Iteratively pop and append to generate new lists using pandas-CodePudding

I have a list of elements mylist = [1, 2, 3, 4, 5, 6, 7, 8] and would like to iteratively:

copy the list
pop the first element of the copied list
and append it to the end of the copied list
repeat this for the next row, etc.

Desired output:

index   A   B   C   D   E   F   G   H
0       1   2   3   4   5   6   7   8
1       2   3   4   5   6   7   8   1
2       3   4   5   6   7   8   1   2
3       4   5   6   7   8   1   2   3
4       5   6   7   8   1   2   3   4
5       6   7   8   1   2   3   4   5
6       7   8   1   2   3   4   5   6
7       8   1   2   3   4   5   6   7

I suspect a for loop is needed but am having trouble iteratively generating rows based on the prior row.

CodePudding user response：

I think slicing (Understanding slicing) is what you are looking for:

next_iteration =  my_list[1:]   [my_list[0]]

and the full loop:

output = []
for i in range(len(my_list)):
    output.append(my_list[i:]   my_list[:i])

CodePudding user response：

Use this numpy solution with rolls create by np.arange:

mylist = [1, 2, 3, 4, 5, 6, 7, 8]

a = np.array(mylist)
rolls = np.arange(0, -8, -1)
print (rolls)
[ 0 -1 -2 -3 -4 -5 -6 -7]

df = pd.DataFrame(a[(np.arange(len(a))[:,None]-rolls) % len(a)], 
                  columns=list('ABCDEFGH'))
print (df)
   A  B  C  D  E  F  G  H
0  1  2  3  4  5  6  7  8
1  2  3  4  5  6  7  8  1
2  3  4  5  6  7  8  1  2
3  4  5  6  7  8  1  2  3
4  5  6  7  8  1  2  3  4
5  6  7  8  1  2  3  4  5
6  7  8  1  2  3  4  5  6
7  8  1  2  3  4  5  6  7

If need loop solution (slow) is possible use numpy.roll:

mylist = [1, 2, 3, 4, 5, 6, 7, 8]

rolls = np.arange(0, -8, -1)

df = pd.DataFrame([np.roll(mylist, i) for i in rolls], 
                  columns=list('ABCDEFGH'))
print (df)
   A  B  C  D  E  F  G  H
0  1  2  3  4  5  6  7  8
1  2  3  4  5  6  7  8  1
2  3  4  5  6  7  8  1  2
3  4  5  6  7  8  1  2  3
4  5  6  7  8  1  2  3  4
5  6  7  8  1  2  3  4  5
6  7  8  1  2  3  4  5  6
7  8  1  2  3  4  5  6  7

CodePudding user response：

try this:

mylist = [1, 2, 3, 4, 5, 6, 7, 8]
ar = np.roll(np.array(mylist), 1)
data = [ar := np.roll(ar, -1) for _ in range(ar.size)]
df = pd.DataFrame(data, columns=[*'ABCDEFGH'])
print(df)
>>>
   A  B  C  D  E  F  G  H
0  1  2  3  4  5  6  7  8
1  2  3  4  5  6  7  8  1
2  3  4  5  6  7  8  1  2
3  4  5  6  7  8  1  2  3
4  5  6  7  8  1  2  3  4
5  6  7  8  1  2  3  4  5
6  7  8  1  2  3  4  5  6
7  8  1  2  3  4  5  6  7