Cast dataframe of tuples to numpy matrix in Python

Question

I have a dataframe

df = pd.DataFrame({'age' : [(1, 2), (1, 3), (1, 1)], \

                   'year' : [(20, 30), (30, 40), (30, 40)]})

df

Out[58]: 

      age      year

0  (1, 2)  (20, 30)

1  (1, 3)  (30, 40)

2  (1, 1)  (30, 40)

I want to convert this as a numpy array like this

 array([[ 1,  2, 20, 30],

        [ 1,  3, 30, 40],

        [ 1,  1, 30, 40]])

i.e. a row in the dataframe is a row in the matrix, and one tuple column in the dataframe is two columns in the matrix. There could concievably be more tuples in the dataframe (resulting in more columns in the array).

So,if

col_names

is an array of the column names (here

col_names = ['age', 'year']

)

I want something like

numpy_array = some_clever_expression(col_names)

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Answer 1

Stack with np.concatenate to get a 1D flattened array and then reshape -

np.concatenate(np.concatenate(df.values)).reshape(df.shape[0],-1)

Sample output -

In [460]: np.concatenate(np.concatenate(df.values)).reshape(df.shape[0],-1)
Out[460]: 
array([[ 1,  2, 20, 30],
       [ 1,  3, 30, 40],
       [ 1,  1, 30, 40]])

Alternatively, we could use np.hstack to get the flattened version -

np.hstack(np.hstack(df.values))

To select specific columns, simple index into those columns, get the array data and proceed. Thus, for a list of column names in col_names, use df[col_names].values instead.

Cast dataframe of tuples to numpy matrix in Python

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