python - Pandas - 在 applymap 期间获取每个元素的行和列名称

Question

我正在尝试比较一个字符串列表的相似性，并在 pandas 数据框中获取结果以供检查；所以我使用一个列表作为索引，另一个作为列列表。然后我想计算它们的“Levenshtein 相似度”(一个比较两个词之间相似度的函数)。

我尝试在每个单元格上使用 applymap 来做到这一点，并将单元格索引与单元格列进行比较。我怎么能那样做？或者更简单的替代方案？

things = ['car', 'bike', 'sidewalk', 'eatery']
action = ['walking', 'caring', 'biking', 'eating']
matrix = pd.DataFrame(index = things, columns = action)

def lev(x):
    x = Levenshtein.distance(x.index, x.column)  
matrix.applymap(lev)

到目前为止，我使用了以下(下方)，但我发现它笨拙且缓慢

matrix = pd.DataFrame(data = [action for i in things], index = things, columns = action)
for i, values in matrix.iterrows():
    for j, value in enumerate(values):
        matrix.ix[i,j] = Levenshtein.distance(i, value)

Answer 1

我想你可以使用 apply在数据框上，并使用 .name 访问列的值:

def lev(x):
    #replace your function
    return x.index + x.name
a = matrix.apply(lev)
print (a)
                  walking          caring          biking          eating
car            carwalking       carcaring       carbiking       careating
bike          bikewalking      bikecaring      bikebiking      bikeeating
sidewalk  sidewalkwalking  sidewalkcaring  sidewalkbiking  sidewalkeating
eatery      eaterywalking    eaterycaring    eaterybiking    eateryeating

编辑:

如果需要一些算术运算使用broadcasting :

a = pd.DataFrame(matrix.index.values + matrix.columns.values[:,None], 
                 index=matrix.index, 
                 columns=matrix.columns)
print (a)
             walking       caring           biking         eating
car       carwalking  bikewalking  sidewalkwalking  eaterywalking
bike       carcaring   bikecaring   sidewalkcaring   eaterycaring
sidewalk   carbiking   bikebiking   sidewalkbiking   eaterybiking
eatery     careating   bikeeating   sidewalkeating   eateryeating

或者:

a = pd.DataFrame(matrix.index.values + matrix.columns.values[:, np.newaxis], 
                 index=matrix.index, 
                 columns=matrix.columns)
print (a)
             walking       caring           biking         eating
car       carwalking  bikewalking  sidewalkwalking  eaterywalking
bike       carcaring   bikecaring   sidewalkcaring   eaterycaring
sidewalk   carbiking   bikebiking   sidewalkbiking   eaterybiking
eatery     careating   bikeeating   sidewalkeating   eateryeating