Converting rows of dataframes into matrices - Python Pandas

Question

I have the following dataframe:

   Pclass   Sex     Fare    Embarked    Title   Family
    3          0    1.0        0          0     1
    1          1    2.0        1          3     1
    3          1    1.0        1          2     0
    1          1    2.0        3          3     1
    3          0    2.0        1          1     0

I need to take each row and turn it into a 6X32 matrix.

(Example of 3X10)

3,3,3,3,3,3,3,3,3,3
0,0,0,0,0,0,0,0,0,0
1,1,1,1,1,1,1,1,1,1

I need that but for every element of the column and 32 times instead of 10.

My code:

from itertools import repeat
array = []
for i in range(0,len(datos)):
    rows = datos.iloc[i,:]
    for j in range(0,len(rows)):
        array.append([rows[j]]*10)

I'm not sure how to do it to separate each row into a unique array.

At least let us know what you have already tried. Don't just expect someone to do it for you. — BikerDude
– BikerDude, Commented Aug 16, 2017 at 16:21

piRSquared · Accepted Answer · 2017-08-16 16:42:04Z

2

Everything at once

np.dstack([df.values.astype(int)] * 32)

You can even turn it into a series with matching index

pd.Series(np.dstack([df.values.astype(int)] * 32).tolist(), df.index).apply(np.array)

0    [[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
1    [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
2    [[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
3    [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
4    [[3, 3, 3, 3, 3,

answered Aug 16, 2017 at 16:42

piRSquared

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Comments

Alz · Accepted Answer · 2017-08-16 16:35:38Z

1

for the first row:

a = df.iloc[0].values.repeat(10).reshape(6,10)


#out: array([[ 3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.],
#   [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#   [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
#   [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#   [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#   [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.]])

now for the whole df:

for i in range(df.shape[1] -1 ):
    a = df.iloc[i].values.repeat(10).reshape(6,10)
    # ...

answered Aug 16, 2017 at 16:35

Alz

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