To rank items in an array using NumPy in Python without sorting the array twice, you can use the `argsort()`

function in conjunction with the `argsort().argsort()`

method. Here’s an example:

```
import numpy as np
# Define an array
arr = np.array([7, 3, 1, 2, 6, 4])
# Get the indices that would sort the array
sorted_indices = np.argsort(arr)
# Get the indices that would sort the sorted indices
ranks = np.argsort(sorted_indices)
# Print the ranks
print(ranks)
```

In this example, we have an array `arr`

with values `[7, 3, 1, 2, 6, 4]`

.

To rank the items in the array, we first use `np.argsort(arr)`

to obtain the indices that would sort the array in ascending order. This gives us `[2, 3, 1, 5, 4, 0]`

, which represents the sorted indices.

Next, we use `np.argsort(sorted_indices)`

to obtain the indices that would sort the sorted indices. This effectively gives us the ranks of the items in the original array. The resulting array `[5, 2, 1, 3, 4, 0]`

represents the ranks of the items in the original array.

The output will be:

`[5 2 1 3 4 0]`

In this case, the ranks of the items in the array are `[5, 2, 1, 3, 4, 0]`

.

By using `argsort()`

and `argsort().argsort()`

in this manner, we avoid sorting the array twice while obtaining the ranks of the items. This approach is efficient and provides the desired result.

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