将numpy数组作为列添加到Pandas数据框 [英] Add numpy array as column to Pandas data frame

问题描述

我有一个形状为(X，Y)的Pandas数据框对象，如下所示:

I have a Pandas data frame object of shape (X,Y) that looks like this:

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

和形状为(X，Z)的Numpy稀疏矩阵(CSC)看起来像这样

and a numpy sparse matrix (CSC) of shape (X,Z) that looks something like this

[[0, 1, 0],
[0, 0, 1],
[1, 0, 0]]

如何将矩阵中的内容添加到新命名列中的数据框中，以使数据框最终像这样:

How can I add the content from the matrix to the data frame in a new named column such that the data frame will end up like this:

[[1, 2, 3, [0, 1, 0]],
[4, 5, 6, [0, 0, 1]],
[7, 8, 9, [1, 0, 0]]]

请注意，数据框现在具有形状(X，Y + 1)，并且矩阵中的行是数据框中的元素.

Notice the data frame now has shape (X, Y+1) and rows from the matrix are elements in the data frame.

推荐答案

import numpy as np
import pandas as pd
import scipy.sparse as sparse

df = pd.DataFrame(np.arange(1,10).reshape(3,3))
arr = sparse.coo_matrix(([1,1,1], ([0,1,2], [1,2,0])), shape=(3,3))
df['newcol'] = arr.toarray().tolist()
print(df)

收益

   0  1  2     newcol
0  1  2  3  [0, 1, 0]
1  4  5  6  [0, 0, 1]
2  7  8  9  [1, 0, 0]

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