您不能只数
td或
th单元格，不。您必须对表进行扫描以获取每一行的列数，并将上一行中任何活动的行跨度添加到该计数中。
在用行跨度解析表的另一种情况下，我跟踪每个列号的行跨数，以确保来自不同单元格的数据以正确的列结尾。这里可以使用类似的技术。
第一计数列；只保留最高的数字。保留行数为2或更大的列表，并为您处理的每一行列的每行减去1。这样，您就知道每一行有多少“额外”列。以最高的列数来构建输出矩阵。
接下来，再次遍历行和单元格，这次跟踪从列号到活动计数的字典中的行跨度。同样，将值大于等于2的任何内容都保留到下一行。然后移动列号以说明活动的任何行跨度；如果在第
td0列上有活动的行跨度，则行的第一个实际上是第二个，等等。
您的代码将复制的列和行的值重复复制到输出中；我通过在给定单元格（每个默认为1）的
colspan和
rowspan数字上创建循环以多次复制值来实现相同目的。我忽略了重叠的单元格；的HTML表格规范指出重叠的小区是一个错误，它是由用户代理来解决冲突。在下面的代码中，colspan胜过rowpan单元。
from itertools import productdef table_to_2d(table_tag): rowspans = [] # track pending rowspans rows = table_tag.find_all('tr') # first scan, see how many columns we need colcount = 0 for r, row in enumerate(rows): cells = row.find_all(['td', 'th'], recursive=False) # count columns (including spanned). # add active rowspans from preceding rows # we *ignore* the colspan value on the last cell, to prevent # creating 'phantom' columns with no actual cells, only extended # colspans. This is achieved by hardcoding the last cell width as 1. # a colspan of 0 means “fill until the end” but can really only apply # to the last cell; ignore it elsewhere. colcount = max( colcount, sum(int(c.get('colspan', 1)) or 1 for c in cells[:-1]) + len(cells[-1:]) + len(rowspans)) # update rowspan bookkeeping; 0 is a span to the bottom. rowspans += [int(c.get('rowspan', 1)) or len(rows) - r for c in cells] rowspans = [s - 1 for s in rowspans if s > 1] # it doesn't matter if there are still rowspan numbers 'active'; no extra # rows to show in the table means the larger than 1 rowspan numbers in the # last table row are ignored. # build an empty matrix for all possible cells table = [[None] * colcount for row in rows] # fill matrix from row data rowspans = {} # track pending rowspans, column number mapping to count for row, row_elem in enumerate(rows): span_offset = 0 # how many columns are skipped due to row and colspans for col, cell in enumerate(row_elem.find_all(['td', 'th'], recursive=False)): # adjust for preceding row and colspans col += span_offset while rowspans.get(col, 0): span_offset += 1 col += 1 # fill table data rowspan = rowspans[col] = int(cell.get('rowspan', 1)) or len(rows) - row colspan = int(cell.get('colspan', 1)) or colcount - col # next column is offset by the colspan span_offset += colspan - 1 value = cell.get_text() for drow, dcol in product(range(rowspan), range(colspan)): try: table[row + drow][col + dcol] = value rowspans[col + dcol] = rowspan except IndexError: # rowspan or colspan outside the confines of the table pass # update rowspan bookkeeping rowspans = {c: s - 1 for c, s in rowspans.items() if s > 1} return table这样可以正确解析您的样本表：
>>> from pprint import pprint>>> pprint(table_to_2d(soup.table), width=30)[['1', '2', '5'], ['3', '4', '4'], ['3', '6', '7']]
并处理您的其他示例；第一张桌子：
>>> table1 = BeautifulSoup('''... <table border="1">... <tr>... <th>A</th>... <th>B</th>... </tr>... <tr>... <td rowspan="2">C</td>... <td rowspan="1">D</td>... </tr>... <tr>... <td>E</td>... <td>F</td>... </tr>... <tr>... <td>G</td>... <td>H</td>... </tr>... </table>''', 'html.parser')>>> pprint(table_to_2d(table1.table), width=30)[['A', 'B', None], ['C', 'D', None], ['C', 'E', 'F'], ['G', 'H', None]]第二个：
>>> table2 = BeautifulSoup('''... <table border="1">... <tr>... <th>A</th>... <th>B</th>... </tr>... <tr>... <td rowspan="2">C</td>... <td rowspan="2">D</td>... </tr>... <tr>... <td>E</td>... <td>F</td>... </tr>... <tr>... <td>G</td>... <td>H</td>... </tr>... </table>... ''', 'html.parser')>>> pprint(table_to_2d(table2.table), width=30)[['A', 'B', None, None], ['C', 'D', None, None], ['C', 'D', 'E', 'F'], ['G', 'H', None, None]]最后但并非最不重要的一点是，代码正确地处理了超出实际表的
"0"跨度和跨度（延伸至末尾），如以下示例所示：
<table border="1"> <tr> <td rowspan="3">A</td> <td rowspan="0">B</td> <td>C</td> <td colspan="2">D</td> </tr> <tr> <td colspan="0">E</td> </tr></table>
即使有rowpan和colspan值会让您相信可能会有3和5，也有两行包含4个单元格：
+---+---+---+---+| | | C | D || A | B +---+---+| | | E |+---+---+-------+
这种超限的处理方式与浏览器相同。它们将被忽略，并且0跨度扩展到其余的行或列：
>>> span_demo = BeautifulSoup('''... <table border="1">... <tr>... <td rowspan="3">A</td>... <td rowspan="0">B</td>... <td>C</td>... <td colspan="2">D</td>... </tr>... <tr>... <td colspan="0">E</td>... </tr>... </table>''', 'html.parser')>>> pprint(table_to_2d(span_demo.table), width=30)[['A', 'B', 'C', 'D'], ['A', 'B', 'E', 'E']]
