导入数据

df1=pd.read_excel('.xlxs',sheetname='')

df2=...

df=pd.concat([df1,df2,df3],axis=0) #axis=0>>>detail_id

df.reset_index(drop=True,inplace=True)

df.shape

df,info()

清洗数据

检查是否存在重复值

df.duplicated().sum()

删除缺失的字段

df.dropna(axis=1,inplace=True)

删除无用的字段

df[''].unique()

df.drop(['A','B'],axis=1,inplace=True)

df.head()

dishes_name字段处理

df['dish_names']=df['dish_names'].str,strip('[]')

[33]:

#繁体简体转换

from langconv import *

def simple2tradition(line):

#将简体转换成繁体

line = Converter('zh-hant').convert(line)

return line

def tradition2simple(line):

# 将繁体转换成简体

line = Converter('zh-hans').convert(line)

return line

simplified_sentence = tradition2simple('憂郁的臺灣烏龜')

print(simplified_sentence)

traditional_sentence = simple2tradition('忧郁的台湾乌龟')

print(traditional_sentence)

忧郁的台湾乌龟

憂郁的臺灣烏龜

from langconv import *

def tradition2simple(line):

# 将繁体转换成简体

line = Converter('zh-hans').convert(line)

return line

df['dish_names']=df['dish_names'].apply(tradition2simple)

添加money列

df.insert(6,'money',df['counts']*df['amounts'])

数据分析

一共有多少笔订单

df['order_id'].nunique()

有多少个不同的菜

df['dish_names'].nunique()

统计菜品价格的平均值

df.drop_duplicates(subset='dish_names')['amounts'].mean()

什么菜最受欢迎(点的次数最多)

df['dish_names'].value_counts()

哪个菜吃的份数最多

df[['dish_names','amounts']].groupby('dish_names').sum().nlargest(10,columns='amounts')

哪个用户id点的菜最多

##把detail_id看作是用户id

df[['detail_id','counts']].groupby('detail_id').sum().nlargest(10,columns='counts')

哪个id吃的钱数多

df[['detail_id','money']].groupby('detail_id').sum().nlargest(10,columns='money')

哪个id吃的平均菜价贵

df1=df[['detail_id','money']].groupby('detail_id').sum()

df2=df[['detail_id','counts']].groupby('detail_id').sum()

s=df1['money']/df2['counts']

s.nlargest(10)

时间相关分析

一天什么时候吃饭最多（点的菜）

df['hour']=df['place_order_time'].dt.hour

df['hour'].value_counts().sort_index().plot(kind='bar')

哪一天吃饭的人最多

df['day']=df['place_order_time'].dt.day

df['day'].value_counts().sort_index().plot(kind='bar')

星期几吃饭的人最多

df['weekday']=df['place_order_time'].dt.weekday+1

df['weekday'].value_counts().sort_index().plot(kind='bar')

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