python - pandas:如何保持每个组的最后 `n` 记录按另一个变量排序？

Question

我想保留每个组的最后 n 行，使用 pandas 按变量 var_to_sort 排序。

这就是我现在要做的，我想按 name 对下面的数据框进行分组，然后按 date sort 然后使用tail(n) 以按组获取最后的 n 元素。

data = [
    ['tom', date(2018,2,1), "I want this"],
    ['tom', date(2018,1,1), "Don't want"],
    ['nick', date(2019,4,1), "Don't want"],
    ['nick', date(2019,5,1), "I want this"]]

# Create the pandas DataFrame
df = pd.DataFrame(data)
df.columns = ["names", "date", "result"]

# sort it
df.sort_values("date", inplace=True)

df.groupby("names").tail(1)

有没有更有效的方法来做到这一点？如果数据集已按 "date" 或 ["date", "name"] 索引怎么办？

Answer 1

我认为你的解决方案非常好，也可以使用 sort_values 而不用 inplace 一起用于链代码。

其他问题:

data = [
    ['tom', date(2018,2,1), "I want this"],
    ['tom', date(2018,1,1), "Don't want"],
    ['nick', date(2019,4,1), "Don't want"],
    ['nick', date(2019,5,1), "I want this"]]

# Create the pandas DataFrame
df = pd.DataFrame(data)
df.columns = ["names", "date", "result"]

---

df1 = df.sort_values("date").groupby("names").tail(1)
print (df1)
  names        date       result
0   tom  2018-02-01  I want this
3  nick  2019-05-01  I want this

---

df2 = df.set_index('date')
print (df2)
           names       result
date                         
2018-02-01   tom  I want this
2018-01-01   tom   Don't want
2019-04-01  nick   Don't want
2019-05-01  nick  I want this

df22 = df2.sort_index().groupby("names").tail(1)
print (df22)
           names       result
date                         
2018-02-01   tom  I want this
2019-05-01  nick  I want this

---

df3 = df.set_index(['date','names'])
print (df3)
                       result
date       names             
2018-02-01 tom    I want this
2018-01-01 tom     Don't want
2019-04-01 nick    Don't want
2019-05-01 nick   I want this

df33 = df3.sort_index().groupby(level=1).tail(1)
print (df33)
                       result
date       names             
2018-02-01 tom    I want this
2019-05-01 nick   I want this