前言：判断数组为空，是一个常见用法。Python与Java的方法不同，需区分

Python–方法：
1.根据长度判断
长度为0时，表示空。（其中”判断条件”成立时（非零），则执行后面的语句）

lst = []
if len(lst):
    print 'c'
else:
    print 'cc'

2.根据逻辑判断
由于一个空 list 本身等同于 False

lst = []
if lst:
    print 'c'
else:
    print 'cc'

注意：
两个以上的判断条件：
代码：

lst = []
if lst[0] == 1 and lst:
    print 'a'

此时执行，会提示 IndexError: list index out of range
需将if的两个条件互换，则不会报错

lst = []
if lst and lst[0] == 1 :
    print 'a'

Java–数组为空

  if(arr==null||arr.length==0)
   return -1;

第一种方案（适用于前端传过来数据判定）

mystr = ''

if mystr:
    list = mystr.split('*')
    print len(list)
    print list
else:
    print 0

第二种方案(适用于自己定义的数据，[]为空是默认为False)

mylist = []

if mylist:
    print 'yes'
else:
    print 'no'

第三种方案（适用于字符数组，非字符数组会报错）

mystr = '[]'

mylist = eval(mystr)

if mylist:
    print 'yes'
else:
    print 'no'

NumPy's main object is the homogeneous multidimensional array. In Numpy dimensions are called axes. The number of axes is rank. Numpy's array class is called ndarray. It is also known by the alias array. The more important attributes of an ndarray object are:

ndarray.ndim

the number of axes (dimensions) of the array. In the Python world, the number of dimensions is referred to as rank.

ndarray.shape

the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the rank, or number of dimensions, ndim.

ndarray.size

the total number of elements of the array. This is equal to the product of the elements of shape.