[SOLVED] Pandas lookup values from DataFrame by index selectors

Issue

Suppose we have an indexed Dataframe with arbitrary but long number of columns:

from numpy.random import randint
import pandas as pd

df = pd.DataFrame(randint(0,100,size=(10, 4)), columns=list('ABCD'))
print(df)

>    A   B   C   D
> 0  78   1  97  98
> 1  93  58  46  45
> 2  50   1  77  27
> 3  63  87  66  21
> 4  26   1  10  46
> 5  26  60  71  79
> 6  74   4  62  98
> 7  93  22  23  89
> 8  30  31  14  46
> 9  51   4  90  22

And have a selector, which contains which index need for each columns, like:

selector = pd.DataFrame({ "other_index": randint(len(df.index),size=len(df.columns))}, 
                        index=df.columns)
print(selector)

>    other_index
> A            9
> B            0
> C            3
> D            4

Now I would like to get the

selected = [df[c].loc[selector.loc[c][0]] for c in df.columns]
print(selected)

> [51, 1, 66, 46]

I’m pretty sure there is a more efficient way in pandas to achieve this, but I can’t find.

Solution

I would use df.lookup before it got deprecated in the future. 🙂

df = pd.DataFrame(randint(0,100,size=(10, 4)), columns=list('ABCD'))
    A   B   C   D
0  93  30  17  42
1  38  55  10  46
2   7  30  86  36
3  25  48  25  62
4   1  61  50   0
5  18  87  98  87
6  61  57  80  34
7  38  50  32  96
8  72  68  75  74
9  70  99  77  28

selector = pd.DataFrame({ "other_index": randint(len(df.index),size=len(df.columns))}, 
                        index=df.columns)
   other_index
A            5
B            7
C            5
D            9

df.lookup(selector.other_index, selector.index)
array([18, 50, 98, 28])

Answered By – hteza

Answer Checked By – Jay B. (BugsFixing Admin)

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