2019-12-02 22:12:02 8 Comments

Currently have a dataframe that is countries by series, with values ranging from 0-25

I want to sort the df so that the highest values appear in the top left (first), while the lowest appear in the bottom right (last).

## FROM

```
A B C D ...
USA 4 0 10 16
CHN 2 3 13 22
UK 2 1 8 14
...
```

## TO

```
D C A B ...
CHN 22 13 2 3
USA 16 10 4 0
UK 14 8 2 1
...
```

In this, the column with the highest values is now first, and the same is true with the index.

I have considered reindexing, but this loses the 'Countries' Index.

```
D C A B ...
0 22 13 2 3
1 16 10 4 0
2 14 8 2 1
...
```

I have thought about creating a new column and row that has the Mean or Sum of values for that respective column/row, but is this the most efficient way?

How would I then sort the DF after I have the new rows/columns??

Is there a way to reindex using...

```
df_mv.reindex(df_mv.mean(or sum)().sort_values(ascending = False).index, axis=1)
```

... that would allow me to keep the country index, and simply sort it accordingly?

Thanks for any and all advice or assistance.

## EDIT

Intended result organizes columns AND rows from largest to smallest.

Regarding the first row of the A and B columns in the intended output, these are supposed to be 2, 3 respectively. This is because the intended result interprets the A column as greater than the B column in both sum and mean (even though either sum or mean can be considered for the 'value' of a row/column).

By saying the higher numbers would be in the top left, while the lower ones would be in the bottom right, I simply meant this as a general trend for the resulting df. It is the columns and rows as whole however, that are the intended focus. I apologize for the confusion.

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### [SOLVED] Adding new column to existing DataFrame in Python pandas

**2012-09-23 19:00:01****tomasz74****2031480**View**1002**Score**24**Answer- Tags: python pandas dataframe chained-assignment

## 4 comments

## @Mykola Zotko 2019-12-02 22:58:56

Using

`numpy`

:Output:

## @jorijnsmit 2019-12-02 22:42:53

Here's another way, this time without transposing but using

`axis=1`

as an argument:## @ansev 2019-12-02 22:37:59

You could use:

## @jorijnsmit 2019-12-02 22:43:51

This does not match the expected result.

## @Erfan 2019-12-02 22:53:31

His actually does, yours does not. @jorijnsmit

## @ansev 2019-12-02 22:54:17

Look well at the expected result, now that I notice it is your answer that does not match

## @Mykola Zotko 2019-12-02 23:00:06

I don't understand why you have

`2, 3`

and not`3, 2`

in the first row at the end.## @jorijnsmit 2019-12-02 23:04:26

Hmm question is ambiguous actually. OP asks for highest number in the top left (

`22`

) and lowest in the bottom right; which is`0`

!## @Alex 2019-12-03 00:31:03

Using df.max unfortunately did not yield the results I was looking for- however, keeping all else the same and changing that df.max to df.mean, worked out great! thank you!

## @ansev 2019-12-03 00:31:39

I am glad to help you:)

## @jorijnsmit 2019-12-02 22:18:31

Use

`.T`

to transpose rows to columns and vice versa:Result:

## @ansev 2019-12-02 22:27:17

Why do you know that you must order by D? I think this is not a general solution.

## @jorijnsmit 2019-12-02 22:35:45

You are right! Edited my answer to sort by

`df.max().idxmax()`

instead of by arbitrarily selecting a column with a highest value.## @Mykola Zotko 2019-12-02 23:04:09

Shouldn't it be

`4, 0`

in the second row and`2, 1`

in the last row at the end?## @jorijnsmit 2019-12-02 23:05:48

¯_(ツ)_/¯ question can be interpreted in multiple ways.

## @ansev 2019-12-02 23:06:52

I think the expected output speaks for itself

## @Mykola Zotko 2019-12-02 23:08:05

@ansev Ok. If you take the expected output of OP you get

`2, 1`

in the last row and not`1, 2`

.## @ansev 2019-12-02 23:10:37

which on the other hand is the output that OP expects