You can't just count td or th cells, no. You'll have to do a scan across the table to get the number of columns on each row, adding to that count any active rowspans from a preceding row.
In a different scenario parsing a table with rowspans I tracked rowspan counts per column number to ensure that data from different cells ended up in the correct column. A similar technique can be used here.
First count columns; keep only the highest number. Keep a list of rowspan numbers of 2 or greater and subtract 1 from each for every row of columns you process. That way you know how many 'extra' columns there are on each row. Take the highest column count to build your output matrix.
Next, loop over the rows and cells again, and this time track rowspans in a dictionary mapping from column number to active count. Again, cary over anything with a value of 2 or over to the next row. Then shift column numbers to account for any rowspans that are active; the first td in a row would actually be the second if there was a rowspan active on column 0, etc.
Your code copies the value for spanned columns and rows into the output repeatedly; I achieved the same by creating a loop over the colspan and rowspan numbers of a given cell (each defaulting to 1) to copy the value multiple times. I’m ignoring overlapping cells; the HTML table specifications state that overlapping cells are an error and it is up to the user agent to resolve conflicts. In the code below colspan trumps rowspan cells.
from itertools import product
def 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
This parses your sample table correctly:
>>> from pprint import pprint
>>> pprint(table_to_2d(soup.table), width=30)
[['1', '2', '5'],
['3', '4', '4'],
['3', '6', '7']]
and handles your other examples; first table:
>>> 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]]
And the second:
>>> 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]]
Last but not least, the code correctly handles spans that extend beyond the actual table, and "0" spans (extending to the ends), like in the following example:
<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>
There are two rows of 4 cells, even though the rowspan and colspan values would have you believe there could be 3 and 5:
+---+---+---+---+
| | | C | D |
| A | B +---+---+
| | | E |
+---+---+-------+
Such overspanning is handled just like the browser would; they are ignored, and the 0 spans extend to the remaining rows or columns:
>>> 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']]
