pandas - Scrape tables into dataframe with BeautifulSoup

Question

I'm trying to scrape the data from the coins catalog.

There is one of the pages. I need to scrape this data into Dataframe

So far I have this code:

import bs4 as bs
import urllib.request
import pandas as pd

source = urllib.request.urlopen('http://www.gcoins.net/en/catalog/view/45518').read()
soup = bs.BeautifulSoup(source,'lxml')

table = soup.find('table', attrs={'class':'subs noBorders evenRows'})
table_rows = table.find_all('tr')

for tr in table_rows:
    td = tr.find_all('td')
    row = [tr.text for tr in td]
    print(row)                    # I need to save this data instead of printing it 

It produces following output:

[]
['', '', '1882', '', '108,000', 'UNC', '—']
[' ', '', '1883', '', '786,000', 'UNC', '~ $3.99']
[' ', " 



$('subGraph55337').on('click', function(event) {
Lightview.show({
href : '/en/catalog/ajax/subgraph?id=55337',
rel : 'ajax',
options : {
autosize : true,
topclose : true,
ajax : {
evalScripts : true
}
} 
});
event.stop();
return false;
});
", '1884', '', '4,604,000', 'UNC', '~ $2.08–$4.47']
[' ', '', '1885', '', '1,314,000', 'UNC', '~ $3.20']
['', '', '1886', '', '444,000', 'UNC', '—']
[' ', '', '1888', '', '413,000', 'UNC', '~ $2.88']
[' ', '', '1889', '', '568,000', 'UNC', '~ $2.56']
[' ', " 



$('subGraph55342').on('click', function(event) {
Lightview.show({
href : '/en/catalog/ajax/subgraph?id=55342',
rel : 'ajax',
options : {
autosize : true,
topclose : true,
ajax : {
evalScripts : true
}
} 
});
event.stop();
return false;
});
", '1890', '', '2,137,000', 'UNC', '~ $1.28–$4.79']
['', '', '1891', '', '605,000', 'UNC', '—']
[' ', '', '1892', '', '205,000', 'UNC', '~ $4.47']
[' ', '', '1893', '', '754,000', 'UNC', '~ $4.79']
[' ', '', '1894', '', '532,000', 'UNC', '~ $3.20']
[' ', '', '1895', '', '423,000', 'UNC', '~ $2.40']
['', '', '1896', '', '174,000', 'UNC', '—']

But when I'm trying to save it to Dataframe and export to excel it contains just the last value:

         0
0         
1         
2     1896
3         
4  174,000
5      UNC
6        —

Answer 1

Pandas already has a built-in method to convert the table on the web to a dataframe:

table = soup.find_all('table')
df = pd.read_html(str(table))[0]