Pandas: return multiple column values based on searching the multiple row values

Question

Assume I have a dataframe in Pandas:

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
                   'B': 'one one two three two two one three'.split(),
                   'C': '0 1 2 3 4 5 6 7'.split(), 
                   'D': '0 2 4 6 8 10 12 14'.split()})

The dataframe df looks like

      A      B  C   D
 0  foo    one  0   0
 1  bar    one  1   2
 2  foo    two  2   4
 3  bar  three  3   6
 4  foo    two  4   8
 5  bar    two  5  10
 6  foo    one  6  12
 7  foo  three  7  14

Note that the type of numbers in C and D columns is string.

I'm thinking about two conditions:

(1) Consecutive search

I want to return the D values when I'm searching C=2:5. If I use df.loc[df['C'] == '2:5', "D"], it returns an error. How can I do this part?

(2) Discrete search

I'd like to return the D values when I'm searching C=0,3,6. Again, if I use df.loc[df['C'] == '0,3,6', "D"], it returns an error. What should I write this code?

You shouldn't be using strings to represent numerical values. — Quang Hoang
– Quang Hoang, Commented May 11, 2021 at 16:48

Shivam Roy · Accepted Answer · 2021-05-11 22:38:38Z

1

Consecutive Search:

The consecutive search method for a range of numerical strings can be taken care of by the isin() and zfill() method. The zfill method in the below code creates a list containing the range of the numbers:

df.D.loc[df.C.isin([str(i).zfill(1) for i in range(2,6)])]

Discrete Search

The discrete searching can be done by using the isin() method:

df.D.loc[(df.C.isin(['0','3','6'])]

edited May 11, 2021 at 22:38

answered May 11, 2021 at 16:48

Shivam Roy

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2 Comments

Thomas D. Over a year ago

Yes, for the first case (consecutive searching), we can list them as you wrote. But if they're large interval, say 1:100, we can no longer list them. Is there a more convenient way?

Shivam Roy Over a year ago

@Thomas Thanks for this question, and it can be done pretty easily, please chek the edited answer.

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