When you save a Python object (non-array), numpy wraps it in an array. The object is pickled:

In [112]: np.save('test.npy', {'foo':34})                                              

In newer numpy versions, you have to explicitly allow it to load pickled items:

In [113]: data = np.load('test.npy')                                                   
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-113-c5835e6fb31e> in <module>
----> 1 data = np.load('test.npy')

/usr/local/lib/python3.6/dist-packages/numpy/lib/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)
    451             else:
    452                 return format.read_array(fid, allow_pickle=allow_pickle,
--> 453                                          pickle_kwargs=pickle_kwargs)
    454         else:
    455             # Try a pickle

/usr/local/lib/python3.6/dist-packages/numpy/lib/format.py in read_array(fp, allow_pickle, pickle_kwargs)
    720         # The array contained Python objects. We need to unpickle the data.
    721         if not allow_pickle:
--> 722             raise ValueError("Object arrays cannot be loaded when "
    723                              "allow_pickle=False")
    724         if pickle_kwargs is None:

ValueError: Object arrays cannot be loaded when allow_pickle=False


In [115]: data = np.load('test.npy',allow_pickle=True)                                 
In [116]: data                                                                         

The result is an object dtype array with 1 element (0d)

Out[116]: array({'foo': 34}, dtype=object)

tolist can extract that object, as will item().

In [117]: data.tolist()                                                                
Out[117]: {'foo': 34}
In [118]: data.tolist()['foo']                                                         
Out[118]: 34
In [119]: data.item()                                                                  
Out[119]: {'foo': 34}
In [120]: data[()]                                                                     
Out[120]: {'foo': 34}

In [7]: np.array([1.0])                                                                        
Out[7]: array([1.])

For a single item array:

In [8]: np.array([1.0]).item()                                                                 
Out[8]: 1.0

In [9]: np.array([1.0]).tolist()                                                               
Out[9]: [1.0]

For a single item 1d array:

In [10]: np.array([1.0])[0]                                                                    
Out[10]: 1.0

Note that the type of the selection differs with the method. Often that doesn't matter.

In [11]: type(Out[10])                                                                         
Out[11]: numpy.float64
In [12]: type(Out[8])                                                                          
Out[12]: float
In [13]: type(Out[9][0])                                                                       
Out[13]: float

If the array is 0d, item is best

In [14]: np.array(1.0).item()                                                                  
Out[14]: 1.0
In [15]: np.array(1.0)[0]                                                                      
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-15-23b6eb4e1c33> in <module>
----> 1 np.array(1.0)[0]

IndexError: too many indices for array
In [16]: np.array(1.0)[()]                                                                     
Out[16]: 1.0

To get an integer, instead of a float, you have to do an int conversion at some point, either in the array (with astype) or after.