genfromtxt-强制生成未知列数的列名 [英] genfromtxt - Force column name generation for unknown number of columns

问题描述

我很难让numpy加载表格数据并自动生成列名.看起来很简单，但我无法钉牢.

I have trouble getting numpy to load tabular data and automatically generate column names. It seems pretty simple but I cannot nail it.

如果我知道可以轻松创建names参数的列数，但是我没有此知识，并且我想避免对数据文件进行事先自省.

If i knew the number of columns I could easily create names parameter, but I don't have this knowledge, and I would like to avoid prior introspection of the data file.

当我不知道文件中有多少列时，如何强制numpy生成列名或自动使用类似tuple的dtype?我想在读取数据后对列名称进行操作.

How can I force numpy to generate the column names, or use tuple-like dtype automatically, when I have no knowledge how many columns there are in file? I want to manipulate the column names after reading the data.

到目前为止，我的方法是

My approaches so far:

data = np.genfromtxt(tar_member, unpack = True, names = '')-我想通过提供一些空"参数来强制自动生成列名.错误为ValueError: size of tuple must match number of fields.

data = np.genfromtxt(tar_member, unpack = True, names = '') - I wanted to force automatic generation of column names by giving some "empty" parameter. Results with error ValueError: size of tuple must match number of fields.

data = np.genfromtxt(tar_member, unpack = True, names = True)-有效"，但占用了第一行数据.

data = np.genfromtxt(tar_member, unpack = True, names = True) - "Works" but consumes 1st row of data.

data = np.genfromtxt(tar_member, unpack = True, dtype = None)-适用于混合类型的数据.自动类型猜测将dtype扩展为一个元组，并分配了名称.但是，对于实际上一切都为float的数据，dtype设置为float64，当我尝试访问data.dtype.names时得到了ValueError: there are no fields defined.

data = np.genfromtxt(tar_member, unpack = True, dtype = None) - Worked for data with mixed types. Automatic type guessing expanded dtype into a tuple, and assigned the names. However, for data where everything was actually float, dtype was set to float64, and I got ValueError: there are no fields defined when I tried accessing data.dtype.names.

推荐答案

我知道有一种更清洁的方法可以执行此操作，但是如果您不介意强制执行此问题，则可以生成dtype结构并将其直接分配给数据数组.

I know there is a cleaner way to do this, but if you don't mind forcing the issue you can generate your dtype structure and assign it directly to the data array.

x = numpy.random.rand(10,10)
numpy.savetxt('test.out', x, delimiter=',')
dataa = numpy.genfromtxt('test.out',delimiter=",", dtype=None)
if dataa.dtype.names is None:#then dataa is homogenous?
  l1 = map(lambda z:('f%d'%(z),dataa.dtype),range(0,dataa.shape[1]))
  dataa.dtype = dtype(l1)

dataa.dtype
dataa.dtype.names

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