Converting pandas.Multindex to numpy.ndarray with dtype float

user_7151242 0
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When converting a pandas.Multiindex to a numpy.ndarray, the output is a one dimensional ndarray with dtype=object as seen in the following example:

df = pd.DataFrame({
    'A': [10, 20, 30, 40, 50, 60],
    'B': [0,1,2,3,4,5],
    'C': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5']
}).set_index(['A','B'])

The df will be:

A B C
10 0 K0
20 1 K1
30 2 K2
40 3 K3
50 4 K4
60 5 K5

The output for df.index.to_numpy() is a one dimensional ndarray with dtype=object:

array([(10, 0), (20, 1), (30, 2), (40, 3), (50, 4), (60, 5)], dtype=object)

but I want:

array([[10,  0],
       [20,  1],
       [30,  2],
       [40,  3],
       [50,  4],
       [60,  5]])

On How to convert a Numpy 2D array with object dtype to a regular 2D array of floats, I found the following solution:

np.vstack(df.index)

Is there any more direct or better solution?

pandas numpy-ndarray numpy python
2 Answers

I am pretty sure you will get what you want by flattening the multi index and taking numpy array from the result. E.g. by using the following syntax

np.array(list(df.index))
user_72816630

turn the index to columns.

df.reset_index()[['A', 'B']].values
user_60063830
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