函数np.nanmean()正确，因为它是ignores the NaN values, read documentation.
对于您的示例，您的数值列是一个异构（多类型）数组。您可以通过使用array.astype()函数将其转换为同构（单类型）数组来解决此问题。
试试这个：

的数据

使用您的示例txt：

In [2]: txt='''Student Name,Math,Science,English,History,Art
   ...: Alice,90,88,94,85,78
   ...: Bob, 85,92,,88,90
   ...: Charlie,78,80,85,85,79
   ...: David,94,,90,92,84
   ...: Eve,92,88,92,90,88
   ...: Frank,,95,94,86,95'''

字符串
你的vw处理genfromtxt以及任何人，我见过的SO：

In [3]: # Defining anything that could be missing in someone else's data
   ...: missing_values = ['N/A', 'NA', 'nan', 'NaN', 'NULL', '', '']
   ...: 
   ...: # Defining each of the data types
   ...: dtype = [('Student Name', 'U50'), ('Math', 'float'), 
   ...:          ('Science', 'float'), ('English', 'float'), 
   ...:          ('History', 'float'), ('Art', 'float')]
   ...: 
   ...: # Load data into a numpy array
   ...: data = np.genfromtxt(txt.splitlines(), delimiter=',', 
   ...:                      names=True, dtype=dtype,
   ...:                      encoding=None, missing_values=missing_values,
   ...:                      filling_values=np.nan, ndmin=2)

型
data是一个结构化数组; ipython's显示的是repr，所以它显示的是dtype：

In [4]: data
Out[4]: 
array([[('Alice', 90., 88., 94., 85., 78.)],
       [('Bob', 85., 92., nan, 88., 90.)],
       [('Charlie', 78., 80., 85., 85., 79.)],
       [('David', 94., nan, 90., 92., 84.)],
       [('Eve', 92., 88., 92., 90., 88.)],
       [('Frank', nan, 95., 94., 86., 95.)]],
      dtype=[('Student_Name', '<U50'), ('Math', '<f8'), ('Science', '<f8'), ('English', '<f8'), ('History', '<f8'), ('Art', '<f8')])

In [5]: field_names = data.dtype.names
   ...: # Extract the numeric columns by checking their data type
   ...: numeric_columns = data[[field for field in field_names if data[field].dtype == float]]

In [6]: numeric_columns
Out[6]: 
array([[(90., 88., 94., 85., 78.)],
       [(85., 92., nan, 88., 90.)],
       [(78., 80., 85., 85., 79.)],
       [(94., nan, 90., 92., 84.)],
       [(92., 88., 92., 90., 88.)],
       [(nan, 95., 94., 86., 95.)]],
      dtype={'names': ['Math', 'Science', 'English', 'History', 'Art'], 'formats': ['<f8', '<f8', '<f8', '<f8', '<f8'], 'offsets': [200, 208, 216, 224, 232], 'itemsize': 240})

型
这是一个（6，1）形状，有5个字段。尺寸1的维度在那里是因为你指定了ndmin=2。如果没有，它将是（6，）。
nanmean不能处理这种复合dtype。有几种方法可以转换为简单的浮点数组。astype/view可以，也可以：

In [7]: x=np.array(numeric_columns.tolist())
In [8]: x.shape
Out[8]: (6, 1, 5)
In [10]: np.nanmean(x[:,0,:], axis=1)
Out[10]: array([87.  , 88.75, 81.4 , 90.  , 90.  , 92.5 ])

型
另一个转换器：

In [12]: import numpy.lib.recfunctions as rf
In [13]: y=rf.structured_to_unstructured(numeric_columns[:,0])
In [14]: y
Out[14]: 
array([[90., 88., 94., 85., 78.],
       [85., 92., nan, 88., 90.],
       [78., 80., 85., 85., 79.],
       [94., nan, 90., 92., 84.],
       [92., 88., 92., 90., 88.],
       [nan, 95., 94., 86., 95.]])

型