获取数据表中分类变量的最小值和最大值

Question

   D   X   Y   Z
   A  22  16  23
   A  21  16  22
   A  20  17  21
   B  33  50  11
   B  34  53  12
   B  34  55  13
   C  44  34  11
   C  45  33  11
   C  45  33  10
   D  55  35  60
   D  57  34  61
   E  66  36  13
   E  67  38  14 
   E  67  37  13

D Xmin Xmax Ymin Ymax Zmin Zmax
A 20    22   16   17   21   23 
B 33    34   50   55   11   13    
C 44    45   33   34   10   11  
D 55    57   34   35   60   61
E 66    67   36   38   13   14

min_max_df = dfObj.groupby('D').agg({'X': [dfObj.min(axis=0), dfObj.max(axis=0)]})

Answer 1

df = df.groupby('D').agg(['min', 'max'])
df.columns = df.columns.map("".join)  # flatten multi-level columns

输出

>>> df

   Xmin  Xmax  Ymin  Ymax  Zmin  Zmax
D                                    
A    20    22    16    17    21    23
B    33    34    50    55    11    13
C    44    45    33    34    10    11
D    55    57    34    35    60    61
E    66    67    36    38    13    14

我做了一些简单的测试，比较了Celius的、Asish的和user17242583/my的解决方案。

设置

import numpy as np 
import pandas as pd 
from itertools import product

n = 1_000_000
rng = np.random.default_rng() 

df = pd.DataFrame({
    'D': rng.choice(list("ABCDEFGH"), size=n),
    'X': rng.integers(100, size=n),
    'Y': rng.integers(100, size=n),
    'Z': rng.integers(100, size=n),
})

>>> df.shape
(1000000, 4)

# Ashish's 
>>> %%timeit -n 50
... aggs = {f"{col}{fn}": (col, fn) for col,fn in product(['X', 'Y', 'Z'], ['min', 'max'])}
... df1 = df.groupby('D').agg(**aggs) 

116 ms ± 5.88 ms per loop (mean ± std. dev. of 7 runs, 50 loops each)

# user17242583's / mine 
>>> %%timeit -n 50
... df1 = df.groupby('D').agg(['min', 'max'])
... df1.columns = df1.columns.map("".join)  # flat multi-level columns

120 ms ± 4.69 ms per loop (mean ± std. dev. of 7 runs, 50 loops each)

#Celius's
>>> %%timeit -n 50
... df1 = df.groupby('D').min().join(df.groupby('D').max(),lsuffix='min',rsuffix='max')

178 ms ± 6.29 ms per loop (mean ± std. dev. of 7 runs, 50 loops each)