1、相关函数说明
2、原始数据
df = pd.DataFrame({"id":["00{}".format(i) for i in range(1,10)], "score":[2,3,4,4,5,6,7,7,8]}) display(df)
结果如下：
3、cumsum()函数：求前n个元素的累积值(很重要的一个函数)
df = pd.DataFrame({"id":["00{}".format(i) for i in range(1,10)], "score":[2,3,4,4,5,6,7,7,8]}) display(df) df["cumsum"] = df["score"].cumsum(axis=0) display(df)
结果如下：
1）cumsum()：分组求累计值
df = pd.DataFrame({"id":["001","001","002","003","001","002","002","003","003"], "score":[2,3,4,4,5,6,7,7,8]}) display(df) df["分组求累计值"] = df.groupby("id").cumsum() df = df.sort_values(by=["id"]) display(df)
结果如下：
4、cummax()函数：求前n个元素中的最大值
df = pd.DataFrame({"score":[1,2,1,5,2,6,3,7,1]}) display(df) df["前n个值中最大值"] = df["score"].cummax(axis=0) display(df)
结果如下：
1）cummax()函数：分组求前n个元素中的最大值
df = pd.DataFrame({"id":["001","001","002","003","001","002","002","003","003"], "date":["2020-01-01","2020-01-09","2020-01-05","2020-01-03", "2020-01-08","2020-01-07","2020-01-02","2020-01-04","2020-01-06"], "score":[1,2,1,5,2,6,3,7,1]}) display(df) df = df.sort_values(by=["id","date"],ascending=[True,True]) df["前n个值中最大值"] = df.groupby("id")["score"].cummax() display(df)
结果如下：
注意：cummin()函数的用法和cummax()函数的用法一致，可以自行下去尝试。
5、cumprod()函数：求前n个元素的累乘积
df = pd.DataFrame({"score":[1,2,1,5,2,6,3,7,1]}) display(df) df["前n个值的累乘积"] = df["score"].cummax(axis=0) display(df)
结果如下：
注意：对于分组求前n个元素的累乘积，和上面用法一致。
