本案例主要在于使用pandas的分组聚合函数和日期时间函数做简单分析。

import os                       #导入必要的库
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
os.chdir("D:\Data\File")       #指定工作目录

%matplotlib inline               #可视化必要设置
plt.rcParams["font.sans-serif"] = ["KAITI"]
plt.rcParams["axes.unicode_minus"] = False

一、导入excel数据

detail1 = pd.read_excel('meal_order_detail.xlsx',sheet_name='meal_order_detail1')#读取订单详情表文件
detail2 = pd.read_excel('meal_order_detail.xlsx',sheet_name='meal_order_detail2')#读取订单详情表文件
detail3 = pd.read_excel('meal_order_detail.xlsx',sheet_name='meal_order_detail3')#读取订单详情表文件

data = pd.concat([detail1,detail2,detail3],axis=0)  #纵向拼接
data.head(3)

df1 = data.copy()

1.1 提出分析问题

# 1.订单表的长度，shape，values，columns
# 2.统计菜品的价格平均值（amount）
# 3.频数统计，什么菜最受欢迎
# 4.哪个id点的菜最多
# 5.哪个id吃的钱数多 
# 5.哪个id吃的平均菜价贵
# 6.一天什么时候吃饭最多
# 7.哪一天人吃饭最多
# 8.星期几人吃饭最多
# 9.每日菜品总价格，均价，中位数

1.2 查看数据集基本信息

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

二、分析用餐数据

2.1 订单表的长度，shape，values，columns

df1.columns
df1.size
df1.shape

Index(['detail_id', 'order_id', 'dishes_id', 'logicprn_name',
       'parent_class_name', 'dishes_name', 'itemis_add', 'counts', 'amounts',
       'cost', 'place_order_time', 'discount_amt', 'discount_reason',
       'kick_back', 'add_inprice', 'add_info', 'bar_code', 'picture_file',
       'emp_id'],
      dtype='object')

190703
(10037, 19)

df1.info(memory_usage="deep")

<class 'pandas.core.frame.DataFrame'>
Int64Index: 10037 entries, 0 to 3610
Data columns (total 19 columns):
 #   Column             Non-Null Count  Dtype         
---  ------             --------------  -----         
 0   detail_id          10037 non-null  int64         
 1   order_id           10037 non-null  int64         
 2   dishes_id          10037 non-null  int64         
 3   logicprn_name      0 non-null      float64       
 4   parent_class_name  0 non-null      float64       
 5   dishes_name        10037 non-null  object        
 6   itemis_add         10037 non-null  int64         
 7   counts             10037 non-null  int64         
 8   amounts            10037 non-null  int64         
 9   cost               0 non-null      float64       
 10  place_order_time   10037 non-null  datetime64[ns]
 11  discount_amt       0 non-null      float64       
 12  discount_reason    0 non-null      float64       
 13  kick_back          0 non-null      float64       
 14  add_inprice        10037 non-null  int64         
 15  add_info           0 non-null      float64       
 16  bar_code           0 non-null      float64       
 17  picture_file       10037 non-null  object        
 18  emp_id             10037 non-null  int64         
dtypes: datetime64[ns](1), float64(8), int64(8), object(2)
memory usage: 3.0 MB

round(df1.isnull().sum()/len(df1),2)

detail_id            0.0
order_id             0.0
dishes_id            0.0
logicprn_name        1.0
parent_class_name    1.0
dishes_name          0.0
itemis_add           0.0
counts               0.0
amounts              0.0
cost                 1.0
place_order_time     0.0
discount_amt         1.0
discount_reason      1.0
kick_back            1.0
add_inprice          0.0
add_info             1.0
bar_code             1.0
picture_file         0.0
emp_id               0.0
dtype: float64

