Sort a alphanumeric column in pandas and replace it with original column of the dataset [duplicate] - pandas

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?

Answer 1

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.

Answer 2

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.

Answer 3

Using column name worked for me.
sorted_df = df.sort_values(by=['Column_name'], ascending=True)

Answer 4

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

Answer 5

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.

Answer 6

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

Answer 7

This worked for me
df.sort_values(by='Column_name', inplace=True, ascending=False)

Answer 8

You probably need to reset the index after sorting:
df = df.sort_values('2')
df = df.reset_index(drop=True)

Answer 9

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

Answer 10

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"

Answer 11

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

Answer 12

This one worked for me:
df=df.sort_values(by=[2])
Whereas:
df=df.sort_values(by=['2'])
is not working.

Answer 13

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)