Distribuição

Python intermediário

Hugo Bowne-Anderson

Data Scientist at DataCamp

Distribuição

Caminhada aleatória

headtailsrw.py

import numpy as np
np.random.seed(123)
tails = [0]
for x in range(10) :
    coin = np.random.randint(0, 2)
    tails.append(tails[x] + coin)

100 execuções

distribution.py

import numpy as np
np.random.seed(123)
final_tails = []

for x in range(100) :
    tails = [0]
    for x in range(10) :
        coin = np.random.randint(0, 2)
        tails.append(tails[x] + coin)

    final_tails.append(tails[-1])

print(final_tails)

[3, 6, 4, 5, 4, 5, 3, 5, 4, 6, 6, 8, 6, 4, 7, 5, 7, 4, 3, 3, ..., 4]

Histograma, 100 execuções

distribution.py

import numpy as np

import matplotlib.pyplot as plt

np.random.seed(123)
final_tails = []
for x in range(100) :
    tails = [0]
    for x in range(10) :
        coin = np.random.randint(0, 2)
        tails.append(tails[x] + coin)     
    final_tails.append(tails[-1])

plt.hist(final_tails, bins = 10)
plt.show()

Histograma, 100 execuções

Histograma, 1.000 execuções

distribution.py

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(123)
final_tails = []
for x in range(1000) : # <--
    tails = [0]
    for x in range(10) :
        coin = np.random.randint(0, 2)
        tails.append(tails[x] + coin)     
    final_tails.append(tails[-1])

plt.hist(final_tails, bins = 10)
plt.show()

Histograma, 1.000 execuções

Histograma, 10.000 execuções

distribution.py

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(123)
final_tails = []
for x in range(10000) : # <--
    tails = [0]
    for x in range(10) :
        coin = np.random.randint(0, 2)
        tails.append(tails[x] + coin)     
    final_tails.append(tails[-1])

plt.hist(final_tails, bins = 10)
plt.show()

Histograma, 10.000 execuções

Vamos praticar!

Python intermediário