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-- coding: utf-8 --
Created on Fri Oct 12 12:31:21 2018
項目 13 社會財富分配問題 （蒙特卡羅模擬）
Note:
1 建立一個空的DataFrame時，只需要index 參數(shù)
2 當在一個列表中隨機選取一個值 可以用random的choice，當需要隨機選取多個值用numpy
的random的choice()
3 Series 的name 參數(shù)設置在Series 中 name=‘’
4 當在DataFrame需要判斷再賦值時，可以先用判斷篩選列 重新賦值 或者 用apply函數(shù)
5 apply在DataFrame中用于判斷賦值（☆）
6 在繪制圖表時 如果不想顯示每一個xticks 不要xlim的?
7 在迭代過程中不要將值賦值給變量名和傳入的變量一致，會導致報錯
8 在篩選時或者標記 當對某些值進行篩選或者按某些條件篩選 即按照此標記
"""
導入模塊
import os
import random
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
定義函數(shù)
def round1():
'''
建立初始模型，第1次交易
'''
# 構建初始財富值100，index的值為每個人的編號
people = pd.DataFrame(index=list(range(1,101)))
people['money'] = 100
people['r1'] = people['money'] - 1
# 構建收錢的隨機對象
people['to'] = np.random.choice(list(range(1,101)), size=100,
replace=True, p=None)
data_to = people['to'].value_counts()
data_to.name = 'count'
data_to = pd.DataFrame(data_to)
people = pd.merge(people, data_to, how='left', left_index=True,
right_index=True).fillna(0)
people['r1_m'] = people['r1'] + people['count']
return people
def roundi(n):
'''
構建借貸模型
不考慮財富值為0，即允許借貸
'''
# 構建初始財富值
people = pd.DataFrame(index=list(range(1,101)))
people['r0'] = 100
# 構建模型
for i in range(1, n+1):
col = 'r' + str(i)
col0 = 'r' + str(i-1)
people[col] = people[col0] - 1
data_to = pd.Series(np.random.choice(list(range(1,101)), size=100,
replace=True, p=None), name='to')
data_to = data_to.value_counts()
data_to = pd.DataFrame(data_to)
people = pd.merge(people, data_to, how='left', left_index=True,
right_index=True).fillna(0)
people[col] = people[col] + people['to']
del people['to']
return people.T
def roundn(n):
'''
構建初始模型
考慮財富值為0，即財富值為0時可以接收 ==> choice的范圍是1-100
但不給出 ==> 所以要統(tǒng)計給出的數(shù)量 == 接收的數(shù)量
'''
# 構建初始財富值
people = pd.DataFrame(index=list(range(1,101)))
people['r0'] = 100
# 構建模型
for i in range(1, n+1):
col = 'r' + str(i)
col0 = 'r' + str(i-1)
people[col] = people[col0] - 1
data_to = pd.Series(np.random.choice(list(range(1,101)), size=100,
replace=True, p=[]), name='to')
data_to = data_to.value_counts()
data_to = pd.DataFrame(data_to)
people = pd.merge(people, data_to, how='left', left_index=True,
right_index=True).fillna(0)
people[col] = people[col] + people['to']
del people['to']
return people.T
def roundm(n):
'''
構建努力人生模型
'''
# 構建初始財富值
people = pd.DataFrame(index=list(range(1,101)))
people['r0'] = 100
person_id= [1, 11, 21, 31, 41, 51, 61, 71, 81, 91] # 努力的Id
# 構建概率
p = [0.899/90 for i in range(100)]
for i in person_id:
p[i-1] = 0.0101
# 構建模型
for i in range(1, n+1):
col = 'r' + str(i)
col0 = 'r' + str(i-1)
people[col] = people[col0] - 1
data_to = pd.Series(np.random.choice(list(range(1,101)), size=100,
replace=True, p=p), name='to')
data_to = data_to.value_counts()
data_to = pd.DataFrame(data_to)
people = pd.merge(people, data_to, how='left', left_index=True,
right_index=True).fillna(0)
people[col] = people[col] + people['to']
del people['to']
return people.T
def graph(data, title):
'''
繪制柱狀圖 == 表排序
'''
plt.figure(figsize=(10, 5))
data.plot(kind='bar', color='gray', edgecolor='gray', figsize=(10, 5))
plt.xlabel('Player Id')
plt.ylabel('Forturn')
plt.title(title)
plt.savefig(title + '.jpg', dpi=200)
