## Question

Asked By – mane

I have a dataframe with some columns like this:

```
A B C
0
4
5
6
7
7
6
5
```

The *possible range of values in A are only from 0 to 7*.

Also, I have a list of 8 elements like this:

```
List=[2,5,6,8,12,16,26,32] //There are only 8 elements in this list
```

If the element in column A is *n*, I need to insert the *n* th element from the List in a new column, say ‘D’.

How can I do this in one go without looping over the whole dataframe?

The resulting dataframe would look like this:

```
A B C D
0 2
4 12
5 16
6 26
7 32
7 32
6 26
5 16
```

Note: The dataframe is huge and iteration is the last option option. But I can also arrange the elements in ‘List’ in any other data structure like dict if necessary.

**Now we will see solution for issue: Add column in dataframe from list **

## Answer

IIUC, if you make your (unfortunately named) `List`

into an `ndarray`

, you can simply index into it naturally.

```
>>> import numpy as np
>>> m = np.arange(16)*10
>>> m[df.A]
array([ 0, 40, 50, 60, 150, 150, 140, 130])
>>> df["D"] = m[df.A]
>>> df
A B C D
0 0 NaN NaN 0
1 4 NaN NaN 40
2 5 NaN NaN 50
3 6 NaN NaN 60
4 15 NaN NaN 150
5 15 NaN NaN 150
6 14 NaN NaN 140
7 13 NaN NaN 130
```

Here I built a new `m`

, but if you use `m = np.asarray(List)`

, the same thing should work: the values in `df.A`

will pick out the appropriate elements of `m`

.

Note that if you’re using an old version of `numpy`

, you might have to use `m[df.A.values]`

instead– in the past, `numpy`

didn’t play well with others, and some refactoring in `pandas`

caused some headaches. Things have improved now.

This question is answered By – DSM

**This answer is collected from stackoverflow and reviewed by FixPython community admins, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 **