【算法竞赛学习】资金流入流出预测-挑战Baseline_数据探索与分析1
赛题简介
蚂蚁金服拥有上亿会员并且业务场景中每天都涉及大量的资金流入和流出，面对如此庞大的用户群，资金管理压力会非常大。在既保证资金流动性风险最小，又满足日常业务运转的情况下，精准地预测资金的流入流出情况变得尤为重要。此届大赛以《资金流入流出预测》为题，期望参赛者能够通过对例如余额宝用户的申购赎回数据的把握，精准预测未来每日的资金流入流出情况。对货币基金而言，资金流入意味着申购行为，资金流出为赎回行为 。
赛题与数据
竞赛中使用的数据主要包含四个部分，分别为用户基本信息数据、用户申购赎回数据、收益率表和银行间拆借利率表。https://tianchi.aliyun.com/competition/entrance/231573/information
官方Baseline
数据探索与分析
import pandas as pd
import numpy as np
import warnings
import datetime
import seaborn as sns
import matplotlib.pyplot as plt
import datetime
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
# 设置数据集路径
dataset_path = 'Data/'
# 读取数据
data_balance = pd.read_csv(dataset_path+'user_balance_table.csv')
# 为数据集添加时间戳
data_balance['date'] = pd.to_datetime(data_balance['report_date'], format= "%Y%m%d")
data_balance['day'] = data_balance['date'].dt.day
data_balance['month'] = data_balance['date'].dt.month
data_balance['year'] = data_balance['date'].dt.year
data_balance['week'] = data_balance['date'].dt.week
data_balance['weekday'] = data_balance['date'].dt.weekday
时间序列分析
# 聚合时间数据，行为'total_purchase_amt'和'total_redeem_amt'，列为不同的'date'，数据为每日的purchase和redeem总和
# 数据清洗时，会将带空值的行删除，此时DataFrame或Series类型的数据不再是连续的索引，可以使用reset_index()重置索引
total_balance = data_balance.groupby(['date'])['total_purchase_amt','total_redeem_amt'].sum().reset_index()
# 生成测试集区段数据
# 总共随机抽取了约 3 万用户，其中部分用户在 2014 年 9 月份第一次出现，这部分用户只在测试数据中
# datetime.timedelta对象代表两个时间之间的时间差
start = datetime.datetime(2014,9,1)
testdata = []
while start != datetime.datetime(2014,10,1):
temp = [start, np.nan, np.nan]
testdata.append(temp)
start += datetime.timedelta(days = 1)
testdata = pd.DataFrame(testdata)
testdata.columns = total_balance.columns
# 拼接数据集
total_balance = pd.concat([total_balance, testdata], axis = 0)
# 为数据集添加时间戳
total_balance['day'] = total_balance['date'].dt.day
total_balance['month'] = total_balance['date'].dt.month
total_balance['year'] = total_balance['date'].dt.year
total_balance['week'] = total_balance['date'].dt.week
total_balance['weekday'] = total_balance['date'].dt.weekday
# 画出每日总购买与赎回量的时间序列图
fig = plt.figure(figsize=(20,6))
plt.plot(total_balance['date'], total_balance['total_purchase_amt'],label='purchase')
plt.plot(total_balance['date'], total_balance['total_redeem_amt'],label='redeem')
plt.legend(loc='best')
plt.title("The lineplot of total amount of Purchase and Redeem from July.13 to Sep.14")
plt.xlabel("Time")
plt.ylabel("Amount")
plt.show()
# 画出4月份以后的时间序列图
total_balance_1 = total_balance[total_balance['date'].dt.date >= datetime.date(2014,4,1)]
fig = plt.figure(figsize=(20,6))
plt.plot(total_balance_1['date'], total_balance_1['total_purchase_amt'])
plt.plot(total_balance_1['date'], total_balance_1['total_redeem_amt'])
plt.legend()
plt.title("The lineplot of total amount of Purchase and Redeem from April.14 to Sep.14")
plt.xlabel("Time")
plt.ylabel("Amount")
plt.show()
# 分别画出每个月中每天购买赎回量的时间序列图
fig = plt.figure(figsize=(15,15))
plt.subplot(4,1,1)
plt.title("The time series of total amount of Purchase and Redeem for August, July, June, May respectively")
total_balance_2 = total_balance[total_balance['date'].dt.date >= datetime.date(2014,8,1)]
plt.plot(total_balance_2['date'], total_balance_2['total_purchase_amt'])
plt.plot(total_balance_2['date'], total_balance_2['total_redeem_amt'])
plt.legend()
total_balance_3 = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,7,1)) & (total_balance['date'].dt.date < datetime.date(2014,8,1))]
plt.subplot(4,1,2)
plt.plot(total_balance_3['date'], total_balance_3['total_purchase_amt'])
plt.plot(total_balance_3['date'], total_balance_3['total_redeem_amt'])
plt.legend()
total_balance_4 = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,6,1)) & (total_balance['date'].dt.date < datetime.date(2014,7,1))]
plt.subplot(4,1,3)
plt.plot(total_balance_4['date'], total_balance_4['total_purchase_amt'])
plt.plot(total_balance_4['date'], total_balance_4['total_redeem_amt'])
