Pandas (55)

How to reshape a dataframe to the largest possible square after removing the negative values?

# Input
df = pd.DataFrame(np.random.randint(-20, 50, 100).reshape(10,-1))
print(df)

# Solution
# Step 1: remove negative values from arr
arr = df[df > 0].values.flatten()
arr_qualified = arr[~np.isnan(arr)]

# Step 2: find side-length of largest possible square
n = int(np.floor(arr_qualified.shape[0]**.5))

# Step 3: Take top n^2 items without changing positions
top_indexes = np.argsort(arr_qualified)[::-1]
output = np.take(arr_qualified, sorted(top_indexes[:n**2])).reshape(n, -1)
print(output)

# output
    0   1   2   3   4   5   6   7   8   9
0  25 -13  17  16   0   6  22  44  10 -19
1  47   4  -1  29 -13  12  41 -13  49  42
2  20 -20   9  16 -17  -1  37  39  41  37
3  27  44  -5   5   3 -12   0 -13  23  45
4   8  27  -8  -3  48 -16  -5  40  16  10
5  12  12  41 -12   3 -17  -3  27 -15  -1
6  -9  -3  41 -13   1   0  28  33  -2  18
7  18 -14  35   5   4  14   4  44  14  34
8   1  24  26  28 -10  17 -14  14  38  17
9  13  12   5   9 -16  -7  12 -18   1  24

[[ 25.  17.  16.   6.  22.  44.  10.  47.]
 [  4.  29.  12.  41.  49.  42.  20.   9.]
 [ 16.  37.  39.  41.  37.  27.  44.   5.]
 [  3.  23.  45.   8.  27.  48.  40.  16.]
 [ 10.  12.  12.  41.   3.  27.  41.  28.]
 [ 33.  18.  18.  35.   5.   4.  14.   4.]
 [ 44.  14.  34.  24.  26.  28.  17.  14.]
 [ 38.  17.  13.  12.   5.   9.  12.  24.]]