# NumPy - Find Indices of Maximum element in large array

## NumPy: Performance Optimization Exercise-12 with Solution

Write a NumPy program that creates a large NumPy array and write a function to find the indices of the maximum element using a for loop. Optimize it using NumPy's argmax() function

**Sample Solution:**

**Python Code:**

```
import numpy as np
# Generate a large 1D NumPy array with random integers
large_array = np.random.randint(1, 1000000, size=1000000)
# Function to find the indices of the maximum element using a for loop
def find_max_index_with_loop(arr):
max_index = 0
max_value = arr[0]
for i in range(1, len(arr)):
if arr[i] > max_value:
max_value = arr[i]
max_index = i
return max_index
# Find the index of the maximum element using the for loop method
max_index_with_loop = find_max_index_with_loop(large_array)
# Find the index of the maximum element using NumPy's argmax() function
max_index_with_numpy = np.argmax(large_array)
# Display the results
print(f"Index of maximum element using for loop: {max_index_with_loop}")
print(f"Index of maximum element using NumPy: {max_index_with_numpy}")
```

Output:

Index of maximum element using for loop: 546157 Index of maximum element using NumPy: 546157

**Explanation:**

- Importing numpy: We first import the numpy library for array manipulations.
- Generating a large array: A large 1D NumPy array with random integers is generated.
- Defining the function: A function find_max_index_with_loop is defined to find the index of the maximum element using a for loop.
- Finding index with loop: The index of the maximum element is found using the for loop method.
- Finding index with numpy: The index of the maximum element is found using NumPy's built-in argmax() function.
- Displaying results: The results from both methods are printed out to verify correctness.

**Python-Numpy Code Editor:**

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