plt.legend()
total_balance_5 = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,5,1)) & (total_balance['date'].dt.date < datetime.date(2014,6,1))]
plt.subplot(4,1,4)
plt.plot(total_balance_5['date'], total_balance_5['total_purchase_amt'])
plt.plot(total_balance_5['date'], total_balance_5['total_redeem_amt'])
plt.legend()
plt.xlabel("Time")
plt.ylabel("Amount")
plt.show()
# 分别画出13年8月与9月每日购买赎回量的时序图
fig = plt.figure(figsize=(15,9))
total_balance_last8 = total_balance[(total_balance['date'].dt.date >= datetime.date(2013,8,1)) & (total_balance['date'].dt.date < datetime.date(2013,9,1))]
plt.subplot(2,1,1)
plt.plot(total_balance_last8['date'], total_balance_last8['total_purchase_amt'],label='purchase')
plt.plot(total_balance_last8['date'], total_balance_last8['total_redeem_amt'],label='redeem')
plt.legend()
total_balance_last9 = total_balance[(total_balance['date'].dt.date >= datetime.date(2013,9,1)) & (total_balance['date'].dt.date < datetime.date(2013,10,1))]
plt.subplot(2,1,2)
plt.plot(total_balance_last9['date'], total_balance_last9['total_purchase_amt'],label='purchase')
plt.plot(total_balance_last9['date'], total_balance_last9['total_redeem_amt'],label='redeem')
plt.legend()
plt.xlabel("Time")
plt.ylabel("Amount")
plt.show()
翌日特征分析
# 画出每个周几的数据分布于整体数据的分布图
a = plt.figure(figsize=(10,10))
scatter_para = {'marker':'.', 's':3, 'alpha':0.3}
line_kws = {'color':'k'}
plt.subplot(2,2,1)
plt.title('The distrubution of total purchase')
sns.violinplot(x='weekday', y='total_purchase_amt', data = total_balance_1, scatter_kws=scatter_para, line_kws=line_kws)
plt.subplot(2,2,2)
plt.title('The distrubution of total purchase')
sns.distplot(total_balance_1['total_purchase_amt'].dropna())
plt.subplot(2,2,3)
plt.title('The distrubution of total redeem')
sns.violinplot(x='weekday', y='total_redeem_amt', data = total_balance_1, scatter_kws=scatter_para, line_kws=line_kws)
plt.subplot(2,2,4)
plt.title('The distrubution of total redeem')
sns.distplot(total_balance_1['total_redeem_amt'].dropna())
# 按周几对数据聚合后取均值
week_sta = total_balance_1[['total_purchase_amt', 'total_redeem_amt', 'weekday']].groupby('weekday', as_index=False).mean()
# 分析周几的中位数特征
plt.figure(figsize=(12, 5))
ax = plt.subplot(1,2,1)
plt.title('The barplot of average total purchase with each weekday')
ax = sns.barplot(x="weekday", y="total_purchase_amt", data=week_sta, label='Purchase')
ax.legend()
ax = plt.subplot(1,2,2)
plt.title('The barplot of average total redeem with each weekday')
ax = sns.barplot(x="weekday", y="total_redeem_amt", data=week_sta, label='Redeem')
ax.legend()
# 画出周几的箱型图
plt.figure(figsize=(12, 5))
ax = plt.subplot(1,2,1)
plt.title('The boxplot of total purchase with each weekday')
ax = sns.boxplot(x="weekday", y="total_purchase_amt", data=total_balance_1)
ax = plt.subplot(1,2,2)
plt.title('The boxplot of total redeem with each weekday')
ax = sns.boxplot(x="weekday", y="total_redeem_amt", data=total_balance_1)
# 使用OneHot方法将周几特征划分，获取划分后特征
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder()
total_balance = total_balance.reset_index()
week_feature = encoder.fit_transform(np.array(total_balance['weekday']).reshape(-1, 1)).toarray()
week_feature = pd.DataFrame(week_feature,columns=['weekday_onehot']*len(week_feature[0]))
feature = pd.concat([total_balance, week_feature], axis = 1)[['total_purchase_amt', 'total_redeem_amt','weekday_onehot','date']]
feature.columns = list(feature.columns[0:2]) + [x+str(i) for i,x in enumerate(feature.columns[2:-1])] + ['date']
# 画出划分后周几特征与标签的斯皮尔曼相关性
f, ax = plt.subplots(figsize = (15, 8))
plt.subplot(1,2,1)
plt.title('The spearman coleration between total purchase and each weekday')
sns.heatmap(feature[[x for x in feature.columns if x not in ['total_redeem_amt', 'date'] ]].corr('spearman'),linewidths = 0.1, vmax = 0.2, vmin=-0.2)
plt.subplot(1,2,2)
plt.title('The spearman coleration between total redeem and each weekday')
