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| from scipy import stats | |
| import numpy as np | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| sns.set() # for aesthetic purposes | |
| def viz(mu, std, min, max, score=None): | |
| # draw a sample | |
| dist = np.random.normal(mu, std, size=10000) | |
| plt.hist(dist, bins=50, density=True, range=(min, max)) | |
| plt.xlabel('SAT score'); plt.ylabel('density') | |
| plt.axvline(score, color='red', label='my score', linewidth=3) | |
| plt.axvline(mu, color='black', label='mean', linewidth=3) | |
| plt.legend(loc='best') | |
| plt.show() | |
| # 1. calculate z-score | |
| def calc_zscore(x, mu, std): | |
| return (x - mu) / std | |
| # define mu, std, my_score, min and max | |
| my_score, mu, std = 1200, 1060, 195 | |
| min, max = 400, 1600 | |
| my_zscore = calc_zscore(my_score, mu, std) | |
| print(f'My score {my_score}={my_zscore}') | |
| # 2. visualize | |
| viz(mu, std, min, max, score=my_score) | |
| viz(0, 1, min=-3, max=3, score=my_zscore) |
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