按X列对数据框进行分组

本文介绍了按X列对数据框进行分组的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有一个数据框，我想将一个函数应用于每2列(或3列，它是变量).

I have a dataframe and I'd like to apply a function to each 2 columns (or 3, it's variable).

例如下面的DataFrame，我想将均值函数应用于0-1、2-3、4-5，.... 28-29列

For example with the following DataFrame, I'd like to apply the mean function to columns 0-1, 2-3, 4-5, ....28-29

d = pd.DataFrame((np.random.randn(360)).reshape(12,30)) 0 1 ... 17 18 19 29 0 0.590293 -2.794911 ... 0.772830 -1.389820 -1.696832 ... 0.615549 1 0.115954 2.179996 ... -0.764384 -0.610713 -0.289050 ... -1.130803 2 0.209405 0.381398 ... -0.317797 0.261590 2.502581 ... 1.750126 3 2.828746 0.831299 ... -0.679128 -1.255643 0.245522 ... -0.612011 4 0.625284 1.141448 ... 0.391047 -1.262303 -0.094523 ... -3.643543 5 0.493923 1.601924 ... -0.935102 -2.416869 0.112278 ... -0.001863 6 -1.213347 0.396682 ... 0.671210 0.122041 -1.469256 ... 1.825214 7 0.026695 -0.482887 ... 0.020123 1.151533 -0.440114 ... -1.407276 8 0.235436 0.763454 ... -0.446333 -0.322420 1.067925 ... -0.622363 9 0.668812 0.537556 ... 0.471777 -0.119756 0.098581 ... 0.007390 10 -1.112536 -2.378293 ... 1.047705 -0.812025 0.771080 ... -0.403167 11 -0.709457 -1.598942 ... -0.568418 -2.095332 -1.970319 ... 1.687536

推荐答案

groupby也可以在axis=1上运行，并且可以接受一组组标签.如果您的列在您的示例中那样是方便的范围，那么这很简单:

groupby can work on axis=1 as well, and can accept a sequence of group labels. If your columns are convenient ranges like in your example, it's trivial:

>>> df = pd.DataFrame((np.random.randn(6*6)).reshape(6,6)) >>> df 0 1 2 3 4 5 0 1.705550 -0.757193 -0.636333 2.097570 -1.064751 0.450812 1 0.575623 -0.385987 0.105516 0.820795 -0.464069 0.728609 2 0.776840 -0.173348 0.878534 0.995937 0.094515 0.098853 3 0.326854 1.297625 2.232534 1.004719 -0.440271 1.548430 4 0.483211 -1.182175 -0.012520 -1.766317 -0.895284 -0.695300 5 0.523011 -1.653557 1.022042 1.201774 -1.118465 1.400537 >>> df.groupby(df.columns//2, axis=1).mean() 0 1 2 0 0.474179 0.730618 -0.306970 1 0.094818 0.463155 0.132270 2 0.301746 0.937235 0.096684 3 0.812239 1.618627 0.554080 4 -0.349482 -0.889419 -0.795292 5 -0.565273 1.111908 0.141036

(之所以起作用，是因为df.columns//2给出了Int64Index([0, 0, 1, 1, 2, 2], dtype='int64').)

(This works because df.columns//2 gives Int64Index([0, 0, 1, 1, 2, 2], dtype='int64').)

即使我们不是很幸运，我们仍然可以自己建立适当的小组:

Even if we're not so fortunate, we can still build the appropriate groups ourselves:

>>> df.groupby(np.arange(df.columns.size)//2, axis=1).mean() 0 1 2 0 0.474179 0.730618 -0.306970 1 0.094818 0.463155 0.132270 2 0.301746 0.937235 0.096684 3 0.812239 1.618627 0.554080 4 -0.349482 -0.889419 -0.795292 5 -0.565273 1.111908 0.141036