sns.heatmap(feature[[x for x in feature.columns if x not in ['total_purchase_amt', 'date'] ]].corr('spearman'),linewidths = 0.1, vmax = 0.2, vmin=-0.2)
月特征分析
# 画出每个月的购买总量分布估计图(kdeplot)
# 核密度估计是概率论上用来估计未知的密度函数，属于非参数检验，通过核密度估计图可以比较直观的看出样本数据本身的分布特征
plt.figure(figsize=(15,10))
plt.title('The Probability Density of total purchase amount in Each Month')
plt.ylabel('Probability')
plt.xlabel('Amount')
for i in range(7, 12):
sns.kdeplot(total_balance[(total_balance['date'].dt.date >= datetime.date(2013,i,1)) & (total_balance['date'].dt.date < datetime.date(2013,i+1,1))]['total_purchase_amt'],label='13Y,'+str(i)+'M')
for i in range(1, 9):
sns.kdeplot(total_balance[(total_balance['date'].dt.date >= datetime.date(2014,i,1)) & (total_balance['date'].dt.date < datetime.date(2014,i+1,1))]['total_purchase_amt'],label='14Y,'+str(i)+'M')
# 画出每个月的赎回总量分布估计图(kdeplot)
plt.figure(figsize=(15,10))
plt.title('The Probability Density of total redeem amount in Each Month')
plt.ylabel('Probability')
plt.xlabel('Amount')
for i in range(7, 12):
sns.kdeplot(total_balance[(total_balance['date'].dt.date >= datetime.date(2013,i,1)) & (total_balance['date'].dt.date < datetime.date(2013,i+1,1))]['total_redeem_amt'],label='13Y,'+str(i)+'M')
for i in range(1, 9):
sns.kdeplot(total_balance[(total_balance['date'].dt.date >= datetime.date(2014,i,1)) & (total_balance['date'].dt.date < datetime.date(2014,i+1,1))]['total_redeem_amt'],label='14Y,'+str(i)+'M')
# 画出14年五六七八月份的分布估计图
total_balance_last_2 = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,7,1)) & (total_balance['date'].dt.date < datetime.date(2014,8,1))]
total_balance_last_3 = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,6,1)) & (total_balance['date'].dt.date < datetime.date(2014,7,1))]
total_balance_last_4 = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,5,1)) & (total_balance['date'].dt.date < datetime.date(2014,6,1))]
total_balance_last_5 = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,4,1)) & (total_balance['date'].dt.date < datetime.date(2014,5,1))]
plt.figure(figsize=(12,10))
ax = plt.subplot(2,1,1)
plt.title('The Probability Density of total purchase and redeem amount from May.14 to August.14')
plt.ylabel('Probability')
plt.xlabel('Amount')
ax = sns.kdeplot(total_balance_last_2['total_purchase_amt'],label='August')
ax = sns.kdeplot(total_balance_last_3['total_purchase_amt'],label='July')
ax = sns.kdeplot(total_balance_last_4['total_purchase_amt'],label='June')
ax = sns.kdeplot(total_balance_last_5['total_purchase_amt'],color='Black',label='May')
ax = plt.subplot(2,1,2)
plt.ylabel('Probability')
plt.xlabel('Amount')
ax = sns.kdeplot(total_balance_last_2['total_redeem_amt'],label='August')
ax = sns.kdeplot(total_balance_last_3['total_redeem_amt'],label='July')
ax = sns.kdeplot(total_balance_last_4['total_redeem_amt'],label='June')
ax = sns.kdeplot(total_balance_last_5['total_redeem_amt'],color='Black',label='May')
# 画出13年八月到九月份的分布估计图
total_balance_last_7 = total_balance[(total_balance['date'].dt.date >= datetime.date(2013,7,1)) & (total_balance['date'].dt.date < datetime.date(2013,8,1))]
total_balance_last_8 = total_balance[(total_balance['date'].dt.date >= datetime.date(2013,8,1)) & (total_balance['date'].dt.date < datetime.date(2013,9,1))]
total_balance_last_9 = total_balance[(total_balance['date'].dt.date >= datetime.date(2013,9,1)) & (total_balance['date'].dt.date < datetime.date(2013,10,1))]
total_balance_last_10 = total_balance[(total_balance['date'].dt.date >= datetime.date(2013,10,1)) & (total_balance['date'].dt.date < datetime.date(2013,11,1))]
plt.figure(figsize=(12,10))
ax = plt.subplot(2,1,1)
plt.title('The Probability Density of total purchase and redeem amount from Aug.13 to Sep.13')
plt.ylabel('Probability')
plt.xlabel('Amount')
ax = sns.kdeplot(total_balance_last_8['total_purchase_amt'],label='August')
