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?
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.
This worked for me
df.sort_values(by='Column_name', inplace=True, ascending=False)
Using column name worked for me.
sorted_df = df.sort_values(by=['Column_name'], ascending=True)
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
This one worked for me:
df=df.sort_values(by=[2])
Whereas:
df=df.sort_values(by=['2'])
is not working.
Panda's sort_values
does the work.
If one doesn't intends to keep the same variable name, don't forget the inplace=True
(this performs the operation in-place)
df.sort_values(by=['2'], inplace=True)
One might as well assigning the change (sort) to a variable, that may have the same name as the df
as
df = df.sort_values(by=['2'])
Forgetting the steps mentioned above may lead one (as this user) to not be able to get the expected result.
Note that if one wants in descending order, one needs to pass ascending=False
, such as
df = df.sort_values(by=['2'], ascending=False)
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.
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
Source: Stackoverflow.com