Combining distinct combinatios of elements of an numpy array into a dataframe

Question

Maybe I could synthesize precisely my problem with my title, however I guess that by explaning it, things will get more clear.

So, what I want to do is the following: I want to create a dataframe which each row combinates each distinct element of 4 different numpy arrays.

I am trying to avoid for loops as much as possible and I am not sure if there are other means to achieve my goal utilizing pandas or python methods which I am not still aware of. The problem that I am trying to solve is naturally more complex and would involve several more arrays as well as more complex data.

I would really appreciate your help on this one!

min_x = 1

max_x = 5


x1_set = np.linspace(min_x, max_x, 5)

x2_set = np.linspace(min_x, max_x, 5)

x3_set = np.linspace(min_x, max_x, 5)

x4_set = np.linspace(min_x, max_x, 5)

X_set_df = pd.DataFrame([x1_set,x2_set,x3_set,x4_set]).T

I would expect a dataframe that would be somehow like this

First row : 1,1,1,1

Second row: 1,1,1,2

Third row: 1,1,1,3

...

n-row: 5,5,5,5

jezrael · Accepted Answer · 2019-08-20 12:36:19Z

1

Use itertools.product for cartesian product, last pass it to DataFrame constructor:

from  itertools import product
df = pd.DataFrame(list(product(*[x1_set,x2_set,x3_set,x4_set])))
print (df)
       0    1    2    3
0    1.0  1.0  1.0  1.0
1    1.0  1.0  1.0  2.0
2    1.0  1.0  1.0  3.0
3    1.0  1.0  1.0  4.0
4    1.0  1.0  1.0  5.0
..   ...  ...  ...  ...
620  5.0  5.0  5.0  1.0
621  5.0  5.0  5.0  2.0
622  5.0  5.0  5.0  3.0
623  5.0  5.0  5.0  4.0
624  5.0  5.0  5.0  5.0

[625 rows x 4 columns]

edited Aug 20, 2019 at 12:36

answered Aug 20, 2019 at 12:30

jezrael

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