Data Analysis Library Learn python numpy

 

import numpy as np 
def asum(a_list,b_list,n1=2,n2=3):
    a = np.array(a_list)
    b = np.array(b_list)
    c = pow(a,n1) + pow(b,n2)
    return c

a_lst = [1,2,3,4]
b_lst = [2,3,4,5]
print(asum(a_lst,b_lst))

#np.array()生成数据对象ndarray
a = np.array([[1,2,3,4],[1,2,3,4]])
print(type(a)) #<class 'numpy.ndarray'>
print(a)
print(a.ndim)#轴数
print(a.shape) # (2,4) 2 4 rows 
Print (a.size) # The total number of elements 
Print (a.itemsize) # element size 
Print (a.dtype) # Int32, element type 

Print (np.arange (10 ))
 Print (np.ones ((3,3), DTYPE = np.float32)) # generates three lines of the matrix 1 are three 
Print (np.ones ([4,3-], DTYPE = NP. Int32)) # supra four rows and three columns, the type of the parameter list is also OK 
Print (np.zeros ((2,3))) # two rows and three columns 0 
Print (np.full ((3,3),. 6)) # 3 are three rows. 6 
print (np.eye (. 6)) # diagonal is a square matrix of rows and 6 columns 1,6 
# generated multidimensional array 
print(np.ones ((2,3,4), DTYPE = np.int32)) 

# mimic the shape of the green list 0 Chengdu matrix 
Print (np.zeros_like ([[2,3,4,5], [. 3, 4,5,6 ], [0,0,0,0]]))
 # mimic the shape of the green list 1 Chengdu matrix 
Print (np.ones_like ([[2,3,4,5], [3,4- , 5,6 ], [0,0,0,0]]))
 # mimic the shape of the green list Chengdu matrix 10 
Print (np.full_like ([[2,3,4,5], [3,4-, 5,6], [0,0,0,0]], 10 ))
 # generates four elements 1 starting end 10, is equally divided into three 
Print (np.linspace (1,10,4 )) 
 # does not contain 10 four elements, it is necessary to cake into four 
Print (np.linspace (1,10,4, Endpoint = False))
 # the two arrays into 
a = np.linspace (1,10,4 ) 
B = NP .linspace (1,10,4, endpoint =False) 
C = np.concatenate ((A, B))
 Print (C) # [4. 1. 10. The 1. 7. The 3.25 5.5 7.75] 
# the RESHAPE 
A = np.ones ((2,3,4), = DTYPE np.int32)
 Print (a) 
C = a.reshape ((3,8)) # does not change the original array 
Print (C)
 Print (a) 
a.resize (( 3,8)) # change the original array a 
Print (a)
 # fatten reduced dimensional array 
a.flatten ()
 Print (a) # no change 
D = a.flatten () # modified based on the new array 
Print (D) 

#asType () array type conversion element group 
A = np.ones ((2,3,4), DTYPE = np.int)
 print (A) 
B = a.astype (np.float) # copy the new data may then change the type of 
print (B) 

# data to the list conversion .ToList () 
A = np.full ((2,3,4), 25, DTYPE = np.int32)
 Print (A)
 Print (a.tolist ()) # in the new array based on the change 
Print (A) # original array unchanged 

# array slice 
A = np.array ([1,2,3,4,5,6 ])
 Print (A [2 ])
 Print (A [. 1:. 4: 2]) # starting number comprising no 

A = np.arange (24 )
 Print (A) 
a.resize ((3,2,4 ))
 Print (A)
 Print (A [2,1, 3]) # outermost row 3, line 2 inside, which fourth element 
Print (A [-2, -1, -3 ])
 Print (A [:,. 1, -3 ])
 Print (A [:,. 1: 2 ,:])
 Print (A [:,:, :: 2 ]) 

B = np.arange (48) .reshape (3,4,4 )
 Print (B)
 # average matrix 
Print (a.mean ())
 Print (np.arange (2) .mean ()) # 0.5 
# a divided by the average 
a = a / a .mean ()
 Print (a) 

# unary functions, square 
a = np.arange (24) .reshape (2,3,4 )
Print (A) 
A = np.square (A)
 Print (A) 

# square root of 
A = np.arange (24) .reshape (2,3,4 ) 
A = np.sqrt (A)
 Print (A)
 # integers and fractional partial peeling 
Print (np.modf (a)) # two matrix, the first matrix is the fractional part, the second matrix is the integer part 

# binary function instance, two matrices 
a = np.arange (24). the RESHAPE (2,3,4 ) 
B = np.sqrt (A)
 Print (A)
 Print (B)
 Print (np.maximum (A, B)) # difference between the two types of data matrices, taken float 
Print (A > b)