用Pandas分割多个列范围

问题描述

假设我在一个数据集中有20列，我想使用19列作为输入.输入列是从1:10到12:20的列.我想使用第11列作为输出.那么如何使用熊猫给这种距离呢?

Suppose I have 20 Columns in a data set and i want to use 19 as an input. and input columns are columns from 1:10 and 12: 20. and I want to use 11th column as an output. so how to give this kind of range using pandas?

例如:示例数据集

考虑上面的数据，它有4列，但是我只需要输入3列，但是那些列是b，d，e，我想跳过c列.现在我正在使用输入= dftrain.loc [:，:'e']考虑所有4列.

consider above data it have 4 columns but i have to take input only 3 columns but those columns are b,d,e and i want to skip c column. Right now i m usinginput = dftrain.loc[:,:'e']which consider all 4 columns.

推荐答案

选项1
np.r_

idx = np.r_[0:11, 12:20]

idx
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 12, 13, 14, 15, 16, 17,
       18, 19])

将此内容传递给iloc-

df.iloc[:, 11] = df.iloc[:, idx].sum(axis=1) # sum, for example

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选项2
pd.IndexSlice

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Option 2
pd.IndexSlice

idx = pd.IndexSlice[0:11, 12:20]

idx
(slice(0, 11, None), slice(12, 20, None))

您可以以与以前相同的方式使用idx.

You can use idx in the same manner as before.

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