python - 如何对 Pandas 数据框中的两列进行字符串比较？

Question

我有一个如下所示的数据框 df:

        a      b
  0     Jon     Jon
  1     Jon     John
  2     Jon     Johnny

我想将这两个字符串进行比较并创建一个这样的新列:

  df['compare'] = df2['a'] = df2['b']


        a      b          compare
  0     Jon     Jon         True
  1     Jon     John        False
  2     Jon     Johnny      False

我还希望能够通过这个 levenshtein 函数传递列 a 和 b:

def levenshtein_distance(a, b):
    """Return the Levenshtein edit distance between two strings *a* and *b*."""
    if a == b:
        return 0
    if len(a) < len(b):
        a, b = b, a
    if not a:
        return len(b)
    previous_row = range(len(b) + 1)
    for i, column1 in enumerate(a):
        current_row = [i + 1]
        for j, column2 in enumerate(b):
            insertions = previous_row[j + 1] + 1
            deletions = current_row[j] + 1
            substitutions = previous_row[j] + (column1 != column2)
            current_row.append(min(insertions, deletions, substitutions))
        previous_row = current_row
    return previous_row[-1] 

并添加这样一列:

  df['compare'] = levenshtein_distance(df2['a'], df2['b'])      

        a      b          compare
   0    Jon     Jon         100
   1    Jon     John        .95
   2    Jon     Johnny      .87

但是当我尝试时出现这个错误:

  ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

我如何格式化我的数据/数据框以允许它比较两列并将比较添加为第三列？

Answer 1

只是做:

df['compare'] = [levenshtein_distance(a, b) for a, b in zip(df2['a'], df2['b'])]

或者，如果你想要相等比较:

df['compare'] = (df['a'] == df['b'])