how to sort pandas dataframe from one column

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

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.

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.

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

Using column name worked for me.

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

This worked for me

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

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
  

This one worked for me:

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

Whereas:

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

is not working.

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)

You probably need to reset the index after sorting:

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

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"