[英]Getting n-th ranked column IDs per row of a dataframe - Python/Pandas
I am trying to find a method for finding the nth ranked value and returning the column name. 我正在尝试找到一种方法来查找第n个排名值并返回列名。 So for example, given a data-frame:
例如,给定一个数据框:
df = pd.DataFrame(np.random.randn(5, 4), columns = list('ABCD'))
# Return column name of "MAX" value, compared to other columns in any particular row.
df['MAX1_NAMES'] = df.idxmax(axis=1)
print df
A B C D MAX1_NAMES
0 -0.728424 -0.764682 -1.506795 0.722246 D
1 1.305500 -1.191558 0.068829 -1.244659 A
2 -0.175834 -0.140273 1.117114 0.817358 C
3 -0.255825 -1.534035 -0.591206 -0.352594 A
4 -2.408806 -1.925055 -1.797020 2.381936 D
This would find the highest value in the rows and return the column name where it occurred. 这将在行中找到最高值并返回其出现的列名称。 But I need the case where I can choose the particular rank of the desired value, and hopefully get a data frame like the following:
但我需要这样的情况,我可以选择所需值的特定排名,并希望得到如下数据框:
A B C D MAX1_NAMES MAX2_NAMES
0 -0.728424 -0.764682 -1.506795 0.722246 D A
1 1.305500 -1.191558 0.068829 -1.244659 A C
2 -0.175834 -0.140273 1.117114 0.817358 C D
3 -0.255825 -1.534035 -0.591206 -0.352594 A D
4 -2.408806 -1.925055 -1.797020 2.381936 D C
Where MAX2_NAMES
is the second largest value in the row. 其中
MAX2_NAMES
是行中的第二大值。
Thanks. 谢谢。
You can apply an argsort()
per row, reverse the index and pick up the one at the second position: 您可以每行应用一个
argsort()
,反转索引并在第二个位置拾取一个:
df['MAX2_NAMES'] = df.iloc[:,:4].apply(lambda r: r.index[r.argsort()[::-1][1]], axis = 1)
df
# A B C D MAX1_NAMES MAX2_NAMES
#0 -0.728424 -0.764682 -1.506795 0.722246 D A
#1 1.305500 -1.191558 0.068829 -1.244659 A C
#2 -0.175834 -0.140273 1.117114 0.817358 C D
#3 -0.255825 -1.534035 -0.591206 -0.352594 A D
#4 -2.408806 -1.925055 -1.797020 2.381936 D C
You are looking to perform the ranking for a particular rank n
only, so I would like to suggest np.argpartition
that would get sorted indices just for the highest n-ranked entries at each row rather than sorting all elements. 您正在寻找仅针对特定排名
n
执行排名,因此我建议np.argpartition
将获得排序索引,仅针对每行中排名最高的n个条目,而不是排序所有元素。 This is aimed at improved performance. 这旨在提高性能。 The performance benefits are discussed in length in answers to
A fast way to find the largest N elements in an numpy array
and hopefully we will reap the benefits here too. 在
A fast way to find the largest N elements in an numpy array
答案中,我们将详细讨论性能优势,希望我们也能从中获益。
Thus, in a function format, we would have - 因此,在函数格式中,我们将 -
def rank_df(df,rank):
coln = 'MAX' + str(rank) + '_NAMES'
sortID = np.argpartition(-df[['A','B','C','D']].values,rank,axis=1)[:,rank-1]
df[coln] = df.columns[sortID]
Sample run - 样品运行 -
In [84]: df
Out[84]:
A B C D
0 -0.124851 0.152432 1.436602 -0.391178
1 0.371932 1.732399 0.340876 -1.340609
2 -1.218608 0.444246 0.169968 -1.437259
3 -0.828132 0.821613 -0.556643 -0.407703
4 -0.390477 0.048824 -2.087323 1.597030
In [85]: rank_df(df,1)
In [86]: rank_df(df,2)
In [87]: df
Out[87]:
A B C D MAX1_NAMES MAX2_NAMES
0 -0.124851 0.152432 1.436602 -0.391178 C B
1 0.371932 1.732399 0.340876 -1.340609 B A
2 -1.218608 0.444246 0.169968 -1.437259 B C
3 -0.828132 0.821613 -0.556643 -0.407703 B D
4 -0.390477 0.048824 -2.087323 1.597030 D B
Runtime test 运行时测试
I am timing np.argpartition
based approach as listed earlier in this post and np.argsort
based one as listed in the other solution by @Psidom on a decent sized dataframe. 我正在
np.argpartition
基于np.argpartition
的方法,如本文前面所列, np.argsort
基于np.argsort
在另一个解决方案中列出的一个体面的大小数据帧。
In [92]: df = pd.DataFrame(np.random.randn(10000, 4), columns = list('ABCD'))
In [93]: %timeit rank_df(df,2)
100 loops, best of 3: 2.36 ms per loop
In [94]: df = pd.DataFrame(np.random.randn(10000, 4), columns = list('ABCD'))
In [95]: %timeit df['MAX2_NAMES'] = df.iloc[:,:4].apply(lambda r: r.index[r.argsort()[::-1][1]], axis = 1)
1 loops, best of 3: 3.32 s per loop
You can do this by combining rank, apply, and idxmin. 您可以通过组合rank,apply和idxmin来完成此操作。
for example: 例如:
df = pd.util.testing.makeTimeDataFrame(5)
df
A B C D
2000-01-03 -1.814888 -0.709120 -0.134390 -0.906183
2000-01-04 0.459742 1.235481 0.109602 -0.226923
2000-01-05 -1.567867 0.562368 -1.185567 -2.176161
2000-01-06 0.747989 -0.160384 1.617100 0.242830
2000-01-07 -1.288061 -1.631342 -0.857830 -0.210695
df['rank_2_col'] = df.rank(1).apply(lambda r: r[r==2].idxmin(), axis=1)
df
A B C D rank_2_col
2000-01-03 -1.814888 -0.709120 -0.134390 -0.906183 D
2000-01-04 0.459742 1.235481 0.109602 -0.226923 C
2000-01-05 -1.567867 0.562368 -1.185567 -2.176161 A
2000-01-06 0.747989 -0.160384 1.617100 0.242830 D
2000-01-07 -1.288061 -1.631342 -0.857830 -0.210695 A
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