Select values from different Dataframe column based on a list of index

Question

Here is my issue, I have a dataframe, let's say:

df = DataFrame({'A' : [5,6,3,4], 'B' : [1,2,3, 5]})

I also have a list of index:

idx = [1, 2]

I would like to store in a list, the corresponding value in each column. Meaning I want the first value of the col1 and the second value of the col2.

I'm sure there is a simple answer to my issue however I'm mixing everything up with iloc and cannot find a way of developing a optimized method in my case (I have 1000 rows and 4 columns).

Nk03 · Accepted Answer · 2021-07-06 16:17:24Z

1

IIUC, you can try:

you can extract the complete rows and then pick the diagonal elements

result = np.diag(df.values[idx])

Alternative:

convert the dataframe to numpy array.
use numpy indexing to access the required values.

result = df.values[idx, range(len(df.columns))]

OUTPUT:

array([6, 3])

edited Jul 6, 2021 at 16:17

answered Jul 6, 2021 at 16:10

Nk03

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2 Comments

Thib Ödlaniug Over a year ago

In fact, converting the dataframe to a numpy array solves the issue I expressed in the comment of @AnuragDabas.

Nk03 Over a year ago

So, If I'm not wrong you need this - np.diag(df.values[idx]) ??

Tom McLean · Accepted Answer · 2021-07-06 16:10:39Z

1

Use:

list(df.values[idx, range(len(idx))])

Output:

[6, 3]

answered Jul 6, 2021 at 16:10

Tom McLean

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Comments

rhug123 · Accepted Answer · 2021-07-06 17:28:34Z

0

Here is a different way:

df.stack().loc[list(zip(idx,df.columns[:(len(idx))]))].to_numpy()

answered Jul 6, 2021 at 17:28

rhug123

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