数据分析3

数据探索：

import pandas as pd
import matplotlib.pyplot as plt
datafile="C:/Users/admin/Documents/WeChat Files/wxid_b0fz4hqogenr22/FileStorage/File/2023-03/air_data.csv"
resultfile="C:/Users/admin/Documents/WeChat Files/wxid_b0fz4hqogenr22/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.colmns=[u'空值数',u'最大值',u'最小值']
explore.to_csv(resultfile)

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)
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('入会年份')
plt.title('各年份入会人数--3029')
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--3029')
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--3029')
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--3029')
plt.grid(axis='y')
plt.show()
plt.close