is return only name C because C is the only one who score over 80 on every subject

Ben.T · Accepted Answer · 2018-08-02 20:52:03Z

3

you can use filter after groupby such as:

print (data.groupby('Name').filter(lambda x: (x['Score'].min()>80)))

and you get

  Course Name Number  Score
5    Eng    C   0003     81
6    Geo    C   0003    100
7    Bio    C   0003     90

answered Aug 2, 2018 at 20:52

Ben.T

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Comments

Rushabh Mehta · Accepted Answer · 2018-08-02 20:55:47Z

3

A groupby is what you want

grouped = data.groupby('Name').min()
print(grouped.loc[grouped['Score']>80].index[0])

This will give you

Out[1]: ['C']

edited Aug 2, 2018 at 20:55

answered Aug 2, 2018 at 20:47

Rushabh Mehta

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Comments

BENY · Accepted Answer · 2018-08-02 20:52:07Z

1

Using transform with all

data.loc[data.Score.gt(80).groupby(data['Name']).transform('all'),'Name'].unique()
Out[9]: array(['C'], dtype=object)

answered Aug 2, 2018 at 20:52

BENY

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Comments

harpan · Accepted Answer · 2018-08-02 20:54:18Z

0

You can use transform with unique

data.loc[data.groupby('Name')['Score'].transform('min')>80].Name.unique()

Output:

array(['C'], dtype=object)

answered Aug 2, 2018 at 20:54

harpan

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Comments

niraj · Accepted Answer · 2018-08-04 00:16:20Z

0

One other way may be as following:

f = lambda group: group.name if all(group>80) else None
r = data.groupby(['Name'])['Score'].apply(f).dropna().values
print(r)

# Result: ['C']

answered Aug 4, 2018 at 0:16

niraj

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