how to sort pandas dataframe from one column

Question

I have a data frame like this:

print(df)

        0          1     2
0   354.7      April   4.0
1    55.4     August   8.0
2   176.5   December  12.0
3    95.5   February   2.0
4    85.6    January   1.0
5     152       July   7.0
6   238.7       June   6.0
7   104.8      March   3.0
8   283.5        May   5.0
9   278.8   November  11.0
10  249.6    October  10.0
11  212.7  September   9.0

As you can see, months are not in calendar order. So I created a second column to get the month number corresponding to each month (1-12). From there, how can I sort this data frame according to calendar months' order?

score 761 · Accepted Answer · edited Jun 30 '19 at 03:56

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.

score 277 · Answer 2 · edited Nov 07 '21 at 10:53

277

I tried the solutions above and I do not achieve results, so I found a different solution that works for me. The ascending=False is to order the dataframe in descending order, by default it is True. I am using python 3.6.6 and pandas 0.23.4 versions.

final_df = df.sort_values(by=['2'], ascending=False)

You can see more details in pandas documentation here.

edited Nov 07 '21 at 10:53

vvvvv

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answered Nov 14 '18 at 14:42

Joel Carneiro

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score 70 · Answer 3 · edited Aug 27 '20 at 13:41

70

Using column name worked for me.

sorted_df = df.sort_values(by=['Column_name'], ascending=True)

edited Aug 27 '20 at 13:41

M Z

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answered Aug 27 '20 at 09:57

Niraj

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Gonçalo Peres · Answer 4 · 2022-10-28T13:06:25.010

Panda's sort_values does the work.

There are various parameters one can pass, such as ascending (bool or list of bool):

Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.

As the default is ascending, and OP's goal is to sort ascending, one doesn't need to specify that parameter (see the last note below for the way to solve descending), so one can use one of the following ways:

Performing the operation in-place, and keeping the same variable name. This requires one to pass inplace=True as follows:

df.sort_values(by=['2'], inplace=True)

# or

df.sort_values(by = '2', inplace = True)

# or

df.sort_values('2', inplace = True)

If doing the operation in-place is not a requirement, one can assign the change (sort) to a variable:
- With the same name of the original dataframe, df as
```
df = df.sort_values(by=['2'])
```
- With a different name, such as df_new, as
```
df_new = df.sort_values(by=['2'])
```

All this previous operations would give the following output

        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       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

Finally, one can reset the index with pandas.DataFrame.reset_index, to get the following

df.reset_index(drop = True, inplace = True)

# or

df = df.reset_index(drop = True)

[Out]:

        0          1     2
0    85.6    January   1.0
1    95.5   February   2.0
2   104.8      March   3.0
3   354.7      April   4.0
4   283.5        May   5.0
5   238.7       June   6.0
6     152       July   7.0
7    55.4     August   8.0
8   212.7  September   9.0
9   249.6    October  10.0
10  278.8   November  11.0
11  176.5   December  12.0

A one-liner that sorts ascending, and resets the index would be as follows

df = df.sort_values(by=['2']).reset_index(drop = True)

[Out]:

        0          1     2
0    85.6    January   1.0
1    95.5   February   2.0
2   104.8      March   3.0
3   354.7      April   4.0
4   283.5        May   5.0
5   238.7       June   6.0
6     152       July   7.0
7    55.4     August   8.0
8   212.7  September   9.0
9   249.6    October  10.0
10  278.8   November  11.0
11  176.5   December  12.0

Notes:

If one is not doing the operation in-place, forgetting the steps mentioned above may lead one (as this user) to not be able to get the expected result.
There are strong opinions on using inplace. For that, one might want to read this.
One is assuming that the column 2 is not a string. If it is, one will have to convert it:
- Using pandas.to_numeric
```
 df['2'] = pd.to_numeric(df['2'])
```
- Using pandas.Series.astype
```
 df['2'] = df['2'].astype(float)
```

If one wants in descending order, one needs to pass ascending=False as

 df = df.sort_values(by=['2'], ascending=False)

 # or

 df.sort_values(by = '2', ascending=False, inplace=True)

 [Out]:

        0          1     2
2   176.5   December  12.0
9   278.8   November  11.0
10  249.6    October  10.0
11  212.7  September   9.0
1    55.4     August   8.0
5     152       July   7.0
6   238.7       June   6.0
8   283.5        May   5.0
0   354.7      April   4.0
7   104.8      March   3.0
3    95.5   February   2.0
4    85.6    January   1.0

alireza yazdandoost · Answer 5 · 2020-10-17T10:04:56.607

Just as another solution:

Instead of creating the second column, you can categorize your string data(month name) and sort by that like this:

df.rename(columns={1:'month'},inplace=True)
df['month'] = pd.Categorical(df['month'],categories=['December','November','October','September','August','July','June','May','April','March','February','January'],ordered=True)
df = df.sort_values('month',ascending=False)

It will give you the ordered data by month name as you specified while creating the Categorical object.

