第三章作业

import pandas as pd
datafile=r"C:\Users\Lenovo\Desktop\air_data.csv"
resultfile=r"D:\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3143')
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3143')
plt.show()
plt.close

 

 

 

#提取不同级别会员的人数
lv_four=pd.value_counts(data['FFP_TIER'])[4]
lv_five=pd.value_counts(data['FFP_TIER'])[5]
lv_six=pd.value_counts(data['FFP_TIER'])[6]
#绘制会员各级别人数条形图
fig=plt.figure(figsize=(8,5))
plt.bar(x=range(3),height=[lv_four,lv_five,lv_six],width=0.4,alpha=0.8,color='skyblue')
plt.xticks([index for index in range(3)],['4','5','6'])
plt.xlabel('会员等级')
plt.ylabel('会员人数')
plt.title('会员各级别人数3143')
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3143')
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3143')
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3143')
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3143')
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3143')
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3143')
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("3143")
plt.subplots(figsize=(10,10))
sns.heatmap(dt_corr,annot=True,vmax=1,square=True,cmap='Blues')
plt.show()
plt.close

 

 

data = StandardScaler().fit_transform(airline_features)
# np.savez(r'G:\data\data\airline_scale.npz',data)
# print('标准化后LRFMC的5个属性为:\n',data[:5,:])

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
import warnings
warnings.filterwarnings('ignore')

from scipy import stats
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, KFold
from sklearn.feature_sele