ax = sns.kdeplot(total_balance_last_7['total_purchase_amt'],label='July')
ax = sns.kdeplot(total_balance_last_9['total_purchase_amt'],color='Red',label='September')
ax = plt.subplot(2,1,2)
plt.ylabel('Probability')
plt.xlabel('Amount')
ax = sns.kdeplot(total_balance_last_8['total_redeem_amt'],label='August')
ax = sns.kdeplot(total_balance_last_7['total_redeem_amt'],label='July')
ax = sns.kdeplot(total_balance_last_9['total_redeem_amt'],color='Red',label='September')
ax = sns.kdeplot(total_balance_last_10['total_redeem_amt'],color='Black',label='Novermber')
日期特征分析
# 按照每天聚合数据集
day_sta = total_balance_2[['total_purchase_amt', 'total_redeem_amt', 'day']].groupby('day', as_index=False).mean()
# 获取聚合后每月购买分布的柱状图
ax = sns.barplot(x="day", y="total_purchase_amt", data=day_sta, label='Purchase')
ax = sns.lineplot(x="day", y="total_purchase_amt", data=day_sta, label='Purchase')
ax.legend()
plt.title("The total Purchase in Aug.14")
# 获取聚合后每月赎回分布的柱状图
ax = sns.barplot(x="day", y="total_redeem_amt", data=day_sta, label='Redeem')
ax = sns.lineplot(x="day", y="total_redeem_amt", data=day_sta, label='Redeem')
ax.legend()
plt.title("The total Redeem in Aug.14")
# 画出13年九月份的分布图
plt.figure(figsize=(15,5))
day_sta = total_balance_last_9[['total_purchase_amt', 'total_redeem_amt', 'day']].groupby('day', as_index=False).mean()
plt.subplot(1,2,1)
plt.title("The total Purchase in Sep.13")
ax = sns.barplot(x="day", y="total_purchase_amt", data=day_sta, label='Purchase')
ax = sns.lineplot(x="day", y="total_purchase_amt", data=day_sta, label='Purchase')
plt.subplot(1,2,2)
plt.title("The total Redeem in Sep.13")
bx = sns.barplot(x="day", y="total_redeem_amt", data=day_sta, label='Redeem')
bx = sns.lineplot(x="day", y="total_redeem_amt", data=day_sta, label='Redeem')
bx.legend()
我们发现，去年9月的数据具有非常有限的星期特征
9月份有一些奇怪的日子。
- 第一天
- 第二天
- 第16天（大量购买）–周一和中秋节前3天
- 第11天和第25天(兑换)-------------周三
- 19 20(购买和赎回都很低)
# 画出历史所有天的热力图
test = np.zeros((max(total_balance_1['week']) - min(total_balance_1['week']) + 1, 7))
test[total_balance_1['week'] - min(total_balance_1['week']), total_balance_1['weekday']] = total_balance_1['total_purchase_amt']
f, ax = plt.subplots(figsize = (10, 4))
sns.heatmap(test,linewidths = 0.1, ax=ax)
ax.set_title("Purchase")
ax.set_xlabel('weekday')
ax.set_ylabel('week')
test = np.zeros((max(total_balance_1['week']) - min(total_balance_1['week']) + 1, 7))
test[total_balance_1['week'] - min(total_balance_1['week']), total_balance_1['weekday']] = total_balance_1['total_redeem_amt']
f, ax = plt.subplots(figsize = (10, 4))
sns.heatmap(test,linewidths = 0.1, ax=ax)
ax.set_title("Redeem")
ax.set_xlabel('weekday')
ax.set_ylabel('week')
从热图中我们发现，第4周的周六的数据非常奇怪，第12周的周二也是
# 对于热力图中异常点的数据分析.1
total_balance_1[(total_balance_1['week'] == 4 + min(total_balance_1['week'])) & (total_balance_1['weekday'] == 6)]
5月4日是劳动节过后的第一天
# 对于热力图中异常点的数据分析.2
total_balance_1[(total_balance_1['week'] == 12 + min(total_balance_1['week'])) & (total_balance_1['weekday'] == 2)]
6月25日赎出很多但是购买很少
对于节假日的分析
# 获取节假日的数据
qingming = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,4,5)) & (total_balance['date'].dt.date < datetime.date(2014,4,8))]
labour = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,5,1)) & (total_balance['date'].dt.date < datetime.date(2014,5,4))]
duanwu = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,5,31)) & (total_balance['date'].dt.date < datetime.date(2014,6,3))]
data618 = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,6,10)) & (total_balance['date'].dt.date < datetime.date(2014,6,20))]
# 画出节假日与平时的均值
fig = plt.figure()
index_list = ['QM','Labour','DW','618','Mean']
label_list = [np.mean(qingming['total_purchase_amt']), np.mean(labour['total_purchase_amt']),np.mean(duanwu['total_purchase_amt']),np.mean(data618['total_purchase_amt']),np.mean(total_balance_1['total_purchase_amt'])]
plt.bar(index_list, label_list, label="Purchase")
index_list = ['QM.','Labour.','DW.','618.','Mean.']
