有没有在python中存储XY数据的标准方法? [英] Is there a standard way to store XY data in python?

问题描述

是否有 standard 方法在python中存储(x，y)，(x，y，z)或(x，y，z，t)数据?

Is there a standard way to store (x,y), (x,y,z), or (x,y,z,t) data in python?

我知道numpy数组经常用于诸如此类的事情，但是我想您也可以使用numpy矩阵来做到这一点.

I know numpy arrays are used often for things like this, but I suppose you could do it also with numpy matrices.

我已经看到使用2个列表压缩在一起，这完全避免了使用numpy.

I've seen the use of 2 lists zipped together, which side steps the use of numpy altogether.

XY_data = zip( [x for x in range(0,10)] , [y for y in range(0,10)] )

是否有标准?如果没有，您最喜欢的方式是什么，或者您最常看到的方式是什么?

Is there a standard? If not, what is your favorite way, or the one which you have seen the most?

推荐答案

一种不错的方法是使用结构化数组.这提供了numpy数组的所有优点，但提供了方便的访问结构.

One nice way is with a structured array. This gives all the advantages of numpy arrays, but a convenient access structure.

使numpy数组成为结构化"数组所需要做的就是给它提供dtype参数.这给每个字段"一个名称和类型.如果您愿意，它们甚至可以具有更复杂的形状和层次结构，但是这就是我保存我的x-y数据的方式:

All you need to do to make your numpy array a "structured" one is to give it the dtype argument. This gives each "field" a name and type. They can even have more complex shapes and hierarchies if you wish, but here's how I keep my x-y data:

In [175]: import numpy as np

In [176]: x = np.random.random(10)

In [177]: y = np.random.random(10)

In [179]: zip(x,y)
Out[179]: 
[(0.27432965895978034, 0.034808254176554643),
 (0.10231729328413885, 0.3311112896885462),
 (0.87724361175443311, 0.47852682944121905),
 (0.24291769332378499, 0.50691735432715967),
 (0.47583427680221879, 0.04048957803763753),
 (0.70710641602121627, 0.27331443495117813),
 (0.85878694702522784, 0.61993945461613498),
 (0.28840423235739054, 0.11954319357707233),
 (0.22084849730366296, 0.39880927226467255),
 (0.42915612628398903, 0.19197320645915561)]

In [180]: data = np.array( zip(x,y), dtype=[('x',float),('y',float)])

In [181]: data['x']
Out[181]: 
array([ 0.27432966,  0.10231729,  0.87724361,  0.24291769,  0.47583428,
        0.70710642,  0.85878695,  0.28840423,  0.2208485 ,  0.42915613])

In [182]: data['y']
Out[182]: 
array([ 0.03480825,  0.33111129,  0.47852683,  0.50691735,  0.04048958,
        0.27331443,  0.61993945,  0.11954319,  0.39880927,  0.19197321])

In [183]: data[0]
Out[183]: (0.27432965895978034, 0.03480825417655464)

其他人可能会建议使用 pandas ，但是如果您的数据相对简单，则简单的numpy可能会更容易.

Others will probably suggest using pandas, but if your data is relatively simple, plain numpy might be easier.

您可以根据需要添加层次结构，但这通常比必要的复杂.

You can add hierarchy if you wish, but often it's more complicated than necessary.

例如:

In [200]: t = np.arange(10)

In [202]: dt = np.dtype([('t',int),('pos',[('x',float),('y',float)])])

In [203]: alldata = np.array(zip(t, zip(x,y)), dtype=dt)

In [204]: alldata
Out[204]: 
array([(0, (0.27432965895978034, 0.03480825417655464)),
       (1, (0.10231729328413885, 0.3311112896885462)),
       (2, (0.8772436117544331, 0.47852682944121905)),
       (3, (0.242917693323785, 0.5069173543271597)),
       (4, (0.4758342768022188, 0.04048957803763753)),
       (5, (0.7071064160212163, 0.27331443495117813)),
       (6, (0.8587869470252278, 0.619939454616135)),
       (7, (0.28840423235739054, 0.11954319357707233)),
       (8, (0.22084849730366296, 0.39880927226467255)),
       (9, (0.429156126283989, 0.1919732064591556))], 
      dtype=[('t', '<i8'), ('pos', [('x', '<f8'), ('y', '<f8')])])

In [205]: alldata['t']
Out[205]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [206]: alldata['pos']
Out[206]: 
array([(0.27432965895978034, 0.03480825417655464),
       (0.10231729328413885, 0.3311112896885462),
       (0.8772436117544331, 0.47852682944121905),
       (0.242917693323785, 0.5069173543271597),
       (0.4758342768022188, 0.04048957803763753),
       (0.7071064160212163, 0.27331443495117813),
       (0.8587869470252278, 0.619939454616135),
       (0.28840423235739054, 0.11954319357707233),
       (0.22084849730366296, 0.39880927226467255),
       (0.429156126283989, 0.1919732064591556)], 
      dtype=[('x', '<f8'), ('y', '<f8')])

In [207]: alldata['pos']['x']
Out[207]: 
array([ 0.27432966,  0.10231729,  0.87724361,  0.24291769,  0.47583428,
        0.70710642,  0.85878695,  0.28840423,  0.2208485 ,  0.42915613])

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