>>> df =DataFrame({'a':[1,2,3,4],'b':[2,4,6,8]})
>>> df['x']=df.a + df.b
>>> df['y']=df.a - df.b
>>> df
a b x y
0 1 2 3 -1
1 2 4 6 -2
2 3 6 9 -3
3 4 8 12 -4
Now I want to rearrange the column sequence, which makes 'x','y' column to be the first & second columns by :
>>> df = df[['x','y','a','b']]
>>> df
x y a b
0 3 -1 1 2
1 6 -2 2 4
2 9 -3 3 6
3 12 -4 4 8
But if I have a long coulmns 'a','b','c','d'....., and I don't want to explictly list the columns. How can I do that ?
Or Does Pandas provide a function like set_column_sequence(dataframe,col_name, seq)
so that I can do : set_column_sequence(df,'x',0)
and set_column_sequence(df,'y',1)
?
You could also do something like this:
df = df[['x', 'y', 'a', 'b']]
You can get the list of columns with:
cols = list(df.columns.values)
The output will produce something like this:
['a', 'b', 'x', 'y']
...which is then easy to rearrange manually before dropping it into the first function
There may be an elegant built-in function (but I haven't found it yet). You could write one:
# reorder columns
def set_column_sequence(dataframe, seq, front=True):
'''Takes a dataframe and a subsequence of its columns,
returns dataframe with seq as first columns if "front" is True,
and seq as last columns if "front" is False.
'''
cols = seq[:] # copy so we don't mutate seq
for x in dataframe.columns:
if x not in cols:
if front: #we want "seq" to be in the front
#so append current column to the end of the list
cols.append(x)
else:
#we want "seq" to be last, so insert this
#column in the front of the new column list
#"cols" we are building:
cols.insert(0, x)
return dataframe[cols]
For your example: set_column_sequence(df, ['x','y'])
would return the desired output.
If you want the seq at the end of the DataFrame instead simply pass in "front=False".
You can do the following:
df =DataFrame({'a':[1,2,3,4],'b':[2,4,6,8]})
df['x']=df.a + df.b
df['y']=df.a - df.b
create column title whatever order you want in this way:
column_titles = ['x','y','a','b']
df.reindex(columns=column_titles)
This will give you desired output
I would suggest you just write a function to do what you're saying probably using drop
(to delete columns) and insert
to insert columns at a position. There isn't an existing API function to do what you're describing.
Feel free to disregard this solution as subtracting a list from an Index does not preserve the order of the original Index, if that's important.
In [61]: df.reindex(columns=pd.Index(['x', 'y']).append(df.columns - ['x', 'y']))
Out[61]:
x y a b
0 3 -1 1 2
1 6 -2 2 4
2 9 -3 3 6
3 12 -4 4 8
Source: Stackoverflow.com