财政收入影响因素分析及预测

import pandas as pd
datafile=r'D:\python学习\data\data.csv'
resultfile=r'D:\python学习\data\explore.csv'
data=pd.read_csv(datafile, encoding='utf-8')
explore=data.describe(percentiles=[],include='all').T
explore['null']=len(data)-explore['count']
explore=explore[['null','max','min']]
explore.columns=[u'空值数',u'最大值',u'最小值']
explore.to_csv(resultfile)

from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt;
ffp=data['FFP_DATE'].apply(lambda x:datetime.strptime(x,'%Y/%m/%d'))
ffp_year=ffp.map(lambda x : x.year)
#绘制各年份会员入会人数直方图
fig=plt.figure(figsize=(8,5))
plt.rcParams['font.sans-serif']='SimHei'
plt.rcParams['axes.unicode_minus']=False
plt.hist(ffp_year,bins='auto',color='#0504aa')
plt.xlabel('年份')
plt.ylabel('入会人数')
plt.title('各年份会员入会人数3103')
plt.show()
plt.close

#提取会员年龄age=data['AGE'].dropna()age=age.astype('int64')#绘制会员年龄分布箱型图fig=plt.figure(figsize=(5,10))plt.boxplot(age,patch_artist=True,labels=['会员年龄'],boxprops={'facecolor':'lightblue'})plt.title('会员年龄分布箱型图3103')plt.grid(axis='y')plt.show()plt.close

lte=data['LAST_TO_END']
fc=data['FLIGHT_COUNT']
sks=data['SEG_KM_SUM']
#绘制最后乘机至结束时长箱型图
fig=plt.figure(figsize=(5,8))
plt.boxplot(lte,patch_artist=True,labels=['时长'],boxprops={'facecolor':'lightblue'})
plt.title('会员最后乘机至结束时长分布箱型图3103')
plt.grid(axis='y')
plt.show()
plt.close

#绘制客户飞行次数箱型图
fig=plt.figure(figsize=(5,8))
plt.boxplot(fc,patch_artist=True,labels=['飞行次数'],boxprops={'facecolor':'lightblue'})
plt.title('会员飞行次数分布箱型图3103')

plt.grid(axis='y')
plt.show()
plt.close

#绘制客户总飞行公里数箱型图
fig=plt.figure(figsize=(5,10))
plt.boxplot(sks,patch_artist=True,labels=['总飞行公里数'],boxprops={'facecolor':'lightblue'})
plt.title('客户总飞行公里数箱型图3103')
plt.show()
plt.close

#提取会员不同性别人数
male=pd.value_counts(data['GENDER'])['男']
female=pd.value_counts(data['GENDER'])['女']
#绘制会员性别比例饼图
fig=plt.figure(figsize=(7,4))
plt.pie([male,female],labels=['男','女'],colors=['lightskyblue','lightcoral'],autopct='%1.1f%%')
plt.title('会员性别比例3103')
plt.show()
plt.close

ec=data['EXCHANGE_COUNT']
#绘制会员兑换积分次数直方图
fig=plt.figure(figsize=(8,5))
plt.hist(ec,bins=5,color='#0504aa')
plt.xlabel('兑换次数')
plt.ylabel('会员人数')
plt.title('会员兑换积分次数分布直方图3103')
plt.show()
plt.close

#提取会员总累计积分
ps=data['Points_Sum']
#绘制会员总乐基积分箱型图
fig=plt.figure(figsize=(5,8))
plt.boxplot(ps,patch_artist=True,labels=['总累计积分'],boxprops={'facecolor':'lightblue'})
plt.title('客户总累计积分箱型图3103')
plt.show()
plt.close

#提取属性并合并为新数据集
data_corr=data[['FFP_TIER','FLIGHT_COUNT','LAST_TO_END','SEG_KM_SUM','EXCHANGE_COUNT','Points_Sum']]
age1=data['AGE'].fillna(0)
data_corr['AGE']=age1.astype('int64')
data_corr['ffp_year']=ffp_year
#计算相关性矩阵
dt_corr=data_corr.corr(method='pearson')
print('相关性矩阵为：\n',dt_corr)
#绘制热力图
import seaborn as sns
plt.title("3103")
plt.subplots(figsize=(10,10))
sns.heatmap(dt_corr,annot=True,vmax=1,square=True,cmap='Blues')
plt.show()
plt.close

import numpy as np
import pandas as pd

datafile=r'D:\python学习\data\data.csv'
cleanedfile=r'D:\python学习\data\data_cleaned.csv'
airline_data=pd.read_csv(datafile,encoding='utf-8')
print('原始数据的形状为：',airline_data.shape)
#去除票价为空的记录
airline_notnull=airline_data.loc[airline_data['SUM_YR_1'].notnull()&airline_data['SUM_YR_2'].notnull(),:]
print('删除确实记录后数据的形状为:',airline_notnull.shape)
#只保留票价非零的，或者平均折扣率部位0且总飞行公里数大于0的记录
index1=airline_notnull['SUM_YR_1']!=0
index2=airline_notnull['SUM_YR_2']!=0
index3=(airline_notnull['SEG_KM_SUM']>0)&(airline_notnull['avg_discount']!=0)
index4=airline_notnull['AGE']>100
airline=airline_notnull[(index1|index2)&index3&~index4]
print('数据清洗后数据的形状为:',airline.shape)
airline.to_csv(cleanedfile)

import pandas as pd
import numpy as np
cleanedfile=r'D:\python学习\data\data.csv'
airline=pd.read_csv(cleanedfile,encoding='utf-8')
#选取需求属性
airline_selection=airline[['FFP_DATE','LOAD_TIME','LAST_TO_END','FLIGHT_COUNT','SEG_KM_SUM','avg_discount']]
print('筛选的属性前5行为:\n',airline_selection.head())

L=pd.to_datetime(airline_selection['LOAD_TIME'])-\
pd.to_datetime(airline_selection['FFP_DATE'])
L=L.astype('str').str.split().str[0]
L=L.astype('int')/30
#合并属性
airline_features = pd.concat([L,airline_selection.iloc[:,2:]],axis = 1) #行合并
print('构建的LRFMC特征前5行为：\n',airline_features.head())
#数据标准化
from sklearn.preprocessing import StandardScaler
data = StandardScaler().fit_transform(airline_features)
np.savez(r'D:\python学习\data\airline_scale.npz',data)
print('标准化后LRFMC的5个属性为:\n',data[:5,:])