过滤 numpy 数组时 0:: 和 0: 之间有什么区别？

Question

我正在尝试使用 Python 和 NumPy 来解决问题的 Kaggle Titanic tutorial。我很难理解 data[0::, ] 和 data[0:, ] 之间的区别。我复制粘贴下面的相关代码片段：

for i in xrange(number_of_classes):       #loop through each class
    for j in xrange(number_of_price_brackets):   #loop through each price bin

        women_only_stats = data[                          # Which element           
                                (data[0::, 4] == "female") &   # is a female and
                                (data[0::, 2].astype(np.float) # was ith class
                                  == i+1) 
                                &                              # and
                                (data[0:, 9].astype(np.float)  # was greater
                                  >= j * fare_bracket_size)    # than this bin 
                                &                              # and
                               (data[0:, 9].astype(np.float)   # less than
                                  < (j+1)*fare_bracket_size)   # the next bin    
                               , 1]                        # in the 2nd col

Answer 1

没有区别，两种方法都会以相同的方式挂接到 __getitem__。

>>> class Thing(object):
...     def __getitem__(self, item):
...         print(repr(item))
...
>>> t = Thing()
>>> t[0:, 4]
(slice(0, None, None), 4)
>>> t[0::, 4]
(slice(0, None, None), 4)

过滤 numpy 数组时 0:: 和 0: 之间有什么区别？

What is the difference between 0:: and 0: when filtering a numpy array?

python

numpy

kaggle