Numpy：重新编码每个元素所属的五分之一的数字数组

Question

我有一个数值向量 a:

import numpy as np

a = np.random.rand(100)

我希望重新编码矢量（或任何其他矢量），以便每个元素为 0、1、2、3 或 4，据此 a 它所在的五分位数（对于任何分位数可能更通用，如四分位数、十分位数等）。

这就是我正在做的。必须有更优雅的东西，不是吗？

from scipy.stats import percentileofscore

n_quantiles = 5

def get_quantile(i, a, n_quantiles):
    if a[i] >= max(a):
        return n_quantiles - 1
    return int(percentileofscore(a, a[i])/(100/n_quantiles))

a_recoded = np.array([get_quantile(i, a, n_quantiles) for i in range(len(a))])

print(a)
print(a_recoded)

[0.04708996 0.86267278 0.23873192 0.02967989 0.42828385 0.58003015
 0.8996666  0.15359369 0.83094778 0.44272398 0.60211289 0.90286434
 0.40681163 0.91338397 0.3273745  0.00347029 0.37471307 0.72735901
 0.93974808 0.55937197 0.39297097 0.91470761 0.76796271 0.50404401
 0.1817242  0.78244809 0.9548256  0.78097562 0.90934337 0.89914752
 0.82899983 0.44116683 0.50885813 0.2691431  0.11676798 0.84971927
 0.38505195 0.7411976  0.51377242 0.50243197 0.89677377 0.69741088
 0.47880953 0.71116534 0.01717348 0.77641096 0.88127268 0.17925502
 0.53053573 0.16935597 0.65521692 0.19042794 0.21981197 0.01377195
 0.61553814 0.8544525  0.53521604 0.88391848 0.36010949 0.35964882
 0.29721931 0.71257335 0.26350287 0.22821314 0.8951419  0.38416004
 0.19277649 0.67774468 0.27084229 0.46862229 0.3107887  0.28511048
 0.32682302 0.14682896 0.10794566 0.58668243 0.16394183 0.88296862
 0.55442047 0.25508233 0.86670299 0.90549872 0.04897676 0.33042884
 0.4348465  0.62636481 0.48201213 0.49895892 0.36444648 0.01410316
 0.46770595 0.09498391 0.96793139 0.03931124 0.64286295 0.50934846
 0.59088907 0.56368594 0.7820928  0.77172038]

[0 4 1 0 2 3 4 0 4 2 3 4 2 4 1 0 1 3 4 2 1 4 3 2 0 3 4 3 4 4 4 2 2 1 0 4 1 
3 2 2 4 3 2 3 0 3 4 0 2 0 3 0 1 0 3 4 2 4 1 1 1 3 1 1 4 1 0 3 1 2 1 1 1 0 
0 3 0 4 2 1 4 4 0 1 2 3 2 2 1 0 2 0 4 0 3 2 3 2 3 3]

更新：只是想说这在 R 中非常简单： How to get the x which belongs to a quintile?

Answer 1

这是使用 pd.cut()

的一种方法

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(100))
df.columns = ['values']
# Apply the quantiles
gdf = df.groupby(pd.cut(df.loc[:, 'values'], np.arange(0, 1.2, 0.2)))['values'].apply(lambda x: list(x)).to_frame()
# Make use of the automatic indexing to assign quantile numbers
gdf.reset_index(drop=True, inplace=True)
# Re-expand the grouped list of values. Method provided by @Zero at 
gdf['values'].apply(pd.Series).stack().reset_index(level=1, drop=True).to_frame('values').reset_index()

Answer 2

您可以使用 argpartition。示例：

>>> a = np.random.random(20)
>>> N = len(a)
>>> nq = 5
>>> o = a.argpartition(np.arange(1, nq) * N // nq)
>>> out = np.empty(N, int)
>>> out[o] = np.arange(N) * nq // N
>>> a
array([0.61238649, 0.37168998, 0.4624829 , 0.28554766, 0.00098016,
       0.41979328, 0.62275886, 0.4254548 , 0.20380679, 0.762435  ,
       0.54054873, 0.68419986, 0.3424479 , 0.54971072, 0.06929464,
       0.51059431, 0.68448674, 0.97009023, 0.16780152, 0.17887862])
>>> out
array([3, 1, 2, 1, 0, 2, 3, 2, 1, 4, 3, 4, 1, 3, 0, 2, 4, 4, 0, 0])

Numpy：重新编码每个元素所属的五分之一的数字数组

Numpy: recode numeric array to which quintile each element belongs

python

numpy

scipy

percentile