如何在 Pandas/numpy 中将一系列数组转换为单个矩阵？

Question

I somehow got a pandas.Series which contains a bunch of arrays in it, as the s in the code below.

data = [[1,2,3],[2,3,4],[3,4,5],[2,3,4],[3,4,5],[2,3,4],
        [3,4,5],[2,3,4],[3,4,5],[2,3,4],[3,4,5]]
s = pd.Series(data = data)
s.shape # output ---> (11L,)
# try to convert s to matrix
sm = s.as_matrix()
# but...
sm.shape # output ---> (11L,)

How can I convert the s into a matrix with shape (11,3)? Thanks!

Answer 1

If, for some reason, you have found yourself with that abomination of a Series , getting it back into the sort of matrix or array you want is straightforward:

In [16]: s
Out[16]:
0     [1, 2, 3]
1     [2, 3, 4]
2     [3, 4, 5]
3     [2, 3, 4]
4     [3, 4, 5]
5     [2, 3, 4]
6     [3, 4, 5]
7     [2, 3, 4]
8     [3, 4, 5]
9     [2, 3, 4]
10    [3, 4, 5]
dtype: object

In [17]: sm = np.matrix(s.tolist())

In [18]: sm
Out[18]:
matrix([[1, 2, 3],
        [2, 3, 4],
        [3, 4, 5],
        [2, 3, 4],
        [3, 4, 5],
        [2, 3, 4],
        [3, 4, 5],
        [2, 3, 4],
        [3, 4, 5],
        [2, 3, 4],
        [3, 4, 5]])

In [19]: sm.shape
Out[19]: (11, 3)

But unless it's something you can't change, having that Series makes little sense to begin with.

Answer 2

Another way is to extract the values of your series and use numpy.stack on them.

np.stack(s.values)

PS. I've run into similar situations often.

Answer 3

对于pandas> = 0.24，您还可以使用np.stack(s.to_numpy())或np.concatenate(s.to_numpy()) ，具体取决于您的要求。

Answer 4

I tested above methods with 5793 of 100D vectors. The old method, converting to list first, is fastest.

%time print(np.stack(df.features.values).shape)
%time print(np.stack(df.features.to_numpy()).shape)
%time print(np.array(df.features.tolist()).shape)
%time print(np.array(list(df.features)).shape)

Result

(5793, 100)
CPU times: user 11.7 ms, sys: 3.42 ms, total: 15.1 ms
Wall time: 22.7 ms
(5793, 100)
CPU times: user 11.1 ms, sys: 137 µs, total: 11.3 ms
Wall time: 11.9 ms
(5793, 100)
CPU times: user 5.96 ms, sys: 0 ns, total: 5.96 ms
Wall time: 6.91 ms
(5793, 100)
CPU times: user 5.74 ms, sys: 0 ns, total: 5.74 ms
Wall time: 6.43 ms