numpy_challenge_2.py

import numpy as np
arr = np. array([112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115,
               126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150,
               178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193,
               181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235,
               229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234,
               264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315,
               364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413,
               405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467,
               404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404,
               359, 310, 337, 360, 342, 406, 396, 420, 472, 548, 559, 463, 407,
               362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390,
               432], dtype=np.float)
arr

arr_miss = arr.copy()

# Introduce missing values
np.random.seed(100)
missing_loc = []
for i in range(5):
    loc = np.random.randint(0, len(arr_miss)); missing_loc.append(loc)
    arr_miss[loc] = np.nan
    
arr_miss