import os
import pandas as pd
os.chdir(r'H:\2019-2-3新華書店筆記以及資料\資料\HW5')
card=pd.read_csv(r"card.csv",encoding="gbk") #讀取數據
disp=pd.read_csv(r"disp.csv",encoding="gbk")
clients=pd.read_csv(r"clients.csv",encoding="gbk")
card.head() #查看數據分佈
| card_id | disp_id | issued | type | |
|---|---|---|---|---|
| 0 | 1005 | 9285 | 1993-11-07 | 普通卡 |
| 1 | 104 | 588 | 1994-01-19 | 普通卡 |
| 2 | 747 | 4915 | 1994-02-05 | 普通卡 |
| 3 | 70 | 439 | 1994-02-08 | 普通卡 |
| 4 | 577 | 3687 | 1994-02-15 | 普通卡 |
disp.head()
| disp_id | client_id | account_id | type | |
|---|---|---|---|---|
| 0 | 1 | 1 | 1 | 所有者 |
| 1 | 2 | 2 | 2 | 所有者 |
| 2 | 3 | 3 | 2 | 用戶 |
| 3 | 4 | 4 | 3 | 所有者 |
| 4 | 5 | 5 | 3 | 用戶 |
clients.head()
| client_id | sex | birth_date | district_id | |
|---|---|---|---|---|
| 0 | 1 | 女 | 1970-12-13 | 18 |
| 1 | 2 | 男 | 1945-02-04 | 1 |
| 2 | 3 | 女 | 1940-10-09 | 1 |
| 3 | 4 | 男 | 1956-12-01 | 5 |
| 4 | 5 | 女 | 1960-07-03 | 5 |
import sqlite3 # sqlite3相當於輕量版，更多功能可使用SQLAlchemy
con = sqlite3.connect(':memory:') # 數據庫連接，使用sql連接查詢表
card.to_sql('card', con)
disp.to_sql('disp', con)
clients.to_sql('clients', con)
car_sql='''select a.*,c.sex,c.birth_date,c.district_id from card a left join disp b on a.disp_id=b.disp_id left join clients c on b.client_id=c.client_id where b.type="所有者"'''
card_t=pd.read_sql(car_sql, con)
card_t.head()
| index | card_id | disp_id | issued | type | sex | birth_date | district_id | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1005 | 9285 | 1993-11-07 | 普通卡 | 女 | 1968-01-28 | 74 |
| 1 | 1 | 104 | 588 | 1994-01-19 | 普通卡 | 女 | 1960-10-20 | 61 |
| 2 | 2 | 747 | 4915 | 1994-02-05 | 普通卡 | 男 | 1963-07-19 | 40 |
| 3 | 3 | 70 | 439 | 1994-02-08 | 普通卡 | 女 | 1968-09-12 | 51 |
| 4 | 4 | 577 | 3687 | 1994-02-15 | 普通卡 | 男 | 1972-02-06 | 49 |
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默認字體
mpl.rcParams['axes.unicode_minus'] = False # 解決保存圖像是負號'-'顯示爲方塊的問題
card_t['type'].value_counts().plot(kind='pie',autopct='%.1f%%') #查看數據分佈 餅圖
<matplotlib.axes._subplots.AxesSubplot at 0x167ec390>
#1、髮卡趨勢 #總體髮卡趨勢 from datetime import * card_t['issued_date']=pd.to_datetime(card_t['issued']) card_t['issued_year']=card_t['issued_date'].map(lambda x:x.year) card_t.card_id.groupby(card_t['issued_year']).count().plot(kind="bar") #年齡和髮卡的關係 <matplotlib.axes._subplots.AxesSubplot at 0x9354358>
#2、不同卡的分佈
card_t.type.value_counts().plot(kind="pie",autopct='%.1f%%') #卡種分佈
<matplotlib.axes._subplots.AxesSubplot at 0x93aa8d0>
pd.crosstab(card_t.issued_year,card_t.type).plot(kind = 'bar') #卡種和年齡的關係分佈 <matplotlib.axes._subplots.AxesSubplot at 0x940cb00>
t1=pd.crosstab(card_t.issued_year,card_t.type) #卡種和年齡佔比對比
t1["sum1"]=t1.sum(1)
t2=t1.div(t1.sum1,axis = 0)
t2.drop("sum1",1).plot(kind = 'bar',stacked= True)
<matplotlib.axes._subplots.AxesSubplot at 0xa8b5d30>
import matplotlib.pyplot as plt
labels=["青年卡","普通卡","金卡"]
y1=t1.loc[:,"青年卡"]#.astype('int')
y2=t1.loc[:,"普通卡"]#.astype('int')
y3=t1.loc[:,"金卡"]#.astype('int')
x=t1.index#.astype('int')
plt.stackplot(x,y1,y2,y3,labels = labels) # 圖表類型
plt.title('髮卡趨勢')
plt.ylabel('髮卡量')
plt.legend(loc = 'upper left')# 標籤位置
plt.show()
?plt.stackplot(x,y1,y2,y3,labels = labels) pd.crosstab(card_t.type,card_t.sex).plot(kind='bar')#性別和卡種數量對比 <matplotlib.axes._subplots.AxesSubplot at 0xa9960b8>
