pandas 按在熊猫中的位置选择多个数据框列
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Selecting multiple dataframe columns by position in pandas
提问by aiden rosenblatt
I have a (large) dataframe. How can I select specific columns by position? e.g. columns 1..3, 5, 6
我有一个（大）数据框。如何按位置选择特定列？例如第 1..3、5、6 列
Rather than just drop column4, I am trying to do it in this way because there are a ton of rows in my dataset and I want to select by position:
我不只是删除 column4，而是尝试以这种方式执行此操作，因为我的数据集中有大量行并且我想按位置选择：
df=df[df.columns[0:2,4:5]]
but that gives IndexError: too many indices for array
但这给 IndexError: too many indices for array
DF input
DF输入
Col1 Col2 Col3 Col4 Col5 Col6
1 apple tomato pear banana banana
1 apple grape nan banana banana
1 apple nan banana banana banana
1 apple tomato banana banana banana
1 apple tomato banana banana banana
1 apple tomato banana banana banana
1 avacado tomato banana banana banana
1 toast tomato banana banana banana
1 grape tomato egg banana banana
DF output - desired
DF 输出 - 所需
Col1 Col2 Col3 Col5 Col6
1 apple tomato banana banana
1 apple grape banana banana
1 apple nan banana banana
1 apple tomato banana banana
1 apple tomato banana banana
1 apple tomato banana banana
1 avacado tomato banana banana
1 toast tomato banana banana
1 grape tomato banana banana
回答by YOBEN_S
回答by jpp
You can select columns 0, 1, 4 in this way:
您可以通过这种方式选择第 0、1、4 列：
df.iloc[:, [0, 1, 4]]
You can read more about this in Indexing and Selecting Data.
您可以在索引和选择数据中阅读有关此内容的更多信息。
? iloc is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with python/numpy slice semantics). Allowed inputs are:
? An integer e.g. 5
? A list or array of integers [4, 3, 0]
? A slice object with ints 1:7
? A boolean array
? A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above)
? iloc 主要基于整数位置（从轴的 0 到长度 1），但也可以与布尔数组一起使用。如果请求的索引器越界，.iloc 将引发 IndexError，但允许越界索引的切片索引器除外。（这符合 python/numpy 切片语义）。允许的输入是：
? 一个整数，例如 5
? 整数列表或数组 [4, 3, 0]
? 一个整数为 1:7 的切片对象
? 一个布尔数组
? 一个可调用函数，带有一个参数（调用 Series、DataFrame 或 Panel）并返回有效的索引输出（上述之一）
回答by YanSym
Use the pandas iloc method:
使用Pandas iloc 方法：
df_filtered = df.iloc[:, [1,2,3,5,6]]
回答by Tai
The error OP face is from df.columns[0:2,4:5]where too many indices were put into. IIUC, you can put all the column names you need together to do a selection.
错误 OP face 来自df.columns[0:2,4:5]放入太多索引的地方。IIUC，你可以把你需要的所有列名放在一起做一个选择。
from itertools import chain
cols_to_select = list(v for v in chain(df.columns[0:2], df.columns[4:5]))
df_filtered = df[cols_to_select]
If there can be name conflicts in cols_to_select, do selection using ilocas jp_data_analysis suggested or np.r_as Wen suggested.
如果 cols_to_select 中可能存在名称冲突，请iloc按照 jp_data_analysis 建议或np.r_Wen 建议进行选择。
回答by student
You can also use rangewith concatenatefrom numpyand get columns where np.concatenateis used to combine two different ranges:
您还可以使用range与concatenate从numpy和获得，其中，列np.concatenate被用于两个不同范围的结合：
import numpy as np
df = df[df.columns[np.concatenate([range(0,3),range(4,6)])]]
df
Output:
输出：
Col1 Col2 Col3 Col5 Col6
0 1 apple tomato banana banana
1 1 apple grape banana banana
2 1 apple NaN banana banana
3 1 apple tomato banana banana
4 1 apple tomato banana banana
5 1 apple tomato banana banana
6 1 avacado tomato banana banana
7 1 toast tomato banana banana
8 1 grape tomato banana banana
