前言

使用包包括，numpy, pandas, matplotlib

产生随机数

np.random.seed() 可以用来设定一个种子，使后续产生相同的随机数。
np.random.rand() 在默认下会产生一个0-1之间的浮点类型的数字。
np.random.randint(x, y) 在x到y-1 中产生一个随机整数。

小游戏

基于上述知识，我们可以写一个丢骰子移动的小游戏。

# 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)

还可以加点东西，用matplotlib 把图画出来。

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

# Plot random_walk
plt.plot(random_walk)

# Show the plot
plt.show()

一次丢骰子不好玩，我们还可以看看同时丢10次的结果。
这里有个小问题，由于一共十次结果，每次结果包含100步的得分数字信息，但进行绘图时，需要将这个10X100 的表格转变为100X10，利用np.transpose 将表格调换。

# 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()

还可以增加一个随机数的判断，比如有1%的概率骰子掉了。（hhh

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

最后可以再看一看结果的分布情况。

# 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()