Python数据挖掘 数据预处理案例（以航空公司数据为例）源代码+文件

import pandas as pd import matplotlib.pyplot as plt datafile= 'cleanedfile_finish.csv' # 航空原始数据,第一行为属性标签 # 读取原始数据，指定UTF-8编码（需要用文本编辑器将数据装换为UTF-8编码） data = pd.read_csv(datafile, encoding = 'utf-8') # 客户信息类别 # 提取会员入会年份 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)) # 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('各年份会员入会人数') 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)) # 设置画布大小 plt.pie([ male, female], labels=['男','女'], colors=['lightskyblue', 'lightcoral'], autopct='%1.1f%%') plt.title('会员性别比例') 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)) # 设置画布大小 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') 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'], # 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') # 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') # 显示y坐标轴的底线 plt.grid(axis='y') plt.show() plt.close # 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') # 显示y坐标轴的底线 plt.grid(axis='y') plt.show() plt.close # 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') # 显示y坐标轴的底线 plt.grid(axis='y') plt.show() plt.close # 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') plt.show() plt.close # 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') # # 显示y坐标轴的底线 # plt.grid(axis='y') # 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.subplots(figsize=(10, 10)) # 设置画面大小 sns.heatmap(dt_corr, annot=True, vmax=1, square=True, cmap='Blues') plt.show() plt.close