,WORK

import pandas as pd
datafile='E:/桌面/air_data.csv'
resultfile='E:/桌面/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.colums=[u'空值数',u'最大值',u'最小值']
explore.to_csv(resultfile)

import matplotlib.pyplot as plt
from datetime import datetime
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,3030')
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']
#回执会员性别比例饼图
fig=plt.figure(figsize=(7,4))
plt.pie([male,female],labels=['男','女'],colors=['lightskyblue','lightcoral'],autopct='%1.1f%%')
plt.title('会员性别比例学号3046')
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]
#会指挥员各级别人数条形图
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('会员各级别人数学号3046')
plt.show()
plt.close()

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学号3030')
#显示y坐标轴的底线
plt.grid(axis='y')
plt.show()
plt.close

#提取会员年龄
#显示y坐标轴的底线
plt.grid(axis='y')
plt.show()
plt.close

# 乘机信息类别
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线图
fig = plt.figure(figsize = (5 ,8))
plt.boxplot(lte,
patch_artist=True,
labels = ['时长'], # 设置x轴标题
boxprops = {'facecolor':'lightblue'}) # 设置填充颜色
plt.title('会员最后乘机至结束时长分布箱线图学号3030')
# 显示y坐标轴的底线
plt.grid(axis='y')
plt.show()

# 绘制客户飞行次数箱线图
fig = plt.figure(figsize = (5 ,8))
plt.boxplot(fc,
patch_artist=True,
labels = ['飞行次数'], # 设置x轴标题
boxprops = {'facecolor':'lightblue'}) # 设置填充颜色
plt.title('会员飞行次数分布箱线图学号3030')
# 显示y坐标轴的底线
plt.grid(axis='y')
plt.show()

# 绘制客户总飞行公里数箱线图
fig = plt.figure(figsize = (5 ,10))
plt.boxplot(sks,
patch_artist=True,
labels = ['总飞行公里数'], # 设置x轴标题
boxprops = {'facecolor':'lightblue'}) # 设置填充颜色
plt.title('客户总飞行公里数箱线图学号3030')
# 显示y坐标轴的底线
plt.grid(axis='y')
plt.show()

# 积分信息类别
# 提取会员积分兑换次数
ec = data['EXCHANGE_COUNT']
# 绘制会员兑换积分次数直方图
fig = plt.figure(figsize = (8 ,5)) # 设置画布大小
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学号3030')
plt.show()

# 提取会员总累计积分
ps = data['Points_Sum']
# 绘制会员总累计积分箱线图
fig = plt.figure(figsize = (5 ,8))
plt.boxplot(ps,
patch_artist=True,
labels = ['总累计积分'], # 设置x轴标题
boxprops = {'facecolor':'lightblue'}) # 设置填充颜色
plt.title('客户总累计积分箱线图学号3030')
# 显示y坐标轴的底线
plt.grid(axis='y')
plt.show()

# 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.subplots(figsize=(10, 10)) # 设置画面大小
sns.heatmap(dt_corr, annot=True, vmax=1, square=True, cmap='Blues')
plt.title('热力图学号3030')
plt.show()

import pandas as pd
a=r"D:/桌面/数据挖掘/city.xls"
data=pd.read_excel(a)
d=data['WORK_CITY'].value_counts()
#统计列“WORK_CITY”中各个类型的重复次数
print(d)
resultfile = 'D:/桌面/city12.csv'
d.to_csv(resultfile)