label_list = [np.mean(qingming['total_redeem_amt']), np.mean(labour['total_redeem_amt']),np.mean(duanwu['total_redeem_amt']),np.mean(data618['total_redeem_amt']),np.mean(total_balance_1['total_redeem_amt'])]
plt.bar(index_list, label_list, label="Redeem")
plt.title("The average of different holiday")
plt.ylabel("Amount")
plt.legend()
plt.show()
# 画出节假日购买量与其所处翌日的对比
import numpy as np
import matplotlib.pyplot as plt
size = 4
x = np.arange(size)
total_width, n = 0.8, 2
width = total_width / n
x = x - (total_width - width) / 2
a = [176250006, 167825284, 162844282,321591063]
b = [225337516, 241859315, 225337516,307635449]
plt.bar(x, a, width=width, label='Holiday_Purchase')
plt.bar(x + width, b, width=width, label='Normal_Purchase')
plt.xticks(x + width / 2, ('QingMing', 'Labour', 'DuanWu', '618'))
plt.legend()
plt.show()
# 画出节假日赎回量与其所处翌日的对比
import numpy as np
import matplotlib.pyplot as plt
size = 4
x = np.arange(size)
total_width, n = 0.8, 2
width = total_width / n
x = x - (total_width - width) / 2
a = [159914308, 154717620, 154366940,291016763]
b = [235439685, 240364238, 235439685,313310347]
plt.bar(x, a, width=width, label='Holiday_Redeem')
plt.bar(x + width, b, width=width, label='Normal_Redeem')
plt.xticks(x + width / 2, ('QingMing', 'Labour', 'DuanWu', '618'))
plt.legend()
plt.show()
对于节假日周边日期的分析
# 画出清明节与周边日期的时序图
qingming_around = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,4,1)) & (total_balance['date'].dt.date < datetime.date(2014,4,13))]
ax = sns.lineplot(x="date", y="total_purchase_amt", data=qingming_around, label='Purchase')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=qingming_around, label='Redeem', ax=ax)
ax = sns.scatterplot(x="date", y="total_purchase_amt", data=qingming, ax=ax)
ax = sns.scatterplot(x="date", y="total_redeem_amt", data=qingming, ax=ax)
plt.title("The data around Qingming Holiday")
ax.legend()
# 画出劳动节与周边日期的时序图
labour_around = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,4,25)) & (total_balance['date'].dt.date < datetime.date(2014,5,10))]
ax = sns.lineplot(x="date", y="total_purchase_amt", data=labour_around, label='Purchase')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=labour_around, label='Redeem', ax=ax)
ax = sns.scatterplot(x="date", y="total_purchase_amt", data=labour, ax=ax)
ax = sns.scatterplot(x="date", y="total_redeem_amt", data=labour, ax=ax)
plt.title("The data around Labour holiday")
ax.legend()
# 画出端午节与周边日期的时序图
duanwu_around = total_balance[(total_balance['date'].dt.date >= datetime.date(2014,5,25)) & (total_balance['date'].dt.date < datetime.date(2014,6,7))]
ax = sns.lineplot(x="date", y="total_purchase_amt", data=duanwu_around, label='Purchase')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=duanwu_around, label='Redeem', ax=ax)
ax = sns.scatterplot(x="date", y="total_purchase_amt", data=duanwu, ax=ax)
ax = sns.scatterplot(x="date", y="total_redeem_amt", data=duanwu, ax=ax)
plt.title("The data around Duanwu Holiday")
ax.legend()
# 画出中秋与周边日期的时序图
zhongqiu = total_balance[(total_balance['date'].dt.date >= datetime.date(2013,9,19)) & (total_balance['date'].dt.date < datetime.date(2013,9,22))]
zhongqiu_around = total_balance[(total_balance['date'].dt.date >= datetime.date(2013,9,14)) & (total_balance['date'].dt.date < datetime.date(2013,9,28))]
ax = sns.lineplot(x="date", y="total_purchase_amt", data=zhongqiu_around, label='Purchase')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=zhongqiu_around, label='Redeem', ax=ax)
ax = sns.scatterplot(x="date", y="total_purchase_amt", data=zhongqiu, ax=ax)
ax = sns.scatterplot(x="date", y="total_redeem_amt", data=zhongqiu, ax=ax)
plt.title("The data around MiddleAutumn Holiday(in 2013)")
ax.legend()
对于异常值的分析
# 画出用户交易纪录的箱型图
sns.boxplot(data_balance['total_purchase_amt'])
plt.title("The abnormal value of total purchase")
# 对于购买2e8的用户的交易行为分析
data_balance[data_balance['user_id'] == 14592].sort_values(by = 'total_redeem_amt',axis = 0,ascending = False).head()
# 画出单笔交易为2e8的那天的总交易量及附近几天的交易量
e2 = total_balance[(total_balance['date'].dt.date >= datetime.date(2013,11,1)) & (total_balance['date'].dt.date < datetime.date(2013,11,10))]
ax = sns.barplot(x="day", y="total_purchase_amt", data=e2, label='2E')
ax = sns.lineplot(x="day", y="total_purchase_amt", data=e2, label='2E')
plt.title("The influence of the big deal with 200 million purchasing(Red Bar)")
ax.legend()
# 画出每日单笔最大交易的时序图
plt.figure(figsize=(20, 6))
