[Python] indexing

numpy 는 정수여야만 했지만 판다스는 정수외에 실수도 사용가능

 

▷

obj = pd.Series(np.arange(4.), index=['a', 'b', 'c', 'd'])
obj
# <출력>
# a    0.0
# b    1.0
# c    2.0
# d    3.0
# dtype: float64

 

•  

obj['b']
# <출력> 1.0

obj[1]
# <출력> 1.0

obj[2:4]
# <출력>
# c    2.0
# d    3.0
# dtype: float64

obj[['b', 'a', 'd']]
# <출력>
# b    1.0
# a    0.0
# d    3.0
# dtype: float64

obj[[1, 3]]
# <출력>
# b    1.0
# d    3.0
# dtype: float64

obj[obj < 2]
# <출력>
# a    0.0
# b    1.0
# dtype: float64

obj['b':'c']
# <출력>
# b    1.0
# c    2.0
# dtype: float64

 

 

 

• 값 대체

obj['b':'c'] =5   # b,c 값을 5로 대체하라
obj
# <출력>
# a    0.0
# b    5.0
# c    5.0
# d    3.0
# dtype: float64

 

▷

하나 이상의 컬럼값 가져오기

data = pd.DataFrame(np.arange(16).reshape((4,4)),
                    index =['Ohio','Colorado','Utah','New York'],columns=['one','two','three','four'])

data
# <출력> 
#           one  two  three  four
# Ohio        0    1      2     3
# Colorado    4    5      6     7
# Utah        8    9     10    11
# New York   12   13     14    15

 

•  

data['two']
# <출력>
# Ohio         1
# Colorado     5
# Utah         9
# New York    13
# Name: two, dtype: int32

 

• Error

data['three','one']
# <출력> KeyError: ('three', 'one')

▼ 옳은것

data[['three','one']]
# <출력>
# three  one
# Ohio          2    0
# Colorado      6    4
# Utah         10    8
# New York     14   12

 

 

▷

슬라이싱, 불리언 배열로 로우 선택가능

# <data>
#           one  two  three  four
# Ohio        0    1      2     3
# Colorado    4    5      6     7
# Utah        8    9     10    11
# New York   12   13     14    15

 

 

• 로우선택

data[:2]
# <출력>
#           one  two  three  four
# Ohio        0    1      2     3
# Colorado    4    5      6     7

 

 

•  data 안의 data가 three 가 5 이상인 로우 값을 모두 가져오기

data[data['three']>5]   
# <출력>
#           one  two  three  four
# Colorado    4    5      6     7
# Utah        8    9     10    11
# New York   12   13     14    15

 

 

• true false 값 추출하기

data<5        
# <출력>
#             one    two  three   four
# Ohio       True   True   True   True
# Colorado   True  False  False  False
# Utah      False  False  False  False
# New York  False  False  False  Fals

 

 

• 데이터 값이 5이하인 데이터는 0으로 대체

data[data<5]=0    
data
# <출력>
#           one  two  three  four
# Ohio        0    0      0     0
# Colorado    0    5      6     7
# Utah        8    9     10    11
# New York   12   13     14    15

 

▷

loc & iloc

# <data>
#           one  two  three  four
# Ohio        0    0      0     0
# Colorado    0    5      6     7
# Utah        8    9     10    11
# New York   12   13     14    15

 

 

• loc

data.loc['Colorado',['two','three']]
# <출력>
# two      5
# three    6
# Name: Colorado, dtype: int32

 

 

• iloc

data.iloc[2,[3,0,1]]
# <출력>
# four    11
# one      8
# two      9
# Name: Utah, dtype: int32

data.iloc[2]
# <출력>
# one       8
# two       9
# three    10
# four     11
# Name: Utah, dtype: int32

data.iloc[[1,2],[3,0,1]]
# <출력>
#           four  one  two
# Colorado     7    0    5
# Utah        11    8    9

 

 

 

▷

문자쓸땐 문자까지 포함

data.loc[:'Utah','two']    
# <출력>
# Ohio        0
# Colorado    5
# Utah        9
# Name: two, dtype: int32

utah까지

 

 

▷

숫자쓰면 숫자 전까지

data.iloc[:,:3][data.three >5]
# <출력>
#           one  two  three
# Colorado    0    5      6
# Utah        8    9     10
# New York   12   13     14

 0부터2 까지, three가 5이상인 애들 ; data.three!!!