2024-heraklion-data/notebooks/.ipynb_checkpoints/which_data_structure_intro-checkpoint.ipynb

In [1]:

import numpy as np

In [5]:

sound_data = np.random.rand(100)

In [6]:

sound_data

Out[6]:

array([0.66709183, 0.55973494, 0.95416669, 0.60810949, 0.05188879,
       0.58619063, 0.25555136, 0.72451477, 0.2646681 , 0.08694215,
       0.75592186, 0.67261696, 0.62847452, 0.06232598, 0.20549438,
       0.11718457, 0.25184725, 0.48625729, 0.8103058 , 0.18100915,
       0.81113341, 0.62055231, 0.9046905 , 0.56664205, 0.73235338,
       0.74382869, 0.64856368, 0.80644398, 0.46199345, 0.78516632,
       0.91298397, 0.48290914, 0.20847714, 0.99162659, 0.26374781,
       0.3602381 , 0.07173351, 0.8584085 , 0.32248766, 0.39167573,
       0.67944923, 0.00930429, 0.21714217, 0.58810089, 0.17668711,
       0.57444803, 0.25760187, 0.43785728, 0.39119371, 0.68268063,
       0.95954499, 0.45934239, 0.03616905, 0.23896063, 0.61872801,
       0.76332531, 0.96272817, 0.57169277, 0.50225193, 0.01361629,
       0.15357459, 0.8057233 , 0.0642748 , 0.95013941, 0.38712684,
       0.97231498, 0.20261775, 0.74184693, 0.26629893, 0.84672705,
       0.67662718, 0.96055977, 0.64942314, 0.66487937, 0.86867536,
       0.40815661, 0.1139344 , 0.95638066, 0.87436447, 0.18407227,
       0.64457074, 0.19233097, 0.24012179, 0.90399279, 0.39093908,
       0.26389161, 0.97537645, 0.14209784, 0.75261696, 0.10078122,
       0.87468408, 0.77990102, 0.92983283, 0.45841805, 0.61470669,
       0.87939755, 0.09266009, 0.41177209, 0.46973971, 0.43152144])

In [ ]:

synonyms = {
    'hot': ['blazing', 'boiling', 'heated'],
    'airplane': ['aircraft', 'airliner', 
                 'cab', 'jet', 'plane'],
    'beach': ['coast', 'shore', 'waterfront'],
    # ...
}