xb=pd.crosstab(card_t.type,card_t.sex) #性別和卡種百分比對比 xb.div(xb.sum(1),axis=0).plot(kind='bar') <matplotlib.axes._subplots.AxesSubplot at 0xaa24860>
from stack2dim import * stack2dim(card_t,'type','sex') #調用之前寫的庫，查看卡種和性別關係（柱子粗細代表數量，縱座標代表百分比）
card_t['age']=(pd.to_datetime(card_t['issued'])-pd.to_datetime(card_t['birth_date'])) card_t['age1']=card_t['age'].map(lambda x:x.days/365) import seaborn as sns sns.boxplot(x='type',y='age1',data=card_t) #查看何須圖，年齡和卡種分佈 <matplotlib.axes._subplots.AxesSubplot at 0xc104940>
trans=pd.read_csv(r"trans.csv",encoding="gbk")
trans.to_sql('trans', con)
card_t.to_sql('card_t', con)
D:\anaconda\lib\site-packages\IPython\core\interactiveshell.py:2785: DtypeWarning: Columns (8) have mixed types. Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
D:\anaconda\lib\site-packages\pandas\io\sql.py:450: UserWarning: the 'timedelta' type is not supported, and will be written as integer values (ns frequency) to the database.
chunksize=chunksize, dtype=dtype)
trans.head()
| trans_id | account_id | date | type | operation | amount | balance | k_symbol | bank | account | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 695247 | 2378 | 1993-01-01 | 貸 | 信貸資金 | $700 | $700 | NaN | NaN | NaN |
| 1 | 171812 | 576 | 1993-01-01 | 貸 | 信貸資金 | $900 | $900 | NaN | NaN | NaN |
| 2 | 207264 | 704 | 1993-01-01 | 貸 | 信貸資金 | $1,000 | $1,000 | NaN | NaN | NaN |
| 3 | 1117247 | 3818 | 1993-01-01 | 貸 | 信貸資金 | $600 | $600 | NaN | NaN | NaN |
| 4 | 579373 | 1972 | 1993-01-02 | 貸 | 信貸資金 | $400 | $400 | NaN | NaN | NaN |
card_t.head()
| index | card_id | disp_id | issued | type | sex | birth_date | district_id | issued_date | issued_year | age | age1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1005 | 9285 | 1993-11-07 | 普通卡 | 女 | 1968-01-28 | 74 | 1993-11-07 | 1993 | 9415 days | 25.794521 |
| 1 | 1 | 104 | 588 | 1994-01-19 | 普通卡 | 女 | 1960-10-20 | 61 | 1994-01-19 | 1994 | 12144 days | 33.271233 |
| 2 | 2 | 747 | 4915 | 1994-02-05 | 普通卡 | 男 | 1963-07-19 | 40 | 1994-02-05 | 1994 | 11159 days | 30.572603 |
| 3 | 3 | 70 | 439 | 1994-02-08 | 普通卡 | 女 | 1968-09-12 | 51 | 1994-02-08 | 1994 | 9280 days | 25.424658 |
| 4 | 4 | 577 | 3687 | 1994-02-15 | 普通卡 | 男 | 1972-02-06 | 49 | 1994-02-15 | 1994 | 8045 days | 22.041096 |
car_sql='''
select a.card_id,a.issued,a.type,c.type as t_type,c.amount,c.balance,c.date as t_date
from card as a
left join disp as b on a.disp_id=b.disp_id
left join trans as c on b.account_id=c.account_id
where b.type="所有者"
order by a.card_id,c.date
''' #使用sql連接表 card_t2=pd.read_sql(car_sql, con) card_t2.head()
| card_id | issued | type | t_type | amount | balance | t_date | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1998-10-16 | 金卡 | 貸 | $900 | $900 | 1996-11-24 |
| 1 | 1 | 1998-10-16 | 金卡 | 貸 | $33975 | $34875 | 1996-12-07 |
| 2 | 1 | 1998-10-16 | 金卡 | 借 | $6,600 | $28275 | 1996-12-24 |
| 3 | 1 | 1998-10-16 | 金卡 | 貸 | $30 | $28305 | 1996-12-31 |
| 4 | 1 | 1998-10-16 | 金卡 | 貸 | $22650 | $50955 | 1997-01-07 |
card_t2['issued']=pd.to_datetime(card_t2['issued']) card_t2['t_date']=pd.to_datetime(card_t2['t_date']) card_t2.head()
| card_id | issued | type | t_type | amount | balance | t_date | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1998-10-16 | 金卡 | 貸 | $900 | $900 | 1996-11-24 |
