前言

使用包包括股囊，numpy, pandas, matplotlib

產(chǎn)生隨機數(shù)

np.random.seed() 可以用來設(shè)定一個種子袜匿，使后續(xù)產(chǎn)生相同的隨機數(shù)。
np.random.rand() 在默認下會產(chǎn)生一個0-1之間的浮點類型的數(shù)字稚疹。
np.random.randint(x, y) 在x到y(tǒng)-1 中產(chǎn)生一個隨機整數(shù)。

小游戲

基于上述知識祭务，我們可以寫一個丟骰子移動的小游戲内狗。

# Numpy is imported, seed is set

# Initialize random_walk
random_walk = [0]

for x in range(100) :
    step = random_walk[-1]
    dice = np.random.randint(1,7)

    if dice <= 2:
        # Replace below: use max to make sure step can't go below 0
        step = max(0, step -1)
    elif dice <= 5:
        step = step + 1
    else:
        step = step + np.random.randint(1,7)

    random_walk.append(step)

print(random_walk)

還可以加點東西，用matplotlib 把圖畫出來义锥。

# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Plot random_walk
plt.plot(random_walk)

# Show the plot
plt.show()

一次丟骰子不好玩柳沙，我們還可以看看同時丟10次的結(jié)果。
這里有個小問題拌倍，由于一共十次結(jié)果赂鲤，每次結(jié)果包含100步的得分數(shù)字信息，但進行繪圖時柱恤，需要將這個10X100 的表格轉(zhuǎn)變?yōu)?code>100X10数初，利用np.transpose 將表格調(diào)換。

# numpy and matplotlib imported, seed set.

# initialize and populate all_walks
all_walks = []
for i in range(10) :
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)
        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)
        random_walk.append(step)
    all_walks.append(random_walk)

# Convert all_walks to Numpy array: np_aw
np_aw = np.array(all_walks)

# Transpose np_aw: np_aw_t
np_aw_t = np.transpose(np_aw)

# Plot np_aw_t and show
plt.plot(np_aw_t)
plt.show()

還可以增加一個隨機數(shù)的判斷梗顺，比如有1%的概率骰子掉了泡孩。（hhh

        if np.random.rand() <= 0.001 :
            step = 0

最后可以再看一看結(jié)果的分布情況。

# numpy and matplotlib imported, seed set

# Simulate random walk 500 times
all_walks = []
for i in range(500) :
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)
        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)
        if np.random.rand() <= 0.001 :
            step = 0
        random_walk.append(step)
    all_walks.append(random_walk)

# Create and plot np_aw_t
np_aw_t = np.transpose(np.array(all_walks))

# Select last row from np_aw_t: ends
ends = np_aw_t[-1, :]

# Plot histogram of ends, display plot
plt.hist(ends)
plt.show()