def lst():
'''
生產(chǎn)繪制圖表數(shù)據(jù)的行數(shù)
'''
lst1 = [x for x in range(0, 100, 10)]
lst2 = [x for x in range(100, 1000, 100)]
lst3 = [x for x in range(1000, 17001, 400)]
return lst1 + lst2 + lst3
def forturn_std(data):
'''
計算每一輪的財富標準差
'''
lst = []
for i in range(17001):
dat = data.iloc[i]
std = dat.std()
lst.append(std)
s = pd.Series(lst)
return s
def line_graph(data, title):
'''
繪制折線圖
'''
fig = plt.figure(figsize=(10,5))
plt.plot(data, color='red')
plt.grid(linestyle='--', color='gray', alpha=0.6, axis='both')
plt.xlim([0, 17000])
plt.ylim([0, 150])
plt.title(title)
plt.savefig(title + '.jpg', dpi=400)
def sign_pc(data):
'''
負債id標記
'''
data = pd.DataFrame(data)
data['color'] = 'gray'
data['color'][data['r6200'] < 0] = 'red'
del data['r6200']
return data
if name == 'main':
# 運行初始模型 得到模型數(shù)據(jù)
r17 = roundn(17000)
# 繪制圖表 -- 不排序
path = r'C:\Users\pj2063150\Desktop\項目\項目13社會財富分配問題模擬\圖表\初始不排序'
os.chdir(path)
lst = lst()
for i in lst:
title = 'Round' + str(i)
#data = r17.iloc[i]
#graph(data, title)
# 繪制圖表 -- 排序
path = r'C:\Users\pj2063150\Desktop\項目\項目13社會財富分配問題模擬\圖表\初始排序'
os.chdir(path)
for i in lst:
title = 'Round' + str(i)
data = r17.iloc[i]
data = data.sort_values(ascending=True)
graph(data, title)
# 運行借貸模型，得到數(shù)據(jù)
ri17 = roundi(17000)
# 繪制圖表
path = r'C:\Users\pj2063150\Desktop\項目\項目13社會財富分配問題模擬\圖表\允許借貸'
os.chdir(path)
for i in lst:
title = 'Round' + str(i)
data = ri17.iloc[i]
data = data.sort_values(ascending=True)
graph(data, title)
# 調(diào)用計算函數(shù)，獲取標準差
data_std = forturn_std(ri17)
# 繪制圖表
path = r'C:\Users\pj2063150\Desktop\項目\項目13社會財富分配問題模擬\圖表'
os.chdir(path)
line_graph(data_std, '財富標準差曲線')
# 35歲破產(chǎn)往后逆襲情況 6200次
data_6200 = ri17.iloc[6200]
id_pc =data_6200[data_6200 < 0].index.tolist()
# 對負債id進行標記
id_sign = sign_pc(data_6200)
# 繪制圖表
path = r'C:\Users\pj2063150\Desktop\項目\項目13社會財富分配問題模擬\圖表\負債逆襲'
os.chdir(path)
for i in range(6200, 17000, 500):
title = 'Round' + str(i)
col = 'r' + str(i)
data = ri17.iloc[i]
data = pd.DataFrame(data)
data1 = pd.merge(data, id_sign, how='right', left_index=True, right_index=True)
data1 = data1.sort_values(by=col, ascending=True)
fig = plt.figure(figsize=(10,5))
data1[col].plot(kind='bar', color=data1['color'], figsize=(10,5))
plt.xlabel('Player Id')
plt.ylabel('Forturn')
plt.title(title)
plt.savefig(title + '.jpg', dpi=200)
# 運行努力人生模型，得到數(shù)據(jù)
rm17 = roundm(17000)
# 對努力id進行標記
id_nl = [1, 11, 21, 31, 41, 51, 61, 71, 81, 91]
id_nl_sign = pd.Series('gray', index=range(1, 101), name='color')
for i in id_nl:
id_nl_sign[i] = 'red'
id_nl_sign = pd.DataFrame(id_nl_sign)
# 繪制圖表
path = r'C:\Users\pj2063150\Desktop\項目\項目13社會財富分配問題模擬\圖表\努力人生'
os.chdir(path)
for i in lst:
title = 'Round' + str(i)
col = 'r' + str(i)
data = rm17.iloc[i]
data = pd.DataFrame(data)
data = pd.merge(data, id_nl_sign, how='right', left_index=True, right_index=True)
data = data.sort_values(by=col, ascending=True)
fig = plt.figure(figsize=(10,5))
data[col].plot(kind='bar', color=data['color'], figsize=(10,5))
plt.xlabel('Player Id')
plt.ylabel('Forturn')
plt.title(title)
plt.savefig(title + '.jpg', dpi=200)
print('Finished')
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