数据分析

标签：数据分析 Files plt WeChat airline csv data

数据清洗：

import numpy as np
import pandas as pd
datafile='D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/air_data.csv'
cleanedfile='D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/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：\n',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('数据清清洗后数据形状为：',airline.shape)
airline.to_csv(cleanedfile)

import numpy as np
import pandas as pd
cleanedfile='D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/data_cleaned.csv'
airline_data=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('D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/airline_scale.npz',data)
print('标准化后LRFMC5个属性为：\n',data[:5,:])

 

 数据探索：

import pandas as pd
datafile='D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/air_data.csv'
resultfile='D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/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']

data.describe
explore.to_csv(resultfile)

from datetime import datetime
from matplotlib import 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3021')
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3021')
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('会员各级别人数3021')
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3021')
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.grid(axis='y')
plt.title('3021')
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3021')
plt.grid(axis='y')
plt.show()
plt.close

fig=plt.figure(figsize=(5,10))
plt.boxplot(sks,patch_artist=True,labels=['总飞行功公里数'],boxprops={'facecolor':'lightblue'})
plt.title('客户总飞行公里数箱型图3021')
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3021')
plt.show()
plt.close()

ps=data['Points_Sum']
fig=plt.figure(figsize=(5,10))
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3021')
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']]
agel=data['AGE'].fillna(0)
data_corr['AGE']=agel.astype('int64')
data_corr['ffp_year']=ffp_year
dt_corr=data_corr.corr(method='pearson')
print('相关性分析:\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3021')
plt.show()
plt.close

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 客户特征分析雷达图：

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

airline_scale=np.load('D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/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('各类样本的类别标签为：\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)

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])
ang=angle*180/np.pi
ax.set_thetagrids(ang[:-1],labels)
plt.title('客户特征分析雷达图3021')
plt.legend(legen)
plt.show()
plt.close

 

 判断一个客户是，已流失，准流失，未流失的客户：

import numpy as np
import pandas as pd
datafile='D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/air_data.csv'
cleanedfile='D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/data_cleaned2.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：\n',airline_notnull.shape)

#选取特征
airline_data['单位里程票价'] = (airline_data['SUM_YR_1'] + airline_data['SUM_YR_2'])/airline_data['SEG_KM_SUM']
airline_data['单位里程积分'] = (airline_data['P1Y_BP_SUM'] + airline_data['L1Y_BP_SUM'])/airline_data['SEG_KM_SUM']
airline_data['飞行次数比例'] = airline_data['L1Y_Flight_Count'] / airline_data['P1Y_Flight_Count'] #第二年飞行次数与第一年飞行次数的比例
#筛选出老客户(飞行次数大于6次的为老客户)
airline_data = airline_data[airline_data['FLIGHT_COUNT'] > 6]
#选择特征
airline_data = airline_data[['FFP_TIER','飞行次数比例','AVG_INTERVAL',
'avg_discount','EXCHANGE_COUNT','Eli_Add_Point_Sum','单位里程票价','单位里程积分']]
#导出
airline_data.to_csv(cleanedfile,index=None)

input_file = 'D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/data_cleaned2.csv'
output_file = 'D:/WeChat Files/WeChat Files/wxid_jomb9ver281c22/FileStorage/File/2023-03/data_cleaned3.csv'
data = pd.read_csv(input_file,encoding='utf-8')
data['客户类型'] = None
for i in range(len(data)):
#第一、二年飞行次数比例小于50%的客户定义为已流失
if data['飞行次数比例'][i] < 0.5:
data['客户类型'][i] = 0 #0代表已流失
#第一、二年飞行次数比例在[0.5,0.9)之间的客户定义为准流失
if (data['飞行次数比例'][i] >= 0.5) & (data['飞行次数比例'][i] < 0.9) :
data['客户类型'][i] = 1 #1代表准流失
#第一、二年飞行次数比例大于等于90%的客户定义为未流失
if data['飞行次数比例'][i] >= 0.9:
data['客户类型'][i] = 2 #2代表未流失
#导出
data.to_csv(output_file,index=None)
print('筛选的属性前5行为：\n',data.head())

 

 

 

标签：数据分析,Files,plt,WeChat,airline,csv,data
From： https://www.cnblogs.com/mo-ling/p/17206907.html