albertyumol

from collections import Counter
def mode(x):
    counts = Counter(x)
    max_count = max(counts.values())
    return [i for i, j in counts.items() if j == max_count]

albertyumol

def quantile(x, p):
  p_index = int(p*len(x))
  return sorted(x)[p_index]

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def range_(x):
  return max(x) - min(x)

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import numpy as np
def variance(x):
    x_bar = np.mean(x)
    N = len(x)
    deviation_mean = [x_i - x_bar for x_i in x]
    return  np.dot(deviation_mean, deviation_mean) / N

albertyumol

def quantile(x, p):
  p_index = int(p*len(x))
  return sorted(x)[p_index]

def interquartile_range(x):
  return quantile(x, 0.75) - quantile(x, 0.25)

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import numpy as np
def standard_deviation(x):
    x_bar = np.mean(x)
    N = len(x)
    deviation_mean = [x_i - x_bar for x_i in x]
    return  np.sqrt(np.dot(deviation_mean, deviation_mean) / N)

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#Provided that x and y have the same length.
import numpy as np
def covariance(x, y):
    N = len(x)
    x_bar = np.mean(x)
    y_bar = np.mean(y)
    deviation_mean_x = [x_i - x_bar for x_i in x]
    deviation_mean_y = [y_i - y_bar for y_i in y]
    return np.dot(deviation_mean_x, deviation_mean_y) / N

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#Provided that x and y have the same length.
import numpy as np
def standard_deviation(x):
    x_bar = np.mean(x)
    N = len(x)
    deviation_mean = [x_i - x_bar for x_i in x]
    return  np.sqrt(np.dot(deviation_mean, deviation_mean) / N)

def covariance(x, y):
    N = len(x)

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import random
def random_kid():
    return random.choice(['boy','girl'])

both_girls = 0
older_girl = 0
either_girl = 0

random.seed(7)
for i in range(1000000):

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import numpy as np
import matplotlib.pyplot as plt
def normal_pdf(x, mu=0, sigma=1):
    sqrt_two_pi = np.sqrt(2*np.pi)
    return (np.exp(-(x-mu)**2/2/sigma**2)/(sqrt_two_pi*sigma))

xs = [x / 10.0 for x in range(-50, 50)]
plt.plot(xs, [normal_pdf(x, sigma=1) for x in xs], '-', label='$\mu=0$, $\sigma=1$')
plt.plot(xs, [normal_pdf(x, sigma=2) for x in xs], '--', label='$\mu=0$, $\sigma=2$')
plt.plot(xs, [normal_pdf(x, sigma=0.5) for x in xs], ':', label='$\mu=0$, $\sigma=0.5$')