【Python 基礎】Numpy 子產品

user_docs

array

import numpy as np
array(object,dtype=None,copy=True,order='K',subok=False,ndmin=0)
# obj: data
# dtype: data type
# order: list
# ndmin: dimintion
nd = np.array((1,2,3,4))
nd.dtype
nd.ndim
np.array(([1,2,3,][1,2]))  # dtype is object, not int32

           

• order C

  np.array([range(1,4),range(4,8)],order=‘C’)

1	2	3	4
5	6	7	8

• order F

  np.array([range(1,4),range(4,8)],order=‘F’)

1	5
2	6
3	7
4	8

ndarray

# create 2 x 2 array
np.ndarray((2,2), buffer=np.array(range(4)), dtype=int)
.shape
.size
.dtype
.data
.reshape(m,n)

# slice
a[:]
b[[0,1]]
b[::, 1]
b[[1,2],[1,2]]?
b[::,[1,4],b[::,::2]]
           

create ndarray ways

np.zeros()
np.zeros_like/ones_likes/empty_like(a, ...)
full(shape,  fill, dtype, order)
arange(start, stop, step, stype)
linspace(start, stop, num=50)
random.rand/randn(d0, d1...)
random.randint(low, high, size)

# dtype： bool, float, int
# character code ('biufcmMOSUV') 
np.typeDict
np.typecodes
np.typeNA

           

create data type

1. date: name, age, eg.[(‘sun’, 10), (‘yang’, 11)]

dt = np.dtype('U16','i4')
a = np.array([('sun', 10), ('yang', 11)], dtype=dt)
a['f0']  # silce
a['f1']  
dt = np.dtype('3S8', '')
           

2. second way

3. third way

dt = np.dtype(['f0',[('f1','U2'),('f2', np.int16)]])
dt = np.dtype([('name', 'U16'),('age', 'i4')])

dt = np.dtype({'x': ('S2', 0), 'y': ('i4', 2)})
dt = np.dtype({'name': ['name', 'age'], 'formats': ['U16', 'i4']})

           

calculate

# broadcasting: + - * /

# boolean index
np.any
np.all
np.where(cond,[x,y])

&  # 邏輯與
|  # 邏輯或

np.all
np.arange(9).reshape(3,3)
# return an array representing the indices of a grid
np.indices(a.shape)
r, c = np.indices(a.shape)