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Using Python to scrape HTML tables with merged cells

The python library pandas makes it easy to scrape HTML tables from the web. Its read_html() method is very straightfoward and, for most tables, does a great job. It reads a page’s tables directly into memory as a list of dataframes.

For example, say we wanted to scrape the Flow of Funds table found on this page. We could do the following:

import pandas as pd 
fof_url = ""
fof = pd.read_html(fof_url)[0]
fof.to_csv("fof.csv", header = False, index = False)

…which scrapes the table then saves it as a CSV which ends up looking like this:

alt text

Perfect! But not all tables are this simple. What if the table has cells that are merged, as is common in multi-level column titles? Take the first table on this page for example. It has a hierarchical column title strucuture, with complicated relationships between headers, subheaders, and even sub-subheaders.

alt text

Pandas does not handle this situation well. Specifically, it has no way of parsing the rowspan and colspan HTML attributes that determine the dimensions of the cells. If we try to use read_html on this type of table, the result will be an unusable mess – often skipping the column headers entirely.

A custom method to handle this

Fortunately it’s not too difficult to approach this from scratch. The following class and method mimics the functionality of read_html, with the added ability to accurately parse merged cells:

import os
import requests
import urllib
import math
import copy
import pandas as pd	
import numpy as np
from bs4 import BeautifulSoup 

class html_tables(object):
    def __init__(self, url):
        self.url      = url
        self.r        = requests.get(self.url)
        self.url_soup = BeautifulSoup(self.r.text)
    def read(self):
        self.tables      = []
        self.tables_html = self.url_soup.find_all("table")
        # Parse each table
        for n in range(0, len(self.tables_html)):
            n_cols = 0
            n_rows = 0
            for row in self.tables_html[n].find_all("tr"):
                col_tags = row.find_all(["td", "th"])
                if len(col_tags) > 0:
                    n_rows += 1
                    if len(col_tags) > n_cols:
                        n_cols = len(col_tags)
            # Create dataframe
            df = pd.DataFrame(index = range(0, n_rows), columns = range(0, n_cols))
			# Create list to store rowspan values 
            skip_index = [0 for i in range(0, n_cols)]
            # Start by iterating over each row in this table...
			row_counter = 0
            for row in self.tables_html[n].find_all("tr"):
                # Skip row if it's blank
                if len(row.find_all(["td", "th"])) == 0:
                    # Get all cells containing data in this row
                    columns = row.find_all(["td", "th"])
                    col_dim = []
                    row_dim = []
                    col_dim_counter = -1
                    row_dim_counter = -1
                    col_counter = -1
                    this_skip_index = copy.deepcopy(skip_index)
                    for col in columns:
                        # Determine cell dimensions
                        colspan = col.get("colspan")
                        if colspan is None:
                        col_dim_counter += 1
                        rowspan = col.get("rowspan")
                        if rowspan is None:
                        row_dim_counter += 1
                        # Adjust column counter
                        if col_counter == -1:
                            col_counter = 0  
                            col_counter = col_counter + col_dim[col_dim_counter - 1]
                        while skip_index[col_counter] > 0:
                            col_counter += 1

                        # Get cell contents  
                        cell_data = col.get_text()
                        # Insert data into cell
                        df.iat[row_counter, col_counter] = cell_data

                        # Record column skipping index
                        if row_dim[row_dim_counter] > 1:
                            this_skip_index[col_counter] = row_dim[row_dim_counter]
                # Adjust row counter 
                row_counter += 1
                # Adjust column skipping index
                skip_index = [i - 1 if i > 0 else i for i in this_skip_index]

            # Append dataframe to list of tables

The gist what’s going on here:

  • First we parse the HTML using BeautifulSoup, finding all tables, then all rows within each table, then each cell within each row.
  • Then when we’re looping over each of these cells, we check to see if there are any colspan and rowspan attributes associated with the cell – this tells us the dimensions of the cell. In spreadsheet terms, you might think of a rowspan value of 2 corresponding to a cell being merged with the cell below it.
    • The colspan information is easy to use. If we register a colspan value greater than 1, we skip that number of columns before we start filling in the next cell.
    • The rowspan information is a bit trickier. If we register a rowspan value greater than 1, we store it in a list (“skip_index”) where each element corresponds to a column in our table. When the loop is filling in data for each cell, it first checks to see if there’s a nonzero value in this column’s skip_index; if that’s true, it skips this column. With every row iteration, we increment each nonzero skip_index element by -1 until it’s back to zero.

Let’s say we wanted to try this with that SSA table from above. We would do the following:

ssa_url = ""
ssa = html_tables(ssa_url)
first_table =[0]
first_table.to_csv("ssa.csv", header = False, index = False)

The resulting CSV now has the correct positions for each header cell:


Not the prettiest table, but it contains the correct positional information, and could easily be beautified in Excel by actually merging the cells.