ax = sns.lineplot(x="date", y="total_purchase_amt", data=data_balance[['total_purchase_amt', 'date']].groupby('date', as_index=False).max(), label='MAX_PURCHASE')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=data_balance[['total_redeem_amt', 'date']].groupby('date', as_index=False).max(), label='MAX_REDEEM')
plt.title("The Biggest deal happend in each day")
# 画出每日单笔最大交易以及总交易额的时序图
plt.figure(figsize=(20, 6))
ax = sns.lineplot(x="date", y="total_purchase_amt", data=data_balance[['total_purchase_amt', 'date']].groupby('date', as_index=False).max(), label='MAX_PURCHASE')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=data_balance[['total_redeem_amt', 'date']].groupby('date', as_index=False).max(), label='MAX_REDEEM')
ax = sns.lineplot(x="date", y="total_purchase_amt", data=data_balance[['total_purchase_amt', 'date']].groupby('date', as_index=False).sum(), label='TOTAL_PURCHASE')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=data_balance[['total_redeem_amt', 'date']].groupby('date', as_index=False).sum(), label='TOTAL_REDEEM')
# 画出每个月大额交易的频次直方图
big_frequancy = data_balance[(data_balance['total_purchase_amt'] > 10000000) | (data_balance['total_redeem_amt'] > 10000000)][['month','year','user_id']].groupby(['year','month'], as_index=False).count()
big_frequancy['i'] = big_frequancy['year'] + big_frequancy['month'] / 100
ax = sns.barplot(x="i", y="user_id", data=big_frequancy)
plt.title("The frequency of super big deal(larger than 100million) in each month")
# 获取大额交易的数据集
data_balance['big_purchase'] = 0
data_balance.loc[data_balance['total_purchase_amt'] > 1000000, 'big_purchase'] = 1
data_balance['big_redeem'] = 0
data_balance.loc[data_balance['total_redeem_amt'] > 1000000, 'big_redeem'] = 1
# 对大额交易按每天做聚合操作
big_purchase = data_balance[data_balance['big_purchase'] == 1].groupby(['date'], as_index=False)['total_purchase_amt'].sum()
small_purchase = data_balance[data_balance['big_purchase'] == 0].groupby(['date'], as_index=False)['total_purchase_amt'].sum()
big_redeem = data_balance[data_balance['big_redeem'] == 1].groupby(['date'], as_index=False)['total_redeem_amt'].sum()
small_redeem = data_balance[data_balance['big_redeem'] == 0].groupby(['date'], as_index=False)['total_redeem_amt'].sum()
# 画出大额交易与小额交易的时序分布图
fig = plt.figure(figsize=(20,6))
plt.plot(big_purchase['date'], big_purchase['total_purchase_amt'],label='big_purchase')
plt.plot(big_redeem['date'], big_redeem['total_redeem_amt'],label='big_redeem')
plt.plot(small_purchase['date'], small_purchase['total_purchase_amt'],label='small_purchase')
plt.plot(small_redeem['date'], small_redeem['total_redeem_amt'],label='small_redeem')
plt.legend(loc='best')
plt.title("The time series of big deal of Purchase and Redeem from July.13 to Sep.14")
plt.xlabel("Time")
plt.ylabel("Amount")
plt.show()
# 画出大额交易与小额交易的分布估计图
plt.figure(figsize=(12,10))
plt.subplot(2,2,1)
for i in range(4, 9):
sns.kdeplot(big_purchase[(big_purchase['date'].dt.date >= datetime.date(2014,i,1)) & (big_purchase['date'].dt.date < datetime.date(2014,i+1,1))]['total_purchase_amt'],label='14Y,'+str(i)+'M')
plt.title('BIG PURCHASE')
plt.subplot(2,2,2)
for i in range(4, 9):
sns.kdeplot(small_purchase[(small_purchase['date'].dt.date >= datetime.date(2014,i,1)) & (small_purchase['date'].dt.date < datetime.date(2014,i+1,1))]['total_purchase_amt'],label='14Y,'+str(i)+'M')
plt.title('SMALL PURCHASE')
plt.subplot(2,2,3)
for i in range(4, 9):
sns.kdeplot(big_redeem[(big_redeem['date'].dt.date >= datetime.date(2014,i,1)) & (big_redeem['date'].dt.date < datetime.date(2014,i+1,1))]['total_redeem_amt'],label='14Y,'+str(i)+'M')
plt.title('BIG REDEEM')
plt.subplot(2,2,4)
for i in range(4, 9):
sns.kdeplot(small_redeem[(small_redeem['date'].dt.date >= datetime.date(2014,i,1)) & (small_redeem['date'].dt.date < datetime.date(2014,i+1,1))]['total_redeem_amt'],label='14Y,'+str(i)+'M')
plt.title('SMALL REDEEM')
# 添加时间戳
big_purchase['weekday'] = big_purchase['date'].dt.weekday
small_purchase['weekday'] = small_purchase['date'].dt.weekday
big_redeem['weekday'] = big_redeem['date'].dt.weekday
small_redeem['weekday'] = small_redeem['date'].dt.weekday
# 分析大额小额的翌日分布
plt.figure(figsize=(12, 10))
ax = plt.subplot(2,2,1)
ax = sns.boxplot(x="weekday", y="total_purchase_amt", data=big_purchase[big_purchase['date'].dt.date >= datetime.date(2014,4,1)])
plt.title('BIG PURCHASE')
ax = plt.subplot(2,2,2)
