Numpy的索引切片

索引

In [72]: arr = np.array([[[1,1,1],[2,2,2]],[[3,3,3],[4,4,4]]])                 In [73]: arr
Out[73]:
array([[[1, 1, 1],[2, 2, 2]],[[3, 3, 3],[4, 4, 4]]])In [74]: arr.ndim
Out[74]: 3In [75]: arr.shape
Out[75]: (2, 2, 3)In [76]: arr[0]           #返回降低一个维度的数组
Out[76]:
array([[1, 1, 1],[2, 2, 2]])

切片

In [78]: arr[:,:,:2]
Out[78]:
array([[[1, 1],[2, 2]],[[3, 3],[4, 4]]])

索引与切片结合

array([[[1, 1, 1],[2, 2, 2]],[[3, 3, 3],[4, 4, 4]]])

In [79]: arr[0,1,:2]
Out[79]: array([2, 2])

Pandas的索引切片

In [8]: obj = pd.Series(np.arange(4),index=['a','b','c','d'])                  In [9]: obj
Out[9]:
a    0
b    1
c    2
d    3
dtype: int64

In [10]: obj['b']
Out[10]: 1In [11]: obj[1]
Out[11]: 1

In [12]: obj['b':'d']    #包含尾部
Out[12]:
b    1
c    2
d    3
dtype: int64In [13]: obj[1:3]
Out[13]:
b    1
c    2
dtype: int64

In [14]: obj[['b','d']]
Out[14]:
b    1
d    3
dtype: int64In [15]: obj[[1,3]]
Out[15]:
b    1
d    3
dtype: int64

In [20]: obj = pd.DataFrame(np.arange(16).reshape((4,4)),index=['a','b','c','d'...: ],columns=['a1','b2','c3','d4'])
In [21]: obj
Out[21]: a1  b2  c3  d4
a   0   1   2   3
b   4   5   6   7
c   8   9  10  11
d  12  13  14  15

In [32]: obj['b2']
Out[32]:
a     1
b     5
c     9
d    13
Name: b2, dtype: int64

In [36]: obj[:3]
Out[36]: a1  b2  c3  d4
a   0   1   2   3
b   4   5   6   7
c   8   9  10  11In [37]: obj[obj['c3']>6]  #根据布尔值数组选择数据
Out[37]: a1  b2  c3  d4
c   8   9  10  11
d  12  13  14  15

In [38]: obj['a1']['c']
Out[38]: 8In [39]: obj['a1'][:2]
Out[39]:
a    0
b    4
Name: a1, dtype: int64

In [55]: obj
Out[55]: a1  b2  c3  d4
a   0   1   2   3
b   4   5   6   7
c   8   9  10  11
d  12  13  14  15In [53]: obj.iloc[2,[2,0,1]]  #变换列顺序
Out[53]:
c3    10
a1     8
b2     9
Name: c, dtype: int64In [54]: obj.iloc[2]         #索引行
Out[54]:
a1     8
b2     9
c3    10
d4    11
Name: c, dtype: int64

In [57]: obj.loc['a',['b2','a1']]
Out[57]:
b2    1
a1    0
Name: a, dtype: int64In [58]: obj.loc['a':'c',['b2','a1']]                                          Out[58]: b2  a1
a   1   0
b   5   4
c   9   8