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

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

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

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

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]]]
```

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

```
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)
```

yields

```
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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