sorting rows in a pandas dataframe in a way which is not alphabetical

Question

I have some dataframes (df) with categorical data starting with: a, b, c and a category for "remaining categories".

I would like to sort the month column in the dataframe ascending=true, but then have the category column sorted so that they are in the following order:

c

a

b

"remaining category"

Is this possible? --> Basically I want a custom sort order for a specific column, but then have the month column sorted in order of date.

Answer 1

docs are here

In [8]: df = DataFrame({'A' : [1,1,1,2,2,3], 'B' : list('bbcdae') })

In [9]: df.dtypes
Out[9]: 
A     int64
B    object
dtype: object

In [10]: df['B'] = pd.Categorical(df['B'],categories=list('ghbaedfc'))

In [11]: df
Out[11]: 
   A  B
0  1  b
1  1  b
2  1  c
3  2  d
4  2  a
5  3  e

In [12]: df.dtypes
Out[12]: 
A       int64
B    category
dtype: object

In [13]: df.sort(['B','A'])
Out[13]: 
   A  B
0  1  b
1  1  b
4  2  a
5  3  e
3  2  d
2  1  c

Answer 2

You can do it with a dict and adding a new 'sort' column to your dataframe. Check out this similar question Custom sorting with Pandas

Answer 3

Pandas sort() was deprecated and removed from Pandas with release 0.20 (2017-05-05).

Pandas sort_values() and sort_index() are now used instead of sort(). To get a df in categorical order use pd.Categorical()

df = pd.DataFrame({'Panel':['Left', 'Right', 'Top', 'Bottom','Left'],
         Value':[70, 50, 30, 40, 60]})
df['Panel'] = pd.Categorical(df['Panel'],
        categories=['Top','Bottom','Left','Right'],ordered=True)

results

    Panel   Value
2   Top     30
3   Bottom  40
0   Left    70
4   Left    60
1   Right   50