生肖属相单变量分析

import pandas as pd
import numpy as np

f = open(r'ft_zodiac.txt', encoding='utf-8')
ft_zodiac = pd.read_csv(f)  
print(ft_zodiac.shape)
ft_zodiac.head()

(23519, 4)

	0	order_id	chinese_zodiac	zodiac
0	0	100000081567592448	狗	处女座
1	0	100000467565182976	牛	双子座
2	0	100000530945323008	羊	射手座
3	0	100000556765458432	鼠	摩羯座
4	0	100000598171623424	虎	水瓶座

pd15作为好坏的分割节点。>15 为坏人，<15为好人？

15天以上的人为坏，5天以内的人为好。

l = open(r'zodiac_label.txt')
zodiac_label=pd.read_csv(l)
zodiac_label.head()

	order_id	overdue_days	repay_time	label
0	100000081567592448	0	2018-07-09	0
1	100000467565182976	1	2018-07-09	0
2	100000530945323008	0	2018-07-09	0
3	100000556765458432	0	2018-07-09	0
4	100000598171623424	0	2018-07-09	0

set(zodiac_label.label)

{0, 1, 2}

# 剔除不等于2的
ft_label = zodiac_label[zodiac_label['label'] != 2]
ft_label.head()

	order_id	overdue_days	repay_time	label
0	100000081567592448	0	2018-07-09	0
1	100000467565182976	1	2018-07-09	0
2	100000530945323008	0	2018-07-09	0
3	100000556765458432	0	2018-07-09	0
4	100000598171623424	0	2018-07-09	0

set(ft_label.label)

{0, 1}

data = pd.merge(ft_label,ft_zodiac,on = 'order_id',how = 'inner')
data.head()

	order_id	overdue_days	repay_time	label	0	chinese_zodiac	zodiac
0	100000081567592448	0	2018-07-09	0	0	狗	处女座
1	100000467565182976	1	2018-07-09	0	0	牛	双子座
2	100000530945323008	0	2018-07-09	0	0	羊	射手座
3	100000556765458432	0	2018-07-09	0	0	鼠	摩羯座
4	100000598171623424	0	2018-07-09	0	0	虎	水瓶座

badrate = bad/toal

zodiac_list = set(data.zodiac)
zodiac_list

{'双子座',
 '双鱼座',
 '处女座',
 '天秤座',
 '天蝎座',
 '射手座',
 '巨蟹座',
 '摩羯座',
 '水瓶座',
 '狮子座',
 '白羊座',
 '金牛座'}

chinese_zodiac_list = set(data.chinese_zodiac)
chinese_zodiac_list

{'兔', '牛', '狗', '猪', '猴', '羊', '虎', '蛇', '马', '鸡', '鼠', '龙'}

# 星座
zodiac_badrate = {}
for x in zodiac_list:
    
    a = data[data.zodiac == x]
    
    bad = a[a.label == 1]['label'].count()  # 坏的计数
    good = a[a.label == 0]['label'].count() # 好的计数
    
    zodiac_badrate[x] = bad/(bad+good)

zodiac_badrate

{'双子座': 0.1312410841654779,
 '巨蟹座': 0.1408351026185421,
 '狮子座': 0.12760416666666666,
 '射手座': 0.14480286738351256,
 '水瓶座': 0.140117994100295,
 '白羊座': 0.13455414012738853,
 '双鱼座': 0.14873646209386282,
 '处女座': 0.13035143769968052,
 '天秤座': 0.12461252324860508,
 '天蝎座': 0.12005028284098052,
 '摩羯座': 0.12920489296636087,
 '金牛座': 0.12259059367771781}

f = zip(zodiac_badrate.keys(), zodiac_badrate.values())
f = sorted(f, key = lambda x : x[1], reverse = True )
zodiac_badrate = pd.DataFrame(f)
zodiac_badrate.columns = pd.Series(['星座', 'badrate'])
zodiac_badrate

	星座	badrate
0	双鱼座	0.148736
1	射手座	0.144803
2	巨蟹座	0.140835
3	水瓶座	0.140118
4	白羊座	0.134554
5	双子座	0.131241
6	处女座	0.130351
7	摩羯座	0.129205
8	狮子座	0.127604
9	天秤座	0.124613
10	金牛座	0.122591
11	天蝎座	0.120050

from pyecharts import Line
x = zodiac_badrate['星座']
y = zodiac_badrate['badrate']
line = Line('星座')
line.add(1, x, y)

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# 生肖
chinese_zodiac_badrate = {}
for x in chinese_zodiac_list:
    
    a = data[data.chinese_zodiac == x]
    
    bad = a[a.label == 1]['label'].count()  # 好的计数
    good = a[a.label == 0]['label'].count()  # 坏的计数
    
    chinese_zodiac_badrate[x] = bad/(bad+good)

chinese_zodiac_badrate

{'猪': 0.14269406392694065,
 '牛': 0.1578112609040444,
 '虎': 0.15165876777251186,
 '龙': 0.1439084219133279,
 '鼠': 0.1340602950609365,
 '兔': 0.1502843216896832,
 '鸡': 0.12846998063266624,
 '蛇': 0.12789827973074047,
 '羊': 0.11335403726708075,
 '猴': 0.12008141112618724,
 '马': 0.12053872053872054,
 '狗': 0.11052009456264776}

f = zip(chinese_zodiac_badrate.keys(),chinese_zodiac_badrate.values())
f = sorted(f,key = lambda x : x[1],reverse = True )
chinese_zodiac_badrate = pd.DataFrame(f)
chinese_zodiac_badrate.columns = pd.Series(['生肖','badrate'])
chinese_zodiac_badrate

	生肖	badrate
0	牛	0.157811
1	虎	0.151659
2	兔	0.150284
3	龙	0.143908
4	猪	0.142694
5	鼠	0.134060
6	鸡	0.128470
7	蛇	0.127898
8	马	0.120539
9	猴	0.120081
10	羊	0.113354
11	狗	0.110520

from pyecharts import Line
x = chinese_zodiac_badrate['生肖']
y = chinese_zodiac_badrate['badrate']
line = Line('生肖')
line.add(1,x,y)

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