df.sort_values(['Total Due']) returns a sorted DF, but it doesn't update DF in place.

So do it explicitly:

df = df.sort_values(['Total Due'])

or

df.sort_values(['Total Due'], inplace=True)

NOTE: the Pandas core team discourages the use of inplace=True parameter because it should be deprecated in the future versions of Pandas.

Use sort_values to sort the df by a specific column's values:

In [18]:
df.sort_values('2')

Out[18]:
        0          1     2
4    85.6    January   1.0
3    95.5   February   2.0
7   104.8      March   3.0
0   354.7      April   4.0
8   283.5        May   5.0
6   238.7       June   6.0
5   152.0       July   7.0
1    55.4     August   8.0
11  212.7  September   9.0
10  249.6    October  10.0
9   278.8   November  11.0
2   176.5   December  12.0

If you want to sort by two columns, pass a list of column labels to sort_values with the column labels ordered according to sort priority. If you use df.sort_values(['2', '0']), the result would be sorted by column 2 then column 0. Granted, this does not really make sense for this example because each value in df['2'] is unique.

If you want to sort the entire DataFrame:

out = myDataTradeSWE.sort_values(by='VAL', ascending=False)

If you want to sort only VAL:

out = myDataTradeSWE['VAL'].sort_values(ascending=False)

@QuangHoang has the correct answer in the comments.

Expanded: Although df.sort_values(by="time", inplace=True, ascending=True) has the effect of re-sorting the values in the column specified, it does not change the index. To do this, I added ignore_index=True and another line df.reset_index(drop=True, inplace=True), making the full code:

df.sort_values(by="time", inplace=True, ascending=True, ignore_index=True)
df.reset_index(drop=True, inplace=True)

As a last check when saving the file, I added index=True which saves the index when exporting as CSV. That way you can directly troubleshoot what is happening to the values and indices with any manipulation.

df.to_csv(f'fname', index=True)