1 import pandas as pd
 2 import numpy as np
 3 
 4 # 加载数据
 5 detail = pd.read_excel("../day05/meal_order_detail.xlsx")
 6 print("detail: \n", detail)
 7 print("detail的列名称: \n", detail.columns)
 8 
 9 # 删除法
10 # 先进行判断
11 drop_list = []
12 for column in detail.columns:
13     # print(column)
14     # 统计每一列非空数据的数量
15     res = detail.loc[:, column].count()
16     # print("res: \n", res)
17     if res == 0:
18         drop_list.append(column)
19 
20 print(drop_list)
21 
22 # 再进行删除:
23 detail.drop(labels=drop_list, axis=1, inplace=True)
24 print("删除全部为空列之后的结果: \n" ,detail.shape)
25 print("删除全部为空列之后的结果的列名称: \n" ,detail.columns)
26 print("^"*60)
27 
28 # 分组进行统计指标
29 # 按照单列进行分组——统计菜品id的最大值
30 res_ = detail.groupby(by="order_id")["dishes_id"].max()
31 res_ = detail.groupby(by=detail["order_id"])["dishes_id"].max()
32 
33 print("res_: \n", res_)
34 
35 # 统计所欲python班级各个小组的平均成绩
36 df = pd.DataFrame(
37     data={
38         "cls_id": ["A", "B", "C", "A", "B", "C", "A", "B", "C", "A", "B","C"],
39         "group_id": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2],
40         "name": ["xixi", "haha", "taotao", "huihui", "ranran", "Island", "Tree" ,"bamao", "simao", "hanhan", "qimao", "sanmao"],
41         "score": [92, 93, 39, 89, 90.5, 80, 91, 92, 65, 73, 34.5, 56],
42         "height": [165, 166, 167, 168, 152, 193, 192, 190, 173, 172, 170, 169]
43     },
44 )
45 print("df: \n", df)
46 
47 # 按照班级分组，统计班级的平均分
48 # 按照单列进行分组
49 res = df.groupby(by="cls_id")["score"].mean()
50 print(res)
51 
52 # 先按照班级分组，再统计各小组的平均成绩
53 res = df.groupby(by=["cls_id", "group_id"])["score"].mean()
54 print("res: \n", res)
55 
56 # 按照多列分组，既要统计成绩的平均值，又要统计身高的平均值
57 res = df.groupby(by=["cls_id", "group_id"])[["score", "height"]].mean()
58 print("res: \n", res)
59 
60 # 对成绩求最大值，身高求平均值
61 # res = detail.agg({"counts": np.max, "height": np.mean})
62 # print("res: \n", res)
63 
64 # 对不同的列求取不同的指标
65 res = detail.agg({"counts": np.sum, "amounts": np.mean})
66 
67 # 对不同的列求取多个相同的指标
68 res = detail[["counts", "amounts"]].agg([np.max, np.mean])
69 #
70 # # 对不同单列求取不同个数的指标
71 res = detail.agg({"counts": [np.mean, np.max], "amounts": np.min})
72 
73 print("res :\n", res)
74 
75 # 对某列进行指定的运算
76 res = detail[["counts", "amounts"]].apply(lambda x: x+1)
77 res = detail[["counts", "amounts"]].transform(lambda x: x+1)
78 # res = detail[["counts", "amounts"]].apply(lambda x, y: x+y)  # 错误的， 不能跨列运算
79 
80 print('detail[["counts", "amounts"]]: \n', detail[["counts", "amounts"]])
81 print("res :\n", res)
82 print(detail["counts"])