Find symmetric pairs quickly in numpy

Question

from itertools import product
import pandas as pd

df = pd.DataFrame.from_records(product(range(10), range(10)))
df = df.sample(90)
df.columns = \"c1 c2\".split()
df         


        

Answer 1

You can sort the values, then groupby:

a= np.sort(df.to_numpy(), axis=1)
df.groupby([a[:,0], a[:,1]], as_index=False, sort=False).first()

Option 2: If you have a lot of pairs c1, c2, groupby can be slow. In that case, we can assign new values and filter by drop_duplicates:

a= np.sort(df.to_numpy(), axis=1) 

(df.assign(one=a[:,0], two=a[:,1])   # one and two can be changed
   .drop_duplicates(['one','two'])   # taken from above
   .reindex(df.columns, axis=1)
)

Answer 2

One way is using np.unique with return_index=True and use the result to index the dataframe:

a = np.sort(df.values)
_, ix = np.unique(a, return_index=True, axis=0)

print(df.iloc[ix, :])

    c1  c2
0    0   0
1    0   1
20   2   0
3    0   3
40   4   0
50   5   0
6    0   6
70   7   0
8    0   8
9    0   9
11   1   1
21   2   1
13   1   3
41   4   1
51   5   1
16   1   6
71   7   1
...

Answer 3

I will do

df[~pd.DataFrame(np.sort(df.values,1)).duplicated().values]

From pandas and numpy tri

s=pd.crosstab(df.c1,df.c2)
s=s.mask(np.triu(np.ones(s.shape)).astype(np.bool) & s==0).stack().reset_index()

Answer 4

frozenset

mask = pd.Series(map(frozenset, zip(df.c1, df.c2))).duplicated()

df[~mask]

Answer 5

If this needs to be fast, and if your variables are integer, then the following trick may help: let v,w be the columns of your vector; construct [v+w, np.abs(v-w)] =: [x, y]; then sort this matrix lexicographically, remove duplicates, and finally map it back to [v, w] = [(x+y), (x-y)]/2.

Answer 6

Here's one NumPy based one for integers -

def remove_symm_pairs(df):
    a = df.to_numpy(copy=False)
    b = np.sort(a,axis=1)
    idx = np.ravel_multi_index(b.T,(b.max(0)+1))
    sidx = idx.argsort(kind='mergesort')
    p = idx[sidx]
    m = np.r_[True,p[:-1]!=p[1:]]
    a_out = a[np.sort(sidx[m])]
    df_out = pd.DataFrame(a_out)
    return df_out

If you want to keep the index data as it is, use return df.iloc[np.sort(sidx[m])].

For generic numbers (ints/floats, etc.), we will use a view-based one -

# https://stackoverflow.com/a/44999009/ @Divakar
def view1D(a): # a is array
    a = np.ascontiguousarray(a)
    void_dt = np.dtype((np.void, a.dtype.itemsize * a.shape[1]))
    return a.view(void_dt).ravel()

and simply replace the step to get idx with idx = view1D(b) in remove_symm_pairs.