航空公司客户价值分析

标签：plt 航空公司 labels 客户 airline pd import 价值 data

import pandas as pd
datafile='air_data.csv'
resultfile='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 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)
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3116')
plt.show()
plt.close

male=pd.value_counts(data['GENDER'])['男']
female=pd.value_counts(data['GENDER'])['女']
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3116')
plt.show()
plt.close

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]

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('会员各级人数3116')
plt.show()
plt.close()

age=data['AGE'].dropna()
age=age.astype('int64')

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3116')
plt.grid(axis='y')
plt.show()
plt.close

 

 

 

 

 

 

 

 

 

 

 

lte=data['LAST_TO_END']
fc=data['FLIGHT_COUNT']
sks=data['SEG_KM_SUM']
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3116')
plt.grid(axis='y')
plt.show()
plt.close

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3116')
plt.grid(axis='y')
plt.show()
plt.close

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3116')
plt.grid(axis='y')
plt.show()
plt.close

 

 

 

 

 

ec=data['EXCHANGE_COUNT']
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3116')
plt.show()
plt.close

ps=data['Points_Sum']
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3116')
plt.grid(axis='y')
plt.show()
plt.close

 

 

 

 

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

dt_corr=data_corr.corr(method='pearson')
print('相关性矩阵为：\n',dt_corr)

import seaborn as sns
plt.subplots(figsize=(10,10))
sns.heatmap(dt_corr,annot=True,vmax=1,square=True,cmap='Blues')

plt.title('热力图3116')
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
plt.show()
plt.close

 

 

 

 

import numpy as np
import pandas as pd

datafile='air_data.csv'
cleanedfile='data_cleaned.csv'

airline_data=pd.read_csv(datafile,encoding='utf-8')
print('原始数据的形状为：',airline_data.shape)

airline_notnull=airline_data.loc[airline_data['SUM_YR_1'].notnull() & airline_data['SUM_YR_2'].notnull(),:]
print('删除缺失记录后数据的形状为：',airline_notnull.shape)

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='data_cleaned.csv'
airline=pd.read_csv(cleanedfile,encoding='utf-8')
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

airline_features=pd.concat([L,airline_selection.iloc[:,2:]],axis=1)
print('构建的LRFMC属性前5行为：\n',airline_features.head())

from sklearn.preprocessing import StandardScaler
data=StandardScaler().fit_transform(airline_features)
np.savez('airline_scale.npz',data)
print('标准化后LRFMC 5个属性为：\n',data[:5,:])

 

 

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans

airline_scale=np.load('airline_scale.npz')['arr_0']
k=5

kmeans_model=KMeans(n_clusters=k,random_state=123)
fit_kmeans=kmeans_model.fit(airline_scale)

kmeans_cc=kmeans_model.cluster_centers_
print('各类聚类中心为：\n',kmeans_cc)
kmeans_labels=kmeans_model.labels_
print('各样本的类别标签为：\n',kmeans_labels)
r1=pd.Series(kmeans_model.labels_).value_counts()
print('最终每个类别的数目为：\n',r1)

cluster_center=pd.DataFrame(kmeans_model.cluster_centers_,columns=['ZL','ZR','ZF','ZM','ZC'])
cluster_center.index=pd.DataFrame(kmeans_model.labels_).drop_duplicates().iloc[:,0]
print(cluster_center)

 

 

%matplotlib inline
import matplotlib.pyplot as plt
labels=['ZL','ZR','ZF','ZM','ZC']
legen=['客户群'+str(i+1) for i in cluster_center.index]
lstype=['-','--',(0,(3,5,1,5,1,5)),':','-.']
kinds=list(cluster_center.iloc[:,0])
cluster_center=pd.concat([cluster_center,cluster_center[['ZL']]],axis=1)
centers=np.array(cluster_center.iloc[:,0:])
n=len(labels)
angle=np.linspace(0,2*np.pi,n,endpoint=False)
angle=np.concatenate((angle,[angle[0]]))

fig=plt.figure(figsize=(8,6))
ax=fig.add_subplot(111,polar=True)
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False

for i in range(len(kinds)):
    ax.plot(angle,centers[i],linestyle=lstype[i],linewidth=2,label=kinds[i])
#ax.set_thetagrids(angle*180/np.pi,labels)
plt.title('客户特征分析雷达图3116')
plt.legend(legen)
plt.show()
plt.close

 

标签：plt,航空公司,labels,客户,airline,pd,import,价值,data
From： https://www.cnblogs.com/Nothingtolose/p/17188279.html