Scraping Columns with Pandas (Column Entries w/ more than 1 word writes two columns)

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Thank you again for this forum! How do I determine the tables[#]? Is it a guessing game or is is there an attribute or property within the browser code that could aid me in finding the correct tables[#]?

A web site can have many tables,so have to look at site(count) or test out like tables[0], tables[1],tables[6].... and see if get wanted result.

There is match in pandas.read_html that can use string or regex to match something i table wanted.
Example Timeline of programming languages ,let say we want Python table we can match name Guido van Rossum.

import pandas as pd
 
df = pd.read_html('https://en.wikipedia.org/wiki/Timeline_of_programming_languages', match='Guido van Rossum')
df[0].head(13)

Output:
	Year	Name	Chief developer, company	Predecessor(s)
0	1990	Sather	Steve Omohundro	Eiffel
1	1990	AMOS BASIC	François Lionet and Constantin Sotiropoulos	STOS BASIC
2	1990	AMPL	Robert Fourer, David Gay and Brian Kernighan a...	NaN
3	1990	Object Oberon	H Mössenböck, J Templ, R Griesemer	Oberon
4	1990	J	Kenneth E. Iverson, Roger Hui at Iverson Software	APL, FP
5	1990	Haskell	NaN	Miranda
6	1990	EuLisp	NaN	Common Lisp, Scheme
7	1990	Z Shell (zsh)	Paul Falstad at Princeton University	ksh
8	1991	GNU E	David J. DeWitt, Michael J. Carey	C++
9	1991	Oberon-2	Hanspeter Mössenböck, Wirth	Object Oberon
10	1991	Oz	Gert Smolka and his students	Prolog
11	1991	Q	Albert Gräf	NaN
12	1991	Python	Guido van Rossum	ABC, C

So if a match it will always be df[0].
Without match it would be table 9:

df[9].head(13)