numpy.zeros(shape, dtype=float, order='C')

numpy.zeros
numpy.zeros(shape, dtype=float, order='C')
Return a new array of given shape and type, filled with zeros.
Parameters:
shape : int or sequence of ints
Shape of the new array, e.g., (2, 3) or 2.
dtype : data-type, optional
The desired data-type for the array, e.g., numpy.int8. Default is numpy.float64.
order : {‘C', ‘F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory.
Returns:
out : ndarray

#指定长度的一维数组
> np.zeros(5)
array([ 0., 0., 0., 0., 0.])
 
#指定数据类型，指定长度的一维数组
> np.zeros((5,), dtype=int)
array([0, 0, 0, 0, 0])
 
#二维数组
> np.zeros((2, 1))
array([[ 0.],
  [ 0.]])
  
> s = (2,2)
> np.zeros(s)
array([[ 0., 0.],
  [ 0., 0.]])
  
 #指定dtype
> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
array([(0, 0), (0, 0)],
  dtype=[('x', '<i4'), ('y', '<i4')])

matrix=[0,1,2,3]

matrix=[0 for i in range(4)]
print(matrix)

#coding=utf-8

import numpy as np
a = np.array([1,2,3,4,5])
print a
b = np.zeros((2,3))
print b
c = np.arange(10)
print c
d = np.arange(2,10,dtype=np.float)
print d
e = np.linspace(1.0,4.0,6)
print e
f = np.indices((3,3))
print f