Python 3.x python：从比较两个直方图的数据集中删除记录

我有两个长度不同的多列（顺序为10）数据集（每一行都是一条记录），它们必须成为相同数量的行：标准是对多个列进行装箱，从2到4，然后删除两个数据集中的一个中超出的记录（在该箱子中的所有记录之间随机选取）

我目前正在使用numpy，但也可以使用熊猫

因为我事先知道一个数据集比另一个小，所以我（天真地说）的想法是计算两个柱状图（先小一点），从另一个柱状图中减去一个柱状图，以在每个箱子中获得差异，然后遍历数据集以删除超出的记录，但是：我必须知道哪个箱子中有什么记录

用于在python中计算直方图的代码段（为了简单起见，使用两列数据集）：

是否有一种方法可以在装箱时跟踪数据集索引？ 我知道

pandas

数据帧可以有索引，所以如果我坚持这个算法，它们可能是一个自然的选择

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有没有更聪明的方法来做到这一点，改变算法但坚持使用python？

我使用

pandas

找到了一个很好的解决方案

import pandas as pd, numpy as np
x = 50 * np.random.randn(50, 5)
dfx = pd.DataFrame(x)
bins = np.linspace(min(dfx[0]), max(dfx[0]), 10)
first_binning = pd.cut(dfx[0], bins)
bins = np.linspace(min(dfx[1]), max(dfx[1]), 5)
second_binning = pd.cut(ddx[1], bins)
groups = dfx.groupby([first_binning, second_binning])

现在，您可以（根据您的数据）：

查看计数，以及

In [163]: groups.indices
Out[163]:
{('(-101.273, -71.403]', '(50.481, 109.902]'): array([20, 37]),
 ('(-11.661, 18.21]', '(-127.783, -68.362]'): array([26, 39]),
 ('(-11.661, 18.21]', '(-8.94, 50.481]'): array([ 4, 14, 18, 34, 35,     45]),
 ('(-11.661, 18.21]', '(50.481, 109.902]'): array([17]),
 ('(-41.532, -11.661]', '(-68.362, -8.94]'): array([ 3, 13, 16, 30]),
 ('(-41.532, -11.661]', '(-8.94, 50.481]'): array([25, 38, 48]),
 ('(-41.532, -11.661]', '(50.481, 109.902]'): array([0, 5]),
 ('(-71.403, -41.532]', '(-68.362, -8.94]'): array([ 1, 24, 32, 47]),
 ('(-71.403, -41.532]', '(-8.94, 50.481]'): array([ 6, 19, 31]),
 ('(-71.403, -41.532]', '(50.481, 109.902]'): array([12]),
 ('(18.21, 48.0806]', '(-127.783, -68.362]'): array([21, 46]),
 ('(18.21, 48.0806]', '(-68.362, -8.94]'): array([ 2, 15, 22, 33, 40]),
 ('(18.21, 48.0806]', '(-8.94, 50.481]'): array([ 7, 28, 36]),
 ('(18.21, 48.0806]', '(50.481, 109.902]'): array([ 9, 23, 49]),
 ('(48.0806, 77.951]', '(-68.362, -8.94]'): array([41, 42]),
 ('(48.0806, 77.951]', '(-8.94, 50.481]'): array([27, 29, 43, 44]),
 ('(77.951, 107.822]', '(-68.362, -8.94]'): array([11])}

当然要查看数据集记录索引。

有帮助吗？

In [160]: groups.size()
Out[160]:
0                    1
(-101.273, -71.403]  (50.481, 109.902]      2
(-71.403, -41.532]   (-68.362, -8.94]       4
                     (-8.94, 50.481]        3
                     (50.481, 109.902]      1
(-41.532, -11.661]   (-68.362, -8.94]       4
                     (-8.94, 50.481]        3
                     (50.481, 109.902]      2
(-11.661, 18.21]     (-127.783, -68.362]    2
                     (-8.94, 50.481]        6
                     (50.481, 109.902]      1
(18.21, 48.0806]     (-127.783, -68.362]    2
                     (-68.362, -8.94]       5
                     (-8.94, 50.481]        3
                     (50.481, 109.902]      3
(48.0806, 77.951]    (-68.362, -8.94]       2
                     (-8.94, 50.481]        4
(77.951, 107.822]    (-68.362, -8.94]       1
dtype: int64

In [163]: groups.indices
Out[163]:
{('(-101.273, -71.403]', '(50.481, 109.902]'): array([20, 37]),
 ('(-11.661, 18.21]', '(-127.783, -68.362]'): array([26, 39]),
 ('(-11.661, 18.21]', '(-8.94, 50.481]'): array([ 4, 14, 18, 34, 35,     45]),
 ('(-11.661, 18.21]', '(50.481, 109.902]'): array([17]),
 ('(-41.532, -11.661]', '(-68.362, -8.94]'): array([ 3, 13, 16, 30]),
 ('(-41.532, -11.661]', '(-8.94, 50.481]'): array([25, 38, 48]),
 ('(-41.532, -11.661]', '(50.481, 109.902]'): array([0, 5]),
 ('(-71.403, -41.532]', '(-68.362, -8.94]'): array([ 1, 24, 32, 47]),
 ('(-71.403, -41.532]', '(-8.94, 50.481]'): array([ 6, 19, 31]),
 ('(-71.403, -41.532]', '(50.481, 109.902]'): array([12]),
 ('(18.21, 48.0806]', '(-127.783, -68.362]'): array([21, 46]),
 ('(18.21, 48.0806]', '(-68.362, -8.94]'): array([ 2, 15, 22, 33, 40]),
 ('(18.21, 48.0806]', '(-8.94, 50.481]'): array([ 7, 28, 36]),
 ('(18.21, 48.0806]', '(50.481, 109.902]'): array([ 9, 23, 49]),
 ('(48.0806, 77.951]', '(-68.362, -8.94]'): array([41, 42]),
 ('(48.0806, 77.951]', '(-8.94, 50.481]'): array([27, 29, 43, 44]),
 ('(77.951, 107.822]', '(-68.362, -8.94]'): array([11])}