将带索引的numpy数组转换为pandas数据帧

I have a numpy array which I want to print with python ggplot's tile. For that I need to have a DataFrame with the columns x, y, value. How can I transform the numpy array efficiently into such a DataFrame. Please consider, that the form of the data I want is in a sparse style, but I want a regular DataFrame. I tried using scipy sparse data structures like in Convert sparse matrix (csc_matrix) to pandas dataframe, but conversions were too slow and memory hungry: My memory was used up.

我有一个numpy数组,我想用python ggplot的tile打印。为此,我需要一个包含x,y,value列的DataFrame。如何将numpy数组有效地转换为这样的DataFrame。请考虑,我想要的数据形式是稀疏样式,但我想要一个常规的DataFrame。我尝试使用scipy稀疏数据结构,如转换稀疏矩阵(csc_matrix)到pandas数据帧,但转换太慢而且内存耗尽:我的内存已用完。

To clarify what I want:

澄清我想要的东西:

I start out with a numpy array like

我从一个像numpy数组开始

array([[ 1,  3,  7],
       [ 4,  9,  8]])

and I would like to end up with the DataFrame

我想最终得到DataFrame

     x    y    value
0    0    0    1
1    0    1    3
2    0    2    7
3    1    0    4
4    1    1    9
5    1    2    8

1 个解决方案

#1

arr = np.array([[1, 3, 7],
                [4, 9, 8]])

df = pd.DataFrame(np.hstack((np.indices(arr.shape).reshape(2, arr.size).T,\
                    arr.reshape(-1, 1))), columns=['x', 'y', 'value'])
print(df)

   x  y  value
0  0  0      1
1  0  1      3
2  0  2      7
3  1  0      4
4  1  1      9
5  1  2      8

You might also consider using the function employed in this answer, as a speedup to np.indices in the solution above:

您也可以考虑使用本答案中使用的函数,作为上述解决方案中np.indices的加速:

def indices_merged_arr(arr):
    m,n = arr.shape
    I,J = np.ogrid[:m,:n]
    out = np.empty((m,n,3), dtype=arr.dtype)
    out[...,0] = I
    out[...,1] = J
    out[...,2] = arr
    out.shape = (-1,3)
    return out

array = np.array([[ 1,  3,  7],
                  [ 4,  9,  8]])

df = pd.DataFrame(indices_merged_arr(array), columns=['x', 'y', 'value'])
print(df)

   x  y  value
0  0  0      1
1  0  1      3
2  0  2      7
3  1  0      4
4  1  1      9
5  1  2      8

---

Performance

arr = np.random.randn(1000, 1000)

%timeit df = pd.DataFrame(np.hstack((np.indices(arr.shape).reshape(2, arr.size).T,\
                         arr.reshape(-1, 1))), columns=['x', 'y', 'value'])
100 loops, best of 3: 15.3 ms per loop

%timeit pd.DataFrame(indices_merged_arr(array), columns=['x', 'y', 'value'])
1000 loops, best of 3: 229 µs per loop