在pandas框架列中查找数组元素位置（a.k.a pd.series） [英] Finding an array elements location in a pandas frame column (a.k.a pd.series)

问题描述

我有一个类似于这个的熊猫框架：

I have a pandas frame similar to this one:

import pandas as pd
import numpy as np

data = {'Col1' : [4,5,6,7], 'Col2' : [10,20,30,40], 'Col3' : [100,50,-30,-50], 'Col4' : ['AAA', 'BBB', 'AAA', 'CCC']}

df = pd.DataFrame(data=data, index = ['R1','R2','R3','R4'])

    Col1  Col2  Col3 Col4
R1     4    10   100  AAA
R2     5    20    50  BBB
R3     6    30   -30  AAA
R4     7    40   -50  CCC

给定一系列目标：

target_array = np.array(['AAA', 'CCC', 'EEE'])

我想找到 Col4 中的单元格元素索引，它们也出现在 target_array 。

I would like to find the cell elements indices in Col4 which also appear in the target_array.

我试图找到一个记录在案的答案，但这似乎超出了我的技能......任何人都有任何建议吗？

I have tried to find a documented answer but it seems beyond my skill... Anyone has any advice?

PS顺便提一下，对于这种特殊情况，我可以输入一个目标数组，其元素是数据帧索引名称 array（['R1'，'R3'，'R5']）。这样会更容易吗？

P.S. Incidentally, for this particular case I can input a target array whose elements are the data frame indices names array(['R1', 'R3', 'R5']). Would it be easier that way?

编辑1：

非常感谢你们所有的好评。可悲的是，我只能选择一个，但每个人似乎都认为@Divakar是最好的。你还应该看看所有可能的piRSquared和MaxU速度比较

Thank you very much for all the great replies. Sadly I can only choose one but everyone seems to point @Divakar as the best. Still you should look at piRSquared and MaxU speed comparisons for all the possibilities available

推荐答案

你可以使用NumPy in1d -

You can use NumPy's in1d -

df.index[np.in1d(df['Col4'],target_array)]

说明

1）创建 1D 掩码告诉我们 col4的元素与 target_array ：

1) Create a 1D mask corresponding to each row telling us whether there is a match between col4's element and any element in target_array :

mask = np.in1d(df['Col4'],target_array)

2）使用掩码从数据帧中选择有效索引作为最终输出：

2) Use the mask to select valid indices from the dataframe as final output :

out = df.index[np.in1d(df['Col4'],target_array)]

这篇关于在pandas框架列中查找数组元素位置（a.k.a pd.series）的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！