<jupyter_start><jupyter_text>1. Write a program to find BMI of the patients whose height and weight is provided in the list given below Use the lists given below:list of weights (in kg) weights = [57, 69, 54, 65 ,71, 62, 56]list of heights (in m)heights = [1.59, 1.75, 1.66, 1.74, 1.64, 1.72, 1.53]<jupyter_code>import numpy as np
weights = [57, 69, 54, 65 ,71, 62, 56]
heights = [1.59, 1.75, 1.66, 1.74, 1.64, 1.72, 1.53]

# convert the list to array
weights = np.asarray(weights)
heights = np.asarray(heights)
# formula to calculate BMI
bmi= weights/(heights**2)
bmi

a = np.array([0, 1, 2])

b = np.array([5, 5, 5])

a + b

a + 5
x = np.array([[3], [5], [7]])

y = np.array([[5], [7], [9]])
np.hstack((x,y))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1810810810810811, 'fraction_numerical': 0.11486486486486487, 'mean_word_length': 4.006756756756757, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '312678', 'license': 'MIT', 'n_tokens_mistral': 370, 'n_tokens_neox': 313, 'n_words': 105, 'path': 'Arvind/Operations on a Array.ipynb', 'repo_name': 'Arvind-collab/Data-Science', 'size': '740'}