python - 在 pandas groupby 之后只过滤少数组元素

Question

我正在学习 Hadly 的“R for Data Science”一书，并试图 复制 pandas 中的代码。

我遇到了这个问题:
我必须根据延迟时间创建一个新的排名列 航类并仅过滤它们的最小值和最大值。

R 代码:

library(nycflights13)
library(dplyr)

# remove nans
not_cancelled = flights %>% 
    filter( !is.na(dep_delay), !is.na(arr_delay))

# create new column of rank based on dep_time for each day.
df = not_cancelled %>%  
    group_by(year,month,day) %>%  
    mutate(r = min_rank(desc(dep_time))) %>%  
    filter(r %in% range(r)) %>%  # filter only first and last value
    select(year,month,day,dep_delay,arr_delay,r)

dim(df)
head(df,10)

这给出:

m=month d =day dl = dep_delay ad = arr_delay r =r
    year    m   d   dl  ad  r
    2013    1   1    2  11  831
    2013    1   1   -3  -12 1
    2013    1   2   43  36  928
    2013    1   2   -5  -24 1
    2013    1   3   33  22  900
    2013    1   3   -10 -11 1
    2013    1   4   26  23  908
    2013    1   4   -1  -8  1 
    2013    1   4   -1  -9  1 # Behold! january 4 has 3 rows!!
    2013    1   5   15  18  717

我正试图在 Pandas 中复制这个:

df = pd.read_csv('https://github.com/bhishanpdl/Datasets/blob/master/nycflights13.csv?raw=true')
# print(df.shape)
# print(df.iloc[:5,:5])

not_cancelled = df.dropna(subset=['dep_delay','arr_delay'])

df['r'] = not_cancelled.groupby(['year','month','day'])['dep_time']\
    .rank('min',ascending=False)

g = df.groupby(['year','month','day'])['r']
g = g.agg([min,max]).reset_index()

f = g.head()
print(f)

Python 输出:

(336776, 19)
   year  month  day  min    max
0  2013      1    1  1.0  831.0
1  2013      1    2  1.0  928.0
2  2013      1    3  1.0  900.0
3  2013      1    4  1.0  908.0
4  2013      1    5  1.0  717.0

这不太对。如何做正确的事？

感谢您的帮助。向 Pandas 致敬!

Answer 1

这是正确的输出，你只需要 reshape 你的输出

方法一堆栈

g = df.groupby(['year','month','day'])['r']
g = g.agg([min,max]).stack()
g=g.reset_index(level=[0,1,2])

方法二 melt

g=df.groupby(['year','month','day'])['r'].agg([min,max])
g.reset_index().melt(['year','month','day'])    

---

更新

g = df.groupby(['year','month','day'])['r']
g_max = g.transform('max')
g_min = g.transform('min')
yourdf=df.loc[(df.r==g_max)|(df.r==g_min),['year','month','day','r']]