how do I parse info from yahoo finance with beautiful soup - beautifulsoup

Question

I have got so far by using soup.findAll('span')
<span data-reactid="12">Previous Close</span>,
<span class="Trsdu(0.3s) " data-reactid="14">5.52</span>,
<span data-reactid="17"></span>,
<span class="Trsdu(0.3s) " data-reactid="19">5.49</span>,
<span data-reactid="38">Volume</span>,
<span class="Trsdu(0.3s) " data-reactid="40">1,164,604</span>,
...
I want a tabkle that shows me
Open 5.49
Volume 1,164,604
...
I tried soup.findAll('span').text but it gives error msg:
ResultSet object has no attribute 'text'. You're probably treating a list of items like a single item. Did you call find_all() when you meant to call find()?
this is the source:
https://finance.yahoo.com/quote/gxl.ax?p=gxl.ax

Answer 1

Luckily the error gives us a hint:
You're probably treating a list of items like a single item. Did you call find_all() when you meant to call find()?
Try one of these:
soup.findAll('span')[0].text
soup.findAll('span')[i].text
soup.find('span').text
This is a generic problem when navigating many selector systems, CSS selectors included. To operate on an element it must be a single element rather than a set. findAll() returns a set (array), so you can either index into that array (e.g. [i]) or find the first match with find().

Answer 2

soup.findAll('span') will return object/elements in ResultSet. You'd have to iterate through those to print the text. So try:
spans = soup.findAll('span')
for ele in spans:
data = ele.text
print(data)
To take your output and put into a dataframe:
your_output = ['Previous Close', '5.52', 'Open', '5.49', 'Bid', 'Ask', "Day's Range", '52 Week Range', 'Volume', '1,164,604', 'Avg. Volume', '660,530']
headers = your_output[::2]
data = your_output[1::2]
df = pd.DataFrame([data], columns = headers)
Additional
You certainly can use BeautifulSoup to parse and throw into a dataframe by iterating through the elements. I would like to offer an aleternative to BeautifulSoup.
Pandas does most of the work for you if it can identify tables within the html, by using .read_html. You can achieve the dataframe type of table you are looking for using that.
import pandas as pd
tables = pd.read_html(url)
df = pd.concat( [ table for table in tables ] )
Output:
print (df)
0 1
0 Previous Close 5.50
1 Open 5.50
2 Bid 5.47 x 0
3 Ask 5.51 x 0
4 Day's Range 5.47 - 5.51
5 52 Week Range 3.58 - 6.49
6 Volume 634191
7 Avg. Volume 675718
0 Market Cap 660.137M
1 Beta (3Y Monthly) 0.10
2 PE Ratio (TTM) 31.49
3 EPS (TTM) 0.17
4 Earnings Date NaN
5 Forward Dividend & Yield 0.15 (2.82%)
6 Ex-Dividend Date 2019-02-12
7 1y Target Est 5.17