ax = sns.boxplot(x="weekday", y="total_redeem_amt", data=big_redeem[big_redeem['date'].dt.date >= datetime.date(2014,4,1)])
plt.title('BIG REDEEM')
ax = plt.subplot(2,2,3)
ax = sns.boxplot(x="weekday", y="total_purchase_amt", data=small_purchase[small_purchase['date'].dt.date >= datetime.date(2014,4,1)])
plt.title('SMALL PURCHASE')
ax = plt.subplot(2,2,4)
ax = sns.boxplot(x="weekday", y="total_redeem_amt", data=small_redeem[small_redeem['date'].dt.date >= datetime.date(2014,4,1)])
plt.title('SMALL REDEEM')
分析用户交易记录表中其他变量
# 截断数据集
data_balance_1 = data_balance[data_balance['date'] > datetime.datetime(2014,4,1)]
# 画出用户交易纪录表中其他变量与标签的相关性图
feature = ['total_purchase_amt','total_redeem_amt', 'report_date', 'tBalance', 'yBalance',
'direct_purchase_amt', 'purchase_bal_amt', 'purchase_bank_amt',
'consume_amt', 'transfer_amt', 'tftobal_amt',
'tftocard_amt', 'share_amt']
sns.heatmap(data_balance_1[feature].corr(), linewidths = 0.05)
plt.title("The coleration between each feature in User_Balance_Table")
对于银行及支付宝利率的分析
# 读取银行利率并添加时间戳
bank = pd.read_csv(dataset_path + "mfd_bank_shibor.csv")
bank = bank.rename(columns = {'mfd_date': 'date'})
bank_features = [x for x in bank.columns if x not in ['date']]
bank['date'] = pd.to_datetime(bank['date'], format= "%Y%m%d")
bank['day'] = bank['date'].dt.day
bank['month'] = bank['date'].dt.month
bank['year'] = bank['date'].dt.year
bank['week'] = bank['date'].dt.week
bank['weekday'] = bank['date'].dt.weekday
# 读取支付宝利率并添加时间戳
share = pd.read_csv(dataset_path + 'mfd_day_share_interest.csv')
share = share.rename(columns = {'mfd_date': 'date'})
share_features = [x for x in share.columns if x not in ['date']]
share['date'] = pd.to_datetime(share['date'], format= "%Y%m%d")
share['day'] = share['date'].dt.day
share['month'] = share['date'].dt.month
share['year'] = share['date'].dt.year
share['week'] = share['date'].dt.week
share['weekday'] = share['date'].dt.weekday
# 画出上一天银行及支付宝利率与标签的相关性图
bank['last_date'] = bank['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.title("The coleration between each lastday bank rate and total purchase")
temp = pd.merge(bank[['last_date']+bank_features], total_balance, left_on='last_date', right_on='date')[['total_purchase_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
plt.subplot(1,3,3)
plt.title("The coleration between each lastday bank rate and total redeem")
temp = pd.merge(bank[['last_date']+bank_features], total_balance, left_on='last_date', right_on='date')[['total_redeem_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
# 画出上一星期银行及支付宝利率与标签的相关性图
bank['last_week'] = bank['week'] + 1
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.title("The coleration between each last week bank rate and total purchase")
temp = pd.merge(bank[['last_week','weekday']+bank_features], total_balance, left_on=['last_week','weekday'], right_on=['week','weekday'])[['total_purchase_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
plt.subplot(1,3,3)
plt.title("The coleration between each last week bank rate and total redeem")
temp = pd.merge(bank[['last_week','weekday']+bank_features], total_balance, left_on=['last_week','weekday'], right_on=['week','weekday'])[['total_redeem_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
# 分别画出上一星期银行及支付宝利率与大额小额数据的相关性图
bank['last_date'] = bank['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.title("The coleration of Small Rate purchase")
temp = pd.merge(bank[['last_date']+bank_features], small_purchase, left_on='last_date', right_on='date')[['total_purchase_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
plt.subplot(1,3,3)
plt.title("The coleration of Small Rate redeem")
temp = pd.merge(bank[['last_date']+bank_features], small_redeem, left_on='last_date', right_on='date')[['total_redeem_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
bank['last_date'] = bank['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.title("The coleration of Big Rate purchase")
temp = pd.merge(bank[['last_date']+bank_features], big_purchase, left_on='last_date', right_on='date')[['total_purchase_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
plt.subplot(1,3,3)
plt.title("The coleration of Big Rate redeem")
temp = pd.merge(bank[['last_date']+bank_features], big_redeem, left_on='last_date', right_on='date')[['total_redeem_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
# 画出银行利率的时序图
plt.figure(figsize=(15,5))
