Pandas中group+apply应用

import pandas as pd

dict_1 = {"time": ["2019-11-02", "2019-11-03", "2019-11-04", "2019-11-05",
                   "2019-12-02", "2019-12-03", "2019-12-04", "2019-12-05"],
          "pos": ["A", "A", "C", "D", "E", "E", "G", "H"],
          "value1": [10, 20, 30, 40, 50, 60, 70, 80],
          "value2": [100, 200, 300, 400, 500, 600, 700, 800],
          "value3": [50, 20, 30, 90, 50, 60, 80, 80],
          "value4": [10, 30, 90, 40, 60, 60, 70, 80]}

df_1 = pd.DataFrame(dict_1, columns=["time", "pos", "value1", "value2", "value3", "value4"])
print("\n", "df_1", "\n", df_1, "\n")

#第一种方式
def get_f5(x):
    print("x的结果是：\n",x) #查看x的类型
    print("x的值是：\n",x.values) #查看x的类型
    print("x转为list是：\n",x.values.tolist()) #查看x的类型
    print("最终输出：\n",dict(x.values.tolist())) #查看x的类型
    return dict(x.values.tolist())

data = df_1.groupby("pos")[["value1","value2"]].apply(get_f5)
print("第一种方式：",data)
# # 第二种方式
dict_map = df_1.groupby('pos')[['value1','value2']].apply(lambda g: dict(g.values.tolist()))
print("第二种方式：",dict_map)
dict_result = df_1.groupby('pos')[['value1','value2']].apply(lambda g: dict(g.values.tolist())).to_dict()
print("最终结果：",dict_result)