python knowledge analysis dice game

The content shared with you in this article is about python knowledge analysis and dice throwing game. It has certain reference value. Friends in need can refer to it. I recently learned some statistics and python knowledge, and tried to analyze the dice game. The dice has a standard 6 sides, and the results of throwing 1, 2, 3, and 4 dice at a time, and throwing 10, 100, 1,000, and 10,000 times are analyzed.

use tools
Jupyter Notebook analysis tool
matplotlib, pygal visualization package

1, number of guesses

1－6中每个数字出现的次数

# 导入包
import pygal
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号
from random import randint

# 利用随机数据模拟掷骰子
# 每次显示1－6中的一个数

num_sides = 6  # 骰子的6个面
def getData(N, times):
    """
    定义函数，获取投掷数据
    N: 表示一次用几个骰子投
    times：表示总共投几次
    """  
    results = []    
    for n in range(1,N+1):    
        for roll_num in range(times):
            result = randint(1,num_sides)
            results.append(result)    
        return results

# 打印投掷结果
print(getData(1,10)) # 1个骰子掷10次
print(getData(2,5))  # 2个骰子掷5次

[2, 2, 2, 2, 1, 6, 4, 4, 5, 5]
[4, 3, 5, 6, 2, 2, 3, 6, 4, 4]

# 分析结果# 统计每个数字出现的次数并显示图片
    # N: 表示一次用几个骰子投
    # data 表示投掷数据def showResult(N, times):
    frequencies = []    for value in range(1, num_sides+1):
        frequency = getData(N, times).count(value)
        frequencies.append(frequency)    # 数据可视化
    # 本次利用 pygal 生成SVG格式矢量图
    hist = pygal.Bar()
    hist.title = str(N)+"个骰子掷"+ str(times) +"次的结果"
    hist.x_labels = ['1','2','3','4','5','6']
    hist.x_title = "点数"
    hist.y_title = "出现次数"

    hist.add(str(N)+'骰子', frequencies)
    hist.render_to_file('1-'+str(N)+str(times)+'.svg') # 储存为矢量图

# 一个骰子掷10，100, 1000, 10000次结果分析showResult(1,10)
showResult(1,100)
showResult(1,1000)
showResult(1,10000)

#

# 2个骰子掷10，100, 1000, 10000次结果分析showResult(2,10)
showResult(2,100)
showResult(2,1000)
showResult(2,10000)

3 dice , 4 dice will no longer take screenshots.

We found that the more times we throw, the closer the probability of each number appears, and finally tend to be the same.

2. Guess the size

每次投掷点数和

# 每次投掷点数和def getData2(N, times):
    """
    定义函数，获取投掷数据
    N: 表示一次用几个骰子投
    times：表示总共投几次
    """  
    results = []    for roll_num in range(times):
        result = 0
        for n in range(1,N+1):
            result += randint(1,num_sides)
        results.append(result)    
    return results

# 打印投掷结果print(getData2(1,10)) # 1个骰子掷10次print(getData2(2,5))  # 2个骰子掷5次

[4, 3, 6, 2, 5, 4, 5, 3, 6, 2]
[6, 10, 5, 8, 7]

# 分析结果# 统计数字和出现的次数并显示图片
    # N: 表示一次用几个骰子投
    # data 表示投掷数据def showResult2(N, times):
    frequencies = []    for value in range(N, N*num_sides+1):
        frequency = getData2(N, times).count(value)
        frequencies.append(frequency)    # 数据可视化
    # 本次利用 matplotlib 生成图片
    x_num = N*num_sides+1-N
    idx = np.arange(x_num)
    width = 0.5
    sn = str(N)
    sm = str(times)
    x_labels = [str(n) for n in range(N, N*num_sides+1)]  # X轴刻度

    plt.bar(idx, frequencies, width, color='red', label=sn+'个骰子')
    plt.xlabel('点数和')
    plt.ylabel('出现次数')
    plt.title(sn+'个骰子投掷'+ sm +'次的结果')
    plt.xticks(idx, x_labels)
    plt.legend()  # 显示图例
    plt.show()

It doesn’t make much sense to guess the size of one dice. Let’s directly analyze the situation of two dice.

#  2个骰子掷10，100, 1000, 10000次结果分析showResult2(2,10)
showResult2(2,100)
showResult2(2,1000)
showResult2(2,10000)

#  3个骰子掷10，100, 1000, 10000次结果分析showResult2(3,10)
showResult2(3,100)
showResult2(3,1000)
showResult2(3,10000)

#  4个骰子掷10，100, 1000, 10000次结果分析showResult2(4,10)
showResult2(4,100)
showResult2(4,1000)
showResult2(4,10000)

We can see from the above pictures that when the number of throws is enough Often, the probability of large/small points appearing is basically the same, and the point size shows the characteristics of a normal distribution.

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