input: Write a function to read data from a CSV file and compute the correlation coefficients between columns.
  assert (read_csv_and_compute_corrcoef('test1.csv') == [(0, 1, 1.0), (0, 2, -1.0), (1, 2, -1.0)])
  assert (read_csv_and_compute_corrcoef('test2.csv') == [(0, 1, 0.5), (0, 2, 0.0), (1, 2, -0.5)])
  assert (read_csv_and_compute_corrcoef('test3.csv') == [(0, 1, 0.8), (0, 2, 0.6), (1, 2, 0.2)])
output: def read_csv_and_compute_corrcoef(filename):
      import csv
      import numpy as np
      
      # Read CSV file into a list of lists
      with open(filename, 'r') as file:
          reader = csv.reader(file)
          data = [list(map(float, row)) for row in reader]
      
      # Convert to numpy array for easy manipulation
      data = np.array(data)
      
      # Compute correlation coefficients
      n_cols = data.shape[1]
      corrcoef_results = []
      for i in range(n_cols):
          for j in range(i + 1, n_cols):
              corr = np.corrcoef(data[:, i], data[:, j])[0, 1]
              corrcoef_results.append((i, j, round(corr, 1)))
      
      return corrcoef_results