第七章--航空预测

1.数据描述与探索

#对数据进行基本探索
#返回缺失值个数及最大最小值

import pandas as pd
datafile = r'C:\Users\Minori\Desktop\python实训\air_data.csv'
resultfile = "C:/Users/Minori/Desktop/python实训/explore.csv"

data = pd.read_csv(datafile, encoding='utf-8')  # 读取原始数据，指定UTF-8编码
explore = data.describe(percentiles=[], include='all').T
# 包括对数据的基本描述，percentiles参数是指定计算多少的分为数表；T是转置，转置后更方便查阅

explore['null'] = len(data)-explore['count']  # describe函数自动计算非空值数，需要手动计算空值数

explore = explore[['null', 'max', 'min']]
explore.columns = ['空值数', '最大值', '最小值']  # 表头重命名
'''
describe()函数自动计算的字段有count(非空值数)、unique(唯一值数)、top(频数最高者)、freq(最高频数)、mean(平均值)、std(方差)、
min(最小值)、50%(中位数)、max(最大值)
'''

explore.to_csv(resultfile)  # 导出结果

 

#客户信息类别
#提取会员入会年份
from datetime import datetime

import matplotlib.pyplot as plt
import pandas as pd

datafile = r'C:\Users\Minori\Desktop\python实训\air_data.csv'
data = pd.read_csv(datafile, encoding='utf-8')  # 读取原始数据，指定UTF-8编码

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))               #设置画布大小
plt.rcParams['font.sans-serif'] = 'SimHei'  #设置中文显示
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('各年份会员入会年数3104')
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3104')
plt.show()
plt.close()

#提取不同级别会员人数
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]
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('会员个级别人数3104')
plt.show()
plt.close()

#提取会员年龄
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'],        #设置x轴标题
            boxprops={'facecolor':'lightblue'})           #设置填充颜色
plt.title('会员年龄分布箱线图3104')
#显示y坐标轴的底线
plt.grid(axis='y')
plt.show()
plt.close()

 

                                       

 

                                              

 

import pandas as pd
import matplotlib.pyplot as plt

# 乘机信息类别
datafile = r'C:\Users\Minori\Desktop\python实训\air_data.csv'
data = pd.read_csv(datafile, encoding='utf-8')  # 读取原始数据，指定UTF-8编码
lte = data['LAST_TO_END']
fc = data['FLIGHT_COUNT']
sks = data['SEG_KM_SUM']
plt.rcParams['font.sans-serif'] = 'SimHei'  #设置中文显示
plt.rcParams['axes.unicode_minus'] = False

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3104')
# 显示y坐标轴的底线
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'],  # 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3104')
# 显示y坐标轴的底线
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'],  # 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3104')
# 显示y坐标轴的底线
plt.grid(axis='y')
plt.show()
plt.close

 

 

                          

 

import pandas as pd
import matplotlib.pyplot as plt

# 提取会员积分兑换次数
datafile = r'C:\Users\Minori\Desktop\python实训\air_data.csv'
data = pd.read_csv(datafile, encoding='utf-8')  # 读取原始数据，指定UTF-8编码
ec = data['EXCHANGE_COUNT']
plt.rcParams['font.sans-serif'] = 'SimHei'  #设置中文显示
plt.rcParams['axes.unicode_minus'] = False


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3104')

# 提取会员总累计积分
ps = data['Points_Sum']

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3104')
# 显示y坐标轴的底线
plt.grid(axis='y')
plt.show()
plt.close

                                                  

 

 

from datetime import datetime
import matplotlib.pyplot as plt
import pandas as pd

datafile = r'C:\Users\Minori\Desktop\python实训\air_data.csv'
data = pd.read_csv(datafile, encoding='utf-8')  # 读取原始数据，指定UTF-8编码

ffp = data['FFP_DATE'].apply(lambda x:datetime.strptime(x,'%Y/%m/%d'))
ffp_year = ffp.map(lambda x : x.year)

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))
sns.heatmap(dt_corr,annot=True,vmax=1,square=True,cmap='Blues')
plt.show()
plt.close