| 1 | 1 | 1998-10-16 | 金卡 | 貸 | $33975 | $34875 | 1996-12-07 |
| 2 | 1 | 1998-10-16 | 金卡 | 借 | $6,600 | $28275 | 1996-12-24 |
| 3 | 1 | 1998-10-16 | 金卡 | 貸 | $30 | $28305 | 1996-12-31 |
| 4 | 1 | 1998-10-16 | 金卡 | 貸 | $22650 | $50955 | 1997-01-07 |
card_t2['balance2'] = card_t2['balance'].map(lambda x: int(''.join(x[1:].split(','))))
card_t2['amount2'] = card_t2['amount'].map(lambda x: int(''.join(x[1:].split(','))))
import datetime #辦卡前一年的平均餘額
card_t3 = card_t2[card_t2.issued>card_t2.t_date][card_t2.issued<card_t2.t_date+datetime.timedelta(days=365)]
card_t3.head()
D:\anaconda\lib\site-packages\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
| card_id | issued | type | t_type | amount | balance | t_date | balance2 | amount2 | |
|---|---|---|---|---|---|---|---|---|---|
| 47 | 1 | 1998-10-16 | 金卡 | 借 | $13600 | $57950 | 1997-10-20 | 57950 | 13600 |
| 48 | 1 | 1998-10-16 | 金卡 | 借 | $15 | $58197 | 1997-10-31 | 58197 | 15 |
| 49 | 1 | 1998-10-16 | 金卡 | 貸 | $262 | $58212 | 1997-10-31 | 58212 | 262 |
| 50 | 1 | 1998-10-16 | 金卡 | 借 | $4,880 | $53317 | 1997-11-06 | 53317 | 4880 |
| 51 | 1 | 1998-10-16 | 金卡 | 貸 | $22650 | $75967 | 1997-11-07 | 75967 | 22650 |
#card_t3["avg_balance"] = card_t3.groupby('card_id')['balance2'].mean() #生成的series 下面agg生成的dataframe
card_t4=card_t3.groupby(['type','card_id'])['balance2'].agg([('avg_balance','mean')])
card_t4.head()
#card_t4.to_sql('card_t4', con)
| avg_balance | ||
|---|---|---|
| type | card_id | |
| 普通卡 | 2 | 38898.610169 |
| 4 | 54222.060976 | |
| 7 | 46888.702381 | |
| 9 | 55854.223881 | |
| 10 | 51387.500000 |
card_t3.groupby(['type','card_id'])['balance2'].agg([('avg_balance','mean'),'count']).head()
| avg_balance | count | ||
|---|---|---|---|
| type | card_id | ||
| 普通卡 | 2 | 38898.610169 | 59 |
| 4 | 54222.060976 | 82 | |
| 7 | 46888.702381 | 84 | |
| 9 | 55854.223881 | 67 | |
| 10 | 51387.500000 | 66 |
card_t5=card_t4.reset_index() #爲透視表添加行的名，type類型不變
#card_t5=pd.read_sql('select * from card_t4', con)
sns.boxplot(x = 'type', y = 'avg_balance', data = card_t5)
<matplotlib.axes._subplots.AxesSubplot at 0x21afb358>
#%%6、不同類型卡的持卡人在辦卡前一年內的平均收入對比
type_dict = {'借':'out','貸':'income'}
card_t3['type1'] = card_t3.t_type.map(type_dict)
card_t6= card_t3.groupby(['type','card_id','type1'])[['amount2']].sum()
card_t6.head()
card_t6.to_sql('card_t6', con)
card_t7=card_t6.reset_index()
#card_t7=pd.read_sql('select * from card_t6', con)
card_t7.to_sql('card_t7', con)
card_t8=pd.read_sql('select * from card_t7 where type1="income"', con)
card_t6.head()
| amount2 | |||
|---|---|---|---|
| type | card_id | type1 | |
| 普通卡 | 2 | income | 193911 |
| out | 196384 | ||
| 4 | income | 474142 | |
| out | 357224 | ||
| 7 | income | 299797 |
sns.boxplot(x = 'type', y = 'amount2', data = card_t8) #收入和卡種的關係 <matplotlib.axes._subplots.AxesSubplot at 0x15fd6588>
card_t8.head()
| index | type | card_id | type1 | amount2 | |
|---|---|---|---|---|---|
| 0 | 0 | 普通卡 | 2 | income | 193911 |
| 1 | 2 | 普通卡 | 4 | income | 474142 |
| 2 | 4 | 普通卡 | 7 | income | 299797 |
| 3 | 6 | 普通卡 | 9 | income | 322548 |
| 4 | 8 | 普通卡 | 10 | income | 644560 |
card_t9=pd.read_sql('select * from card_t7 where type1="out"', con) #支出和卡種的關係
sns.boxplot(x = 'type', y = 'amount2', data = card_t9)
<matplotlib.axes._subplots.AxesSubplot at 0xa993a58>