#处理缺失值
df1.dropna(how="all",axis=1,inplace=True)
df1

10037 rows × 11 columns

其中有8列特征缺失值达到100%，采用删除的方式

2.2 统计菜品的价格平均值（amount）

df2 = df1[["dishes_name","amounts"]].groupby("dishes_name").agg("mean")
df2

2.3 频数统计，什么菜最受欢迎

df1["order_id"] = df1["order_id"].astype("int32")

dishes_count=df1['dishes_name'].value_counts()[0:10]
dishes_count

白饭/大碗        323
凉拌菠菜         269
谷稻小庄         239
麻辣小龙虾        216
辣炒鱿鱼         189
芝士烩波士顿龙虾     188
五色糯米饭(七色)    187
白饭/小碗        186
香酥两吃大虾       178
焖猪手          173
Name: dishes_name, dtype: int64

df3 = df1[["dishes_name","order_id"]].groupby("dishes_name").agg("count").sort_values(by="order_id",ascending=False)
df3.head(10).T

df3.plot(figsize=(18,4),kind="bar")

可以看到最受欢迎的菜，除了第一名是白米饭，前五名就是凉拌菠菜、谷稻小庄、麻辣小龙虾、辣炒鱿鱼、芝士烩波士顿龙虾

2.4 哪个id定的次数最多

df1["order_id"].value_counts()[:10]

398     36
1295    29
582     27
465     27
1078    27
1311    26
1033    25
672     24
426     24
777     24
Name: order_id, dtype: int64

2.5 哪个id吃的钱数最多

df1["total_amount"] = df1["counts"]* df1["amounts"]
df4 = df1[["order_id","counts","amounts","total_amount"]].groupby("order_id").agg("sum").sort_values(by="total_amount",ascending=False)
df4[:10]

2.6 哪个id平均菜品最贵

df4['average']=df4['total_amount']/df4['counts']
sort_average=df4.sort_values('average',ascending=False) 
sort_average['average'][0:10].plot.bar()

2.7 一天的什么时间吃饭比较多,需要进行计数

df1.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 10037 entries, 0 to 3610
Data columns (total 12 columns):
 #   Column            Non-Null Count  Dtype         
---  ------            --------------  -----         
 0   detail_id         10037 non-null  int64         
 1   order_id          10037 non-null  int64         
 2   dishes_id         10037 non-null  int64         
 3   dishes_name       10037 non-null  object        
 4   itemis_add        10037 non-null  int64         
 5   counts            10037 non-null  int64         
 6   amounts           10037 non-null  int64         
 7   place_order_time  10037 non-null  datetime64[ns]
 8   add_inprice       10037 non-null  int64         
 9   picture_file      10037 non-null  object        
 10  emp_id            10037 non-null  int64         
 11  total_amount      10037 non-null  int64         
dtypes: datetime64[ns](1), int64(9), object(2)
memory usage: 1.2+ MB

df1.head(3)

#方法一
df1["month"] = df1["place_order_time"].dt.month
df1["day"] = df1["place_order_time"].dt.day
df1["weekday"] = df1["place_order_time"].dt.weekday
df1["hour"] = df1["place_order_time"].dt.hour

df5 = df1["hour"].value_counts()
df5.sort_index(inplace=True)
df5.plot(kind="bar")

由上图可以看出，一天中18点到21点是用餐高峰时间。

#方法二
df2 = data.copy()
df2.dropna(how="all",axis=1,inplace=True)
df2['hourcount'] = 1 #添加一列
df2['time'] = pd.to_datetime(df2['place_order_time'])#解析时间
type(df2['time'][1])#查是否是时间类型
df2['hour'] = df2['time'].map(lambda x : x.hour)
gp_by_hour = df2['hourcount'].groupby(df2['hour']).count()
gp_by_hour.plot.bar()

pandas.core.series.Series

2.8 一天订餐数量最多

df1["month"].unique()

array([8], dtype=int64)

df6 = df1["day"].value_counts()
df6.sort_index(inplace=True)
df6.plot(kind="bar")

由上图可以看出，8月这一个月中用餐最多的时间呈现以星期为单位的周期性变化。

df7 = df1["weekday"].value_counts()
df7.sort_index(inplace=True)
df7.plot(kind="bar")

由上图可以看出周六周日用餐人多

2.9 每日菜品总价格

df8 = df1[['day','counts','amounts']].groupby(by='day').agg({
    
    'amounts':np.sum})
df8.plot.bar(); 

由上图可以看出，周六周天总价高

df8 = df1[['day','counts','amounts']].groupby(by='day').agg({
    
    'counts':np.sum})
df8.plot.bar();      #每日菜品的数量