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[英]How do I sort two parameters The largest number should be displayed first, and then from the smallest to the largest
[英]How to sort a group in a way that I get the largest number in the first row and smallest in the second and the second largest in the third and so on
所以我有一个这样的df
In [1]:data= {'Group': ['A','A','A','A','A','A','B','B','B','B'],
'Name': [ ' Sheldon Webb',' Traci Dean',' Chad Webster',' Ora Harmon',' Elijah Mendoza',' June Strickland',' Beth Vasquez',' Betty Sutton',' Joel Gill',' Vernon Stone'],
'Performance':[33,64,142,116,122,68,95,127,132,80]}
In [2]:df = pd.DataFrame(data, columns = ['Group', 'Name','Performance'])
Out[1]:
Group Name Performance
0 A Sheldon Webb 33
1 A Traci Dean 64
2 A Chad Webster 142
3 A Ora Harmon 116
4 A Elijah Mendoza 122
5 A June Strickland 68
6 B Beth Vasquez 95
7 B Betty Sutton 127
8 B Joel Gill 132
9 B Vernon Stone 80
我想以这样一种交替的方式对它进行排序,在一个组中,比如说“A”组,第一行应该有表现最好的人(在这种情况下是“Chad Webster”),然后在第二行中表现最差的人(这是“谢尔顿·韦伯”)。
我正在寻找的 output 看起来像这样:
Out[2]:
Group Name Performance
0 A Chad Webster 142
1 A Sheldon Webb 33
2 A Elijah Mendoza 122
3 A Traci Dean 64
4 A Ora Harmon 116
5 A June Strickland 68
6 B Joel Gill 132
7 B Vernon Stone 80
8 B Betty Sutton 127
9 B Beth Vasquez 95
您可以看到该序列在组内的最高和最低之间交替。
采用排序顺序,然后对其应用二次 function ,其中根是数组长度的 1/2(加上一些小的偏移量)。 这样,最高等级被赋予极值( eps
偏移量的符号决定您是否想要一个高于最低值的最高值)。 我在最后添加了一个小组,以展示它如何正确处理重复值或奇数组大小。
def extremal_rank(s):
eps = 10**-4
y = (pd.Series(np.arange(1, len(s)+1), index=s.sort_values().index)
- (len(s)+1)/2 + eps)**2
return y.reindex_like(s)
df['rnk'] = df.groupby('Group')['Performance'].apply(extremal_rank)
df = df.sort_values(['Group', 'rnk'], ascending=[True, False])
Group Name Performance rnk
2 A Chad Webster 142 6.2505
0 A Sheldon Webb 33 6.2495
4 A Elijah Mendoza 122 2.2503
1 A Traci Dean 64 2.2497
3 A Ora Harmon 116 0.2501
5 A June Strickland 68 0.2499
8 B Joel Gill 132 2.2503
9 B Vernon Stone 80 2.2497
7 B Betty Sutton 127 0.2501
6 B Beth Vasquez 95 0.2499
11 C b 110 9.0006
12 C c 68 8.9994
10 C a 110 4.0004
13 C d 68 3.9996
15 C f 70 1.0002
16 C g 70 0.9998
14 C e 70 0.0000
如果您在 Performace 上使用sort_values
一次升序一次降序,则可以避免groupby
, concat
两个排序的数据帧,然后使用sort_index
和drop_duplicates
获得预期的 output:
df_ = (pd.concat([df.sort_values(['Group', 'Performance'], ascending=[True, False])
.reset_index(), #need the original index for later drop_duplicates
df.sort_values(['Group', 'Performance'], ascending=[True, True])
.reset_index()
.set_index(np.arange(len(df))+0.5)], # for later sort_index
axis=0)
.sort_index()
.drop_duplicates('index', keep='first')
.reset_index(drop=True)
[['Group', 'Name', 'Performance']]
)
print(df_)
Group Name Performance
0 A Chad Webster 142
1 A Sheldon Webb 33
2 A Elijah Mendoza 122
3 A Traci Dean 64
4 A Ora Harmon 116
5 A June Strickland 68
6 B Joel Gill 132
7 B Vernon Stone 80
8 B Betty Sutton 127
9 B Beth Vasquez 95
对每个组应用nlargest
和nsmallest
的排序连接:
>>> (df.groupby('Group')[df.columns[1:]]
.apply(lambda x:
pd.concat([x.nlargest(x.shape[0]//2,'Performance').reset_index(),
x.nsmallest(x.shape[0]-x.shape[0]//2,'Performance').reset_index()]
)
.sort_index()
.drop('index',1))
.reset_index().drop('level_1',1))
Group Name Performance
0 A Chad Webster 142
1 A Sheldon Webb 33
2 A Elijah Mendoza 122
3 A Traci Dean 64
4 A Ora Harmon 116
5 A June Strickland 68
6 B Joel Gill 132
7 B Vernon Stone 80
8 B Betty Sutton 127
9 B Beth Vasquez 95
使用自定义 function 和np.empty
的另一种方法:
def mysort(s):
arr = s.to_numpy()
c = np.empty(arr.shape, dtype=arr.dtype)
idx = arr.shape[0]//2 if not arr.shape[0]%2 else arr.shape[0]//2+1
c[0::2], c[1::2] = arr[:idx], arr[idx:][::-1]
return pd.DataFrame(c, columns=s.columns)
print (df.sort_values("Performance", ascending=False).groupby("Group").apply(mysort))
Group Name Performance
Group
A 0 A Chad Webster 142
1 A Sheldon Webb 33
2 A Elijah Mendoza 122
3 A Traci Dean 64
4 A Ora Harmon 116
5 A June Strickland 68
B 0 B Joel Gill 132
1 B Vernon Stone 80
2 B Betty Sutton 127
3 B Beth Vasquez 95
基准:
让我们尝试使用groupby().transform()
检测min, max
行,然后排序:
groups = df.groupby('Group')['Performance']
mins, maxs = groups.transform('min'), groups.transform('max')
(df.assign(temp=df['Performance'].eq(mins) | df['Performance'].eq(maxs))
.sort_values(['Group','temp','Performance'],
ascending=[True, False, False])
.drop('temp', axis=1)
)
Output:
Group Name Performance
2 A Chad Webster 142
0 A Sheldon Webb 33
4 A Elijah Mendoza 122
3 A Ora Harmon 116
5 A June Strickland 68
1 A Traci Dean 64
8 B Joel Gill 132
9 B Vernon Stone 80
7 B Betty Sutton 127
6 B Beth Vasquez 95
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