Parsing An Html Table With Pd.read_html Where Cells Contain Full-tables Themselves

I need to parse a table from html that has other tables nested within the larger table. As called below with pd.read_html, each of these nested tables are parsed and then 'inserted

Solution 1:

You can't use read_html to read nested tables, but you can roll your own html reader and use read_html for table cells:

import pandas as pd
import bs4

withopen('up_pf00344.test.html') as f:
    html = f.read()
soup = bs4.BeautifulSoup(html, 'lxml')
results = soup.find(attrs = {'id': 'results'})

# get first visible header row as dataframe headersfor row in results.thead.find_all('tr'):
    if'display:none'notin row.get('style',''):
        df = pd.DataFrame(columns=[col.get_text() for col in row.find_all('th')])
    break# append all table rows to dataframefor row in results.tbody.find_all('tr', recursive=False):
    if'display:none'in row.get('style',''):
        continue
    df_row = []
    for col in row.find_all('td', recursive=False):
        table = col.find_all('table')
        df_row.append(pd.read_html(str(col))[0] if table else col.get_text())
    df.loc[len(df)] = df_row

Result of df.iloc[0].map(type):

                                                            <class'str'>
Entry                                                       <class'str'>
Organism                                                    <class'str'>
Protein names                                               <class'str'>
Gene names                                                  <class'str'>
Length                                                      <class'str'>
Cross-reference (Pfam)                                      <class'str'>
Cross-reference (InterPro)                                  <class'str'>
Taxonomic lineage IDs                                       <class'str'>
Subcellular location [CC]                                   <class'str'>
Signal peptide                                              <class'str'>
Transit peptide                                             <class'str'>
Topological domain                  <class'pandas.core.frame.DataFrame'>
Transmembrane                       <class'pandas.core.frame.DataFrame'>
Intramembrane                       <class'pandas.core.frame.DataFrame'>
Sequence caution                                            <class'str'>
Caution                                                     <class'str'>
Taxonomic lineage (SUPERKINGDOM)                            <class'str'>
Taxonomic lineage (KINGDOM)                                 <class'str'>
Taxonomic lineage (PHYLUM)                                  <class'str'>
Cross-reference (RefSeq)                                    <class'str'>
Cross-reference (EMBL)                                      <class'str'>
e                                                           <class'str'>

Bonus: As your table rows have an id, you could use it as index of your dataframe df.loc[row.get('id')] = df_row instead of df.loc[len(df)] = df_row.