Optimizing row-wise Mean calculation of large NumPy arrays
4. Large 2D Array Row-wise Mean Optimization
Write a NumPy program to create a large 2D NumPy array and write a function to calculate the row-wise mean using a for loop. Optimize it using vectorized operations.
Sample Solution:
Python Code:
Output:
Row-wise mean using for loop (first 10 rows): [0.49248002 0.53484555 0.50524083 0.53057524 0.48671578 0.44682543 0.52395333 0.56720315 0.5154937 0.54655315] Row-wise mean using vectorized operations (first 10 rows): [0.49248002 0.53484555 0.50524083 0.53057524 0.48671578 0.44682543 0.52395333 0.56720315 0.5154937 0.54655315]
Explanation:
- Create a large 2D array: A 2D NumPy array with shape (10000, 100) is created using np.random.rand().
- Function with for loop: A function row_wise_mean_using_loop calculates the row-wise mean of the array elements using a for loop.
- Calculate mean with for loop: The row-wise mean is calculated using the for loop, and the first 10 row means are printed.
- Optimize with vectorization: The row-wise mean calculation is optimized using NumPy's vectorized np.mean() function along the rows (axis=1), and the first 10 row means are printed.
For more Practice: Solve these Related Problems:
- Write a Numpy program to calculate the row-wise median of a large 2D array using loops, then optimize with np.median along the correct axis.
- Write a Numpy program to compute both the row-wise mean and variance using a for loop, then optimize using np.mean and np.var.
- Write a Numpy program to compute the row-wise mean of a large 2D array while ignoring NaN values using a loop, then optimize with np.nanmean.
- Write a Numpy program to calculate a weighted row-wise mean on a large 2D array using loops, then optimize it with broadcasting and np.average.
Python-Numpy Code Editor:
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