如何将2d列表转换为2d numpy数组？

I have a 2D list something like

我有一个类似的2D列表

a = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

and I want to convert it to a 2d numpy array. Can we do it without allocating memory like

我想将它转换为2d numpy数组。我们可以在不分配内存的情况下完成

numpy.zeros((3,3))

and then storing values to it?

然后将值存储到它？

4 个解决方案

#1

Just pass the list to np.array:

只需将列表传递给np.array：

a = np.array(a)

You can also take this opportunity to set the dtype if the default is not what you desire.

如果默认值不是您想要的，您也可以借此机会设置dtype。

a = np.array(a, dtype=...)

#2

I am using large data sets exported to a python file in the form

我正在使用导出到表单中的python文件的大型数据集

XVals1 = [.........] 
XVals2 = [.........]

Each list is of identical length. I use

每个列表的长度相同。我用

>>> a1 = np.array(SV.XVals1)

>>> a2 = np.array(SV.XVals2)

Then

然后

>>> A = np.matrix([a1,a2])

#3

just use following code

只需使用以下代码

c = np.matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
matrix([[1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]])

Then it will give you

然后它会给你

you can check shape and dimension of matrix by using following code

您可以使用以下代码检查矩阵的形状和尺寸

c.shape

c.ndim

#4

np.array() is even more powerful than what unutbu said above. You also could use it to convert a list of np arrays to a higher dimention array, the following is a simple example:

np.array（）比unutbu上面说的更强大。您还可以使用它将np数组列表转换为更高维度的数组，以下是一个简单的示例：

aArray=np.array([1,1,1])

bArray=np.array([2,2,2])

aList=[aArray, bArray]

xArray=np.array(aList)

xArray's shape is (2,3), it's a standard np array. This operation avoids a loop programming.

xArray的形状是（2,3），它是一个标准的np数组。此操作可避免循环编程。

#1