パンダの列の値に基づいてデータを分類する方法は？

Question

みましょう私は、このデータフレームを持っていると言う：

Cat1 : 0 <= x <= 1 
Cat2 : 1 < x <= 2 
Cat3 : 2 < x <= 3 
Cat4 : 3 < x <= 4 

と列postTestscore

の合計を構築する：今、私はコラム「支払い」に基づいて、これらのカテゴリを構築したい

raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
     'payout': [.1, .15, .2, .3, 1.2, 1.3, 1.45, 2, 2.04, 3.011, 3.45, 1], 
     'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
     'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3], 
     'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]} 
df = pd.DataFrame(raw_data, columns = ['regiment', 'payout', 'name', 'preTestScore', 'postTestScore']) 

私はブーリアンインデックスを使ってこのようにします：

df.loc[(df['payout'] > 0) & (df['payout'] <= 1), 'postTestScore'].sum() 
df.loc[(df['payout'] > 1) & (df['payout'] <= 2), 'postTestScore'].sum() 
etc... 

それはうまくいきますが、誰かがこれのより簡潔な（pythonic）解決法を知っていますか？

Answer 1

groupbyでpd.cutをお試しください：

df.groupby(pd.cut(df.payout, [0, 1, 2, 3, 4])).postTestScore.sum() 

payout 
(0, 1] 308 
(1, 2] 246 
(2, 3]  62 
(3, 4] 132 
Name: postTestScore, dtype: int64 

Answer 2

はcutでカテゴリを作成し、合計額とgroupby：

df1 = df.groupby(pd.cut(df['payout'], 
         bins=[0,1,2,3,4], 
         labels=['Cat1','Cat2','Cat3','Cat4']))['postTestScore'].sum() 
print (df1) 

payout 
Cat1 308 
Cat2 246 
Cat3  62 
Cat4 132 
Name: postTestScore, dtype: int64 

別：

bins = [0,1,2,3,4] 
labels=['Cat{}'.format(x) for x in range(1, len(bins))] 
binned = pd.cut(df['payout'], bins=bins, labels=labels) 
print (binned) 
0  Cat1 
1  Cat1 
2  Cat1 
3  Cat1 
4  Cat2 
5  Cat2 
6  Cat2 
7  Cat2 
8  Cat3 
9  Cat4 
10 Cat4 
11 Cat1 
Name: payout, dtype: category 
Categories (4, object): [Cat1 < Cat2 < Cat3 < Cat4] 

df1 = df.groupby(binned)['postTestScore'].sum().reset_index() 
print (df1) 
    payout postTestScore 
0 Cat1   308 
1 Cat2   246 
2 Cat3    62 
3 Cat4   132 

それは同じ1つのラインソリューションですで非常に高速なソリューション：

labs = ['Cat{}'.format(x) for x in range(len(bins))] 
a = np.array(labs)[np.array(bins).searchsorted(df['payout'].values)] 
print (a) 

['Cat1' 'Cat1' 'Cat1' 'Cat1' 'Cat2' 'Cat2' 'Cat2' 'Cat2' 'Cat3' 'Cat4' 
'Cat4' 'Cat1'] 

df1 = df.groupby(a)['postTestScore'].sum().rename_axis('cats').reset_index() 
print (df1) 
    cats postTestScore 
0 Cat1   308 
1 Cat2   246 
2 Cat3    62 
3 Cat4   132