20230102B.py

# Normal distribution
data1 = norm.rvs(loc=MEAN, scale=ST_DEV, size=NUM_VALUES, random_state=RANDOM_SEED)
data_binned1 = np.histogram(data1, bins=NUM_BINS)
x_values1 = data_binned1[1][:-1]  # Boundary values - omit the last so that we have just the left boundaries.
y_values1 = data_binned1[0]       # Count of items in this bin.

data1_min, data1_q1, data1_median, data1_q3, data1_max, data1_mean = np.min(data1), np.quantile(data1, 0.25), np.quantile(data1, 0.5), np.quantile(data1, 0.75), np.max(data1), np.mean(data1)

# Positive skew (alpha = 5)
data2 = skewnorm.rvs(5, loc=MEAN, scale=ST_DEV, size=NUM_VALUES, random_state=RANDOM_SEED)
data_binned2 = np.histogram(data2, bins=NUM_BINS)
x_values2 = data_binned2[1][:-1]
y_values2 = data_binned2[0]

data2_min, data2_q1, data2_median, data2_q3, data2_max, data2_mean = np.min(data2), np.quantile(data2, 0.25), np.quantile(data2, 0.5), np.quantile(data2, 0.75), np.max(data2), np.mean(data2)

# Negative skew (alpha = -5)
data3 = skewnorm.rvs(-5, loc=MEAN, scale=ST_DEV, size=NUM_VALUES, random_state=RANDOM_SEED)
data_binned3 = np.histogram(data3, bins=NUM_BINS)
x_values3 = data_binned3[1][:-1]
y_values3 = data_binned3[0]

data3_min, data3_q1, data3_median, data3_q3, data3_max, data3_mean = np.min(data3), np.quantile(data3, 0.25), np.quantile(data3, 0.5), np.quantile(data3, 0.75), np.max(data3), np.mean(data3)