I have a pandas dataframe like the following:
A B C D
0 7 2 5 2
1 3 3 1 1
2 0 2 6 1
3 3 6 2 9
There can be 100s of columns, in the above example I have only shown 4.
I would like to extract top-k columns for each row and their values.
I can get the top-k columns using:
pd.DataFrame({n: df.T[column].nlargest(k).index.tolist() for n, column in enumerate(df.T)}).T
which, for k=3 gives:
0 1 2
0 A C B
1 A B C
2 C B D
3 D B A
But what I would like to have is:
0 1 2 3 4 5
0 A 7 C 5 B 2
1 A 3 B 3 C 1
2 C 6 B 2 D 1
3 D 9 B 6 A 3
Is there a pand(a)oic way to achieve this?
You can use numpy
solution:
numpy.argsort
for columns names values by indices
interweave
for new array DataFrame
constructor k = 3
vals = df.values
arr1 = np.argsort(-vals, axis=1)
a = df.columns[arr1[:,:k]]
b = vals[np.arange(len(df.index))[:,None], arr1][:,:k]
c = np.empty((vals.shape[0], 2 * k), dtype=a.dtype)
c[:,0::2] = a
c[:,1::2] = b
print (c)
[['A' 7 'C' 5 'B' 2]
['A' 3 'B' 3 'C' 1]
['C' 6 'B' 2 'D' 1]
['D' 9 'B' 6 'A' 3]]
df = pd.DataFrame(c)
print (df)
0 1 2 3 4 5
0 A 7 C 5 B 2
1 A 3 B 3 C 1
2 C 6 B 2 D 1
3 D 9 B 6 A 3
>>> def foo(x):
... r = []
... for p in zip(list(x.index), list(x)):
... r.extend(p)
... return r
...
>>> pd.DataFrame({n: foo(df.T[row].nlargest(k)) for n, row in enumerate(df.T)}).T
0 1 2 3 4 5
0 A 7 C 5 B 2
1 A 3 B 3 C 1
2 C 6 B 2 D 1
3 D 9 B 6 A 3
Or, using list comprehension:
>>> def foo(x):
... return [j for i in zip(list(x.index), list(x)) for j in i]
...
>>> pd.DataFrame({n: foo(df.T[row].nlargest(k)) for n, row in enumerate(df.T)}).T
0 1 2 3 4 5
0 A 7 C 5 B 2
1 A 3 B 3 C 1
2 C 6 B 2 D 1
3 D 9 B 6 A 3
This does the job efficiently : It uses argpartition that found the n biggest in O(n), then sort only them.
values=df.values
n,m=df.shape
k=4
I,J=mgrid[:n,:m]
I=I[:,:1]
if k<m: J=(-values).argpartition(k)[:,:k]
values=values[I,J]
names=np.take(df.columns,J)
J2=(-values).argsort()
names=names[I,J2]
values=values[I,J2]
names_and_values=np.empty((n,2*k),object)
names_and_values[:,0::2]=names
names_and_values[:,1::2]=values
result=pd.DataFrame(names_and_values)
For
0 1 2 3 4 5
0 A 7 C 5 B 2
1 B 3 A 3 C 1
2 C 6 B 2 D 1
3 D 9 B 6 A 3
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