Python：pandasデータフレームのペア間のコラボレーションを数える方法は？

Question

私は同じアイテムを持つ人々のリストは考慮せずにコラボレーションの回数をカウントするためにweight

df1 
Name    People       Times 
Tom  [John, Paul, Frank, Richard, James]  [(1/4+1/5),1/4,1/3,1/5,1/5] 
John [Tom, Richard, James]      [(1/4+1/5),1/5,1/5] 
Paul [Tom, John]        [1/4,1/4] 
Frank [Tom]          [1/3] 
Richard [Tom, John, James]      [1/5,1/5,1/5] 
James [Tom, John, Richard]      [1/5,1/5,1/5] 

で平均化する各人のために、この

df = pd.DataFrame({'Item':['A','A','A','B','B','C','C','C','C'], 
'Name':[Tom,John,Paul,Tom,Frank,Tom, John, Richard, James], 
'Weight:[2,2,2,3,3,5, 5, 5, 5]'}) 
df 
Item Name Weight 
A Tom  4 
A John 4 
A Paul 4 
B Tom  3 
B Frank 3 
C Tom  5 
C John 5 
C Richard 5 
C James 5 

のようなデータフレームを持っていますweight、私がやった：

#merge M:N by column Item 
df1 = pd.merge(df, df, on=['Item']) 

#remove duplicity - column Name_x == Name_y 
df1 = df1[~(df1['Name_x'] == df1['Name_y'])] 
#print df1 

#create lists 
df1 = df1.groupby('Name_x')['Name_y'].apply(lambda x: x.tolist()).reset_index() 
print df1 
    Name_x          Name_y 
0 Frank          [Tom] 
1 James      [Tom, John, Richard] 
2  John   [Tom, Paul, Tom, Richard, James] 
3  Paul        [Tom, John] 
4 Richard       [Tom, John, James] 
5  Tom [John, Paul, Frank, John, Richard, James] 


#get count by np.unique 
df1['People'] = df1['Name_y'].apply(lambda a: np.unique((a), return_counts =True)[0]) 
df1['times'] = df1['Name_y'].apply(lambda a: np.unique((a), return_counts =True)[1]) 
#remove column Name_y 
df1 = df1.drop('Name_y', axis=1).rename(columns={'Name_x':'Name'}) 
print df1 
     Name        People   times 
0 Frank        [Tom]    [1] 
1 James     [John, Richard, Tom]  [1, 1, 1] 
2  John   [James, Paul, Richard, Tom]  [1, 1, 1, 2] 
3  Paul       [John, Tom]   [1, 1] 
4 Richard     [James, John, Tom]  [1, 1, 1] 
5  Tom [Frank, James, John, Paul, Richard] [1, 1, 2, 1, 1] 

を最後のデータフレームでは、私はコラの数を持っていますすべてのペア間のホウ素化、しかし私はコラボレーション

Answer 1

以降での彼らの加重カウントをしたいと思います：

df = pd.DataFrame({'Item': ['A', 'A', 'A', 'B', 'B', 'C', 'C', 'C', 'C'], 
        'Name': ['Tom', 'John', 'Paul', 'Tom', 'Frank', 'Tom', 'John', 'Richard', 'James'], 
        'Weight': [2, 2, 2, 3, 3, 5, 5, 5, 5]}) 

df1 = pd.merge(df, df, on=['Item']) 
df1 = df1[~(df1['Name_x'] == df1['Name_y'])].set_index(['Name_x', 'Name_y']).drop(['Item', 'Weight_y'], axis=1) 

あなたはワイドフォーマットの値と.unstack()を作成するために.apply()を使用することができます。

collab = df1.groupby(level=['Name_x', 'Name_y']).apply(lambda x: np.sum(1/x)).unstack().loc[:, 'Weight_x'] 

Name_y  Frank James John Paul Richard  Tom 
Name_x             
Frank   NaN NaN NaN NaN  NaN 0.333333 
James   NaN NaN 0.2 NaN  0.2 0.200000 
John   NaN 0.2 NaN 0.5  0.2 0.700000 
Paul   NaN NaN 0.5 NaN  NaN 0.500000 
Richard  NaN 0.2 0.2 NaN  NaN 0.200000 
Tom  0.333333 0.2 0.7 0.5  0.2  NaN 

次に、行を繰り返してリストに変換します。

df = pd.DataFrame(columns=['People', 'Times']) 
for p, data in collab.iterrows(): 
    s = data.dropna() 
    df.loc[p] = [s.index.tolist(), s.values] 

             People \ 
Frank         [Tom] 
James     [John, Richard, Tom] 
John    [James, Paul, Richard, Tom] 
Paul        [John, Tom] 
Richard     [James, John, Tom] 
Tom  [Frank, James, John, Paul, Richard] 

             Times 
Frank      [0.333333333333] 
James       [0.2, 0.2, 0.2] 
John      [0.2, 0.5, 0.2, 0.7] 
Paul        [0.5, 0.5] 
Richard      [0.2, 0.2, 0.2] 
Tom  [0.333333333333, 0.2, 0.7, 0.5, 0.2]