for i in bank_features:
plt.plot(bank['date'], bank[[i]] ,label=i)
plt.legend()
plt.title("The time series of bank rate")
plt.xlabel("Time")
plt.ylabel("Rate")
# 画出部分银行利率与购买量的时序图
fig,ax1 = plt.subplots(figsize=(15,5))
plt.plot(bank['date'], bank['Interest_3_M'],'b',label="Interest_3_M")
plt.plot(bank['date'], bank['Interest_6_M'],'cyan',label="Interest_6_M")
plt.plot(bank['date'], bank['Interest_9_M'],'skyblue',label="Interest_9_M")
plt.legend()
ax2=ax1.twinx()
plt.plot(total_balance['date'], total_balance['total_purchase_amt'],'g',label="Total purchase")
plt.legend(loc=2)
plt.title("The time series of bank rate and purchase")
plt.xlabel("Time")
plt.ylabel("Rate & Amount")
plt.show()
# 画出部分银行利率与赎回量的时序图
fig,ax1 = plt.subplots(figsize=(15,5))
plt.plot(bank['date'], bank['Interest_3_M'],'b',label="Interest_3_M")
plt.plot(bank['date'], bank['Interest_6_M'],'cyan',label="Interest_6_M")
plt.plot(bank['date'], bank['Interest_9_M'],'skyblue',label="Interest_9_M")
plt.legend()
ax2=ax1.twinx()
plt.plot(total_balance['date'], total_balance['total_redeem_amt'],'g',label="Total redeem")
plt.legend(loc=2)
plt.title("The time series of bank rate and redeem")
plt.xlabel("Time")
plt.ylabel("Rate & Amount")
plt.show()
利率信息
# 画出支付宝利率与标签的相关性图
share['last_date'] = share['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
temp = pd.merge(share[['last_date']+share_features], total_balance, left_on='last_date', right_on='date')[['total_purchase_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin = 0)
plt.subplot(1,3,3)
temp = pd.merge(share[['last_date']+share_features], total_balance, left_on='last_date', right_on='date')[['total_redeem_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin = 0)
# 画出银行利率与标签的相关性图
share['last_week'] = share['week'] + 1
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
temp = pd.merge(share[['last_week','weekday']+share_features], total_balance, left_on=['last_week','weekday'], right_on=['week','weekday'])[['total_purchase_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin = 0)
plt.subplot(1,3,3)
temp = pd.merge(share[['last_week','weekday']+share_features], total_balance, left_on=['last_week','weekday'], right_on=['week','weekday'])[['total_redeem_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin = 0)
# 画出支付宝利率与购买量的时序图
fig,ax1 = plt.subplots(figsize=(15,5))
for i in share_features:
plt.plot(share['date'], share[i],'b',label=i)
break
plt.legend()
ax2=ax1.twinx()
plt.plot(total_balance['date'], total_balance['total_purchase_amt'],'g',label="Total purchase")
plt.legend(loc=2)
plt.show()
# 画出支付宝利率与赎回量的时序图
fig,ax1 = plt.subplots(figsize=(15,5))
for i in share_features:
plt.plot(share['date'], share[i],'b',label=i)
break
plt.legend()
ax2=ax1.twinx()
plt.plot(total_balance['date'], total_balance['total_redeem_amt'],'g',label="Total redeem")
plt.legend(loc=2)
plt.show()
# 画出大额小额数据与支付宝利率的相关性图
share['last_date'] = share['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
temp = pd.merge(share[['last_date']+share_features], small_purchase, left_on='last_date', right_on='date')[['total_purchase_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin=0)
plt.title("SMALL PURCHASE")
plt.subplot(1,3,3)
plt.title("SMALL REDEEM")
temp = pd.merge(share[['last_date']+share_features], small_redeem, left_on='last_date', right_on='date')[['total_redeem_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin=0)
share['last_date'] = share['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.title("BIG PURCHASE")
temp = pd.merge(share[['last_date']+share_features], big_purchase, left_on='last_date', right_on='date')[['total_purchase_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin=0)
plt.subplot(1,3,3)
plt.title("BIG REDEEM")
temp = pd.merge(share[['last_date']+share_features], big_redeem, left_on='last_date', right_on='date')[['total_redeem_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin=0)
# 画出银行利率与支付宝利率的时序图
fig,ax1 = plt.subplots(figsize=(15,5))
plt.plot(bank['date'], bank['Interest_3_M'],c='g',label= 'BANK')
plt.legend()
ax2=ax1.twinx()
plt.plot(share['date'], share['mfd_daily_yield'],label='SHARE')
plt.legend(loc=2)
plt.show()
对利率的分析可以总结以下几点：
- 支付宝利率的影响更有可能作用于购买
- 银行利率的影响更有可能作用于赎回
- 支付宝利率的影响是短暂的
- 银行利率的影响是长期的
根据上述分析，可以发现以下几点会对购买赎回行为有影响：
- 是工作日
- 是否是周末
- 是否是假日
- 与一周的开始（周一）的距离
- 与周末（周日）的距离
- 与假日中心（清明节、端午节、中秋节）的距离
- 与月初的距离
- 与月末的距离
- 上个月同一周的平均值/最大值/最小值
- 上个月最后一天的数值