Hari_pb · Answer 6 · 2020-03-22T16:30:32.887

Just adding some more operations on data. Suppose we have a dataframe df, we can do several operations to get desired outputs

ID         cost      tax    label
1       216590      1600    test      
2       523213      1800    test 
3          250      1500    experiment

(df['label'].value_counts().to_frame().reset_index()).sort_values('label', ascending=False)

will give sorted output of labels as a dataframe

    index   label
0   test        2
1   experiment  1

score 9 · Answer 7 · answered Dec 18 '20 at 04:16

9

This worked for me

df.sort_values(by='Column_name', inplace=True, ascending=False)

answered Dec 18 '20 at 04:16

suzanne chen

135
1
7

score 8 · Answer 8 · answered Dec 26 '21 at 06:47

8

You probably need to reset the index after sorting:

df = df.sort_values('2')
df = df.reset_index(drop=True)

answered Dec 26 '21 at 06:47

mojtaba rezaei

81
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score 6 · Answer 9 · edited Aug 27 '20 at 13:40

Here is template of sort_values according to pandas documentation.

DataFrame.sort_values(by, axis=0,
                          ascending=True,
                          inplace=False,
                          kind='quicksort',
                          na_position='last',
                          ignore_index=False, key=None)[source]

In this case it will be like this.

df.sort_values(by=['2'])

API Reference pandas.DataFrame.sort_values

score 6 · Answer 10 · edited Jul 03 '22 at 08:10

6

Just adding a few more insights

df=raw_df['2'].sort_values() # will sort only one column (i.e 2)

but ,

df =raw_df.sort_values(by=["2"] , ascending = False)  # this  will sort the whole df in decending order on the basis of the column "2"

edited Jul 03 '22 at 08:10

Suraj Rao

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answered Jul 03 '22 at 08:08

Prateek Mohapatra

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if ['2'] is working that means 2 is a char and if [2] is working then 2 is int . That's the only diff. – Prateek Mohapatra Jul 03 '22 at 08:14

score 5 · Answer 11 · answered Nov 24 '22 at 14:44

If you want to sort column dynamically but not alphabetically. and dont want to use pd.sort_values(). you can try below solution.

Problem : sort column "col1" in this sequence ['A', 'C', 'D', 'B']

import pandas as pd
import numpy as np

## Sample DataFrame ##
df = pd.DataFrame({'col1': ['A', 'B', 'D', 'C', 'A']})

>>> df
   col1
0    A
1    B
2    D
3    C
4    A
## Solution ##

conditions = []
values = []

for i,j in enumerate(['A','C','D','B']):
    conditions.append((df['col1'] == j))
    values.append(i)

df['col1_Num'] = np.select(conditions, values)

df.sort_values(by='col1_Num',inplace = True)

>>> df

    col1  col1_Num
0    A         0
4    A         0
3    C         1
2    D         2
1    B         3

score 1 · Answer 12 · edited Feb 15 '21 at 08:54

1

This one worked for me:

df=df.sort_values(by=[2])

Whereas:

df=df.sort_values(by=['2'])

is not working.

edited Feb 15 '21 at 08:54

41 72 6c

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answered Feb 15 '21 at 07:55

Hemapriya R.

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score -1 · Answer 13 · edited Jul 03 '22 at 08:10

Example: Assume you have a column with values 1 and 0 and you want to separate and use only one value, then:

// furniture is one of the columns in the csv file.
 

allrooms = data.groupby('furniture')['furniture'].agg('count')
allrooms


myrooms1 = pan.DataFrame(allrooms, columns = ['furniture'], index = [1])

myrooms2 = pan.DataFrame(allrooms, columns = ['furniture'], index = [0])

print(myrooms1);print(myrooms2)

how to sort pandas dataframe from one column

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