Sort Columns of a Dataframe by Column Name

Sorting columns in pandas dataframe based on column name

df = df.reindex(sorted(df.columns), axis=1)

This assumes that sorting the column names will give the order you want. If your column names won't sort lexicographically (e.g., if you want column Q10.3 to appear after Q9.1), you'll need to sort differently, but that has nothing to do with pandas.

Sort a subset of columns of a pandas dataframe alphabetically by column name

You can split your your dataframe based on column names, using normal indexing operator [], sort alphabetically the other columns using sort_index(axis=1), and concat back together:

>>> pd.concat([df[['subject','timepoint']],
           df[df.columns.difference(['subject', 'timepoint'])]\
               .sort_index(axis=1)],ignore_index=False,axis=1)

    subject  timepoint  a  b  c  d
0         1          1  2  2  2  2
1         1          2  3  3  3  3
2         1          3  4  4  4  4
3         1          4  5  5  5  5
4         1          5  6  6  6  6
5         1          6  7  7  7  7
6         2          1  3  3  3  3
7         2          2  4  4  4  4
8         2          3  1  1  1  1
9         2          4  2  2  2  2
10        2          5  3  3  3  3
11        2          6  4  4  4  4
12        3          1  5  5  5  5
13        3          2  4  4  4  4
14        3          4  5  5  5  5
15        4          1  8  8  8  8
16        4          2  4  4  4  4
17        4          3  5  5  5  5
18        4          4  6  6  6  6
19        4          5  2  2  2  2
20        4          6  3  3  3  3

Python/Pandas: Sort dataframe columns based on a column name

You can change order of columns like:

data = {'X1':  ['11', '12'],
        'X2': ['21', '22'],
        'X3': ['31', '32']
        }

df = pd.DataFrame(data)
df
    X1  X2  X3
0   11  21  31
1   12  22  32

df = df.reindex(['X3','X1','X2'], axis=1)
df

    X3  X1  X2
0   31  11  21
1   32  12  22

Note : You need to provide desired order.

You can create a function to change order by given column:

def sorter(desired, df):
    columns = df.columns.tolist()
    columns.remove(desired)
    columns.insert(0,desired)
    return df.reindex(columns, axis=1)

sorter('X2',df)

    X2  X1  X3
0   21  11  31
1   22  12  32

Sorting pandas dataframe by column index instead of column name

sort_values is not an indexer but a method. You use it with [] instead of () but it doesn't seem to be the problem.

If you want to sort your dataframe by the second column whatever the name, use:

>>> df.sort_values(df.columns[1])
     name  score
1     Joe      3
0    Mary     10
2  Jessie     13

Pandas sort columns by name

natsort

from natsort import natsorted

df = df.reindex(natsorted(df.columns), axis=1)

#   1  2  3  13_1  13_2  13_10
#0  9  3  4    31     1      2

Re-ordering columns in pandas dataframe based on column header names, where the name of the columns are string, contains the numeric number at the end

It will be :

df = df.reindex(sorted(df.columns[1:], key=lambda x: int(x.split('_')[-1][1:])), axis=1)

Pandas: How to sort dataframe on columns with same column labels

You can create temporary helper columns copying the 2 columns by position using iloc, sort by the temporary helper columns. Finally, drop the temporary helper columns, as follows:

df_test = df_test.assign(A=df_test.iloc[:, 0], B=df_test.iloc[:, 1]).sort_values(by=['A', 'B'], ascending=(False,False)).drop(['A', 'B'], axis=1)

Result:

print(df_test)


          Region  Region
2  San Francisco     4.0
4  San Francisco     1.0
1       Portland     1.0
0      Peninsula     2.0
3    Los Angeles     3.0

Reordering Pandas Columns based on Column name

This type of sorting is called natural sorting. (Full details in the linked duplicate Naturally sorting Pandas DataFrame)

Via natsort

import pandas as pd
from natsort import natsorted

df = pd.DataFrame(columns=[f'company_{i}' for i in [5, 2, 3, 4, 1, 10]])

print(df)

Before sort:

Empty DataFrame
Columns: [company_5, company_2, company_3, company_4, company_1, company_10]
Index: []

natsorted:

df = df.reindex(natsorted(df.columns), axis=1)

Empty DataFrame
Columns: [company_1, company_2, company_3, company_4, company_5, company_10]
Index: []

Lexicographic sorting with sorted:

df = df.reindex(sorted(df.columns), axis=1)

Empty DataFrame
Columns: [company_1, company_10, company_2, company_3, company_4, company_5]
Index: []

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